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Journal articles on the topic 'Automatic Motion Detection and Analysis'
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Li, Zhe, Aya Kanazuka, Atsushi Hojo, Takane Suzuki, Kazuyo Yamauchi, Shoichi Ito, Yukihiro Nomura, and Toshiya Nakaguchi. "Automatic Puncture Timing Detection for Multi-Camera Injection Motion Analysis." Applied Sciences 13, no. 12 (June 14, 2023): 7120. http://dx.doi.org/10.3390/app13127120.
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Precisely detecting puncture times has long posed a challenge in medical education. This challenge is attributable not only to the subjective nature of human evaluation but also to the insufficiency of effective detection techniques, resulting in many medical students lacking full proficiency in injection skills upon entering clinical practice. To address this issue, we propose a novel detection method that enables automatic detection of puncture times during injection without needing wearable devices. In this study, we utilized a hardware system and the YOLOv7 algorithm to detect critical features of injection motion, including puncture time and injection depth parameters. We constructed a sample of 126 medical injection training videos of medical students, and skilled observers were employed to determine accurate puncture times. Our experimental results demonstrated that the mean puncture time of medical students was 2.264 s and the mean identification error was 0.330 s. Moreover, we confirmed that there was no significant difference (p = 0.25 with a significance level of α = 0.05) between the predicted value of the system and the ground truth, which provides a basis for the validity and reliability of the system. These results show our system’s ability to automatically detect puncture times and provide a novel approach for training healthcare professionals. At the same time, it provides a key technology for the future development of injection skill assessment systems.
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Fu, Eugene Yujun, Hong Va Leong, Grace Ngai, and Stephen C. F. Chan. "Automatic fight detection in surveillance videos." International Journal of Pervasive Computing and Communications 13, no. 2 (June 5, 2017): 130–56. http://dx.doi.org/10.1108/ijpcc-02-2017-0018.
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Purpose Social signal processing under affective computing aims at recognizing and extracting useful human social interaction patterns. Fight is a common social interaction in real life. A fight detection system finds wide applications. This paper aims to detect fights in a natural and low-cost manner. Design/methodology/approach Research works on fight detection are often based on visual features, demanding substantive computation and good video quality. In this paper, the authors propose an approach to detect fight events through motion analysis. Most existing works evaluated their algorithms on public data sets manifesting simulated fights, where the fights are acted out by actors. To evaluate real fights, the authors collected videos involving real fights to form a data set. Based on the two types of data sets, the authors evaluated the performance of their motion signal analysis algorithm, which was then compared with the state-of-the-art approach based on MoSIFT descriptors with Bag-of-Words mechanism, and basic motion signal analysis with Bag-of-Words. Findings The experimental results indicate that the proposed approach accurately detects fights in real scenarios and performs better than the MoSIFT approach. Originality/value By collecting and annotating real surveillance videos containing real fight events and augmenting with well-known data sets, the authors proposed, implemented and evaluated a low computation approach, comparing it with the state-of-the-art approach. The authors uncovered some fundamental differences between real and simulated fights and initiated a new study in discriminating real against simulated fight events, with very good performance.
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DAIMON, Tatsuru, Kazuhide MOTEGI, and Hironao KAWASHIMA. "Automatic detection of driver's eye motion using video image sequence analysis." Japanese journal of ergonomics 31, no. 1 (1995): 39–50. http://dx.doi.org/10.5100/jje.31.39.
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Kotoku, Jun’ichi, Shinobu Kumagai, Ryouhei Uemura, Susumu Nakabayashi, and Takenori Kobayashi. "Automatic Anomaly Detection of Respiratory Motion Based on Singular Spectrum Analysis." International Journal of Medical Physics, Clinical Engineering and Radiation Oncology 05, no. 01 (2016): 88–95. http://dx.doi.org/10.4236/ijmpcero.2016.51009.
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Zhang, Peng Jun, Yu Cheng Bo, Hui Yuan Wang, and Qiang Li. "Fault Detection of Artillery Automatic Loading System Based on PCA." Advanced Materials Research 590 (November 2012): 459–64. http://dx.doi.org/10.4028/www.scientific.net/amr.590.459.
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The motion process of the automatic loading system is a high overloading and intermittent motion environment will bring about motor windings loosening, transmission system wear and tear, fracture, sensor failure and other security risks or system failures. In the paper no-stationary signal analysis by wavelet transform through wavelet decomposition and non-linear threshold de-noising. And use PCA established system model for on-line monitor. By calculate and analysis four kind of result to find fault source. Finally through the experimental prove the reliability of the method.
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D’Aloia, Matteo, Annalisa Longo, and Maria Rizzi. "Noisy ECG Signal Analysis for Automatic Peak Detection." Information 10, no. 2 (January 22, 2019): 35. http://dx.doi.org/10.3390/info10020035.
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Cardiac signal processing is usually a computationally demanding task as signals are heavily contaminated by noise and other artifacts. In this paper, an effective approach for peak point detection and localization in noisy electrocardiogram (ECG) signals is presented. Six stages characterize the implemented method, which adopts the Hilbert transform and a thresholding technique for the detection of zones inside the ECG signal which could contain a peak. Subsequently, the identified zones are analyzed using the wavelet transform for R point detection and localization. The conceived signal processing technique has been evaluated, adopting ECG signals belonging to MIT-BIH Noise Stress Test Database, which includes specially selected Holter recordings characterized by baseline wander, muscle artifacts and electrode motion artifacts as noise sources. The experimental results show that the proposed method reaches most satisfactory performance, even when challenging ECG signals are adopted. The results obtained are presented, discussed and compared with some other R wave detection algorithms indicated in literature, which adopt the same database as a test bench. In particular, for a signal to noise ratio (SNR) equal to 6 dB, results with minimal interference from noise and artifacts have been obtained, since Se e +P achieve values of 98.13% and 96.91, respectively.
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Schütz, Anne K., Verena Schöler , E. Tobias Krause , Mareike Fischer , Thomas Müller , Conrad M. Freuling, Franz J. Conraths , Mario Stanke, Timo Homeier-Bachmann, and Hartmut H. K. Lentz. "Application of YOLOv4 for Detection and Motion Monitoring of Red Foxes." Animals 11, no. 6 (June 9, 2021): 1723. http://dx.doi.org/10.3390/ani11061723.
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Animal activity is an indicator for its welfare and manual observation is time and cost intensive. To this end, automatic detection and monitoring of live captive animals is of major importance for assessing animal activity, and, thereby, allowing for early recognition of changes indicative for diseases and animal welfare issues. We demonstrate that machine learning methods can provide a gap-less monitoring of red foxes in an experimental lab-setting, including a classification into activity patterns. Therefore, bounding boxes are used to measure fox movements, and, thus, the activity level of the animals. We use computer vision, being a non-invasive method for the automatic monitoring of foxes. More specifically, we train the existing algorithm ‘you only look once’ version 4 (YOLOv4) to detect foxes, and the trained classifier is applied to video data of an experiment involving foxes. As we show, computer evaluation outperforms other evaluation methods. Application of automatic detection of foxes can be used for detecting different movement patterns. These, in turn, can be used for animal behavioral analysis and, thus, animal welfare monitoring. Once established for a specific animal species, such systems could be used for animal monitoring in real-time under experimental conditions, or other areas of animal husbandry.
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Hsu, Yu-Cheng, Hailiang Wang, Yang Zhao, Frank Chen, and Kwok-Leung Tsui. "Automatic Recognition and Analysis of Balance Activity in Community-Dwelling Older Adults: Algorithm Validation." Journal of Medical Internet Research 23, no. 12 (December 20, 2021): e30135. http://dx.doi.org/10.2196/30135.
