Citation:
Lingling WANG, Shiqi GUO, Ying ZHOU, Kunhui CHEN. Study on Indoor Risky Behavior Monitoring and Early Warning System for the Elderly. Journal of Information Technologyin Civil Engineering and Architecture,
2023, 15(1): 7-12.
doi: 10.16670/j.cnki.cn11-5823/tu.2023.01.02
2023, 15(1): 7-12. doi: 10.16670/j.cnki.cn11-5823/tu.2023.01.02
Study on Indoor Risky Behavior Monitoring and Early Warning System for the Elderly
School of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China |
In order to ensure the safety of the elderly in indoor environment, and to detect dangerous situations and provide rescue support in time, a set of indoor risky behavior monitoring and early warning system for the elderly was developed, which can identify common risky behaviors of the elderly and give early warning. For the abnormal prone and falling, two common risky behaviors of the elderly, the RGB camera was used to collect human posture information and extract key feature points of human skeleton.. Support vector machine was used to classify all kinds of risky behaviors and further combine the behavior status and location information to provide remote warning and emergency call to the relevant personnel. The results show that the true classification rate of risky behavior recognition by the system is 97.14%. The system can accurately monitor and warn indoor risky behaviors of the elderly, thus effectively slowing down the expansion of accidental injuries and ensuring the home safety of the elderly.
| [1] | |
| [2] |
Skubic M, Alexander G, Popescu M, et al. A smart home application to eldercare: current status and lessons learned[J]. Technology and Health Care, 2019;17(3): 183–201. |
| [3] |
苌飞霸, 尹军, 张和华, 等. 可穿戴式健康监测系统研究与展望[J]. 中国医疗器械杂志, 2015, 39(01): 40-43. |
| [4] |
A.J.A. Majumder, I. Zerin, S.I. Ahamed, et al. A multi-sensor approach for fall risk prediction and prevention in the elderly[J]. SIGAPP Appl. Comput. Rev., 2014, 14: 41–52.doi: 10.1145/2600617.2600621 |
| [5] |
Sathyanarayana A, Joty S, Fernandez-Luque L, et al. Correction of: Sleep Quality Prediction From Wearable Data Using Deep Learning[J]. JMIR mhealth and uhealth, 2016, 4(4): 130.doi: 10.2196/mhealth.6953 |
| [6] |
Wang Z, Wu D, Chen J, et al. A triaxial accelerometer-based human activity recognition via eemd-based features and game-theory-based feature selection[J]. IEEE Sens. |
| [7] |
ALARIFI A, ALWADAIN A. Killer heuristic optimized convolution neural network-based fall detection with wearable IoT sensor devices[J]. Measurement, 2020, 167: 108258. |
| [8] |
Khan, Z. A, Sohn, W. A hierarchical abnormal human activity recognition system based on R-transform and kernel discriminant analysis for elderly health care[J]. Computing, 2013, 95 (2): 109–127. |
| [9] |
M. S. Hossain. Patient status monitoring for smart home healthcare[C]. 2016 IEEE International Conference on Multimedia & Expo Workshops (ICMEW), 2016. |
| [10] |
Espinosa R, Ponce H, GUTIéRREZ S, et al. A vision-based approach for fall detection using multiple cameras and convolutional neural networks: A case study using the UP-Fall detection dataset[J]. Computers in Biology and Medicine, 2019, 115: 103520. |
| [11] |
王世刚, 孙爱朦, 赵文婷, 等. 基于时空兴趣点的单人行为及交互行为识别[J]. 吉林大学学报(工学版), 2015, 45(01): 304-308. |
| [12] |
Lai YL, Chen CL, Chang CH, et al. An intelligent health monitoring system using radio-frequency identification technology[J]. Technol Health Care. |
| [13] |
Riboni, D, Civitarese, G, Bettini, C. Analysis of long-term abnormal behaviors for early detection of cognitive decline[C]//. In: IEEE International Workshop on PervAsive Technologies and care systems for sustainable Aging-in-place, Sydney, 2016. |
| [14] |
Fleury A, Vacher M, Noury N. SVM-Based Multi-Modal Classification of Activities of Daily Living in Health Smart Homes: Sensors, Algorithms and First Experimental Results[J]. IEEE Transactions on Information Technology in Biomedicine, 2010, 14(2): 274-83. |
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