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Journal articles on the topic 'Human activity monitoring'

Author: Grafiati

Published: 3 September 2022

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1

Fiore, Loren, Duc Fehr, Robot Bodor, Andrew Drenner, Guruprasad Somasundaram, and Nikolaos Papanikolopoulos. "Multi-Camera Human Activity Monitoring." Journal of Intelligent and Robotic Systems 52, no. 1 (January 29, 2008): 5–43. http://dx.doi.org/10.1007/s10846-007-9201-6.

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2

Maenaka, Kazusuke. "Human Activity Monitoring with MEMS Technology." IEEJ Transactions on Sensors and Micromachines 134, no. 12 (2014): 372–77. http://dx.doi.org/10.1541/ieejsmas.134.372.

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3

Vettier, Benoit, and Catherine Garbay. "Abductive Agents for Human Activity Monitoring." International Journal on Artificial Intelligence Tools 23, no. 01 (February 2014): 1440002. http://dx.doi.org/10.1142/s0218213014400028.

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We propose in this paper a novel architecture for human activity monitoring, following conceptual, technical and experimental claims. From a conceptual viewpoint, we propose to approach the interpretation of sensor data as embedded into a multidimensional frame involving functional and non-functional requirements. Functional requirements involve considering the monitored person's specificities as well as the task to be performed. Non-functional requirements qualify the system activity. This frame of interpretation is continuously refined, to cope with evolving situations or expectations from the Observer. From a technical viewpoint, we propose to develop a multi-Agent architecture as a means for dependable, flexible monitoring. This paradigm allows to handle multiple, heterogeneous entities in a unified way. The Agents process incoming data with a dynamic population of hypotheses on several abstraction levels. This reasoning is abductive and fuzzy in nature. From the experimental viewpoint, we propose a dedicated evaluation approach to estimate the interpretative process unfolding. Functional and non-functional properties are presented to discuss the system's effectiveness, informativeness, sensitivity, efficiency and robustness, some of which are supported by qualitative, analytical discussions, others by quantitative measures.

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4

MAENAKA, Kazusuke. "Human Activity Monitoring System by MEMS Devices." Journal of The Institute of Electrical Engineers of Japan 132, no. 3 (2012): 148–51. http://dx.doi.org/10.1541/ieejjournal.132.148.

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5

Nii, Manabu, Yoshihiro Kakiuchi, Kazunobu Takahama, Kazusuke Maenaka, Kohei Higuchi, and Takayuki Yumoto. "Human Activity Monitoring Using Fuzzified Neural Networks." Procedia Computer Science 22 (2013): 960–67. http://dx.doi.org/10.1016/j.procs.2013.09.180.

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6

Fonollosa, Jordi, Irene Rodriguez-Lujan, Abhijit V. Shevade, Margie L. Homer, Margaret A. Ryan, and Ramón Huerta. "Human activity monitoring using gas sensor arrays." Sensors and Actuators B: Chemical 199 (August 2014): 398–402. http://dx.doi.org/10.1016/j.snb.2014.03.102.

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7

Zhongna Zhou, Xi Chen, Yu-Chia Chung, Zhihai He, T. X. Han, and J. M. Keller. "Activity Analysis, Summarization, and Visualization for Indoor Human Activity Monitoring." IEEE Transactions on Circuits and Systems for Video Technology 18, no. 11 (November 2008): 1489–98. http://dx.doi.org/10.1109/tcsvt.2008.2005612.

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8

Maenaka, Kazusuke. "Miniaturized Human Activity Monitoring System with MEMS Technology." Journal of The Japan Institute of Electronics Packaging 23, no. 5 (August 1, 2020): 331–36. http://dx.doi.org/10.5104/jiep.23.331.

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9

Washino, Fumihiro, Yuki Matsumoto, Tomoya Tanaka, Koji Sonoda, Kensuke Kanda, Takayuki Fujita, and Kazusuke Maenaka. "Low Power ASIC for Monitoring Human Motion Activity." IEEJ Transactions on Sensors and Micromachines 135, no. 5 (2015): 178–83. http://dx.doi.org/10.1541/ieejsmas.135.178.

