Activity Detection using Fusion of Multi-Pressure Sensors in Insoles
Omid Dehzangi, Bhavani Anantapur Bache, Omar Iftikhar · 2018
Daily monitoring of activities performed by patients is important for remote health monitoring. To identify activities Numerous motion-based activity monitoring systems have been developed using wearable motion sensors, that include an accelerometer and gyroscope. are used to identify human activities. However, such systems are invasive making it unsuited for continuous monitoring. In this paper, we designed and developed a non-invasive way of monitoring and identifying activities. We acquire data from high density pressure sensors that are embedded at 13 different locations of insoles. The insole data is characterized and analyzed to identify different activities including sitting, standing, walking, running, cycling and jumping. In this paper we developed a unique methodology that investigates the discriminative capability of individual pressure sensors and further conducted fusion of multiple pressure sensors for improved activity monitoring. Based on our experimental results, 91.8% activity recognition accuracy was achieved using the best individual insole pressure sensor. We then investigated multi-sensor fusion to capture an improved class discriminative score space using a Multi Layer Neural Network (MLNN), which improved the activity identification accuracy of the system to the promising value of 97.63%.