Journal articles on the topic 'Movement-based signal'
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Liang, Sensong, Jiansheng Peng, and Yong Xu. "Passive Fetal Movement Signal Detection System Based on Intelligent Sensing Technology." Journal of Healthcare Engineering 2021 (August 25, 2021): 1–11. http://dx.doi.org/10.1155/2021/1745292.
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Fetal movement (FM) is an essential physiological parameter to determine the health status of the fetus. To address the problems of harrowing FM signal extraction and the low recognition rate of traditional machine learning classifiers in FM signal detection, this paper develops a passive FM signal detection system based on intelligent sensing technology. FM signals are obtained from the abdomen of the pregnant woman by using accelerometers. The FM signals are extracted and identified according to the clinical nature of the features hidden in the amplitude and waveform of the FM signals that fluctuate in duration. The system consists of four main stages: (i) FM signal preprocessing, (ii) maternal artifact signal preidentification, (iii) FM signal identification, and (iv) FM classification. Firstly, Kalman filtering is used to reconstruct the FM signal in a continuous low-amplitude noise background. Secondly, the maternal artifact signal is identified using an amplitude threshold algorithm. Then, an innovative dictionary learning algorithm is used to construct a dictionary of FM features, and orthogonal matching pursuit and adaptive filtering algorithms are used to identify the FM signals, respectively. Finally, mask fusion classification is performed based on the multiaxis recognition results. Experiments are conducted to evaluate the performance of the proposed FM detection system using publicly available and self-built accelerated FM datasets. The classification results showed that the orthogonal matching pursuit algorithm was more effective than the adaptive filtering algorithm in identifying FM signals, with a positive prediction value of 89.74%. The proposed FM detection system has great potential and promise for wearable FM health monitoring.
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Peters, Richard A. "Environmental motion delays the detection of movement-based signals." Biology Letters 4, no. 1 (October 30, 2007): 2–5. http://dx.doi.org/10.1098/rsbl.2007.0422.
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Animal signals are constrained by the environment in which they are transmitted and the sensory systems of receivers. Detection of movement-based signals is particularly challenging against the background of wind-blown plants. The Australian lizard Amphibolurus muricatus has recently been shown to compensate for greater plant motion by prolonging the introductory tail-flicking component of its movement-based display. Here I demonstrate that such modifications to signal structure are useful because environmental motion lengthens the time lizard receivers take to detect tail flicks. The spatio-temporal properties of animal signals and environmental motion are thus sufficiently similar to make signal detection more difficult. Environmental motion, therefore, must have had an influence on the evolution of movement-based signals and motion detection mechanisms.
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Td New, Shaun, and Richard A Peters. "A framework for quantifying properties of 3-dimensional movement-based signals." Current Zoology 56, no. 3 (June 1, 2010): 327–36. http://dx.doi.org/10.1093/czoolo/56.3.327.
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Abstract Understanding how signal properties are optimized for the reliable transmission of information requires accurate description of the signal in time and space. For movement-based signals where movement is restricted to a single plane, measurements from a single viewpoint can be used to consider a range of viewing positions based on simple geometric calculations. However, considerations of signal properties from a range of viewing positions for movements extending into three-dimensions (3D) are more problematic. We present here a new framework that overcomes this limitation, and enables us to quantify the extent to which movement-based signals are view-specific. To illustrate its application, a Jacky lizard tail flick signal was filmed with synchronized cameras and the position of the tail tip digitized for both recordings. Camera alignment enabled the construction of a 3D display action pattern profile. We analyzed the profile directly and used it to create a detailed 3D animation. In the virtual environment, we were able to film the same signal from multiple viewing positions and using a computational motion analysis algorithm (gradient detector model) to measure local image velocity in order to predict view dependent differences in signal properties. This approach will enable consideration of a range of questions concerning movement-based signal design and evolution that were previously out of reach.
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Ban, Dahee, Syed Shahid, and Sungoh Kwon. "Movement Noise Cancellation in Second Derivative of Photoplethysmography Signals with Wavelet Transform and Diversity Combining." Applied Sciences 8, no. 9 (September 1, 2018): 1531. http://dx.doi.org/10.3390/app8091531.
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In this paper, we propose an algorithm to remove movement noise from second derivative of photoplethysmography (SDPPG) signals. SDPPG is widely used in healthcare applications because of its easy and comfortable measurement. However, an SDPPG signal is vulnerable to movement, which degrades the signal. Degradation of SDPPG signal shapes can result in incorrect diagnosis. The proposed algorithm detects movement noise in a measurement signal using wavelet transform, and removes movement noise by selecting the best signal from among multiple signals measured at different locations. Experiment results show that the proposed algorithm outperforms the previous filter-based algorithm, and that movement noise with 30% time duration can be reduced by up to 70.89%.
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Rahul, Yumlembam, and Rupam Kumar Sharma. "EEG Signal-Based Movement Control for Mobile Robots." Current Science 116, no. 12 (June 25, 2019): 1993. http://dx.doi.org/10.18520/cs/v116/i12/1993-2000.
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Turnip, Arjon, Grace Gita Redhyka, Hilman S. Alam, and Iwan R. Setiawan. "An Experiment of Spike Detection Based Mental Task with Ayes Movement Stimuli." Applied Mechanics and Materials 780 (July 2015): 87–96. http://dx.doi.org/10.4028/www.scientific.net/amm.780.87.
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In this paper, an experiment of spike detection based mental task with ayes movement stimuli is reported. The approximation of ICA algorithm is required to eliminate artifacts and detect a pike of brain activity according to the given stimuli which are normal, closed, and blinking ayes. A comparison of ICA algorithms based Extended Fourth Order Blind Identification and Algorithm for Multiple Unknown Signal Extraction is tested. The quality of the extracted signals is measured through the value of the signal to interference ratio and signal to distortion ratio. The extracted results indicate that the best spike detection is achieved using AMUSE algorithm.Keywords:EEG,spike, IndependentComponent Analysis (ICA).
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Rahim, Md, and Jungpil Shin. "Hand Movement Activity-Based Character Input System on a Virtual Keyboard." Electronics 9, no. 5 (May 8, 2020): 774. http://dx.doi.org/10.3390/electronics9050774.
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Nowadays, gesture-based technology is revolutionizing the world and lifestyles, and the users are comfortable and care about their needs, for example, in communication, information security, the convenience of day-to-day operations and so forth. In this case, hand movement information provides an alternative way for users to interact with people, machines or robots. Therefore, this paper presents a character input system using a virtual keyboard based on the analysis of hand movements. We analyzed the signals of the accelerometer, gyroscope, and electromyography (EMG) for movement activity. We explored potential features of removing noise from input signals through the wavelet denoising technique. The envelope spectrum is used for the analysis of the accelerometer and gyroscope and cepstrum for the EMG signal. Furthermore, the support vector machine (SVM) is used to train and detect the signal to perform character input. In order to validate the proposed model, signal information is obtained from predefined gestures, that is, “double-tap”, “hold-fist”, “wave-left”, “wave-right” and “spread-finger” of different respondents for different input actions such as “input a character”, “change character”, “delete a character”, “line break”, “space character”. The experimental results show the superiority of hand gesture recognition and accuracy of character input compared to state-of-the-art systems.
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Suberbiola, Aaron, Ekaitz Zulueta, Jose Manuel Lopez-Guede, Ismael Etxeberria-Agiriano, and Manuel Graña. "Arm Orthosis/Prosthesis Movement Control Based on Surface EMG Signal Extraction." International Journal of Neural Systems 25, no. 03 (April 8, 2015): 1550009. http://dx.doi.org/10.1142/s0129065715500094.
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This paper shows experimental results on electromyography (EMG)-based system control applied to motorized orthoses. Biceps and triceps EMG signals are captured through two biometrical sensors, which are then filtered and processed by an acquisition system. Finally an output/control signal is produced and sent to the actuators, which will then perform the actual movement, using algorithms based on autoregressive (AR) models and neural networks, among others. The research goal is to predict the desired movement of the lower arm through the analysis of EMG signals, so that the movement can be reproduced by an arm orthosis, powered by two linear actuators. In this experiment, best accuracy has achieved values up to 91%, using a fourth-order AR-model and 100ms block length.