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Background Clinical mobility and balance assessments identify older adults who have a high risk of falls in clinics. In the past two decades, sensors have been a popular supplement to mobility and balance assessment to provide quantitative information and a cost-effective solution in the community environment. Nonetheless, the current sensor-based balance assessment relies on manual observation or motion-specific features to identify motions of research interest. Objective The objective of this study was to develop an automatic motion data analytics framework using signal data collected from an inertial sensor for balance activity analysis in community-dwelling older adults. Methods In total, 59 community-dwelling older adults (19 males and 40 females; mean age = 81.86 years, SD 6.95 years) were recruited in this study. Data were collected using a body-worn inertial measurement unit (including an accelerometer and a gyroscope) at the L4 vertebra of each individual. After data preprocessing and motion detection via a convolutional long short-term memory (LSTM) neural network, a one-class support vector machine (SVM), linear discriminant analysis (LDA), and k-nearest neighborhood (k-NN) were adopted to classify high-risk individuals. Results The framework developed in this study yielded mean accuracies of 87%, 86%, and 89% in detecting sit-to-stand, turning 360°, and stand-to-sit motions, respectively. The balance assessment classification showed accuracies of 90%, 92%, and 86% in classifying abnormal sit-to-stand, turning 360°, and stand-to-sit motions, respectively, using Tinetti Performance Oriented Mobility Assessment-Balance (POMA-B) criteria by the one-class SVM and k-NN. Conclusions The sensor-based approach presented in this study provided a time-effective manner with less human efforts to identify and preprocess the inertial signal and thus enabled an efficient balance assessment tool for medical professionals. In the long run, the approach may offer a flexible solution to relieve the community’s burden of continuous health monitoring.
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Marc, O., and N. Hovius. "Amalgamation in landslide maps: effects and automatic detection." Natural Hazards and Earth System Sciences 15, no. 4 (April 2, 2015): 723–33. http://dx.doi.org/10.5194/nhess-15-723-2015.
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Abstract. Inventories of individually delineated landslides are a key to understanding landslide physics and mitigating their impact. They permit assessment of area–frequency distributions and landslide volumes, and testing of statistical correlations between landslides and physical parameters such as topographic gradient or seismic strong motion. Amalgamation, i.e. the mapping of several adjacent landslides as a single polygon, can lead to potentially severe distortion of the statistics of these inventories. This problem can be especially severe in data sets produced by automated mapping. We present five inventories of earthquake-induced landslides mapped with different materials and techniques and affected by varying degrees of amalgamation. Errors on the total landslide volume and power-law exponent of the area–frequency distribution, resulting from amalgamation, may be up to 200 and 50%, respectively. We present an algorithm based on image and digital elevation model (DEM) analysis, for automatic identification of amalgamated polygons. On a set of about 2000 polygons larger than 1000 m2, tracing landslides triggered by the 1994 Northridge earthquake, the algorithm performs well, with only 2.7–3.6% incorrectly amalgamated landslides missed and 3.9–4.8% correct polygons incorrectly identified as amalgams. This algorithm can be used broadly to check landslide inventories and allow faster correction by automating the identification of amalgamation.
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Marc, O., and N. Hovius. "Amalgamation in landslide maps: effects and automatic detection." Natural Hazards and Earth System Sciences Discussions 2, no. 12 (December 16, 2014): 7651–78. http://dx.doi.org/10.5194/nhessd-2-7651-2014.
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Abstract. Inventories of individually delineated landslides are a key to understanding landslide physics and mitigating their impact. They permit assessment of area-frequency distributions and landslide volumes, and testing of statistical correlations between landslides and physical parameters such as topographic gradient or seismic strong motion. Amalgamation, i.e. the mapping of several adjacent landslides as a single polygon, can lead to potentially severe distortion of the statistics of these inventories. This problem can be especially severe in datasets produced by automated mapping. We present 5 inventories of earthquake-induced landslides mapped with different materials and techniques and affected by varying degrees of amalgamation. Errors on the total landslide volume and power-law exponent of the area-frequency distribution, resulting from amalgamation, may be up to 200 and 50%, respectively. We present an algorithm based on image and DEM analysis, for automatic identification of amalgamated polygons. On a set of about 2000 polygons larger than 1000 m2, tracing landslides triggered by the 1994 Northridge earthquake, the algorithm performs well, with only 2.7–3.6% wrongly amalgamated landslides missed and 3.9–4.8% correct polygons wrongly identified as amalgams. This algorithm can be used broadly to check landslide inventories and allow faster correction by automating the identification of amalgamation.
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Poursoltanmohammadi, Amirsaman, and Matthew Sorell. "Reliable Motion Detection, Location and Audit in Surveillance Video." International Journal of Digital Crime and Forensics 1, no. 4 (October 2009): 19–31. http://dx.doi.org/10.4018/jdcf.2009062402.
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The review of video captured by fixed surveillance cameras is a time consuming, tedious, expensive and potentially unreliable human process, but of very high evidentiary value. Two key challenges stand out in such a task: 1.) ensuring that all motion events are captured for analysis, and 2.) demonstrating that all motion events have been captured so that the evidence survives being challenged in court. In previous work (Zhao, Poursoltanmohammadi & Sorell, 2008), it was demonstrated that tracking the average brightness of video frames or frame segment provided a more robust metric of motion than other commonly hypothesized motion measures. This article extends that work in three ways: 1.) by setting automatic localized motion detection thresholds, 2.) by maintaining a frame-by-frame single parameter normalized motion metric, and 3.) by locating regions of motion events within the footage. A tracking filter approach is used for localized motion analysis, which adapts to localized background motion or noise within each image segment. When motion is detected, location and size estimates are reported to provide some objective description of the motion event.
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Cheoi, Kyung Joo. "Temporal Saliency-Based Suspicious Behavior Pattern Detection." Applied Sciences 10, no. 3 (February 4, 2020): 1020. http://dx.doi.org/10.3390/app10031020.
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The topic of suspicious behavior detection has been one of the most emergent research themes in computer vision, video analysis, and monitoring. Due to the huge number of CCTV (closed-circuit television) systems, it is not easy for people to manually identify CCTV for suspicious motion monitoring. This paper is concerned with an automatic suspicious behavior detection method using a CCTV video stream. Observers generally focus their attention on behaviors that vary in terms of magnitude or gradient of motion and behave differently in rules of motion with other objects. Based on these facts, the proposed method detected suspicious behavior with a temporal saliency map by combining the moving reactivity features of motion magnitude and gradient extracted by optical flow. It has been tested on various video clips that contain suspicious behavior. The experimental results show that the performance of the proposed method is good at detecting the six designated types of suspicious behavior examined: sudden running, colliding, falling, jumping, fighting, and slipping. The proposed method achieved an average accuracy of 93.89%, a precision of 96.21% and a recall of 94.90%.
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Chacon-Murguia, Mario I., and Graciela Ramirez-Alonso. "Fuzzy-neural self-adapting background modeling with automatic motion analysis for dynamic object detection." Applied Soft Computing 36 (November 2015): 570–77. http://dx.doi.org/10.1016/j.asoc.2015.08.007.
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Ye-Lin, Yiyao, Javier Garcia-Casado, Gema Prats-Boluda, José Alberola-Rubio, and Alfredo Perales. "Automatic Identification of Motion Artifacts in EHG Recording for Robust Analysis of Uterine Contractions." Computational and Mathematical Methods in Medicine 2014 (2014): 1–11. http://dx.doi.org/10.1155/2014/470786.