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10

Fujita, Takayuki, Jun Okada, Sayaka Okochi, Kohei Higuchi, and Kazusuke Maenaka. "Autonomous Environmental Sensing System for Human Activity Monitoring." Journal of Advanced Computational Intelligence and Intelligent Informatics 15, no. 3 (May 20, 2011): 383–88. http://dx.doi.org/10.20965/jaciii.2011.p0383.

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Continuous observation of human activities and circumstances are quite important for healthcare applications that collect a lot of data from the various MEMS (Micro Electromechanical Systems) sensors. This study demonstrates the multi-environmental sensing system for human applications that can measure the time-based three-axes acceleration (threeaxes shock), barometric pressure, temperature and relative humidity, simultaneously. The system has battery and large sized memory for autonomous sensing. The measured data are stored in a flash memory via an onboard microcontroller. The detailed configurations of the prototype device and some experimental results are investigated.

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11

Hirobayashi, Shigeki, Yusuke Tamura, Tatsuo Yamabuchi, and Takashi Oyabu. "Monitoring of Human Activity Using Plant Bioelectric Potential." IEEJ Transactions on Sensors and Micromachines 127, no. 4 (2007): 258–59. http://dx.doi.org/10.1541/ieejsmas.127.258.

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12

Mukhopadhyay, Subhas Chandra. "Wearable Sensors for Human Activity Monitoring: A Review." IEEE Sensors Journal 15, no. 3 (March 2015): 1321–30. http://dx.doi.org/10.1109/jsen.2014.2370945.

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13

Hen, Gideon, Sera Yosefi, Ana Ronin, Paz Einat, Charles I. Rosenblum, Robert J. Denver, and Miriam Friedman-Einat. "Monitoring leptin activity using the chicken leptin receptor." Journal of Endocrinology 197, no. 2 (March 5, 2008): 325–33. http://dx.doi.org/10.1677/joe-08-0065.

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We report on the construction of a leptin bioassay based on the activation of chicken leptin receptor in cultured cells. A human embryonic kidney (HEK)-293 cell line, stably transfected with the full-length cDNA of chicken leptin receptor together with a STAT3-responsive reporter gene specifically responded to recombinant human and Xenopus leptins. The observed higher sensitivity of chicken leptin receptor to the former is in agreement with the degree of sequence similarity among these species (about 60 and 38% identical amino acids between humans and chickens, and between humans and Xenopus respectively). The specific activation of signal transduction through the chicken leptin receptor, shown here for the first time, suggests that the transition of Gln269 (implicated in the Gln-to-Pro Zucker fatty mutation in rats) to Glu in chickens does not impair its activity. Analysis of leptin-like activity in human serum samples of obese and lean subjects coincided well with leptin levels determined by RIA. Serum samples of pre- and post partum cows showed a tight correlation with the degree of adiposity. However, specific activation of the chicken leptin receptor in this assay was not observed with serum samples from broiler or layer chickens (representing fat and lean phenotypes respectively) or with those from turkey. Similar leptin receptor activation profiles were observed with cells transfected with human leptin receptor. Further work is needed to determine whether the lack of leptin-like activity in the chicken serum samples is due to a lack of leptin in this species or simply to a serum level of leptin that is below the detection threshold.

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14

Park, Homin, Seokhyun Hwang, Myounggyu Won, and Taejoon Park. "Activity-Aware Sensor Cycling for Human Activity Monitoring in Smart Homes." IEEE Communications Letters 21, no. 4 (April 2017): 757–60. http://dx.doi.org/10.1109/lcomm.2016.2619700.

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15

Simon, Béla-Csaba, Stefan Oniga, and Iuliu Alexandru Pap. "Activity and health monitoring systems." Carpathian Journal of Electronic and Computer Engineering 11, no. 1 (September 1, 2018): 11–14. http://dx.doi.org/10.2478/cjece-2018-0003.

