Multitask Deep Learning for Human Activity, Speed, and Body Weight Estimation Using Commercial Smart Insoles

Abstract

Healthcare professionals and individual users use wearable devices equipped with various sensors for healthcare management. Recently, the joint usage of artificial intelligence and these wearable sensors has played an essential role in healthcare management by providing a wide range of applications, such as fitness tracking, gym activity monitoring, patient rehabilitation monitoring, and disease detection. These tasks eventually aim to enhance personal well-being and better manage the user's physical health by monitoring different activity types and body weight changes. Here, we present an efficient multitask learning (MTL) framework based on commercial smart insoles that can solve three tasks related to physical health management: 1) activity classification; 2) speed estimation; and 3) body weight estimation. Our multitask framework converts the sensor data from the smart insole to a recurrence plot, which shows significant performance improvement compared to processing the raw time-series data. In addition, we utilized a modified MobileNetV2 as our backbone network, which has a total parameter of less than 100K and a computational budget of 0.34G of multiply-accumulate operations. Furthermore, we collected a vast data set from 72 users carrying out 16 experiments, which contains the largest number of people for MTL purposes using smart insoles. Extensive experiments show that the proposed MTL framework is extremely efficient while outperforming or leading to comparable performance against single-task models.