Abstract
Fall events in elderly populations often result in serious injury and may lead to long-term disability and/or death. While many fall detection systems have been developed using wearable sensors to distinguish falls from other daily activities, detection sensitivity and specificity decreases when exposed to more rigorous activities such as running and jumping. This research uses time-frequency analysis of accelerometer-only activity data to develop a strategy for improving fall detection accuracy. In this study, a wireless sensor system (WSS) consisting of a three-axis accelerometer, microprocessor and wireless communications module is used to collect daily activities performed following a script in the laboratory setting. Experiments were conducted on 36 healthy human subjects performing four types of falls (i.e., forward, backward, and left/right sideway falls) as well as normal movements such as standing, walking, stand-to-sit, sit-to-stand, stepping, running and jumping. In total, 1227 different activities were collected and analyzed. The developed algorithm computes the magnitude of three-axis accelerometer data to detect if a critical fall threshold is passed, then analyzes the power spectral density within a critical fall duration window (500 ms) to differentiate fall events from other rigorous activities. Fall events were observed with high energy in the 2–3.5 Hz range and distinct from other rigorous activities such as running (3.5–5.5 Hz) and jumping (1–2 Hz). Preliminary results indicate the power spectral density (PSD)-based algorithm can detect falls with high sensitivity (98.4%) and specificity (98.6%) using lab-based daily activity data. The proposed algorithm has the benefit of improved accuracy over existing time-domain only strategies and multisensor strategies.
Highlights
Fall detection, especially in older populations, has been receiving increasing attention in recent years
In our previous study [18], we proposed a multisensor combination of accelerometer and gyroscope to improve fall detection accuracy
Certain walking, running) exhibit periodicity can bewhich characterized in the frequency dowalking, running) exhibitwhich periodicity can be characterized in the frequency domain
Summary
Especially in older populations, has been receiving increasing attention in recent years. Falls are a significant cause of injury for elderly individuals resulting in disabling fractures that can potentially lead to death due to complications, such as infection or pneumonia or lasting disability. More than one-third of elderly individuals over 75 years old fall each year, 24% of whom experience serious injuries [1]. Accurate and reliable fall detection is necessary for protecting health and mitigating long-term disability. Automated recognition of a fall event can provide round-the-clock surveillance for elders living at home or in nursing homes. Timely alerts to caregivers and family members may mitigate the lasting, long-term damage resulting from a fall
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