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Silaban, Freddy Artadima, Setiyo Budiyanto, and Wahyu Kusuma Raharja. "Stepper motor movement design based on FPGA." International Journal of Electrical and Computer Engineering (IJECE) 10, no. 1 (February 1, 2020): 151. http://dx.doi.org/10.11591/ijece.v10i1.pp151-159.
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<p>A stepper motor is an electro mechanical device that can convert electrical pulses to the axis of movement. The finding problem in the movement of a stepper motor is cannot respond to the clock signal directly because the motor windings require a clock (sequence) in the correct order. If the control signal given is not correct, the motor is not moving according to the specified precision. To answer these problems, it is necessary to move the stepper motor with a clock signal that works in real time. The research method is done by designing and testing the stepper motor movement in full stepp and half step with the direction of Clock Wise (CW) and Counter Clock Wise (CCW) movement. These are simulated by using FPGA Isim and implementation using a stepper motor. The results of several experiments have been carried out the stepper motor movement degree according to the input value entered,responding timely movement, and the direction of movement stepper motor.</p>
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Wang, Nian Nian, Ying Zhi Wang, Li Fu Zhu, Ze Xiang Tan, Di Wang, Yue Sun, Ming Yue Li, and Guo Zhong Liu. "The Design of Control System of Cursor Movement Based EEG." Applied Mechanics and Materials 665 (October 2014): 635–39. http://dx.doi.org/10.4028/www.scientific.net/amm.665.635.
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Brain-Computer Interface (BCI) systems support direct communication and control between brain and external devices without use of peripheral nerves system and muscles. BCI can convert electro-encephalogram (EEG) to the control signal to try repairing function for patients. So the study of BCI can improve the life quality of the patients. This system acquires EEG signals due to the left/right hand motor imagery among the normal subjects. For the processing of motor imagery EEG, we adopt the feature extraction method of second order moment in specific frequency band and the feature classification of linear discriminate analysis. Through the analysis of motor imagery EEG, we convert the data results into external control signal to control the movement of the cursor displayed on the computer. The experimental results show that the EEG analysis method makes it feasible and effective for disabled patients communicating with the outside world, and provides the basis for further study of brain-machine interface. Keywords: EEG; motor imagery; cursor movement; second-order moment.
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Wang, Jiu Hui, and Qiang Ji. "Research on Signal Acquisition Based on Wireless Sensor for Foot Compressive Characteristics on Basketball Movement." Applied Mechanics and Materials 483 (December 2013): 401–4. http://dx.doi.org/10.4028/www.scientific.net/amm.483.401.
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The signal acquisition system (SAS) operated by battery is designed in this paper. SAS includes signal acquisition and statistics function based on movement joints of basketball player. SAS is a recording of the electrical activity of the brain and pulse from the scalp and the recorded waveforms provide insights into the dynamic aspects of brain activity. The amplified SAS signals are digitized by an A/D converter. The digitized signal is transmitted to PC by a wireless serial port or stored in secure digital memory card. Experimental result shows that the system could implement the acquisition and storage of the foot compressive mechanical characteristics signals efficiently. This system would be of benefit to all involved in the use of SAS for sports training.
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Feng, Yongfei, Mingwei Zhong, Xusheng Wang, Hao Lu, Hongbo Wang, Pengcheng Liu, and Luige Vladareanu. "Active triggering control of pneumatic rehabilitation gloves based on surface electromyography sensors." PeerJ Computer Science 7 (April 19, 2021): e448. http://dx.doi.org/10.7717/peerj-cs.448.
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The portable and inexpensive hand rehabilitation robot has become a practical rehabilitation device for patients with hand dysfunction. A pneumatic rehabilitation glove with an active trigger control system is proposed, which is based on surface electromyography (sEMG) signals. It can trigger the hand movement based on the patient’s hand movement trend, which may improve the enthusiasm and efficiency of patient training. Firstly, analysis of sEMG sensor installation position on human’s arm and signal acquisition process were carried out. Then, according to the statistical law, three optimal eigenvalues of sEMG signals were selected as the follow-up neural network classification input. Using the back propagation (BP) neural network, the classifier of hand movement is established. Moreover, the mapping relationship between hand sEMG signals and hand actions is built by training and testing. Different patients choose the same optimal eigenvalues, and the calculation formula of eigenvalues’ amplitude is unique. Due to the differences among individuals, the weights and thresholds of each node in the BP neural network model corresponding to different patients are not the same. Therefore, the BP neural network model library is established, and the corresponding network is called for operation when different patients are trained. Finally, based on sEMG signal trigger, the pneumatic glove training control algorithm was proposed. The combination of the trigger signal waveform and the motion signal waveform indicates that the pneumatic rehabilitation glove is triggered to drive the patient’s hand movement. Preliminary tests have confirmed that the accuracy rate of trend recognition for hand movement is about 90%. In the future, clinical trials of patients will be conducted to prove the effectiveness of this system.
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Harezlak, Katarzyna, Michal Blasiak, and Pawel Kasprowski. "Biometric Identification Based on Eye Movement Dynamic Features." Sensors 21, no. 18 (September 8, 2021): 6020. http://dx.doi.org/10.3390/s21186020.
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The paper presents studies on biometric identification methods based on the eye movement signal. New signal features were investigated for this purpose. They included its representation in the frequency domain and the largest Lyapunov exponent, which characterizes the dynamics of the eye movement signal seen as a nonlinear time series. These features, along with the velocities and accelerations used in the previously conducted works, were determined for 100-ms eye movement segments. 24 participants took part in the experiment, composed of two sessions. The users’ task was to observe a point appearing on the screen in 29 locations. The eye movement recordings for each point were used to create a feature vector in two variants: one vector for one point and one vector including signal for three consecutive locations. Two approaches for defining the training and test sets were applied. In the first one, 75% of randomly selected vectors were used as the training set, under a condition of equal proportions for each participant in both sets and the disjointness of the training and test sets. Among four classifiers: kNN (k = 5), decision tree, naïve Bayes, and random forest, good classification performance was obtained for decision tree and random forest. The efficiency of the last method reached 100%. The outcomes were much worse in the second scenario when the training and testing sets when defined based on recordings from different sessions; the possible reasons are discussed in the paper.
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Dhamija, Srishti, Alolika Gon, Pradeep Varakantham, and William Yeoh. "Online Traffic Signal Control through Sample-Based Constrained Optimization." Proceedings of the International Conference on Automated Planning and Scheduling 30 (June 1, 2020): 366–74. http://dx.doi.org/10.1609/icaps.v30i1.6682.
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Traffic congestion reduces productivity of individuals by increasing time spent in traffic and also increases pollution. To reduce traffic congestion by better handling dynamic traffic patterns, recent work has focused on online traffic signal control. Typically, the objective in traffic signal control is to minimize expected delay over all vehicles given the uncertainty associated with the vehicle turn movements at intersections. In order to ensure responsiveness in decision making, a typical approach is to compute a schedule that minimizes the delay for the expected scenario of vehicle movements instead of minimizing expected delay over the feasible vehicle movement scenarios. Such an approximation degrades schedule quality with respect to expected delay as vehicle turn uncertainty at intersections increases. We introduce TUSERACT (TUrn-SamplE-based Real-time trAffic signal ConTrol), an approach that minimizes expected delay over samples of turn movement uncertainty of vehicles. Specifically, our key contributions are: (a) By exploiting the insight that vehicle turn movements do not change with traffic signal control schedule, we provide a scalable constraint program formulation to compute a schedule that minimizes expected delay across multiple vehicle movement samples for a traffic signal; (b) a novel mechanism to coordinate multiple traffic signals through vehicle turn movement samples; and (c) a comprehensive experimental evaluation to demonstrate the utility of TUSERACT over SURTRAC, a leading approach for online traffic signal control which makes the aforementioned approximation. Our approach provides substantially lower (up to 60%) mean expected delay relative to SURTRAC with very few turn movement samples while providing real-time decision making on both real and synthetic networks.
