AnkleSens: Foot Posture Prediction Using Photo Reflective Sensors on Ankle

Abstract

Recognizing foot gestures can be useful for subtle inputs to appliances and machines in everyday life, but for a system to be useful, it must allow users to assume various postures and work in different spaces. Camera-based and pressure-based systems have limitations in these areas. In this paper, we introduce AnkleSens, a novel ankle-worn foot sensing device that estimates a variety of foot postures using photo reflective sensors. Since our device is not placed between the foot and the floor, it can predict foot posture, even if we keep the foot floating in the air. We developed a band prototype with 16 sensors that can be wrapped around the leg above the ankle. To evaluate the performance of the proposed method, we used eight foot postures and four foot states as preliminary classes. After assessing a test dataset with the preliminary classes, we integrated the eight foot postures into five. Finally, we classified the dataset with five postures in four foot states. For the resulting 20 classes, the average classification accuracy with our proposed method was 79.57% with user-dependent training. This study showed the potential of foot posture sensing as a new subtle input method in daily life.

Keywords

• Photo reflective sensor
• wearable device
• machine learning
• foot posture prediction