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Abstract This paper presents an Open Platform Activity and health monitoring systems which are also called e-Health systems. These systems measure and store parameters that reflect changes in the human body. Due to continuous monitoring (e.g. in rest state and in physical effort state), a specialist can learn about the individual's physiological parameters. Because the human body is a complex system, the examiner can notice some changes within the body by looking at the physiological parameters. Six different sensors ensure us that the patient's individual parameters are monitored. The main components of the device are: A Raspberry Pi 3 small single-board computer, an e-Health Sensor Platform by Cooking-Hacks, a Raspberry Pi to Arduino Shields Connection Bridge and a 7-inch Raspberry Pi 3 touch screen. The processing unit is the Raspberry Pi 3 board. The Raspbian operating system runs on the Raspberry Pi 3, which provides a solid base for the software. Every examination can be controlled by the touch screen. The measurements can be started with the graphical interface by pressing a button and every measured result can be represented on the GUI’s label or on the graph. The results of every examination can be stored in a database. From that database the specialist can retrieve every personalized data.

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16

Majidzadeh Gorjani, Ojan, Radek Byrtus, Jakub Dohnal, Petr Bilik, Jiri Koziorek, and Radek Martinek. "Human Activity Classification Using Multilayer Perceptron." Sensors 21, no. 18 (September 16, 2021): 6207. http://dx.doi.org/10.3390/s21186207.

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The number of smart homes is rapidly increasing. Smart homes typically feature functions such as voice-activated functions, automation, monitoring, and tracking events. Besides comfort and convenience, the integration of smart home functionality with data processing methods can provide valuable information about the well-being of the smart home residence. This study is aimed at taking the data analysis within smart homes beyond occupancy monitoring and fall detection. This work uses a multilayer perceptron neural network to recognize multiple human activities from wrist- and ankle-worn devices. The developed models show very high recognition accuracy across all activity classes. The cross-validation results indicate accuracy levels above 98% across all models, and scoring evaluation methods only resulted in an average accuracy reduction of 10%.

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17

Peixoto, Paulo, Jorge Batista, and Helder J. Araujo. "Real-time human activity monitoring exploring multiple vision sensors." Robotics and Autonomous Systems 35, no. 3-4 (June 2001): 221–28. http://dx.doi.org/10.1016/s0921-8890(01)00117-8.

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18

Hbali, Youssef, Sara Hbali, Lahoucine Ballihi, and Mohammed Sadgal. "Skeleton‐based human activity recognition for elderly monitoring systems." IET Computer Vision 12, no. 1 (November 2017): 16–26. http://dx.doi.org/10.1049/iet-cvi.2017.0062.

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19

Folgado, Encarnación, Mariano Rincón, Enrique J. Carmona, and Margarita Bachiller. "A block-based model for monitoring of human activity." Neurocomputing 74, no. 8 (March 2011): 1283–89. http://dx.doi.org/10.1016/j.neucom.2010.05.023.

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20

Mustafa, N. H., M. N. Husain, M. Z. A. Abd Aziz, M. A. Othman, and F. Malek. "Energy monitoring based on human activity in the workplace." Journal of Physics: Conference Series 495 (April 4, 2014): 012029. http://dx.doi.org/10.1088/1742-6596/495/1/012029.

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21

Ohtaki, Yasuaki, Akihiro Suzuki, Xiumin Zhan, Koichi Sagawa, Ryoichi Nagatomi, and Hikaru Inooka. "2505 Development of Portable Device for Human Activity Monitoring." Proceedings of the Conference on Information, Intelligence and Precision Equipment : IIP 2005 (2005): 341–44. http://dx.doi.org/10.1299/jsmeiip.2005.341.

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22

Suriani, Nor Surayahani, and Fadilla ‘Atyka Nor Rashid. "Smartphone Sensor Accelerometer Data for Human Activity Recognition Using Spiking Neural Network." International Journal of Machine Learning and Computing 11, no. 4 (August 2021): 298–303. http://dx.doi.org/10.18178/ijmlc.2021.11.4.1051.