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Hwang, Su, Yu Lee, Do Jeong, and Kwang Park. "Unconstrained Sleep Stage Estimation Based on Respiratory Dynamics and Body Movement." Methods of Information in Medicine 55, no. 06 (2016): 545–55. http://dx.doi.org/10.3414/me15-01-0140.
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SummaryObjectives: The aim of this study is to establish a sleep monitoring method that can classify sleep into four stages in an unconstrained manner using a polyvinylidene fluoride (PVDF) sensor for continuous and accurate estimation of sleep stages.Methods: The study participants consisted of 12 normal subjects and 13 obstructive sleep apnea (OSA) patients. The physiological signals of the subjects were unconstrainedly measured using the PVDF sensor during polysomnography. The respiration and body movement signals were extracted from the PVDF data. Rapid eye movement (REM) sleep was estimated based on the average rate and variability of the respiratory signal. Wakefulness was detected based on the body movement signal. Variability of the respira -tory rate was chosen as an indicator for slow-wave sleep (SWS) detection. Sleep was divided into four stages (wake, light, SWS, and REM) based on the detection results.Results: The performance of the method was assessed by comparing the results with a manual scoring by a sleep physician. In an epoch-by-epoch analysis, the method classified the sleep stages with an average accuracy of 70.9 % and kappa statistics of 0.48. No significant differences were observed in the detection performance between the normal and OSA groups.Conclusions: The developed system and methods can be applied to a home sleep monitoring system.
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Mulam, Harikrishna, and Malini Mudigonda. "EOG-based eye movement recognition using GWO-NN optimization." Biomedical Engineering / Biomedizinische Technik 65, no. 1 (January 28, 2020): 11–22. http://dx.doi.org/10.1515/bmt-2018-0109.
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AbstractIn recent times, the control of human-computer interface (HCI) systems is triggered by electrooculography (EOG) signals. Eye movements recognized based on the EOG signal pattern are utilized to govern the HCI system and do a specific job based on the type of eye movement. With the knowledge of various related examinations, this paper intends a novel model for eye movement recognition based on EOG signals by utilizing Grey Wolf Optimization (GWO) with neural network (NN). Here, the GWO is used to minimize the error function from the classifier. The performance of the proposed methodology was investigated by comparing the developed model with conventional methods. The results reveal the loftier performance of the adopted method with the error minimization analysis and recognition performance analysis in correspondence with varied performance measures such as accuracy, sensitivity, specificity, precision, false-positive rate (FPR), false-negative rate (FNR), negative predictive value (NPV), false discovery rate (FDR) and the F1 score.
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LIU, JIANLI, XUWEN LI, SONG ZHANG, QIANG ZHANG, LIN YANG, YIMIN YANG, and DONGMEI HAO. "FETAL MOVEMENT SIGNAL DETECTION METHOD BASED ON MULTIPLE PRESSURE SENSORS." Journal of Mechanics in Medicine and Biology 21, no. 05 (April 17, 2021): 2140024. http://dx.doi.org/10.1142/s0219519421400248.
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Objective: Fetal movement (FM) is one of the objective phenomena of fetal life. The decrease of FM is a clinical indicator of fetal hypoxia. A fetal motion detection method is based on multiple pressure sensors and multi features. Methods: Collecting the abdominal data of pregnant women using multi-pressure sensors, preprocessing the signal with a digital filter, extracting the time–frequency characteristics of FM signal. The designed classifier is used for recognition of the extracted features. Results: The detection system used for FM time series can provide reliable results with a detection rate of 93.0% and a positive rate is 75.9%. Conclusion: The portable detection system proposed in this paper is a good alternative that will optimize medical use, professionals and hospital resources and has potential application prospects in the field of home electronic medical treatment.
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Zhang, Dong Heng, Xiu Lin Xu, and Xu Dong Guo. "Development of Stimulator Based on Audio-Visual Feedback Signal." Applied Mechanics and Materials 568-570 (June 2014): 359–62. http://dx.doi.org/10.4028/www.scientific.net/amm.568-570.359.
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To improve the muscle function handicap and enhance the body movement function of the stroke patients, a new medical instrument, based on audio-visual feedback, is developed. The designed stimulator regards sound (voice) and light signal (flash lamp) as the command signals. With remind of both voice and visualization signal, trainers can take the initiative to participate in training and try their best to generate a weak electromyographic signal. It provides a new treatment platform for stroke patients, which can play a positive role in the rebuilding of cerebral nerve net, the rehabilitation of body movement function diseases, the protection of brain function and psychological rehabilitation. The real-time monitoring and regulating function for the stimulating current was innovatively achieved. With the above functions, it’s easy for doctors to set up different stimulating intensities for different patients. This stimulator also has advantages of safer noninvasive, easy to carry and advanced human-computer interaction function, all of these make contributions to building up the muscle strength and the rehabilitation of body movement.
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Kuo, Chao-Hung, Timothy M. Blakely, Jeremiah D. Wander, Devapratim Sarma, Jing Wu, Kaitlyn Casimo, Kurt E. Weaver, and Jeffrey G. Ojemann. "Context-dependent relationship in high-resolution micro-ECoG studies during finger movements." Journal of Neurosurgery 132, no. 5 (May 2020): 1358–66. http://dx.doi.org/10.3171/2019.1.jns181840.
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OBJECTIVEThe activation of the sensorimotor cortex as measured by electrocorticographic (ECoG) signals has been correlated with contralateral hand movements in humans, as precisely as the level of individual digits. However, the relationship between individual and multiple synergistic finger movements and the neural signal as detected by ECoG has not been fully explored. The authors used intraoperative high-resolution micro-ECoG (µECoG) on the sensorimotor cortex to link neural signals to finger movements across several context-specific motor tasks.METHODSThree neurosurgical patients with cortical lesions over eloquent regions participated. During awake craniotomy, a sensorimotor cortex area of hand movement was localized by high-frequency responses measured by an 8 × 8 µECoG grid of 3-mm interelectrode spacing. Patients performed a flexion movement of the thumb or index finger, or a pinch movement of both, based on a visual cue. High-gamma (HG; 70–230 Hz) filtered µECoG was used to identify dominant electrodes associated with thumb and index movement. Hand movements were recorded by a dataglove simultaneously with µECoG recording.RESULTSIn all 3 patients, the electrodes controlling thumb and index finger movements were identifiable approximately 3–6-mm apart by the HG-filtered µECoG signal. For HG power of cortical activation measured with µECoG, the thumb and index signals in the pinch movement were similar to those observed during thumb-only and index-only movement, respectively (all p > 0.05). Index finger movements, measured by the dataglove joint angles, were similar in both the index-only and pinch movements (p > 0.05). However, despite similar activation across the conditions, markedly decreased thumb movement was observed in pinch relative to independent thumb-only movement (all p < 0.05).CONCLUSIONSHG-filtered µECoG signals effectively identify dominant regions associated with thumb and index finger movement. For pinch, the µECoG signal comprises a combination of the signals from individual thumb and index movements. However, while the relationship between the index finger joint angle and HG-filtered signal remains consistent between conditions, there is not a fixed relationship for thumb movement. Although the HG-filtered µECoG signal is similar in both thumb-only and pinch conditions, the actual thumb movement is markedly smaller in the pinch condition than in the thumb-only condition. This implies a nonlinear relationship between the cortical signal and the motor output for some, but importantly not all, movement types. This analysis provides insight into the tuning of the motor cortex toward specific types of motor behaviors.
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Kaur, Amanpreet, Amod Kumar, and Ravinder Agarwal. "Wavelet Based Machine Learning Technique to Classify the Different Shoulder Movement of Upper Limb Amputee." Journal of Biomimetics, Biomaterials and Biomedical Engineering 31 (March 2017): 32–43. http://dx.doi.org/10.4028/www.scientific.net/jbbbe.31.32.
