Abstract
Accelerometer-based motion sensing has been extensively applied to fall detection. However, such applications can only detect fall accidents; therefore, a system that can prevent fall accidents is desirable. Bed falls account for more than half of patient falls and are preceded by a clear warning indicator: the patient attempting to get out of bed. This study designed and implemented an Internet of Things module, namely, Bluetooth low-energy-enabled Accelerometer-based Sensing In a Chip-packaging (BASIC) module, with a tilt-sensing algorithm based on the patented low-complexity COordinate Rotation DIgital Computer (CORDIC)-based algorithm for tilt angle conversions. It is applied for detecting the postural changes (from lying down to sitting up) and to protect individuals at a high risk of bed falls by prompting caregivers to take preventive actions and assist individuals trying to get up. This module demonstrates how motion and tilt sensing can be applied to bed fall prevention. The module can be further miniaturized or integrated into a wearable device and commercialized in smart health-care applications for bed fall prevention in hospitals and homes.
Highlights
Wearable sensors have gained attention in recent decades because they allow for noninvasive, real-time physical, and physiological monitoring, especially in smart care and remote digital medicines [1,2,3]
Wearable devices have been used by various groups of individuals, such as athletes [4], to maximize their health, monitor their training and improve their performance, and purposes such as rehabilitation [5]
Wearable sensors are a part of Internet of Things (IoT) system
Summary
Wearable sensors have gained attention in recent decades because they allow for noninvasive, real-time physical, and physiological monitoring, especially in smart care and remote digital medicines [1,2,3]. A wearable sensor, its connectivity, and its health-care applications form a complete IoT system. In this system, data acquired by the sensor are algorithmically processed for dedicated applications, and users interact with the processed data via graphical user interfaces. Common types of sensing include biochemical sensing [6,7,8]; biological sensing, such as electrocardiography (ECG) [9], electromyography (EMG) [10], and electroencephalography (EEG)-based neural status [11]; and motion sensing [12,13]. Motion sensing systems most commonly include an accelerometer that can detect four of the five types of inertial motions: acceleration, vibration, shock, and tilt (but not rotation) [14]
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