Abstract
Wearable devices for fall detection have received attention in academia and industry, because falls are very dangerous, especially for elderly people, and if immediate aid is not provided, it may result in death. However, some predictive devices are not easily worn by elderly people. In this work, a huge dataset, including 2520 tests, is employed to determine the best sensor placement location on the body and to reduce the number of sensor nodes for device ergonomics. During the tests, the volunteer’s movements are recorded with six groups of sensors each with a triaxial (accelerometer, gyroscope and magnetometer) sensor, which is placed tightly on different parts of the body with special straps: head, chest, waist, right-wrist, right-thigh and right-ankle. The accuracy of individual sensor groups with their location is investigated with six machine learning techniques, namely the k-nearest neighbor (k-NN) classifier, Bayesian decision making (BDM), support vector machines (SVM), least squares method (LSM), dynamic time warping (DTW) and artificial neural networks (ANNs). Each technique is applied to single, double, triple, quadruple, quintuple and sextuple sensor configurations. These configurations create 63 different combinations, and for six machine learning techniques, a total of 63 × 6 = 378 combinations is investigated. As a result, the waist region is found to be the most suitable location for sensor placement on the body with 99.96% fall detection sensitivity by using the k-NN classifier, whereas the best sensitivity achieved by the wrist sensor is 97.37%, despite this location being highly preferred for today’s wearable applications.
Highlights
Wearable device applications have gained incredible popularity in many areas of daily life, such as health [1], entertainment [2], communication [3], rehabilitation [4] and education [5]
When all six sensor nodes are used for classification, the k-nearest neighbor (k-NN) algorithm gives 99.91% accuracy; the best accuracy (99.94%) is achieved using three different sensor combinations, which are ECA, FDBA and FECA, right-thigh_waist_head, right-ankle_right-wrist_chest_head and right-ankle_right-thigh-waist-head, respectively
When single sensor behaviors are taken into consideration, it is clear that the waist sensor, labeled C, gives alone the best result, with 98.46% accuracy
Summary
Wearable device applications have gained incredible popularity in many areas of daily life, such as health [1], entertainment [2], communication [3], rehabilitation [4] and education [5]. Thanks to the reduced service cost of communication networks, users can access the Internet at very reasonable prices. These advances in wearable devices and Internet technologies have resulted in today’s wearable and Internet of Things (IoT) patent wars. Wearable fall detection devices are one of the most popular fields in both academia and industry. This is because falls are a serious and common cause of morbidity and mortality among elderly people [6]. If a falling person stays unattended for a long time, physical and psychological complications can be observed. Falls induce fear of falls and Sensors 2016, 16, 1161; doi:10.3390/s16081161 www.mdpi.com/journal/sensors
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