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Recognizing human actions is a challenging task and actively research in computer vision community. The task of human activity recognition has been widely used in various application such as human monitoring in a hospital or public spaces. This work applied open dataset of smartphones accelerometer data for various type of activities. The analogue input data is encoded into the spike trains using some form of a rate-based method. Spiking neural network is a simplified form of dynamic artificial network. Therefore, this network is expected to model and generate action potential from the leaky integrate-and-fire spike response model. The leaning rule is adaptive and efficient to present synapse exciting and inhibiting firing neuron. The result found that the proposed model presents the state-of-the-art performance at a low computational cost.

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23

Hudspith, Sydney. "Applying Human Factors in Anesthesia Monitoring." Proceedings of the Human Factors Society Annual Meeting 31, no. 9 (September 1987): 988–92. http://dx.doi.org/10.1177/154193128703100913.

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The ergonomics of the anesthesia workstation have been examined, and a novel apparatus for monitoring depth of anesthesia has been developed. The depth measuring apparatus works by detecting esophageal motility during anesthesia and displaying the resulting data to the clinician. Esophageal activity is mediated by the vagus nerve and brain stem, and co-varies with the degree of brain stem activity, therefore it can be used to estimate depth of anesthesia. Moreover, the location and availability of the esophagus allow minimally-invasive monitoring of many other vital functions and the displaying of these functions on a single integrated monitor. This enables the clinician to derive from a single low-risk monitoring site a more comprehensive picture of the patient's physiological state. Data derived from clinical trials and research now underway in the UK and US tend to support these views. Now in its embryonic form, this research is being directed toward an integrated anesthesia monitor which will ultimately improve the ergonomics and economics of anesthesia delivery.

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24

Gadebe, Moses Lesiba, and Okuthe Paul Kogeda. "Top-K Human Activity Recognition Dataset." International Journal of Interactive Mobile Technologies (iJIM) 14, no. 18 (November 10, 2020): 68. http://dx.doi.org/10.3991/ijim.v14i18.16965.

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<span lang="EN-US">The availability of Smartphones has increased the possibility of self-monitoring to increase physical activity and behavior change to prevent obesity. However self-monitoring on a Smartphtone comes with some challenges such as unavailability of lightweight classification algorithm, personalized dataset to completely capture bodily postures, subject sensitivity, limited storage and computational power. However, most classification algorithms such as Support Vector Machines, C4.5, Naïve Bayes and K Neighbor relies on larger dataset to accurately predict human activities. In this paper, we present top-k of compressed small personalized dataset to reduce computational cost with increased accuracy. We collected top-k personalized dataset from 13 recruited subjects. After benchmarking our collected dataset we found that the dataset is suitable for tree-oriented algorithm, especially the Random Forest, C4.5 and Boosted tree with accuracy and precision of 100% except for KNN, Support Vector and Naïve Bayes. Further, our top-k personalized dataset improves pruning and overfitting of tree-oriented algorithms. Moreover, the linear consistence of static human activities reveals the potential of our top-k dataset to be replicated to multiple-subject to close subject sensitivity challenge.</span>

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25

Soffer, E. E., P. Scalabrini, and D. L. Wingate. "Prolonged ambulant monitoring of human colonic motility." American Journal of Physiology-Gastrointestinal and Liver Physiology 257, no. 4 (October 1, 1989): G601—G606. http://dx.doi.org/10.1152/ajpgi.1989.257.4.g601.

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The study of human colonic motility under physiological conditions has proved to be an elusive goal. We have used a two-stage pernasal technique to position sensors in the human colon for the prolonged monitoring of motility in freely ambulant subjects. Nine healthy volunteers were studied for a total recording time of 263 h, each study lasting between 13 and 48 (mean 29) h. Motor activity in all regions of the large bowel was characterized by scant and irregular contractions with infrequent bursts that did not conform to any pattern. No motor coordination was apparent between different regions of the large bowel. Contractile activity throughout the large bowel was reduced to a minimum during sleep and was enhanced on waking. Meals were an inconsistent stimulus to motor activity. The technique obviates the need for colonic preparation and allows complete freedom of the subjects throughout the study. In demonstrating the practical feasibility of this mode of studying the colon, these preliminary data highlight a requirement for the availability of appropriate equipment but raise questions about the design and use of such equipment and methods of data analysis.