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Electrohysterography (EHG) is a noninvasive technique for monitoring uterine electrical activity. However, the presence of artifacts in the EHG signal may give rise to erroneous interpretations and make it difficult to extract useful information from these recordings. The aim of this work was to develop an automatic system of segmenting EHG recordings that distinguishes between uterine contractions and artifacts. Firstly, the segmentation is performed using an algorithm that generates the TOCO-like signal derived from the EHG and detects windows with significant changes in amplitude. After that, these segments are classified in two groups: artifacted and nonartifacted signals. To develop a classifier, a total of eleven spectral, temporal, and nonlinear features were calculated from EHG signal windows from 12 women in the first stage of labor that had previously been classified by experts. The combination of characteristics that led to the highest degree of accuracy in detecting artifacts was then determined. The results showed that it is possible to obtain automatic detection of motion artifacts in segmented EHG recordings with a precision of 92.2% using only seven features. The proposed algorithm and classifier together compose a useful tool for analyzing EHG signals and would help to promote clinical applications of this technique.
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Zhang, Yan Hua, and Niu Dong. "The Design of Control System Based on Motion Control Card for Automatic Biochemical Testing Device." Advanced Materials Research 580 (October 2012): 270–74. http://dx.doi.org/10.4028/www.scientific.net/amr.580.270.
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A control system based on GE-300-SV-PCI-G motion control card is designed and developed. The hardware of the control system is achieved with the division of functional modules with the analysis of this biochemical testing device. Then the software realizing corresponding detection process is programmed with the method of open and independent programming. In this paper, the multi-threaded programming method is used to solve the problem of software compatibility. Also the movement accuracy of the biochemical testing device is verified with a motion accuracy detection experiment.
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Chen, Tainsong, Tzu-Pei Chen, and Liang Miin Tsai. "Computerized Quantification Analysis of Left Ventricular Wall Motion from Echocardiograms." Ultrasonic Imaging 19, no. 2 (April 1997): 138–44. http://dx.doi.org/10.1177/016173469701900204.
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Two-dimensional echocardiography (2-D echo) imaging is a more attractive clinical tool than other modalities that either involve radiation exposure or are too slow to image heart motion in real-time. Computer-aided analysis of left ventricular (LV) wall motion provides quantitative parameters for diagnosis. This study presents a computerized model for quantitative analysis of left ventricular wall motion from two-dimensional echocardiography by the application of image processing algorithms, including automatic threshold estimation, contrast stretching, boundary detection and border smoothing. The wall motion measurements rely primarily on sequential changes from end-diastolic to end-systolic frames in the left ventricular contours of apical four-chamber view echocardiograms. Left ventricular wall motion was analyzed on the 30 segments of 5 patients with acute myocardial infarction. The results from the computerized model were compared to those obtained from qualitative analysis of echocardiograms by an experienced clinical cardiologist who was unaware of the results of quantitative data.
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Iwamura, Kiyohiko, Jun Younes Louhi Kasahara, Alessandro Moro, Atsushi Yamashita, and Hajime Asama. "Image Captioning Using Motion-CNN with Object Detection." Sensors 21, no. 4 (February 10, 2021): 1270. http://dx.doi.org/10.3390/s21041270.
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Automatic image captioning has many important applications, such as the depiction of visual contents for visually impaired people or the indexing of images on the internet. Recently, deep learning-based image captioning models have been researched extensively. For caption generation, they learn the relation between image features and words included in the captions. However, image features might not be relevant for certain words such as verbs. Therefore, our earlier reported method included the use of motion features along with image features for generating captions including verbs. However, all the motion features were used. Since not all motion features contributed positively to the captioning process, unnecessary motion features decreased the captioning accuracy. As described herein, we use experiments with motion features for thorough analysis of the reasons for the decline in accuracy. We propose a novel, end-to-end trainable method for image caption generation that alleviates the decreased accuracy of caption generation. Our proposed model was evaluated using three datasets: MSR-VTT2016-Image, MSCOCO, and several copyright-free images. Results demonstrate that our proposed method improves caption generation performance.
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Negied, Nermin Kamal Abdel-Wahab, Elsayed B. Hemayed, and Magda Fayek. "HSBS: A Human’s Heat Signature and Background Subtraction Hybrid Approach for Crowd Counting and Analysis." International Journal of Pattern Recognition and Artificial Intelligence 30, no. 08 (July 17, 2016): 1655025. http://dx.doi.org/10.1142/s0218001416550259.
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This work presents a new approach for crowd counting and classification based upon human thermal and motion features. The technique is efficient for automatic crowd density estimation and type of motion determination. Crowd density is measured without any need for camera calibration or assumption of prior knowledge about the input videos. It does not need any human intervention so it can be used successfully in a fully automated crowd control systems. Two new features are introduced for crowd counting purpose: the first represents thermal characteristics of humans and is expressed by the ratio between their temperature and their ambient environment temperature. The second describes humans motion characteristics and is measured by the ratio between humans motion velocity and the ambient environment rigidity. Each ratio should exceed a certain predetermined threshold for human beings. These features have been investigated and proved to give accurate crowd counting performance in real time. Moreover, the two features are combined and used together for crowd classification into one of the three main types, which are: fully mobile, fully static, or mix of both types. Last but not least, the proposed system offers several advantages such as being a privacy preserving crowd counting system, reliable for homogeneous and inhomogeneous crowds, does not depend on a certain direction in motion detection, has no restriction on crowd size. The experimental results demonstrate the effectiveness of the approach.
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Schwalbe, Ellen, and Hans-Gerd Maas. "The determination of high-resolution spatio-temporal glacier motion fields from time-lapse sequences." Earth Surface Dynamics 5, no. 4 (December 21, 2017): 861–79. http://dx.doi.org/10.5194/esurf-5-861-2017.
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Abstract. This paper presents a comprehensive method for the determination of glacier surface motion vector fields at high spatial and temporal resolution. These vector fields can be derived from monocular terrestrial camera image sequences and are a valuable data source for glaciological analysis of the motion behaviour of glaciers. The measurement concepts for the acquisition of image sequences are presented, and an automated monoscopic image sequence processing chain is developed. Motion vector fields can be derived with high precision by applying automatic subpixel-accuracy image matching techniques on grey value patterns in the image sequences. Well-established matching techniques have been adapted to the special characteristics of the glacier data in order to achieve high reliability in automatic image sequence processing, including the handling of moving shadows as well as motion effects induced by small instabilities in the camera set-up. Suitable geo-referencing techniques were developed to transform image measurements into a reference coordinate system.The result of monoscopic image sequence analysis is a dense raster of glacier surface point trajectories for each image sequence. Each translation vector component in these trajectories can be determined with an accuracy of a few centimetres for points at a distance of several kilometres from the camera. Extensive practical validation experiments have shown that motion vector and trajectory fields derived from monocular image sequences can be used for the determination of high-resolution velocity fields of glaciers, including the analysis of tidal effects on glacier movement, the investigation of a glacier's motion behaviour during calving events, the determination of the position and migration of the grounding line and the detection of subglacial channels during glacier lake outburst floods.
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Muhammad Buhari, Adamu, Chee-Pun Ooi, Vishnu Monn Baskaran, and Wooi-Haw Tan. "Motion and Geometric Feature Analysis for Real-time Automatic Micro-expression Recognition Systems." F1000Research 10 (October 11, 2021): 1029. http://dx.doi.org/10.12688/f1000research.72970.1.
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The trend of real-time micro-expression recognition systems has increased with recent advancements in human-computer interaction (HCI) in security and healthcare. Several studies in this field contributed towards recognition accuracy, while few studies look into addressing the computation costs. In this paper, two approaches for micro-expression feature extraction are analyzed for real-time automatic micro-expression recognition. Firstly, motion-based approach, which calculates motion of subtle changes from an image sequence and present as features. Then, secondly, a low computational geometric-based feature extraction technique, a very popular method for facial expression recognition in real-time. These approaches were integrated in a developed system together with a facial landmark detection algorithm and a classifier for real-time analysis. Moreover, the recognition performance were evaluated using SMIC, CASME, CAS(ME)2 and SAMM datasets. The results suggest that the optimized Bi-WOOF (leveraging on motion-based features) yields the highest accuracy of 68.5%, while the full-face graph (leveraging on geometric-based features) yields 75.53% on the SAMM dataset. On the other hand, the optimized Bi-WOOF processes sample at 0.36 seconds and full-face graph processes sample at 0.10 seconds with a 640x480 image size. All experiments were performed on an Intel i5-3470 machine.
