Adaptive time method for fall detection in elderly

Safa Hussein Mohammed, Yangyu Fan, Guoyun Lv, Shiya Liu

科研成果: 书/报告/会议事项章节会议稿件同行评审

1 引用 (Scopus)

摘要

Falls are the largest risk to the health of elderly people. Different researchers have studied threshold algorithms and machine learning for fall detection; however, these methods do not efficiently detect the possibility of falls in the elderly. Recently, sensors have been developed that can observe human joints to determine how prone a person is to fall. However, the use of a single sensor exhibits several drawbacks such as low accuracy, limited information, and a high false alarm rate. Therefore, multiple sensors at the waist, thigh, and ankle were the most comfortable positions for elderly people wearing sensors. In this paper, a hybrid approach to dimension reduction and discrete wavelet transform (DWT) is proposed to extract features from the dataset. A unique approach employing k Nearest Neighbor (KNN) and Support Vector Machine (SVM) was investigated to accurately detect if a person is prone to fall or not. The results after comparative analysis with current methods show a significant increase in accuracy of 94%.

源语言英语
主期刊名8th International Conference on Recent Advances and Innovations in Engineering
主期刊副标题Empowering Computing, Analytics, and Engineering Through Digital Innovation, ICRAIE 2023
出版商Institute of Electrical and Electronics Engineers Inc.
ISBN(电子版)9798350315516
DOI
出版状态已出版 - 2023
活动8th IEEE International Conference on Recent Advances and Innovations in Engineering, ICRAIE 2023 - Kuala Lumpur, 马来西亚
期限: 2 12月 20233 12月 2023

出版系列

姓名8th International Conference on Recent Advances and Innovations in Engineering: Empowering Computing, Analytics, and Engineering Through Digital Innovation, ICRAIE 2023

会议

会议8th IEEE International Conference on Recent Advances and Innovations in Engineering, ICRAIE 2023
国家/地区马来西亚
Kuala Lumpur
时期2/12/233/12/23

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