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26

Gravina, Raffaele, Congcong Ma, Pasquale Pace, Gianluca Aloi, Wilma Russo, Wenfeng Li, and Giancarlo Fortino. "Cloud-based Activity-aaService cyber–physical framework for human activity monitoring in mobility." Future Generation Computer Systems 75 (October 2017): 158–71. http://dx.doi.org/10.1016/j.future.2016.09.006.

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27

Maenaka, Kazusuke. "Current Topics in Adhesive Plaster Type Human Activity Monitoring Systems." Journal of Japan Institute of Electronics Packaging 18, no. 6 (2015): 428–34. http://dx.doi.org/10.5104/jiep.18.428.

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28

Sekine, Masatoshi, and Kurato Maeno. "Activity Recognition Using Radio Doppler Effect for Human Monitoring Service." Journal of Information Processing 20, no. 2 (2012): 396–405. http://dx.doi.org/10.2197/ipsjjip.20.396.

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29

FUJITA, Takayuki, Kentaro MASAKI, and Kazusuke MAENAKA. "Human Activity Monitoring System Using MEMS Sensors and Machine Learning." Journal of Japan Society for Fuzzy Theory and Intelligent Informatics 20, no. 1 (2008): 3–8. http://dx.doi.org/10.3156/jsoft.20.3.

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30

Choi, Sangil, and Gangman Yi. "Energy Consumption and Efficiency Issues in Human Activity Monitoring System." Wireless Personal Communications 91, no. 4 (April 18, 2016): 1799–815. http://dx.doi.org/10.1007/s11277-016-3321-x.

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31

Fernández-Caballero, Antonio, José Carlos Castillo, and José María Rodríguez-Sánchez. "Human activity monitoring by local and global finite state machines." Expert Systems with Applications 39, no. 8 (June 2012): 6982–93. http://dx.doi.org/10.1016/j.eswa.2012.01.050.

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32

Di Benedetto, Marco, Fabio Carrara, Luca Ciampi, Fabrizio Falchi, Claudio Gennaro, and Giuseppe Amato. "An embedded toolset for human activity monitoring in critical environments." Expert Systems with Applications 199 (August 2022): 117125. http://dx.doi.org/10.1016/j.eswa.2022.117125.

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33

Coley, David A. "Emission factors for human activity." Energy Policy 30, no. 1 (January 2002): 3–5. http://dx.doi.org/10.1016/s0301-4215(01)00061-1.

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34

Mann, T. M., K. E. Williams, P. C. Pearce, and E. A. M. Scott. "A novel method for activity monitoring in small non-human primates." Laboratory Animals 39, no. 2 (April 1, 2005): 169–77. http://dx.doi.org/10.1258/0023677053739783.

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Patterns of spontaneous activity are valuable reflections of well-being in animals and humans and, because of this, investigations have frequently incorporated some form of activity monitoring into their studies. It is widely believed that activity monitoring, alongside assessments of general behaviour, should be included in initial CNS safety pharmacology screening. As the number of marmoset studies having actimetry as their focus, or as an adjunct, is increasing, we wished to evaluate an alternative approach to those commonly used. The method is based on miniaturized accelerometer technologies, currently used for human activity monitoring. Actiwatch®-Minis were used to monitor the activity of two groups of differently housed marmosets for 14 consecutive days. Group A consisted of four mixed-sex pairs of animals and group B comprised eight group-housed males. Activity profiles were generated for weekday and weekend periods. The devices captured quantifiable data which showed differences in total activity between the two differently housed groups and revealed intragroup variations in the temporal spread of activity between weekdays and weekends. The Actiwatch®-Mini has been shown to generate retrospective, data-logged activity counts recorded from multiple animals in a single arena by means of non-invasive monitoring.

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35

Preusse, Kimberly C., Tracy L. Mitzner, Cara B. Fausset, and Wendy A. Rogers. "Activity Monitoring Technologies and Older Adult Users." Proceedings of the International Symposium on Human Factors and Ergonomics in Health Care 3, no. 1 (June 2014): 23–27. http://dx.doi.org/10.1177/2327857914031003.