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Sieberth, T. "OPTICAL BLUR DISTURBS – THE INFLUENCE OF OPTICAL-BLURRED IMAGES IN PHOTOGRAMMTRY." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B1-2020 (August 6, 2020): 383–88. http://dx.doi.org/10.5194/isprs-archives-xliii-b1-2020-383-2020.
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Abstract. Photogrammetric processes such as camera calibration, feature and target detection and referencing are assumed to strongly depend on the quality of the images that are provided for the process. Consequently, motion and optically blurred images are usually excluded from photogrammetric processes to supress their negative influence. To evaluate how much optical blur is acceptable and how large the influence of optical blur is on photogrammetric procedures a variety of test environments were established. These were based upon previous motion blur research and included test fields for the analysis of camera calibration. For the evaluation, a DSLR camera as well as Lytro Illum light field camera were used. The results show that optical blur has a negative influence on photogrammetric procedures, mostly automatic target detection. With the intervention of an experienced operator and the use of semi-automatic tools, acceptable results can be established.
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Tsai, Du-Ming, and Ching-Ying Huang. "A motion and image analysis method for automatic detection of estrus and mating behavior in cattle." Computers and Electronics in Agriculture 104 (June 2014): 25–31. http://dx.doi.org/10.1016/j.compag.2014.03.003.
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Syahputra, Eswin, Irpan Nursukmi, Sony Putra, Bayu Sukma Sani, and Rian Farta Wijaya. "EYE ASPECT RATIO ADJUSTMENT DETECTION FOR STRONG BLINKING SLEEPINESS BASED ON FACIAL LANDMARKS WITH EYE-BLINK DATASET." ZERO: Jurnal Sains, Matematika dan Terapan 6, no. 2 (February 10, 2023): 147. http://dx.doi.org/10.30829/zero.v6i2.14751.
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<span lang="id">Blink detection is an important technique in a variety of settings, including facial motion analysis and signal processing. However, automatic blink detection is challenging due to its blink rate. This paper proposes a real-time method for detecting eye blinks in a video series. The method is based on automatic facial landmark detection trained on real-world datasets and demonstrates robustness against various environmental factors, including lighting conditions, facial emotions, and head position. The proposed algorithm calculates the position of facial landmarks, extracts scalar values using the Eye Aspect Ratio (EAR), and characterises eye proximity in each frame. For each video frame, the proposed method calculates the location of the facial landmark and extracts the vertical distance between the eyelids using the position of the facial landmark. Blinks are detected by using the EAR threshold value and recognising the pattern of EAR values in a short temporal window. According to the results from a common data set, it is shown that the proposed approach is more efficient than state-of-the-art techniques.</span>
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Andrade, Carlos I., and Daniel E. Hurtado. "Inelastic Deformable Image Registration (i-DIR): Capturing Sliding Motion through Automatic Detection of Discontinuities." Mathematics 9, no. 1 (January 5, 2021): 97. http://dx.doi.org/10.3390/math9010097.
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Deformable image registration (DIR) is an image-analysis method with a broad range of applications in biomedical sciences. Current applications of DIR on computed-tomography (CT) images of the lung and other organs under deformation suffer from large errors and artifacts due to the inability of standard DIR methods to capture sliding between interfaces, as standard transformation models cannot adequately handle discontinuities. In this work, we aim at creating a novel inelastic deformable image registration (i-DIR) method that automatically detects sliding surfaces and that is capable of handling sliding discontinuous motion. Our method relies on the introduction of an inelastic regularization term in the DIR formulation, where sliding is characterized as an inelastic shear strain. We validate the i-DIR by studying synthetic image datasets with strong sliding motion, and compare its results against two other elastic DIR formulations using landmark analysis. Further, we demonstrate the applicability of the i-DIR method to medical CT images by registering lung CT images. Our results show that the i-DIR method delivers accurate estimates of a local lung strain that are similar to fields reported in the literature, and that do not exhibit spurious oscillatory patterns typically observed in elastic DIR methods. We conclude that the i-DIR method automatically locates regions of sliding that arise in the dorsal pleural cavity, delivering significantly smaller errors than traditional elastic DIR methods.
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MARTELL, CRAIG, and JOSHUA KROLL. "CORPUS-BASED GESTURE ANALYSIS: AN EXTENSION OF THE FORM DATASET FOR THE AUTOMATIC DETECTION OF PHASES IN A GESTURE." International Journal of Semantic Computing 01, no. 04 (December 2007): 521–36. http://dx.doi.org/10.1142/s1793351x07000287.
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We present the results of using an extension of the FORM gesture dataset to predict the mid-level phenomenon of phase. We compare the results of human phase prediction with automated prediction using machine-learning techniques. Specifically, we present the results of hidden Markov model experiments using an extended version of the FORM data to predict phase labels. Additionally, we compare FORM to the currently most accurate method of data gathering in this field — motion capture — by comparing the predictive accuracy of the physical gesture models produced by FORM and by motion capture for phase labeling.
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Xu, Ju. "Sequence Analysis and Feature Extraction of Sports Images Using Recurrent Neural Network." Mobile Information Systems 2022 (April 7, 2022): 1–11. http://dx.doi.org/10.1155/2022/2845115.
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Image sequence analysis is attracting significant attention at present, but its principles and techniques have rarely been applied to the field of sports biomechanics. As far as the technology of automatic recognition of joint points by computers is concerned, it is still in the experimental stage. The purpose of this paper is to study and analyze the sequence analysis and feature extraction of sports images based on cyclic neural network. This paper puts forward the basic concepts of sports image sequence analysis and feature extraction and analyzes the importance of sports in this context. As the experimental results demonstrates, the application rate of detecting human motion by using template matching technology detection is between 15% and 47%, while the accuracy of image sequence analysis method has increased from 17% to about 65%. Generally speaking, although the template matching technology detection method is more popular than the image sequence analysis method at the beginning, the popularity of the image sequence analysis method is significantly higher than that of the template matching technology detection method after time precipitation. Therefore, it is very important to study the sequence analysis and feature extraction of sports image based on cyclic neural network.
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Kasai, Ryuji, Takashi Goso, and Tetsuro Osawa. "Development of a program for automatic identification of productivity of construction workers." IOP Conference Series: Earth and Environmental Science 1195, no. 1 (June 1, 2023): 012042. http://dx.doi.org/10.1088/1755-1315/1195/1/012042.
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Abstract In the Japanese construction industry, work environment and productivity data of construction projects are collected and analyzed by on-site engineers as individual data based on attendance and performance records. Therefore, productivity data are not uniformly collected and cannot be considered as highly reliable. Therefore, in this study, a prototype of a system is developed that automatically collects data regarding the workers’ activities using sensors and analyzes the working environment and productivity of construction projects. Although image analysis is typically employed for capturing workers’ activities, it is extremely costly and time-consuming for collecting and discriminating data. In addition, it is not practical because it requires adjusting the system to suit the changing environment of the construction site each time. Accordingly, we have conducted motion-detection using accelerometers, gyro-sensors, and GPS that can be attached to individual workers. We have constructed a hierarchical system that combines a phase to detect large acceleration changes that trigger motion, a phase to discriminate periodic motion, and a phase to classify motion by machine learning. The system classified motions into three productivity types: “direct work,” “support,” and “delay.” As a result of applying this system to the sensing data of workers performing road pavement construction-related works, the system discriminated with an overall accuracy rate of 42%. Furthermore, because of the wide range of behaviors that correspond to each productivity behavior category, a large amount of data must be read from the site adopting the system for obtaining training data to perform final behavior classification by machine learning based on the sensing data. Therefore, a system relying on machine learning may not be able to achieve a higher accuracy; thus, in the near future, we aim to automatically discriminate the nine categories of productivity behaviors shown in previous studies.