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The wavelet transform is an accurate, efficient and efficacious method to improve the quality of the myoelectric signal. Classification of the signal from upper limb using Surface Electromyogram (SEMG) signal has been the matter of extensive research. Number of methods and algorithms have been described by researchers to classify biomedical signals. The main aim of this paper to extract the different coefficient values from the given SEMG data by using Discrete Wavelet Transform (DWT). Afterward, random forest machine learning algorithm was used to identify the different shoulder movement of an upper limb amputee. The combination of wavelet coefficients and random forest exhibited the best performance with 99.2% accuracy for the classification of different shoulder motions. It was found that the different motion can be identified accurately and provide the fundamental information to develop an efficient prosthetic device.
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Mohd Noor, Nurul Muthmainnah, Salmiah Ahmad, and Sharul Naim Sidek. "Implementation of Wheelchair Motion Control Based on Electrooculography Using Simulation and Experimental Performance Testing." Applied Mechanics and Materials 554 (June 2014): 551–55. http://dx.doi.org/10.4028/www.scientific.net/amm.554.551.
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The aim of this study is to perform the experimental verification on the fuzzy-based control designed for wheelchair motion. This motion control based on the eye movement signals using electrooculograhphy (EOG) technique. The EOG is a technique to acquire the eye movement data from a person, i.e tetraplegia, which the data obtained, can be used as a main communication tool. This study is about the implementation of the designed controller using PD-type fuzzy controller and tested on the hardware of the wheelchair system using the eye movement signal obtained through EOG technique as the motion input references. The results obtained show that the PD-type fuzzy logic controller designed has successfully managed to track the input reference for linear motion set (forward and backward direction) by the EOG signal.
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Perpetuini, David, Daniela Cardone, Chiara Filippini, Antonio Maria Chiarelli, and Arcangelo Merla. "A Motion Artifact Correction Procedure for fNIRS Signals Based on Wavelet Transform and Infrared Thermography Video Tracking." Sensors 21, no. 15 (July 28, 2021): 5117. http://dx.doi.org/10.3390/s21155117.
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Functional near infrared spectroscopy (fNIRS) is a neuroimaging technique that allows to monitor the functional hemoglobin oscillations related to cortical activity. One of the main issues related to fNIRS applications is the motion artefact removal, since a corrupted physiological signal is not correctly indicative of the underlying biological process. A novel procedure for motion artifact correction for fNIRS signals based on wavelet transform and video tracking developed for infrared thermography (IRT) is presented. In detail, fNIRS and IRT were concurrently recorded and the optodes’ movement was estimated employing a video tracking procedure developed for IRT recordings. The wavelet transform of the fNIRS signal and of the optodes’ movement, together with their wavelet coherence, were computed. Then, the inverse wavelet transform was evaluated for the fNIRS signal excluding the frequency content corresponding to the optdes’ movement and to the coherence in the epochs where they were higher with respect to an established threshold. The method was tested using simulated functional hemodynamic responses added to real resting-state fNIRS recordings corrupted by movement artifacts. The results demonstrated the effectiveness of the procedure in eliminating noise, producing results with higher signal to noise ratio with respect to another validated method.
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Huang, Pingao, Hui Wang, Yuan Wang, Zhiyuan Liu, Oluwarotimi Williams Samuel, Mei Yu, Xiangxin Li, Shixiong Chen, and Guanglin Li. "Identification of Upper-Limb Movements Based on Muscle Shape Change Signals for Human-Robot Interaction." Computational and Mathematical Methods in Medicine 2020 (April 14, 2020): 1–14. http://dx.doi.org/10.1155/2020/5694265.
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Towards providing efficient human-robot interaction, surface electromyogram (EMG) signals have been widely adopted for the identification of different limb movement intentions. Since the available EMG signal sensors are highly susceptible to external interferences such as electromagnetic artifacts and muscle fatigues, the quality of EMG recordings would be mostly corrupted, which may decay the performance of EMG-based control systems. Given the fact that the muscle shape changes (MSC) would be different when doing various limb movements, the MSC signal would be nonsensitive to electromagnetic artifacts and muscle fatigues and maybe promising for movement intention recognition. In this study, a novel nanogold flexible and stretchable sensor was developed for the acquisition of MSC signals utilized for decoding multiple classes of limb movement intents. More precisely, four sensors were used to measure the MSC signals from the right forearm of each subject when they performed seven classes of movements. Also, six different features were extracted from the measured MSC signals, and a linear discriminant analysis- (LDA-) based classifier was built for movement classification tasks. The experimental results showed that using MSC signals could achieve an average recognition rate of about 96.06 ± 1.84% by properly placing the four flexible and stretchable sensors on the forearm. Additionally, when the MSC sampling rate was greater than 100 Hz and the analysis window length was greater than 20 ms, the movement recognition accuracy would be only slightly increased. These pilot results suggest that the MSC-based method should be feasible in movement identifications for human-robot interaction, and at the same time, they provide a systematic reference for the use of the flexible and stretchable sensors in human-robot interaction systems.
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Huo, Yingda, Fubao Li, Qin Li, Enqiu He, and Jichi Chen. "A Novel Method for Hand Movement Recognition Based on Wavelet Packet Transform and Principal Component Analysis with Surface Electromyogram." Computational Intelligence and Neuroscience 2022 (November 8, 2022): 1–12. http://dx.doi.org/10.1155/2022/8125186.
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As an input method of signal language, the hand movement classification technology has developed into one of the ways of natural human-computer interaction. The surface electromyogram (sEMG) signal contains abundant human movement information and has significant advantages as the input signal of human-computer interaction. However, how to effectively extract components from sEMG signals to improve the accuracy of hand motion classification is a difficult problem. Therefore, this work proposes a novel method based on wavelet packet transform (WPT) and principal component analysis (PCA) to classify six kinds of hand motions. The method applies WPT to decompose the sEMG signal into multiple sub-band signals. To efficiently extract the intrinsic components of the sEMG signal, the classification performance of different wavelet packet basis functions is evaluated. The PCA algorithm is used to reduce the dimension of the feature space composed of the features reflecting hand motions extracted from each sub-band signal. Besides, to ensure higher classification performance while reducing the dimension of the feature space by the PCA algorithm, the classification performance of different dimensions of the feature space is compared. In addition, the effects of the variability of the sEMG signal and the size of the window on the proposed method are further analyzed. The proposed method was tested on the sEMG for Basic Hand Movements Data Set and achieved an average accuracy of 96.03%. Compared with the existing research, the proposed method has better classification performance, which indicates that the research results can be applied to the fields of exoskeleton robot, rehabilitation training, and intelligent prosthesis.
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Crenna, Francesco, Giovanni Battista Rossi, and Marta Berardengo. "Filtering Biomechanical Signals in Movement Analysis." Sensors 21, no. 13 (July 4, 2021): 4580. http://dx.doi.org/10.3390/s21134580.
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Biomechanical analysis of human movement is based on dynamic measurements of reference points on the subject’s body and orientation measurements of body segments. Collected data include positions’ measurement, in a three-dimensional space. Signal enhancement by proper filtering is often recommended. Velocity and acceleration signal must be obtained from position/angular measurement records, needing numerical processing effort. In this paper, we propose a comparative filtering method study procedure, based on measurement uncertainty related parameters’ set, based upon simulated and experimental signals. The final aim is to propose guidelines to optimize dynamic biomechanical measurement, considering the measurement uncertainty contribution due to the processing method. Performance of the considered methods are examined and compared with an analytical signal, considering both stationary and transient conditions. Finally, four experimental test cases are evaluated at best filtering conditions for measurement uncertainty contributions.
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Schepens, Bénédicte, and Trevor Drew. "Independent and Convergent Signals From the Pontomedullary Reticular Formation Contribute to the Control of Posture and Movement During Reaching in the Cat." Journal of Neurophysiology 92, no. 4 (October 2004): 2217–38. http://dx.doi.org/10.1152/jn.01189.2003.