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Older adults may benefit from using activity monitoring technologies to support health and wellness management. However, adoption of such technologies is contingent upon ease of use considerations. Age-related changes in cognitive and physical capabilities impact usability. Heuristic evaluation of two such technologies revealed important ease of use design issues that may be particularly problematic for this user group. Case studies of four older adults’ usage experiences over a two-week period revealed additional usability challenges. Human factors involvement in product development should address these issues to enable older adults to use these potentially beneficial technologies.

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36

Wang, Yuan-Kai, Hong-Yu Chen, and Jian-Ru Chen. "Unobtrusive Sleep Monitoring Using Movement Activity by Video Analysis." Electronics 8, no. 7 (July 20, 2019): 812. http://dx.doi.org/10.3390/electronics8070812.

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Sleep healthcare at home is a new research topic that needs to develop new sensors, hardware and algorithms with the consideration of convenience, portability and accuracy. Monitoring sleep behaviors by visual sensors represents one new unobtrusive approach to facilitating sleep monitoring and benefits sleep quality. The challenge of video surveillance for sleep behavior analysis is that we have to tackle bad image illumination issue and large pose variations during sleeping. This paper proposes a robust method for sleep pose analysis with human joints model. The method first tackles the illumination variation issue of infrared videos to improve the image quality and help better feature extraction. Image matching by keypoint features is proposed to detect and track the positions of human joints and build a human model robust to occlusion. Sleep poses are then inferred from joint positions by probabilistic reasoning in order to tolerate occluded joints. Experiments are conducted on the video polysomnography data recorded in sleep laboratory. Sleep pose experiments are given to examine the accuracy of joint detection and tacking, and the accuracy of sleep poses. High accuracy of the experiments demonstrates the validity of the proposed method.

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37

Alcalá, José, Jesús Ureña, Álvaro Hernández, and David Gualda. "Assessing Human Activity in Elderly People Using Non-Intrusive Load Monitoring." Sensors 17, no. 2 (February 11, 2017): 351. http://dx.doi.org/10.3390/s17020351.

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38

Gul, Malik Ali, Muhammad Haroon Yousaf, Shah Nawaz, Zaka Ur Rehman, and HyungWon Kim. "Patient Monitoring by Abnormal Human Activity Recognition Based on CNN Architecture." Electronics 9, no. 12 (November 24, 2020): 1993. http://dx.doi.org/10.3390/electronics9121993.

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Human action recognition has emerged as a challenging research domain for video understanding and analysis. Subsequently, extensive research has been conducted to achieve the improved performance for recognition of human actions. Human activity recognition has various real time applications, such as patient monitoring in which patients are being monitored among a group of normal people and then identified based on their abnormal activities. Our goal is to render a multi class abnormal action detection in individuals as well as in groups from video sequences to differentiate multiple abnormal human actions. In this paper, You Look only Once (YOLO) network is utilized as a backbone CNN model. For training the CNN model, we constructed a large dataset of patient videos by labeling each frame with a set of patient actions and the patient’s positions. We retrained the back-bone CNN model with 23,040 labeled images of patient’s actions for 32 epochs. Across each frame, the proposed model allocated a unique confidence score and action label for video sequences by finding the recurrent action label. The present study shows that the accuracy of abnormal action recognition is 96.8%. Our proposed approach differentiated abnormal actions with improved F1-Score of 89.2% which is higher than state-of-the-art techniques. The results indicate that the proposed framework can be beneficial to hospitals and elder care homes for patient monitoring.

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39

San-Segundo, Ruben, Julian Echeverry-Correa, Christian Salamea, and Jose Manuel Pardo. "Human activity monitoring based on hidden Markov models using a smartphone." IEEE Instrumentation & Measurement Magazine 19, no. 6 (December 2016): 27–31. http://dx.doi.org/10.1109/mim.2016.7777649.