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Wael, Mai, El-Sayed H. Ibrahim, and Ahmed S. Fahmy. "Detection of Cardiac Function Abnormality from MRI Images Using Normalized Wall Thickness Temporal Patterns." International Journal of Biomedical Imaging 2016 (2016): 1–6. http://dx.doi.org/10.1155/2016/4301087.
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Purpose. To develop a method for identifying abnormal myocardial function based on studying the normalized wall motion pattern during the cardiac cycle.Methods. The temporal pattern of the normalized myocardial wall thickness is used as a feature vector to assess the cardiac wall motion abnormality. Principal component analysis is used to reduce the feature dimensionality and the maximum likelihood method is used to differentiate between normal and abnormal features. The proposed method was applied on a dataset of 27 cases from normal subjects and patients.Results. The developed method achieved 81.5%, 85%, and 88.5% accuracy for identifying abnormal contractility in the basal, midventricular, and apical slices, respectively.Conclusions. A novel feature vector, namely, the normalized wall thickness, has been introduced for detecting myocardial regional wall motion abnormality. The proposed method provides assessment of the regional myocardial contractility for each cardiac segment and slice; therefore, it could be a valuable tool for automatic and fast determination of regional wall motion abnormality from conventional cine MRI images.
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Le Boité, Hugo, Mardoche Chetrit, Ali Erginay, Sophie Bonnin, Carlo Lavia, Ramin Tadayoni, and Aude Couturier. "Impact of image averaging on vessel detection using optical coherence tomography angiography in eyes with macular oedema and in healthy eyes." PLOS ONE 16, no. 10 (October 22, 2021): e0257859. http://dx.doi.org/10.1371/journal.pone.0257859.
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Purpose To assess the repeatability of multiple automatic vessel density (VD) measurements and the effect of image averaging on vessel detection by optical coherence tomography angiography (OCTA). Methods An observational study was conducted in a series of healthy volunteers and patients with macular oedema. Five sequential OCTA images were acquired for each eye using the OptoVue HD device. The effect of the averaging of the 5 acquisitions on vessel detection was analysed quantitatively using a pixel-by-pixel automated analysis. In addition, two independent retina experts qualitatively assessed the change in vessel detection in averaged images segmented in 9 boxes and compared to the first non-averaged image. Results The automatic VD measurement in OCTA images showed a good repeatability with an overall mean intra-class correlation coefficient (ICC) of 0.924. The mean ICC was higher in healthy eyes compared to eyes with macular oedema (0.877 versus 0.960; p < 0.001) and in the superficial vascular plexus versus the deep vascular complex (0.967 versus 0.888; p = 0.001). The quantitative analysis of the effect of the averaging showed that averaged images had a mean gain of 790.4 pixels/box, located around or completing interruptions in the vessel walls, and a mean loss of 727.2 pixels/box. The qualitative analysis of the averaged images showed that 99.6% of boxes in the averaged images had a gain in vessel detection (i.e., vessels detected in the averaged image but not in the non-averaged image). The loss of pixels was due to a reduction in background noise and motion artifacts in all cases and no case of loss of vessel detection was observed. Conclusion The automatic VD measurement using the OptoVue HD device showed a good repeatability in 5 acquisitions in a row setting. Averaging images increased vessel detection, and in about a third of boxes, decreased the background noise, both in healthy eyes and, in a greater proportion, in eyes with macular oedema.
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Sarmadi, Sorena, James J. Winkle, Razan N. Alnahhas, Matthew R. Bennett, Krešimir Josić, Andreas Mang, and Robert Azencott. "Stochastic Neural Networks for Automatic Cell Tracking in Microscopy Image Sequences of Bacterial Colonies." Mathematical and Computational Applications 27, no. 2 (March 2, 2022): 22. http://dx.doi.org/10.3390/mca27020022.
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Our work targets automated analysis to quantify the growth dynamics of a population of bacilliform bacteria. We propose an innovative approach to frame-sequence tracking of deformable-cell motion by the automated minimization of a new, specific cost functional. This minimization is implemented by dedicated Boltzmann machines (stochastic recurrent neural networks). Automated detection of cell divisions is handled similarly by successive minimizations of two cost functions, alternating the identification of children pairs and parent identification. We validate the proposed automatic cell tracking algorithm using (i) recordings of simulated cell colonies that closely mimic the growth dynamics of E. coli in microfluidic traps and (ii) real data. On a batch of 1100 simulated image frames, cell registration accuracies per frame ranged from 94.5% to 100%, with a high average. Our initial tests using experimental image sequences (i.e., real data) of E. coli colonies also yield convincing results, with a registration accuracy ranging from 90% to 100%.
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Hocke, Lia Maria, Yunjie Tong, and Blaise deBonneval Frederick. "An Automatic Motion-Based Artifact Reduction Algorithm for fNIRS in Concurrent Functional Magnetic Resonance Imaging Studies (AMARA–fMRI)." Algorithms 16, no. 5 (April 28, 2023): 230. http://dx.doi.org/10.3390/a16050230.
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Multimodal functional near-infrared spectroscopy–functional magnetic resonance imaging (fNIRS–fMRI) studies have been highly beneficial for both the fNIRS and fMRI field as, for example, they shed light on the underlying mechanism of each method. However, several noise sources exist in both methods. Motion artifact removal is an important preprocessing step in fNIRS analysis. Several manual motion–artifact removal methods have been developed which require time and are highly dependent on expertise. Only a few automatic methods have been proposed. AMARA (acceleration-based movement artifact reduction algorithm) is one of the most promising automatic methods and was originally tested in an fNIRS sleep study with long acquisition times (~8 h). However, it relies on accelerometry data, which is problematic when performing concurrent fNIRS–fMIRI experiments. Most accelerometers are not MR compatible, and in any case, existing datasets do not have this data. Here, we propose a new way to retrospectively determine acceleration data for motion correction methods, such as AMARA in multimodal fNIRS–fMRI studies. We do so by considering the individual slice stack acquisition times of simultaneous multislice (SMS) acquisition and reconstructing high-resolution motion traces from each slice stack time. We validated our method on 10 participants during a memory task (2- and 3-back) with 6 fNIRS channels over the prefrontal cortex (limited field of view with fMRI). We found that this motion correction significantly improved the detection of activation in deoxyhemoglobin and outperformed up-sampled motion traces. However, we found no improvement in oxyhemoglobin. Furthermore, our data show a high overlap with fMRI activation when considering activation in channels according to both deoxyhemoglobin and oxyhemoglobin.
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Chan, Si-Wa, Yung-Chieh Chang, Po-Wen Huang, Yen-Chieh Ouyang, Yu-Tzu Chang, Ruey-Feng Chang, Jyh-Wen Chai, et al. "Breast Tumor Detection and Classification Using Intravoxel Incoherent Motion Hyperspectral Imaging Techniques." BioMed Research International 2019 (July 28, 2019): 1–15. http://dx.doi.org/10.1155/2019/3843295.