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We have addressed the nature of the postural control signals contained within the discharge activity of neurons in the pontomedullary reticular formation, including reticulospinal neurons, during a reaching task in the cat. We recorded the activity of 142 neurons during ipsilateral reaching movements that required anticipatory postural adjustments (APAs) in the supporting limbs to maintain equilibrium. Discharge activity in 82/142 (58%) neurons was significantly increased before the onset of the reach. Most of these neurons discharged either in a phasic (22/82), tonic (10/82), or phasic/tonic (41/82) pattern. In each of these 3 groups, the onset of the discharge activity in some neurons was temporally related either to the go signal or to the onset of the movement. In many neurons, one component of the discharge sequence was better related to the go signal and another to the onset of the movement. Based on our previous behavioral study during the same task, we suggest that reticular neurons in which the discharge activity is better related to the go signal contribute to the initiation of the APAs that precede the movement. Neurons in which the discharge activity is better related to the movement signal might contribute to the initiation of the movement and to the production of the postural responses that accompany that movement. Together our results suggest the existence of neurons that signal posture and movement independently and others that encode a convergent signal that contributes to the control of both posture and movement.
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Liu, Bin, Yuxi Ruan, and Yanguang Yu. "Determining System Parameters and Target Movement Directions in a Laser Self-Mixing Interferometry Sensor." Photonics 9, no. 9 (August 29, 2022): 612. http://dx.doi.org/10.3390/photonics9090612.
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Self-mixing interferometry (SMI) is a promising sensing technology. As well as its compact structure, self-alignment and low implementation cost, it has an important advantage that conventional two-beam interferometry does not have, i.e., SMI signal fringe evolves into asymmetrical shape with increasing optical feedback level, which leads to discrimination of target movement directions for unambiguous displacement measurement possible by a single-channel interferometric signal. It is usually achieved by using SMI signals in moderate feedback regime, where the signals exhibit hysteresis and discontinuity. However, in some applications, e.g., in biomedical sensing where the target has a low reflectivity, it is hard for the SMI system to operate in a moderate feedback regime. In this work, we present comprehensive analyses on SMI signal waveforms for determining system parameters and movement directions by a single-channel weak feedback SMI signal. We first investigated the influence of two system parameters, i.e., linewidth enhancement factor and optical feedback factor, on the symmetry of SMI signals. Based on the analyses on signal waveform, we then proposed a method of estimating the system parameters and displacement directions. The method was finally verified by experiments. The results are helpful for developing sensing applications based on weak feedback SMI systems.
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NAMAZI, HAMIDREZA, and SAJAD JAFARI. "DECODING OF WRIST MOVEMENTS’ DIRECTION BY FRACTAL ANALYSIS OF MAGNETOENCEPHALOGRAPHY (MEG) SIGNAL." Fractals 27, no. 02 (March 2019): 1950001. http://dx.doi.org/10.1142/s0218348x19500014.
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Analysis of human movements is an important category of research in biomedical engineering, especially for the rehabilitation purpose. The human’s different movements are usually investigated by analyzing the movement signals. Based on the literatures, fewer efforts have been made in order to investigate how human movements are represented in the brain. In this paper, we decode the movements’ directions of wrist by complexity analysis of Magnetoencephalography (MEG) signal. For this purpose, we employ fractal theory. In fact, we investigate how the complexity of MEG signal changes in case of different wrist movements’ directions. The results of our analysis showed that MEG signal has different level of complexity in response to different movement’s directions. The employed methodology in this research is not limited to the analysis of MEG signal in response to wrist movement, however, it can be applied widely to analyze the influence of different factors (stimuli) on complex structure of other brain signals such as Electroencephalography (EEG) and fMRI signals.
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Prisukhina, Ilona, Dmitry Borisenko, and Sergey Lunev. "Simulation Model of Electric Code-Modulated Signal in Russian Systems of Interval Control of Train Movement Based on Track Circuit." SPIIRAS Proceedings 18, no. 5 (September 19, 2019): 1212–38. http://dx.doi.org/10.15622/sp.2019.18.5.1212-1238.
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Systems of interval control of train movement Signaling systems, which are currently in service in Russian railways, use the electric track circuit as the main data channel between signals and locomotives. Code-modulated electric signals transferred through that channel are frequently get corrupted which leads to railway traffic delays.
Decoding of the electric signal received from a track circuit can be represented as an image classification problem, and thus the stability of the data channel could be significantly improved.
However, to build such a classifier based on some machine learning algorithm, one needs a large dataset. In this article, a simulation model to synthesize this dataset is proposed.
The structure of the computer model matches the main stages of the electric code-modulated signal generation in a track circuit: code signal generator, rails, locomotive receiver.
Based on code signal generator schematic and waveform diagrams, a generator algorithm is developed. At this stage, we modeled timings of electric code signals according to the specification as well as their random deviations caused by various factors.
The analysis of substitution circuits of the rail line revealed that it has the properties of a low-pass filter. So, the rail line using the Butterworth digital filter with corresponding parameters is modeled. Additionally, at this stage, random noise during transmission was taken into account.
A similar technique is applied for modeling of a locomotive receiver which has a band-pass filter as the first signal processing block.
Thus, the proposed simulation model consists of a set of algorithms which run in series. By varying the parameters of the model, one can synthesize waveform diagrams of the electric code-modulated signal received by the locomotive equipment from a track circuit working in various modes and conditions.
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Norali, A. N., M. N. Anas, Z. Zakaria, M. Asymawi, A. H. Abu Bakar, and Y. F. Chong. "Electromyography Signal Pattern Recognition for Movement of Shoulder." Journal of Physics: Conference Series 2071, no. 1 (October 1, 2021): 012049. http://dx.doi.org/10.1088/1742-6596/2071/1/012049.
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Abstract Pectoralis major and deltoid are two muscles that are associated with the movement of the shoulder. Electromyography (EMG) signal acquired from these two muscles can be used to classify the movement of the shoulder based on pattern recognition. In this paper, an experiment for EMG data collection involves eight healthy male subjects who perform four shoulder movements which are flexion, extension, internal rotation and external rotation. Feature extraction of EMG data is done using root mean square (RMS), variance (VAR) and zero crossing (ZC). For pattern recognition, the classifiers that are used are Support Vector Machine (SVM), K-Nearest Neighbour (KNN), Linear Discriminant Analysis (LDA) and Quadratic Discriminant Analysis (QDA). Classification results shows highest accuracy on ZC feature using an SVM classifier with cubic kernel. The study on shoulder movement using EMG of pectoralis and deltoid muscles could be extended on arm amputees based on hypothesis that the EMG signal could be utilized for control of robotic prosthetic arm.
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Harezlak, Katarzyna, and Pawel Kasprowski. "Understanding Eye Movement Signal Characteristics Based on Their Dynamical and Fractal Features." Sensors 19, no. 3 (February 1, 2019): 626. http://dx.doi.org/10.3390/s19030626.
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Eye movement is one of the biological signals whose exploration may reveal substantial information, enabling greater understanding of the biology of the brain and its mechanisms. In this research, eye movement dynamics were studied in terms of chaotic behavior and self-similarity assessment to provide a description of young, healthy, oculomotor system characteristics. The first of the investigated features is present and advantageous for many biological objects or physiological phenomena, and its vanishing or diminishment may indicate a system pathology. Similarly, exposed self-similarity may prove useful for indicating a young and healthy system characterized by adaptability. For this research, 24 young people with normal vision were involved. Their eye movements were registered with the usage of a head-mounted eye tracker, using infrared oculography, embedded in the sensor, measuring the rotations of the left and the right eye. The influence of the preprocessing step in the form of the application of various filtering methods on the assessment of the final dynamics was also explored. The obtained results confirmed the existence of chaotic behavior in some parts of eye movement signal; however, its strength turned out to be dependent on the filter used. They also exposed the long-range correlation representing self-similarity, although the influence of the applied filters on these outcomes was not unveiled.
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Jo, Hyeong Geun. "Moving object detection and tracking based on Doppler ultrasound." INTER-NOISE and NOISE-CON Congress and Conference Proceedings 263, no. 2 (August 1, 2021): 4565–69. http://dx.doi.org/10.3397/in-2021-2745.