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40

Plomp, Johan, Mikko Heiskanen, Mika Hillukkala, Tapio Heikkilä, Jari Rehu, Niek Lambert, Victor van Acht, and Tom Ahola. "Considerations for Synchronization in Body Area Networks for Human Activity Monitoring." International Journal of Wireless Information Networks 18, no. 4 (May 3, 2011): 280–94. http://dx.doi.org/10.1007/s10776-011-0136-2.

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41

Zhuang, Zhendong, and Yang Xue. "Sport-Related Human Activity Detection and Recognition Using a Smartwatch." Sensors 19, no. 22 (November 16, 2019): 5001. http://dx.doi.org/10.3390/s19225001.

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As an active research field, sport-related activity monitoring plays an important role in people’s lives and health. This is often viewed as a human activity recognition task in which a fixed-length sliding window is used to segment long-term activity signals. However, activities with complex motion states and non-periodicity can be better monitored if the monitoring algorithm is able to accurately detect the duration of meaningful motion states. However, this ability is lacking in the sliding window approach. In this study, we focused on two types of activities for sport-related activity monitoring, which we regard as a human activity detection and recognition task. For non-periodic activities, we propose an interval-based detection and recognition method. The proposed approach can accurately determine the duration of each target motion state by generating candidate intervals. For weak periodic activities, we propose a classification-based periodic matching method that uses periodic matching to segment the motion sate. Experimental results show that the proposed methods performed better than the sliding window method.

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42

Damaševičius, Robertas, Mindaugas Vasiljevas, Justas Šalkevičius, and Marcin Woźniak. "Human Activity Recognition in AAL Environments Using Random Projections." Computational and Mathematical Methods in Medicine 2016 (2016): 1–17. http://dx.doi.org/10.1155/2016/4073584.

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Automatic human activity recognition systems aim to capture the state of the user and its environment by exploiting heterogeneous sensors attached to the subject’s body and permit continuous monitoring of numerous physiological signals reflecting the state of human actions. Successful identification of human activities can be immensely useful in healthcare applications for Ambient Assisted Living (AAL), for automatic and intelligent activity monitoring systems developed for elderly and disabled people. In this paper, we propose the method for activity recognition and subject identification based on random projections from high-dimensional feature space to low-dimensional projection space, where the classes are separated using the Jaccard distance between probability density functions of projected data. Two HAR domain tasks are considered: activity identification and subject identification. The experimental results using the proposed method with Human Activity Dataset (HAD) data are presented.

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43

Wang, Yuchen, Mantao Wang, Zhouyu Tan, Jie Zhang, Zhiyong Li, Jiong Mu, Zhihao Zhou, and Lixing Luo. "Construction and Application of Indoor Video Surveillance System Based on Human Activity Recognition." MATEC Web of Conferences 232 (2018): 04024. http://dx.doi.org/10.1051/matecconf/201823204024.

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With the growth of building monitoring network, increasing human resource and funds have been invested into building monitoring system. Computer vision technology has been widely used in image recognition recently, and this technology has also been gradually applied to action recognition. There are still many disadvantages of traditional monitoring system. In this paper, a human activity recognition system which based on the convolution neural network is proposed. Using the 3D convolution neural network and the transfer learning technology, the human activity recognition engine is constructed. The Spring MVC framework is used to build the server end, and the system page is designed in HBuilder. The system not only enhances efficiency and functionality of building monitoring system, but also improves the level of building safety.

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44

Reiss, Attila, and Didier Stricker. "Aerobic activity monitoring: towards a long-term approach." Universal Access in the Information Society 13, no. 1 (March 12, 2013): 101–14. http://dx.doi.org/10.1007/s10209-013-0292-5.

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45

Ghasemzadeh, Hassan, Pasquale Panuccio, Simone Trovato, Giancarlo Fortino, and Roozbeh Jafari. "Power-Aware Activity Monitoring Using Distributed Wearable Sensors." IEEE Transactions on Human-Machine Systems 44, no. 4 (August 2014): 537–44. http://dx.doi.org/10.1109/thms.2014.2320277.

