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In the elderly, due to the degeneration of the muscles along with visual impairment, mobility becomes more difficult than in other ages. Upon moving, the elderly can be susceptible to several factors such as slips or obstacles which can cause unintended fall events and can lead to different degrees of injury, from minor trauma to more severe and even life-threatening injuries. In this work, the characteristics of movement and falling in the elderly are first studied, thereby finding thresholds for determining fall events for motion parameters to detect ahead of time the fall event. Therefore, necessary warnings can be promptly delivered to the users or caregivers, otherwise distress signal can be sent wirelessly to request assistance if the elderly is unable to stand up. This will help minimize the negative impact on the elderly caused by the fall event. The paper proposes to base the research on motion and fall features of the old people to build a wearable device which combines gyroscope accelerometer, a self-developed fall sensor and heart rate sensor for fall detection and warning. The device also employs parameter thresholds to detect forward and backward fall events as well as to provide accurate information about other familiar activities such as standing and sitting, lean forward, backward, left or right. The threshold-based method we used in determining the body states correctly identified the states: 93.33% steady state, 86.67% fallible state and 96.67% slip state. Moreover, the device can also achieve 90% accurate information about the user's heart rate.
Nguyên et al. (Wed,) studied this question.