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Fetal health monitoring during pregnancy has become a necessary procedure. Fetal heart rate (FHR) monitoring can determine fetal development or presence of heart disease and evaluate fetal well-being. The FHR measurement uses typically an acoustic probe-based Doppler ultrasound. Doppler ultrasound method transmits a continuous wave signal to the abdomen of a pregnant woman to receive a reflected signal from the fetal heart. Periodic displacement of the heart tissue produces the Doppler effect and the phase change of the reflected wave is proportional to the velocity of the fetal heart. The reflected signal is modulated into a phase signal and the received signal is demodulated to detect the heart rate. The current clinician system consists of a single probe and requires the probe to be manipulated to the optimal position to measure FHR. The system is highly dependent on trained diagnostic experts. The movement of the pregnant woman and the fetus leads to the misaligned acoustic beam which degrades the reliability of the measurement. This work presents a detection and tracking system using a Doppler signal to compensate for the target's movement. The system is implemented by integrating multi-channel probes interfaced to a Doppler signal converter with a 2-degree of freedom (DOF) motor device. This work describes the characteristics of two key components: Doppler signals of multi-channel probes according to the direction of the acoustic beam and the algorithm with a 2-DOF tracking system.
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Jeong, Won Ho, Hong-Rak Choi, and Kyung-Seok Kim. "Empirical Path-Loss Modeling and a RF Detection Scheme for Various Drones." Wireless Communications and Mobile Computing 2018 (December 6, 2018): 1–17. http://dx.doi.org/10.1155/2018/6795931.
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This paper presents a path-loss model based on a radio-frequency (RF) detection scheme for various drones using 5G aerial communication over an industrial, scientific, and medical radio band (ISM band) network. We considered three communication modes of the ISM band for the channel characteristics analysis: the DJI Enhanced Spread Spectrum Technology (DESST) protocol, Wi-Fi, and Bluetooth. The drone signal detection scheme extracts the drone signal from the environment mixed with the general signal. The drone DESST signal is identified through cross-correlation of the received signal. The Wi-Fi and Bluetooth signals are identified with the singular-value decomposition (SVD) algorithm by using the hopping characteristics. General and drone Wi-Fi signals are separated by in-phase/quadrature (I/Q) phase analysis over the measurement time. The windowed received signal strength indicator (RSSI) moving detection (WRMD) analysis identifies the drone Bluetooth signal according to the movement of the drone. The detected drone signal is channel modeled by the horizontal distance d according to the altitude θ. Finally, they verify their model by a ray-tracing simulation similar to the real environment. The model provides a simple and accurate prediction for designing future aerial communications systems according to changes in drone movement.
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Iqbal, Nadeem, Tufail Khan, Mukhtaj Khan, Tahir Hussain, Tahir Hameed, and Syed Ahmad Chan Bukhari. "Neuromechanical Signal-Based Parallel and Scalable Model for Lower Limb Movement Recognition." IEEE Sensors Journal 21, no. 14 (July 15, 2021): 16213–21. http://dx.doi.org/10.1109/jsen.2021.3076114.
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Gall, Roman. "CONSIDERATION OF PHASE DISTORTIONS CAUSED BY THE MOVEMENT OF GEOSTATIONARY REPEATERS WHEN LOCATING GROUND-BASED RADIO EMISSION SOURCES." T-Comm 15, no. 8 (2021): 22–29. http://dx.doi.org/10.36724/2072-8735-2021-15-8-22-29.
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Currently, there are often cases of illegal use of the resource of relay satellites located in geostationary orbit, and the creation of unintentional and deliberate interference with legal users of satellite communication systems, for example due to non-compliance with the power standards of radio transmitting devices and antenna radiation patterns, as well as the rules for the frequency spectrum regulating. One of the possible stages of the response by the radio frequency service and satellite systems operators to such situations may be an operational assessment of locating of the interference ground radio emission sources that violate the established requirements. The existing methods for estimating the coordinates of radio emission sources operating through geostationary satellites-repeaters involve calculating the Cross-Ambiguity Function (CAF) of signals received from several satellites that relay the signals of the main and side lobes of the antenna pattern of the geolocated source. In the case of a low received signals SNR, it is required to record signals for a long time, and in such cases, to achieve a sufficient SNR at the correlator output, it is necessary to take into account not only the Doppler frequency shift between the signals, but also the change in the frequency shift caused by the change in the velocity vectors of the repeater satellites. The aim of this work is to study the recording duration, at which it is required to take into account phase distortions caused by a change in the speed of the repeater satellite and their effect on the SNR at the correlator output, as well as to develop a method for accounting for such distortions. The theory of digital signal processing and the method of simulation were used as research methods. As a result of the study, an assessment was made of the duration of the signal recording, at which the Doppler frequency shift can be considered constant; introduced the concept of a modified CAF, which takes into account the change in the Doppler frequency shift due to its approximation by a linear function; the maximum duration of signal recording was estimated, at which the proposed linear approximation is valid. It is concluded that in the case of using the modified CAF, the minimum duration of signal recording, at which the absence of correlator output SNR degradation will be guaranteed, is 8.1 times greater than when using the traditional CAF.
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Zhao, Huayu. "Design and Application of Human Movement Respiratory and ECG Signal Acquisition System." Journal of Medical Imaging and Health Informatics 10, no. 4 (April 1, 2020): 890–97. http://dx.doi.org/10.1166/jmihi.2020.2950.
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To realize the design of mobile phone human movement breathing and electrocardiograph (ECG) signal acquisition system based on Bluetooth transmission, the principle of the generation and detection of ECG and respiratory signal and the guide system of signal acquisition are analyzed. Additionally, the hardware of the system is designed, including the hardware of the signal acquisition system, the design of ADS1292R ECG and respiratory signal acquisition module, the design of the main control chip and the design of the Bluetooth module. Then, the digital filtering processing of the ECG and respiratory signals is completed, including the baseline drift filtering and the suppression of the power frequency interference. The results show that the monitoring system runs well and it can effectively collect ECG and respiratory signals, calculate heart rate and respiratory frequency in real time, and display ECG waveform in real time. To sum up, the monitoring system is of great significance for real-time monitoring of the patient's condition.
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Khorev, V. S., V. A. Maksimenko, E. N. Pitsik, A. E. Runnova, S. A. Kurkin, and A. E. Hramov. "Analysis of motor activity using electromyogram signals." Information and Control Systems, no. 3 (June 21, 2019): 114–20. http://dx.doi.org/10.31799/1684-8853-2019-3-114-120.
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Introduction: Methods of detecting the start of a movement and moments of movement planning are important in neuroscience. Using the signals of electrical activity of muscles (electromyograms) in order to precisely detect the moment of movement is a special problem, because the initial signals are complex, non-stationary and affected by noise. It is especially important in experiments with simultaneous registration of an EEG and an electromyogram, when you have to analyze the interaction between brain structures.Purpose: Development of methods for electromyogram data analysis and techniques for their use in a detailed study of motor activity.Methods: We use the threshold detection method based on calculating the derivative of the original signal filtered and smoothed. Such an approach makes it possible to estimate the starting points of the onset of motion relatively quickly and accurately, even along a part of a time series.Results: We have developed a technique which allows you to automatically detect the precursor of a movement start, based on the analysis of electromyographic signals. We have calculated the distribution of the delay between the presentation of a sound signal and the beginning of a movement, and evaluated the statistical properties of this distribution.Practical relevance: The results of this research can be used to automatically detect starting points in experiments with simultaneous EEG recording, and later be applied to solve practical problems related to the development of controlled prostheses for the rehabilitation of people with disabilities.
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Azlan Abu, Mohd, Syazwani Rosleesham, Mohd Zubir Suboh, Mohd Syazwan Md Yid, Zainudin Kornain, and Nurul Fauzani Jamaluddin. "Classification of EMG signal for multiple hand gestures based on neural network." Indonesian Journal of Electrical Engineering and Computer Science 17, no. 1 (January 1, 2020): 256. http://dx.doi.org/10.11591/ijeecs.v17.i1.pp256-263.
