CapWalk: A Capacitive Recognition of Walking-Based Activities as a Wearable Assistive Technology

Abstract

In this research project, we present an alternative approach to recognize various walking-based activities based on the technology of capacitive sensing. While accelerometry-based walking detections suffer from reduced accuracy at low speeds, the technology of capacitive sensing uses physical distance parameters, which makes it invariant to the duration of step performance. Determining accurate levels of walking activity is a crucial factor for people who perform walking with tiny step lengths such as elderlies or patients with pathologic conditions. In contrast to other gait analysis solutions, CapWalk is mobile and less affected by external influences such as bad lighting conditions, while it is also invariant to external acceleration artifacts. Our approach enables a reliable recognition of very slow walking speeds, in which accelerometer-based implementations can fail or provide high deviations. In CapWalk we present three different capacitive sensing prototypes (Leg Band, Chest Band, Insole) in the setup of loading mode to demonstrate recognition of sneaking, normal walking, fast walking, jogging, and walking while carrying weight. Our designs are wearable and could easily be integrated into wearable objects, such as shoes, pants or jackets. We envision such gathered information to be used to assist certain user groups such as diabetics, whose optimal insulin dose is depending on bread units and physical activity or elderlies whose personalized dosage of medication can be better determined based on their physical activity.