Abstract
The population of aged people has been increasing globally due to various factors like life expectancy and declining birth rates. So, there is a reduction in physical or cognitive decline, which affects the quality of life of the people where people compromise comfort. It is essential to bring the technology to assist the elderly, which helps them to serve effectively in terms of cost and reliability. As the technologies are more accessible in terms of price, size and speed, these can be adapted to assist the people. Human activity recognition (HAR) system plays a vital role in understanding human actions to assist such people. In this work, we carry out an extensive survey on research in the field of HAR. We propose a review, based on the various sensory modalities used in the HAR system. Along with this, the issues and challenges faced by the various sensor-based HAR are listed.
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References
Farah JD, Baddour N, Lemaire ED (2019) Design, development, and evaluation of a local sensor-based gait phase recognition system using a logistic model decision tree for orthosis-control. J Neuroeng Rehabil 16(1):22
Ministry of Statistics, G.o.I. (2016) Elderly in India. National Sample Survey Office, pp 1–95
Carmeli E, Imam B, Merrick J (2016) Assistive technology and older adults. In: Health Care for People with Intellectual and Developmental Disabilities across the Lifespan, pp 1465–1471. Springer
Guo J, Zhou X, Sun Y, Ping G, Zhao G, Li Z (2016) Smartphone-based patients’ activity recognition by using a self-learning scheme for medical monitoring. J Med Syst 40(6):140
Terada T, Tanaka K (2010) A framework for constructing entertainment contents using flash and wearable sensors. In: International Conference on Entertainment Computing, pp 334–341. Springer
Dempsey PG, McGorry RW, Maynard WS (2005) A survey of tools and methods used by certified professional ergonomists. Appl Ergon 36(4):489–503
Cornacchia M, Ozcan K, Zheng Y, Velipasalar S (2016) A survey on activity detection and classification using wearable sensors. IEEE Sens J 17(2):386–403
Bulling A, Blanke U, Schiele B (2014) A tutorial on human activity recognition using body-worn inertial sensors. ACM Comput Surv (CSUR) 46(3):33
Hassan MM, Uddin MZ, Mohamed A, Almogren A (2018) A robust human activity recognition system using smartphone sensors and deep learning. Future Gener Comput Syst 81:307–313
Chernbumroong S, Cang S, Atkins A, Yu H (2013) Elderly activities recognition and classification for applications in assisted living. Expert Syst Appl 40(5):1662–1674
Gjoreski H, Gams M (2011) Activity/posture recognition using wearable sensors placed on different body locations. In: Proceedings of (738) Signal and Image Processing and Applications, Crete, Greece 2224, pp 716–724
Anwary AR, Yu H, Vassallo M (2018) Optimal foot location for placing wearable IMU sensors and automatic feature extraction for gait analysis. IEEE Sens J 18(6):2555–2567
Laudanski A, Brouwer B, Li Q (2015) Activity classification in persons with stroke based on frequency features. Med Eng Phys 37(2):180–186
Lorussi F, Carbonaro N, De Rossi D, Paradiso R, Veltink P, Tognetti A (2016) Wearable textile platform for assessing stroke patient treatment in daily life conditions. Front Bioeng Biotechnol 4(28):19
Uddin M, Khaksar W, Torresen J et al (2018) Ambient sensors for elderly care and independent living: a survey. Sensors 18(7):2027
Wang Y, Cang S, Yu H (2019) A survey on wearable sensor modality centred human activity recognition in health care. Expert Syst Appl:167–190
Zhang S, Wei Z, Nie J, Huang L, Wang S, Li Z (2017) A review on human activity recognition using vision-based method. J Healthc Eng 2017:3090343
Debes C, Merentitis A, Sukhanov S, Niessen M, Frangiadakis N, Bauer A (2016) Monitoring activities of daily living in smart homes: understanding human behavior. IEEE Signal Process Mag 33(2):81–94
Al-Shaqi R, Mourshed M, Rezgui Y (2016) Progress in ambient assisted systems for independent living by the elderly. SpringerPlus 5(1):624
Alam MR, Reaz MBI, Ali MAM (2012) A review of smart homes—past, present, and future. IEEE Trans Syst Man Cybern, Part C (Appl Rev) 42(6):190–1203
Liu AA, Xu N, Nie WZ, Su YT, Wong Y, Kankanhalli M (2016) Benchmarking a multimodal and multiview and interactive dataset for human action recognition. IEEE Trans Cybern 47(7):1781–1794
Dawn DD, Shaikh SH (2016) A comprehensive survey of human action recognition with spatio-temporal interest point (stip) detector. Visual Comput 32(3):289–306
Jalal A, Kamal S, Kim D (2017) A depth video-based human detection and activity recognition using multi-features and embedded hidden markov models for health care monitoring systems. Int J Interact Multimedia Artif Intell 4(4)
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Kolkar, R., Geetha, V. (2021). Issues and Challenges in Various Sensor-Based Modalities in Human Activity Recognition System. In: Kumar, R., Dohare, R.K., Dubey, H., Singh, V.P. (eds) Applications of Advanced Computing in Systems. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-33-4862-2_18
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DOI: https://doi.org/10.1007/978-981-33-4862-2_18
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