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<span>This paper presents the classification of EMG signal for multiple hand gestures based on neural network. In this study, the Electromyography is used to measure the muscle cell’s electrical activities which is commonly represented in a function time. Every muscle has their own signals, which was produced in every movement. Surface electromyography (sEMG) is used as a non-invasive technique for acquiring the EMG signal. The development of sensors’ detection and measuring the EMG have been improved and have become more precise while maintaining a small size. In this paper, the main objective is to identify the hand gestures based on: (1) Cylindrical Grasp, (2) Supination (Twist Left), (3) Pronation (Twist Right), (4) Resting Hand and (5) Open Hand that are predefined by using Arduino IDE, CoolTerm software and Microsoft Excel before using artificial neural network for classifying purposes in MATLAB. Finally, the extraction of the EMG patterns for each movement went through features extraction of the signals which is used to train the classifier in MATLAB to classify signals in the neural network. The features extracted are using mean absolute value (MAV), median, waveform length (WL) and root mean square (RMS). The Artificial Neural Network (ANN) produced accuracy of 80% for training and testing for 10 hidden neurons layer.</span>
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Wang, Ai Yu, Hong Xia Pan, and Hui Ling Liu. "Fault Feature Extraction of High-Speed Automaton Based on Motion Morphology Decomposition." Applied Mechanics and Materials 347-350 (August 2013): 224–27. http://dx.doi.org/10.4028/www.scientific.net/amm.347-350.224.
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In order to obtain the characteristic parameters reflecting fault state of high-speed automaton (HSA), the fault feature extraction method based on motion morphology decomposition and wavelet packet transform (WPT) was presented. According to the movement law of the automaton, the vibration signal generated in three bursts of fire was decomposed into three separate signals, then the response signal in each shooting is a separate signal. Then using WPT to respectively extract wavelet packet energy from three separate signals as the fault characteristic parameters of HSA. By the example, the results show that the extracted fault features can well reflect the working conditions of automaton. Thus the proposed method could be used to extract the fault feature of automaton for monitoring the condition and diagnosing the fault of automaton.
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Urtnasan, Erdenebayar, Jong-Uk Park, Jung-Hun Lee, Sang-Baek Koh, and Kyoung-Joung Lee. "Deep Learning for Automatic Detection of Periodic Limb Movement Disorder Based on Electrocardiogram Signals." Diagnostics 12, no. 9 (September 3, 2022): 2149. http://dx.doi.org/10.3390/diagnostics12092149.
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In this study, a deep learning model (deepPLM) is shown to automatically detect periodic limb movement syndrome (PLMS) based on electrocardiogram (ECG) signals. The designed deepPLM model consists of four 1D convolutional layers, two long short-term memory units, and a fully connected layer. The Osteoporotic Fractures in Men sleep (MrOS) study dataset was used to construct the model, including training, validating, and testing the model. A single-lead ECG signal of the polysomnographic recording was used for each of the 52 subjects (26 controls and 26 patients) in the MrOS dataset. The ECG signal was normalized and segmented (10 s duration), and it was divided into a training set (66,560 episodes), a validation set (16,640 episodes), and a test set (20,800 episodes). The performance evaluation of the deepPLM model resulted in an F1-score of 92.0%, a precision score of 90.0%, and a recall score of 93.0% for the control set, and 92.0%, 93.0%, and 90.0%, respectively, for the patient set. The results demonstrate the possibility of automatic PLMS detection in patients by using the deepPLM model based on a single-lead ECG. This could be an alternative method for PLMS screening and a helpful tool for home healthcare services for the elderly population.
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Ling, Yin. "Advanced information processing of MEMS motion sensors for gesture interaction." Journal of Sensors and Sensor Systems 5, no. 2 (December 13, 2016): 419–31. http://dx.doi.org/10.5194/jsss-5-419-2016.
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Abstract. Sensor-based gesture interaction technology has been widely adopted in consumer electronics. Nevertheless, bias, drift, and noise existing in sensor signals are difficult to eliminate, and accurate movement trajectory information is still needed to achieve flexible interaction application. This paper presents micro-electro-mechanical system (MEMS) motion sensor information processing algorithms designed on a gesture interaction system which integrates multiple low-cost MEMS motion sensors with ZigBee wireless technology to support embodied communication while acting together with machines. Sensor signal processing systems mainly solve noise removal, signal smoothing, gravity influence separation, coordinate system conversion, and position information retrieval. The attitude information which is an important movement parameter and required by position estimation is calculated with a quaternion-based extended Kalman filter (EKF). The effectiveness of the movement information retrieval of this gesture interface is verified by experiments and test analysis, both in static and moving cases. In the end, related applications of the described sensor information processing are discussed.
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NAMAZI, HAMIDREZA, and TIRDAD SEIFI ALA. "DECODING OF SIMPLE AND COMPOUND LIMB MOTOR IMAGERY MOVEMENTS BY FRACTAL ANALYSIS OF ELECTROENCEPHALOGRAM (EEG) SIGNAL." Fractals 27, no. 03 (May 2019): 1950041. http://dx.doi.org/10.1142/s0218348x19500415.
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One of the major attempts in rehabilitation science is to decode different movements of human using physiological signals. Since human movements are mainly controlled by the brain, decoding of movements by analysis of the brain activity has great importance. In this paper, we apply fractal analysis to Electroencephalogram (EEG) signal in order to decode simple and compound limb motor imagery movements. The fractal dimension of EEG signal is analyzed in case of left hand, right hand, both hands, feet, left hand combined with right foot, and right hand combined with left foot movements. Based on the obtained results, EEG signal experiences the lowest and greatest fractal dimension in case of both hands movement, and feet movement, respectively. Besides obtaining different fractal dimension for EEG signal in case of different movements, no significant difference was observed in fractal dimension of EEG signal between different movements. The method of analysis employed in this research can be widely applied to analysis of EEG signal for decoding of different movements of human.
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Deng, Yanxia, Farong Gao, and Huihui Chen. "Angle Estimation for Knee Joint Movement Based on PCA-RELM Algorithm." Symmetry 12, no. 1 (January 8, 2020): 130. http://dx.doi.org/10.3390/sym12010130.
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Surface electromyogram (sEMG) signals are easy to record and offer valuable motion information, such as symmetric and periodic motion in human gait. Due to these characteristics, sEMG is widely used in human-computer interaction, clinical diagnosis and rehabilitation medicine, sports medicine and other fields. This paper aims to improve the estimation accuracy and real-time performance, in the case of the knee joint angle in the lower limb, using a sEMG signal, in a proposed estimation algorithm of the continuous motion, based on the principal component analysis (PCA) and the regularized extreme learning machine (RELM). First, the sEMG signals, collected during the lower limb motion, are preprocessed, while feature samples are extracted from the acquired and preconditioned sEMG signals. Next, the feature samples dimensions are reduced by the PCA, as well as the knee joint angle system is measured by the three-dimensional motion capture system, are followed by the normalization of the feature variable value. The normalized sEMG feature is used as the input layer, in the RELM model, while the joint angle is used as the output layer. After training, the RELM model estimates the knee joint angle of the lower limbs, while it uses the root mean square error (RMSE), Pearson correlation coefficient and model training time as key performance indicators (KPIs), to be further discussed. The RELM, the traditional BP neural network and the support vector machine (SVM) estimation results are compared. The conclusions prove that the RELM method, not only has ensured the validity of results, but also has greatly reduced the learning train time. The presented work is a valuable point of reference for further study of the motion estimation in lower limb.
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Wong, Aaron L., and Mark Shelhamer. "Sensorimotor adaptation error signals are derived from realistic predictions of movement outcomes." Journal of Neurophysiology 105, no. 3 (March 2011): 1130–40. http://dx.doi.org/10.1152/jn.00394.2010.
