An Energy-Efficient Algorithm for Classification of Fall Types Using a Wearable Sensor

Abstract

Objective: To mitigate damage from falls, it is essential to provide medical attention expeditiously. Many previous studies have focused on detecting falls and have shown that falls can be accurately detected at least in a laboratory setting. However, very few studies have classified the different types of falls. To this end, in this study, a novel energy-efficient algorithm that can discriminate the five most common fall types was developed for wearable systems. Methods: A wearable system with an inertia measurement unit sensor was first developed. Then, our novel algorithm, temporal signal angle measurement (TSAM), was used to classify the different types of falls at various sampling frequencies and the results were compared with those from three different machine learning algorithms. Results: The overall performance of TSAM and that of the machine learning algorithms were similar. However, TSAM outperformed the machine learning algorithms at frequencies in the range of 10–20 Hz. As the sampling frequency dropped from 200Hz to 10Hz, the accuracy of TSAM ranged from 93.3 % to 91.8%. The sensitivity and specificity ranged from 93.3% to 91.8%, and 98.3% to 97.9%, respectively for the same frequency range. Conclusion: Our algorithm can be utilized with energy-efficient wearable devices at low sampling frequencies to classify different types of falls. Significance: Our system can expedite medical assistance in emergency situations caused by falls by providing the necessary information to medical doctors or clinicians.