Abstract
The issue of elderly people’s travel safety has attracted widespread attention in society. To address this problem and in line with current research trends, this study proposes an analysis and prediction of elderly people’s fall behavior based on ZigBee signal strength features. Due to the significant changes in radial range caused by movements, this paper investigates how ZigBee signal attenuation features can be used to perceive different angles. Various phenomena such as refraction, diffraction, and scattering can cause different degrees of interference in the normal signal propagation when ZigBee signals encounter different situations. By analyzing the signs of signal reception and detecting changes in signal strength, the physical condition of individuals during signal transmission can be determined. Furthermore, to address the issue of low accuracy in fall detection estimation based on broader spectral indices, this paper proposes an improvement. It presents an algorithm for extracting fall features based on a wider range of spectral indices, namely the fall behavior recognition algorithm.
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References
Pravin, K., Kumar, C.D., et al.: AI based elderly fall prediction system using wearable sensors: a smart home-care technology with IOT. Measurement Sens. 25 (2023)
Anitha, G., Priya, B.S.: Vision based real time monitoring system for elderly fall event detection using deep learning. Comput. Syst. Sci. Eng. 42(1) (2022)
Paolo, B., Gianluca, C., Mario, D., et al.: Preface on advanced infrared technology and applications – AITA 2021. Infrared Phys. Technol. 130 (2023)
Akari, H., Sakiko, F., Haruka, T., et al.: An Academic-Industry collaborative research activity related to “care DX” from a nursing perspective: focusing on night-time itching in older adults using sensor technology. Geriatrics Gerontol. Int. (2023)
Saad, F.A., Chiew, Y.W., Bin, S.A.I.M.: WiFi-based human activity recognition through wall using deep learning. Eng. Appl. Artif. Intell. 127(PA) (2024)
Hajar, S., Ahmadreza, E., Pramod, C., et al.: Classification of activities of daily living based on grasp dynamics obtained from a leap motion controller. Sensors 22(21) (2022)
Fayad, M., Hachani, Y.M., Ghoumid, K., et al.: Fall detection approaches for monitoring elderly healthcare using Kinect technology: a survey. Appl. Sci. 13(18) (2023)
Taghvaei, S., Kosuge, K.: Image-based fall detection and classification of a user with a walking support system. Front. Mech. Eng. 13(3) (2018)
Khanh, T.T., Nguyen, V., Pham, X.Q., et al.: Wi-Fi indoor positioning and navigation: a cloudlet-based cloud computing approach. Hum. Cent. Comput. Inf. Sci. 10, 32 (2020). https://doi.org/10.1186/s13673-020-00236-8
Xu, Z., Yu, J., Xiang, W., et al.: A novel SE-CNN attention architecture for sEMG-based hand gesture recognition. Comput. Model. Eng. Sci. 134(1) (2022)
Yaxin, R., Jiang, Y., Jun, C., Xiaowei, L.: A method of fall detection based on CSI. J. Yunnan Univ. Nat. Sci. Ed. 42(2), 220–227 (2020)
Acknowledgement
This project is supported by Jilin Provincial Department of Education Science and Technology Project (JJKH20210457KJ, JJKH20221262KJ), the innovation project of The People’s Republic of China ministry of education science and technology development center (No. 2022IT096), Jilin province science and technology development program (20220508038RC).
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Song, X., Sun, H., Dong, Y., Pei, Y. (2024). Analysis and Prediction of Elderly Fall Behavior Based on ZigBee Signal Strength Features. In: Dong, J., Zhang, L., Cheng, D. (eds) Proceedings of the 2nd International Conference on Internet of Things, Communication and Intelligent Technology. IoTCIT 2023. Lecture Notes in Electrical Engineering, vol 1197. Springer, Singapore. https://doi.org/10.1007/978-981-97-2757-5_16
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DOI: https://doi.org/10.1007/978-981-97-2757-5_16
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