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Breast cancer is a main cause of disease and death for women globally. Because of the limitations of traditional mammography and ultrasonography, magnetic resonance imaging (MRI) has gradually become an important radiological method for breast cancer assessment over the past decades. MRI is free of the problems related to radiation exposure and provides excellent image resolution and contrast. However, a disadvantage is the injection of contrast agent, which is toxic for some patients (such as patients with chronic renal disease or pregnant and lactating women). Recent findings of gadolinium deposits in the brain are also a concern. To address these issues, this paper develops an intravoxel incoherent motion- (IVIM-) MRI-based histogram analysis approach, which takes advantage of several hyperspectral techniques, such as the band expansion process (BEP), to expand a multispectral image to hyperspectral images and create an automatic target generation process (ATGP). After automatically finding suspected targets, further detection was attained by using kernel constrained energy minimization (KCEM). A decision tree and histogram analysis were applied to classify breast tissue via quantitative analysis for detected lesions, which were used to distinguish between three categories of breast tissue: malignant tumors (i.e., central and peripheral zone), cysts, and normal breast tissues. The experimental results demonstrated that the proposed IVIM-MRI-based histogram analysis approach can effectively differentiate between these three breast tissue types.
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Li, Zhou Yang, Wen Tao Gu, and Yan Ni Lei. "Process Data Driven Based Process Equipment Automatic Control Technology." Applied Mechanics and Materials 101-102 (September 2011): 913–17. http://dx.doi.org/10.4028/www.scientific.net/amm.101-102.913.
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Process equipment is essential for product manufacturing, especially for complex products such as aircraft and rocket. In order to improve the motion precision and automation level of process equipment, a new approach is brought forward. A data driven based process equipment automatic control system is introduced to control process equipment automatically according to process data. Fig. 1 in the full paper gives the system’s architecture. Based on analysis of the relationship among manufacturing processes, process plan information and process equipment, process equipment automatic control oriented information model was established. In order to realize automatic control of process equipment, a process equipment automatic control and management framework was established. The key technologies including motion control and motion detecting are discussed in detail. At last, the technology was applied to the design and manufacturing of a horizontal rocket rivet fixture. The application results show that this technology could remarkably increase the efficiency and accuracy of the manufacturing process.
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Singh, Chandra Has, Vishal Mishra, Kamal Jain, and Anoop Kumar Shukla. "FRCNN-Based Reinforcement Learning for Real-Time Vehicle Detection, Tracking and Geolocation from UAS." Drones 6, no. 12 (December 9, 2022): 406. http://dx.doi.org/10.3390/drones6120406.
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In the last few years, uncrewed aerial systems (UASs) have been broadly employed for many applications including urban traffic monitoring. However, in the detection, tracking, and geolocation of moving vehicles using UAVs there are problems to be encountered such as low-accuracy sensors, complex scenes, small object sizes, and motion-induced noises. To address these problems, this study presents an intelligent, self-optimised, real-time framework for automated vehicle detection, tracking, and geolocation in UAV-acquired images which enlist detection, location, and tracking features to improve the final decision. The noise is initially reduced by applying the proposed adaptive filtering, which makes the detection algorithm more versatile. Thereafter, in the detection step, top-hat and bottom-hat transformations are used, assisted by the Overlapped Segmentation-Based Morphological Operation (OSBMO). Following the detection phase, the background regions are obliterated through an analysis of the motion feature points of the obtained object regions using a method that is a conjugation between the Kanade–Lucas–Tomasi (KLT) trackers and Density-Based Spatial Clustering of Applications with Noise (DBSCAN) clustering. The procured object features are clustered into separate objects on the basis of their motion characteristics. Finally, the vehicle labels are designated to their corresponding cluster trajectories by employing an efficient reinforcement connecting algorithm. The policy-making possibilities of the reinforcement connecting algorithm are evaluated. The Fast Regional Convolutional Neural Network (Fast-RCNN) is designed and trained on a small collection of samples, then utilised for removing the wrong targets. The proposed framework was tested on videos acquired through various scenarios. The methodology illustrates its capacity through the automatic supervision of target vehicles in real-world trials, which demonstrates its potential applications in intelligent transport systems and other surveillance applications.
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Skvortsov, Dmitry, Victor Anisimov, and Alina Aizenshtein. "Experimental Study of Military Crawl as a Special Type of Human Quadripedal Automatic Locomotion." Applied Sciences 11, no. 16 (August 20, 2021): 7666. http://dx.doi.org/10.3390/app11167666.
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The biomechanics of military crawl locomotion is poorly covered in scientific literature so far. Crawl locomotion may be used as a testing procedure which allows for the detection of not only obvious, but also hidden locomotor dysfunctions. The aim of the study was to investigate the biomechanics of crawling among healthy adult participants. Eight healthy adults aged 15–31 (four women and four men) were examined by means of a 3D kinematic analysis with Optitrack optical motion-capture system which consists of 12 Flex 13 cameras. The movements of the shoulder, elbow, knee, and hip joints were recorded. A person was asked to crawl 4 m on his/her belly. The obtained results including space-time data let us characterize military crawling in terms of pelvic and lower limb motions as a movement similar to walking but at a more primitive level. Progressive and propulsive motions are characterized as normal; additional right–left side motions—with high degree of reciprocity. It was found that variability of the left-side motions is significantly lower than that of the right side (Z = 4.49, p < 0.0001). The given normative data may be used as a standard to estimate the test results for patients with various pathologies of motor control (ataxia, abasia, etc.).
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Li, Zhi Hua, and Qiu Luan Li. "Automated Alarm Based on Intelligent Visual Analysis." Applied Mechanics and Materials 340 (July 2013): 701–5. http://dx.doi.org/10.4028/www.scientific.net/amm.340.701.
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Abnormal event detection and automated alarm are the important tasks in visual surveillance applications. In this paper, a novel automated alarm method based on intelligent visual analysis is proposed for alarm of abandoned objects and virtual cordon protection. Firstly the monitoring regions and cordons position are set artificially in the surveillance background scenes. The forground motion regions are segmented based on background subtraction model, and then are clustered by connected component analysis. After motion region segmentation and cluster, object tracking based on discriminative appearance model for monocular multi-target tracking is utilized. According to the motion segmentation and tracking results, alarm is triggered in comparison with the monitoring regions and cordons position. Experimental results show that the proposed automated alarm algorithms are sufficient to detect the abnormal events for alarm of abandoned objects and virtual cordon protection.
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García Rubio, Víctor, Juan Antonio Rodrigo Ferrán, Jose Manuel Menéndez García, Nuria Sánchez Almodóvar, José María Lalueza Mayordomo, and Federico Álvarez. "Automatic Change Detection System over Unmanned Aerial Vehicle Video Sequences Based on Convolutional Neural Networks." Sensors 19, no. 20 (October 16, 2019): 4484. http://dx.doi.org/10.3390/s19204484.
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In recent years, the use of unmanned aerial vehicles (UAVs) for surveillance tasks has increased considerably. This technology provides a versatile and innovative approach to the field. However, the automation of tasks such as object recognition or change detection usually requires image processing techniques. In this paper we present a system for change detection in video sequences acquired by moving cameras. It is based on the combination of image alignment techniques with a deep learning model based on convolutional neural networks (CNNs). This approach covers two important topics. Firstly, the capability of our system to be adaptable to variations in the UAV flight. In particular, the difference of height between flights, and a slight modification of the camera’s position or movement of the UAV because of natural conditions such as the effect of wind. These modifications can be produced by multiple factors, such as weather conditions, security requirements or human errors. Secondly, the precision of our model to detect changes in diverse environments, which has been compared with state-of-the-art methods in change detection. This has been measured using the Change Detection 2014 dataset, which provides a selection of labelled images from different scenarios for training change detection algorithms. We have used images from dynamic background, intermittent object motion and bad weather sections. These sections have been selected to test our algorithm’s robustness to changes in the background, as in real flight conditions. Our system provides a precise solution for these scenarios, as the mean F-measure score from the image analysis surpasses 97%, and a significant precision in the intermittent object motion category, where the score is above 99%.
