Pre-impact Alarm System for Fall Detection Using MEMS Sensors and HMM-based SVM Classifier

• Published in: Conference proceedings (IEEE Engineering in Medicine and Biology Society. Conf.) Vol. 2018; pp. 4401 - 4405
• Main Authors: Liang, Shengyun, Chu, Tianyue, Lin, Dan, Ning, Yunkun, Li, Huiqi, Zhao, Guoru
• Format: Conference Proceeding Journal Article
• Language: English
• Published: United States IEEE 01.07.2018
• Subjects: Acceleration; Alarm systems; Angular velocity; Feature extraction; Hidden Markov models; Support vector machines; Time series analysis
• ISSN: 1557-170X; 1558-4615
• DOI: 10.1109/EMBC.2018.8513119

Accidental fall can cause physical injury, fracture and other health complication, especially for elderly people living alone. Aimed to provide timely assistance after the occurrence of falling down, a pre-fall alarm system was proposed. In order to test the reliability of pre-fall alarm system, eighteen subjects who worn this device on the waist were required to participate in a series of experiments. The acceleration and angular velocity time series extracted from human motion processes were used to described human motion features. HMM-based SVM classifier was used to determine the maximum separation boundary between fall and Activities of Daily Living (ADLs). The fall detection results showed 94.91% accuracy, 97.22% Sensitivity and 93.75% Specificity. The proposed device can accurately recognize fall event, achieve additional functions, and have advantages of small size and low power consumption. Based on the findings, this pre-impact fall alarm system with detection algorithm could potentially be useful for monitoring the state of physical function in elderly population.