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Neural systems that control movement maintain accuracy by adaptively altering motor commands in response to errors. It is often assumed that the error signal that drives adaptation is equivalent to the sensory error observed at the conclusion of a movement; for saccades, this is typically the visual (retinal) error. However, we instead propose that the adaptation error signal is derived as the difference between the observed visual error and a realistic prediction of movement outcome. Using a modified saccade-adaptation task in human subjects, we precisely controlled the amount of error experienced at the conclusion of a movement by back-stepping the target so that the saccade is hypometric (positive retinal error), but less hypometric than if the target had not moved (smaller retinal error than expected). This separates prediction error from both visual errors and motor corrections. Despite positive visual errors and forward-directed motor corrections, we found an adaptive decrease in saccade amplitudes, a finding that is well-explained by the employment of a prediction-based error signal. Furthermore, adaptive changes in movement size were linearly correlated to the disparity between the predicted and observed movement outcomes, in agreement with the forward-model hypothesis of motor learning, which states that adaptation error signals incorporate predictions of motor outcomes computed using a copy of the motor command (efference copy).
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Yauri, Ricardo, Antero Castro, Rafael Espino, and Segundo Gamarra. "Implementation of a sensor node for monitoring and classification of physiological signals in an edge computing system." Indonesian Journal of Electrical Engineering and Computer Science 28, no. 1 (October 1, 2022): 98. http://dx.doi.org/10.11591/ijeecs.v28.i1.pp98-105.
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We describe the design and development of sensor nodes, based on Edge computing technologies, for the processing and classification of events detected in physiological signals such as the electrocardiographic signal (ECG is the electrical signal of the heart), temperature, heart rate, and human movement. The edge device uses a 32-bit Tensilica microcontroller-based module with the ability to transmit data wirelessly using Wi-Fi. In addition, algorithms for classification and detection of movement patterns were implemented to be implemented in devices with limited resources and not only in high-performance computers. The Internet of Things and its application in smart environments can help non-intrusive monitoring of daily activities by implementing support vector machine (SVM is a machine learning algorithm) for implementation in embedded systems with low hardware resources. This paper shows experimental results obtained during the acquisition, transmission, and processing of physiological signals in a edge computing system and their visualization in a web application.
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Gong, Jiangkun, Jun Yan, Deren Li, Huiping Hu, Deyong Kong, Wenjing Bao, and Shangde Wu. "Measurement and Analysis of Radar Signals Modulated by the Respiration Movement of Birds." Applied Sciences 12, no. 16 (August 12, 2022): 8101. http://dx.doi.org/10.3390/app12168101.
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Once, bird respiration was thought to be responsible for the 10 dB-level fluctuations in the radar signals of birds. Although, recently, many researchers provide evidence against this, there are almost no quantification measurements of the contribution of respiration to bird signals in microwave anechoic chambers. Here, we first measured the radar signals modulated by the respiration of birds in a microwave anechoic chamber. Theoretically, the simulated signal fluctuation caused by the respiration of a 1 kg standard avian target (SAT) duck is approximately 1.2 dB based on the water sphere model. Then, experimentally, in a microwave anechoic chamber, we measured the signal fluctuations produced by the respiration movement of ducks using a dynamic system composed of a network analyzer and a high-speed camera. We tracked continuous radar data of a living duck and a dead duck within the S-band, X-band, and Ku-band, and then presented them using low-resolution range profiles (LRRP) and high-resolution range profiles (HRRP). The results indicate that respiration movement causes periodic signal fluctuation with a respiration rate of approximately 0.7 Hz, but the amplitudes within S-band, X-band, and Ku-band are approximately 1 dB level, much less than the 10 dB level. Respiration is not responsible for the 10 dB-level periodic signal fluctuation in radar echoes from birds.
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Exaudi, Kemahyanto, Rendyansyah Rendyansyah, and Aditya Putra Perdana Prasetyo. "Kontrol Robot Menggunakan Gerakan Mata Berbasis Sinyal Electrooculography (EOG)." Jurnal ELTIKOM 5, no. 2 (September 10, 2021): 100–109. http://dx.doi.org/10.31961/eltikom.v5i2.464.
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Biomedical technology has now been widely adopted as a means of monitoring the human body in real-time. For example, to detect eye movement. In the medical world, eye movement can be used to determine the type of disease. With the application of human-machine interface (HMI) technology, eyeball movement can be developed in the robotics industry as robot navigation. For example, by moving the eyeball left and right, the robot can interpret the eye signal to move left and right. The interaction between the eyeball movement and the robot is of particular concern in this study. This study aimed to design a measuring instrument for eye movement detection using Electrooculography (EOG) techniques to move a wheeled robot. The EOG measuring instrument consisting of an instrument differential amplifier, a low pass filter, and a high pass filter has been applied in this research. The signal generator technique on EOG is carried out by placing electrodes on three sides of the face, namely forehead (G), left horizontal (H-), right horizontal (H +). The experimental results showed a significant difference between the left and right eye movement amplitude signals. This amplitude is used to classify the movement of the robot wheel towards the left and right. The process of sending robot signals and EOG measuring instruments uses Bluetooth HC-05 serial communication. Based on the research results, it is proven that the robot manages to move left and right according to the eyeball movement.
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Marasanov, Volodymyr, and Artem Sharko. "Mathematical Models for Interrelation of Characteristics of the Developing Defects with the Parameters of Acoustic Emission Signals." International Frontier Science Letters 10 (December 2016): 37–44. http://dx.doi.org/10.18052/www.scipress.com/ifsl.10.37.
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Mathematical models and mechanisms of acoustic emission signal generation are presented. It is shown that the reasons of acoustic emission signal origin are related to the local alterations of microstructure of materials and processes of movement of distribution at formation of tensions in solids. It is proved that the origination of signals is based on the analysis of acoustic wave energy released per load cycle and the work of the external forces at elastic deflection.
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Islam, Sheikh Md Rabiul, and Md Shakibul Islam. "Neural Mass Model-Based Different EEG Signal Generation and Analysis in Simulink." Indian Journal of Signal Processing 1, no. 3 (August 10, 2021): 1–7. http://dx.doi.org/10.35940/ijsp.c1008.081321.
Full textAbstract:
The electroencephalogram (EEG) is an electrophysiological monitoring strategy that records the spontaneous electrical movement of the brain coming about from ionic current inside the neurons of the brain. The importance of the EEG signal is mainly the diagnosis of different mental and brain neurodegenerative diseases and different abnormalities like seizure disorder, encephalopathy, dementia, memory problem, sleep disorder, stroke, etc. The EEG signal is very useful for someone in case of a coma to determine the level of brain activity. So, it is very important to study EEG generation and analysis. To reduce the complexity of understanding the pathophysiological mechanism of EEG signal generation and their changes, different simulation-based EEG modeling has been developed which are based on anatomical equivalent data. In this paper, Instead of a detailed model a neural mass model has been used to implement different simulation-based EEG models for EEG signal generation which refers to the simplified and straightforward method. This paper aims to introduce obtained EEG signals of own implementation of the Lopes da Silva model, Jansen-Rit model, and Wendling model in Simulink and to compare characteristic features with real EEG signals and better understanding the EEG abnormalities especially the seizure-like signal pattern.
50
Islam, Sheikh Md Rabiul, and Md Shakibul Islam. "Neural Mass Model-Based Different EEG Signal Generation and Analysis in Simulink." Indian Journal of Signal Processing 1, no. 3 (August 10, 2021): 1–7. http://dx.doi.org/10.54105/ijsp.c1008.081321.
Full textAbstract:
The electroencephalogram (EEG) is an electrophysiological monitoring strategy that records the spontaneous electrical movement of the brain coming about from ionic current inside the neurons of the brain. The importance of the EEG signal is mainly the diagnosis of different mental and brain neurodegenerative diseases and different abnormalities like seizure disorder, encephalopathy, dementia, memory problem, sleep disorder, stroke, etc. The EEG signal is very useful for someone in case of a coma to determine the level of brain activity. So, it is very important to study EEG generation and analysis. To reduce the complexity of understanding the pathophysiological mechanism of EEG signal generation and their changes, different simulation-based EEG modeling has been developed which are based on anatomical equivalent data. In this paper, Instead of a detailed model a neural mass model has been used to implement different simulation-based EEG models for EEG signal generation which refers to the simplified and straightforward method. This paper aims to introduce obtained EEG signals of own implementation of the Lopes da Silva model, Jansen-Rit model, and Wendling model in Simulink and to compare characteristic features with real EEG signals and better understanding the EEG abnormalities especially the seizure-like signal pattern.