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Nie, Pin, Zhenjie Chen, Nan Xia, Qiuhao Huang, and Feixue Li. "Trajectory Similarity Analysis with the Weight of Direction and k-Neighborhood for AIS Data." ISPRS International Journal of Geo-Information 10, no. 11 (November 10, 2021): 757. http://dx.doi.org/10.3390/ijgi10110757.
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Automatic Identification System (AIS) data have been widely used in many fields, such as collision detection, navigation, and maritime traffic management. Similarity analysis is an important process for most AIS trajectory analysis topics. However, most traditional AIS trajectory similarity analysis methods calculate the distance between trajectory points, which requires complex and time-consuming calculations, often leading to substantial errors when processing AIS trajectory data characterized by substantial differences in length or uneven trajectory points. Therefore, we propose a cell-based similarity analysis method that combines the weight of the direction and k-neighborhood (WDN-SIM). This method quantifies the similarity between trajectories based on the degree of proximity and differences in motion direction. In terms of its effectiveness and efficiency, WDN-SIM outperformed seven traditional methods for trajectory similarity analysis. Particularly, WDN-SIM has a high robustness to noise and can distinguish the similarities between trajectories under complex situations, such as when there are opposing directions of motion, large differences in length, and uneven point distributions.
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Merrouche, Fairouz, and Nadia Baha. "Fall Detection Depth-Based Using Tilt Angle and Shape Deformation." International Journal of Computer Vision and Image Processing 8, no. 4 (October 2018): 26–40. http://dx.doi.org/10.4018/ijcvip.2018100103.
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The population of elderly people is in growth. Falls risk their life, to disabilities, and to fears. Automatic fall detection systems provide them secure living; helping them to be independent at home. Computer vision offers efficient systems over many developed systems. In this article, the authors propose a new vision-based fall detection using depth camera. It combines human shape analysis, centroid detection and motion where it exploits the 3D information provided by a Kinect to compute the tilt angle to discriminate falls. Experimental tests were done with SDUFall dataset that contains 20 subjects performing five daily activities and falls, demonstrate the efficiency of the proposed system, and show that our method is promising achieving satisfactory results up to 84.66%.
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Tufvesson, Jane, Erik Hedström, Katarina Steding-Ehrenborg, Marcus Carlsson, Håkan Arheden, and Einar Heiberg. "Validation and Development of a New Automatic Algorithm for Time-Resolved Segmentation of the Left Ventricle in Magnetic Resonance Imaging." BioMed Research International 2015 (2015): 1–12. http://dx.doi.org/10.1155/2015/970357.
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Introduction.Manual delineation of the left ventricle is clinical standard for quantification of cardiovascular magnetic resonance images despite being time consuming and observer dependent. Previous automatic methods generally do not account for one major contributor to stroke volume, the long-axis motion. Therefore, the aim of this study was to develop and validate an automatic algorithm for time-resolved segmentation covering the whole left ventricle, including basal slices affected by long-axis motion.Methods.Ninety subjects imaged with a cine balanced steady state free precession sequence were included in the study (training setn=40, test setn=50). Manual delineation was reference standard and second observer analysis was performed in a subset (n=25). The automatic algorithm uses deformable model with expectation-maximization, followed by automatic removal of papillary muscles and detection of the outflow tract.Results.The mean differences between automatic segmentation and manual delineation were EDV −11 mL, ESV 1 mL, EF −3%, and LVM 4 g in the test set.Conclusions.The automatic LV segmentation algorithm reached accuracy comparable to interobserver for manual delineation, thereby bringing automatic segmentation one step closer to clinical routine. The algorithm and all images with manual delineations are available for benchmarking.
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Ye, Hua Cong, and Yue Ming Hu. "Study of Self-Adaptive Control System in CNC Machine." Applied Mechanics and Materials 271-272 (December 2012): 504–8. http://dx.doi.org/10.4028/www.scientific.net/amm.271-272.504.
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A set of solving scheme of full-closed loop adaptive servocontrol control system, which can on-line monitoring and real-time control ,was proposed based on the analysis of a number of factors that affect the processing quality of high-precision slender shaft during CNC machining process.Adopt the "PC+motion controller" mode,on-line detection accuracy index of the workpiece, and feed bake the detection signal to CNC system, through the automatic processing of CNC system, timely adjust cutting parameters, so that the detected error factors remain within a reasonable limits, to ensure the stability of the system and improve the accuracy of workpiece.
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Mendez, Martin O., Elvia R. Palacios-Hernandez, Alfonso Alba, Juha M. Kortelainen, Mirja L. Tenhunen, and Anna M. Bianchi. "Detection of the Sleep Stages Throughout Non-Obtrusive Measures of Inter-Beat Fluctuations and Motion: Night and Day Sleep of Female Shift Workers." Fluctuation and Noise Letters 16, no. 04 (November 21, 2017): 1750033. http://dx.doi.org/10.1142/s021947751750033x.
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Automatic sleep staging based on inter-beat fluctuations and motion signals recorded through a pressure bed sensor during sleep is presented. The analysis of the sleep was based on the three major divisions of the sleep time: Wake, non-rapid eye movement (nREM) and rapid eye movement (REM) sleep stages. Twelve sleep recordings, from six females working alternate shift, with their respective annotations were used in the study. Six recordings were acquired during the night and six during the day after a night shift. A Time-Variant Autoregressive Model was used to extract features from inter-beat fluctuations which later were fed to a Support Vector Machine classifier. Accuracy, Kappa index, and percentage in wake, REM and nREM were used as performance measures. Comparison between the automatic sleep staging detection and the standard clinical annotations, shows mean values of [Formula: see text]% for accuracy [Formula: see text] for kappa index, and mean errors of 5% for sleep stages. The performance measures were similar for night and day sleep recordings. In this sample of recordings, the results suggest that inter-beat fluctuations and motions acquired in non-obtrusive way carried valuable information related to the sleep macrostructure and could be used to support to the experts in extensive evaluation and monitoring of sleep.
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Baccinelli, Walter, Maria Bulgheroni, Valentina Simonetti, Francesca Fulceri, Angela Caruso, Letizia Gila, and Maria Luisa Scattoni. "Movidea: A Software Package for Automatic Video Analysis of Movements in Infants at Risk for Neurodevelopmental Disorders." Brain Sciences 10, no. 4 (March 31, 2020): 203. http://dx.doi.org/10.3390/brainsci10040203.
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Early detecting the presence of neurodevelopmental disorders plays an important role in the effectiveness of the treatment. In this paper, we present a novel tool to extract motion features using single camera video recordings of infants. The Movidea software was developed to allow the operator to track the movement of end-effectors of infants in free moving conditions and extract movement features automatically. Movidea was used by different operators to analyze a set of video recordings and its performance was evaluated. The results showed that Movidea performance did not vary with the operator, and the tracking was also stable in home-video recordings. Even if the setup allowed for a two-dimensional analysis, most of the informative content of the movement was maintained. The reliability of the measures and features extracted, as well as the easiness of use, may boost the uptake of the proposed solution in clinical settings. Movidea overcomes the current limitation in the clinical practice in early detection of neurodevelopmental disorders by providing objective measures based on reliable data, and adds a new tool for the motor analysis of infants through unobtrusive technology.
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Tsai, L. M., T. P. Chen, T. S. Chen, and J. H. Chen. "Application of automatic boundary detection for computerized quantitative analysis of left ventricular regional wall motion by two-dimensional echocardiography." Journal of Ultrasound in Medicine 16, no. 3 (March 1997): 177–82. http://dx.doi.org/10.7863/jum.1997.16.3.177.
