Poster: Motion Sensor Based Dragging Feet Detection Using Lightweight Classification Model - Zhou, Michelle; Wang, Shuangquan (IEEE/ACM Conference on Connected Health: Applications, Systems and Engineering Technologies (Online), 2024)
Dragging feet is an important indicator of potential falls. We propose using a motion sensor-embedded wearable device deployed near the ankle to sense walking motions and detect dragging feet continuously. To adapt to the limited computation ability and power supply of the wearable device, only several most representative features are selected to build a lightweight classification model. This model can achieve high recognition accuracy while reducing computation and energy costs. Experimental results show that using only five features common to all users, the proposed method can accurately distinguish normal walking from dragging feet. The average accuracy of the four algorithms is greater than or equal to 93.0% for all five users and up to 98.7% for user 2.