Multi-sensor-based fall detection and activity daily living classification by using ensemble learning

Falls are a serious problem that are often experienced by the elderly in performing activities in daily living. In recent years, the use of smartphone sensors in fall detection and activity daily living (ADL) classification has been studied to explore and understand human behaviours. In this paper, we investigate the role of the multi-sensors in fall detection and the ADL classification problem. We present ensemble learning-based approaches to improve recognition performance. We evaluate their roles on two body positions, which are the arm position and the waist position, while recognizing six ADL activities: standing, sitting, laying, walking, walking upstairs, and walking downstairs, and two different falls: falling after walking and falling after standing. From the experimental results, the ensemble learning-based approaches can improve the recognition performance for using only accelerometer data at the arm position with an accuracy of 94.8750 percent. Moreover, at the waist position, the ensemble approaches can improve the recognition performance for using both accelerometer data and gyroscope data with an accuracy of 100.00 percent.