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Benard, Thierry, Michel Bouchoucha, Michel Dupres, and Paul-Henri Cugnenc. "In vitro analysis of rat intestinal wall movements at rest and during propagated contraction: a new method." American Journal of Physiology-Gastrointestinal and Liver Physiology 273, no. 4 (October 1, 1997): G776—G784. http://dx.doi.org/10.1152/ajpgi.1997.273.4.g776.
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Intestinal wall motions are not easily studied and are frequently deduced from manometric and electromyographic measurements. This study aimed to establish a method of wall movement analysis based on an automatic technique of image processing. Segments of rat jejunum were fixed in an organ bath under isometric conditions. A real-time edge-detection algorithm was used to find the contours of the intestine using video imaging. After the measurement, a mapping of intestinal wall movements was performed based on diameter variations. In the 260 experiments without stimulation, intestinal wall activity was always detected. Propagated activity was found in 40% of the experiments and periodic wall motion in 60%, with 0.5-Hz activity found more frequently (41%) than 0.24-Hz activity (19%). These cyclic activities, related to intestinal slow waves, had their amplitude decreased by acetylcholine and were modified by vapreotide. Analysis of a propagated wave after cholinergic stimulation showed that it is characterized by an increase of the diameter of the intestine followed by a decrease. Moreover, this methodology allows analysis of the initiation of a propagated wave.
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Liu, Dianting, Yilin Yan, Mei-Ling Shyu, Guiru Zhao, and Min Chen. "Spatio-Temporal Analysis for Human Action Detection and Recognition in Uncontrolled Environments." International Journal of Multimedia Data Engineering and Management 6, no. 1 (January 2015): 1–18. http://dx.doi.org/10.4018/ijmdem.2015010101.
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Understanding semantic meaning of human actions captured in unconstrained environments has broad applications in fields ranging from patient monitoring, human-computer interaction, to surveillance systems. However, while great progresses have been achieved on automatic human action detection and recognition in videos that are captured in controlled/constrained environments, most existing approaches perform unsatisfactorily on videos with uncontrolled/unconstrained conditions (e.g., significant camera motion, background clutter, scaling, and light conditions). To address this issue, the authors propose a robust human action detection and recognition framework that works effectively on videos taken in controlled or uncontrolled environments. Specifically, the authors integrate the optical flow field and Harris3D corner detector to generate a new spatial-temporal information representation for each video sequence, from which the general Gaussian mixture model (GMM) is learned. All the mean vectors of the Gaussian components in the generated GMM model are concatenated to create the GMM supervector for video action recognition. They build a boosting classifier based on a set of sparse representation classifiers and hamming distance classifiers to improve the accuracy of action recognition. The experimental results on two broadly used public data sets, KTH and UCF YouTube Action, show that the proposed framework outperforms the other state-of-the-art approaches on both action detection and recognition.
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Baer, M., and U. Kradolfer. "An automatic phase picker for local and teleseismic events." Bulletin of the Seismological Society of America 77, no. 4 (August 1, 1987): 1437–45. http://dx.doi.org/10.1785/bssa0770041437.
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Abstract An automatic detection algorithm has been developed which is capable of time P-phase arrivals of both local and teleseismic earthquakes, but rejects noise bursts and transient events. For each signal trace, the envelope function is calculated and passed through a nonlinear amplifier. The resulting signal is then subjected to a statistical analysis to yield arrival time, first motion, and a measure of reliability to be placed on the P-arrival pick. An incorporated dynamic threshold lets the algorithm become very sensitive; thus, even weak signals are timed precisely. During an extended performance evaluation on a data set comprising 789 P phases of local events and 1857 P phases of teleseismic events picked by an analyst, the automatic picker selected 66 per cent of the local phases and 90 per cent of the teleseismic phases. The accuracy of the automatic picks was “ideal” (i.e., could not be improved by the analyst) for 60 per cent of the local events and 63 per cent of the teleseismic events.
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Khlamov, Sergii V., Vadym E. Savanevych, Olexandr B. Briukhovetskyi, and Artem V. Pohorelov. "CoLiTec software - detection of the near-zero apparent motion." Proceedings of the International Astronomical Union 12, S325 (October 2016): 349–52. http://dx.doi.org/10.1017/s1743921316012539.
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AbstractIn this article we described CoLiTec software for full automated frames processing. CoLiTec software allows processing the Big Data of observation results as well as processing of data that is continuously formed during observation. The scope of solving tasks includes frames brightness equalization, moving objects detection, astrometry, photometry, etc. Along with the high efficiency of Big Data processing CoLiTec software also ensures high accuracy of data measurements. A comparative analysis of the functional characteristics and positional accuracy was performed between CoLiTec and Astrometrica software. The benefits of CoLiTec used with wide field and low quality frames were observed. The efficiency of the CoLiTec software was proved by about 700.000 observations and over 1.500 preliminary discoveries.
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Nurhopipah, Ade, and Agus Harjoko. "Motion Detection and Face Recognition for CCTV Surveillance System." IJCCS (Indonesian Journal of Computing and Cybernetics Systems) 12, no. 2 (July 31, 2018): 107. http://dx.doi.org/10.22146/ijccs.18198.
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Closed Circuit Television (CCTV) is currently used in daily life for a variety purpose. Development of the use of CCTV has transformed from a simple passive surveillance into an integrated intelligent control system. In this research, motion detection and facial recognation in CCTV video is done to be a base for decision making to produce automated, effective and efficient integrated system. This CCTV video processing provides three outputs, a motion detection information, a face detection information and a face identification information. Accumulative Differences Images (ADI) used for motion detection, and Haar Classifiers Cascade used for facial segmentation. Feature extraction is done with Speeded-Up Robust Features (SURF) and Principal Component Analysis (PCA). The features was trained by Counter-Propagation Network (CPN). Offline tests performed on 45 CCTV video. The test results obtained a motion detection success rate of 92,655%, a face detection success rate of 76%, and a face detection success rate of 60%. The results concluded that the process of faces identification through CCTV video with natural background have not been able to obtain optimal results. The motion detection process is ideal to be applied to real-time conditions. But in combination with face recognition process, there is a significant delay time.
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Fedjajevs, Andrejs, Willemijn Groenendaal, Carlos Agell, and Evelien Hermeling. "Platform for Analysis and Labeling of Medical Time Series." Sensors 20, no. 24 (December 19, 2020): 7302. http://dx.doi.org/10.3390/s20247302.
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Reliable and diverse labeled reference data are essential for the development of high-quality processing algorithms for medical signals, such as electrocardiogram (ECG) and photoplethysmogram (PPG). Here, we present the Platform for Analysis and Labeling of Medical time Series (PALMS) designed in Python. Its graphical user interface (GUI) facilitates three main types of manual annotations—(1) fiducials, e.g., R-peaks of ECG; (2) events with an adjustable duration, e.g., arrhythmic episodes; and (3) signal quality, e.g., data parts corrupted by motion artifacts. All annotations can be attributed to the same signal simultaneously in an ergonomic and user-friendly manner. Configuration for different data and annotation types is straightforward and flexible in order to use a wide range of data sources and to address many different use cases. Above all, configuration of PALMS allows plugging-in existing algorithms to display outcomes of automated processing, such as automatic R-peak detection, and to manually correct them where needed. This enables fast annotation and can be used to further improve algorithms. The GUI is currently complemented by ECG and PPG algorithms that detect characteristic points with high accuracy. The ECG algorithm reached 99% on the MIT/BIH arrhythmia database. The PPG algorithm was validated on two public databases with an F1-score above 98%. The GUI and optional algorithms result in an advanced software tool that allows the creation of diverse reference sets for existing datasets.