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Al-Naime, Khalid, Adnan Al-Anbuky, and Grant Mawston. "Human Movement Monitoring and Analysis for Prehabilitation Process Management." Journal of Sensor and Actuator Networks 9, no. 1 (January 21, 2020): 9. http://dx.doi.org/10.3390/jsan9010009.

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Cancer patients assigned for abdominal surgery are often given exercise programmes (prehabilitation) prior to surgery, which aim to improve fitness in order to reduce pre-operative risk. However, only a small proportion of patients are able to partake in supervised hospital-based prehabilitation because of inaccessibility and a lack of resources, which often makes it difficult for health professionals to accurately monitor and provide feedback on exercise and activity levels. The development of a simple tool to detect the type and intensity of physical activity undertaken outside the hospital setting would be beneficial to both patients and clinicians. This paper aims to describe the key exercises of a prehabilitation programme and to determine whether the types and intensity of various prehabilitation exercises could be accurately identified using Fourier analysis of 3D accelerometer sensor data. A wearable sensor with an inbuilt 3D accelerometer was placed on both the ankle and wrist of five volunteer participants during nine prehabilitation exercises which were performed at low to high intensity. Here, the 3D accelerometer data are analysed using fast Fourier analysis, where the dominant frequency and amplitude components are extracted for each activity performed at low, moderate, and high intensity. The findings indicate that the 3D accelerometer located at the ankle is suitable for detecting activities such as cycling and rowing at low, moderate, and high exercise intensities. However, there is some overlap in the frequency and acceleration amplitude components for overland and treadmill walking at a moderate intensity.

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Murakami, D., and M. Makikawa. "Ambulatory Behavior Map, Physical Activity and Biosignal Monitoring System." Methods of Information in Medicine 36, no. 04/05 (October 1997): 360–63. http://dx.doi.org/10.1055/s-0038-1636848.

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Abstract:In this study, we have developed an ambulatory human behavior map and physical activity monitoring system. This was accomplished by equipping our portable digital biosignal memory device developed previously with GPS sensors and piezoresistive accelerometers. Using this new system, we can get a subject’s behavior map, and estimate his physical activities and posture changes in daily life.

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Pires, Ivan Miguel, Faisal Hussain, Nuno M. Garcia, and Eftim Zdravevski. "Improving Human Activity Monitoring by Imputation of Missing Sensory Data: Experimental Study." Future Internet 12, no. 9 (September 17, 2020): 155. http://dx.doi.org/10.3390/fi12090155.

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The automatic recognition of human activities with sensors available in off-the-shelf mobile devices has been the subject of different research studies in recent years. It may be useful for the monitoring of elderly people to present warning situations, monitoring the activity of sports people, and other possibilities. However, the acquisition of the data from different sensors may fail for different reasons, and the human activities are recognized with better accuracy if the different datasets are fulfilled. This paper focused on two stages of a system for the recognition of human activities: data imputation and data classification. Regarding the data imputation, a methodology for extrapolating the missing samples of a dataset to better recognize the human activities was proposed. The K-Nearest Neighbors (KNN) imputation technique was used to extrapolate the missing samples in dataset captures. Regarding the data classification, the accuracy of the previously implemented method, i.e., Deep Neural Networks (DNN) with normalized and non-normalized data, was improved in relation to the previous results without data imputation.

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jmal Hasan, DR K. P. Kaliyamurthie, H. A. "Legal Implication of Human and Physical-Activity Monitoring System Using Android Smartphone." International Journal of Innovative Research in Computer and Communication Engineering 03, no. 03 (March 30, 2015): 1522–28. http://dx.doi.org/10.15680/ijircce.2015.0303017.

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Chuo, Yindar, Marcin Marzencki, Benny Hung, Camille Jaggernauth, Kouhyar Tavakolian, Philip Lin, and Bozena Kaminska. "Mechanically Flexible Wireless Multisensor Platform for Human Physical Activity and Vitals Monitoring." IEEE Transactions on Biomedical Circuits and Systems 4, no. 5 (October 2010): 281–94. http://dx.doi.org/10.1109/tbcas.2010.2052616.

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