A wearable-based posture recognition system with AI-assisted approach for healthcare IoT

Human posture recognition is a challenging task in the medical healthcare industry, when pursuing intelligence, accuracy, security, privacy, and efficiency, etc. Currently, the main posture recognition methods are captured-behaviors-based visual image analysis and wearable devices-based signal analysis. However, these methods suffer from issues such as high misjudgment rate, high-cost and low-efficiency. To address these issues, we propose a collaborative AI-IoT-based solution (namely, WMHPR) that embeds with advanced AI-assisted approach. In WMHPR, we propose the multi-posture recognition (MPR), an offline algorithm is implemented on wearable hardware, to identify posture based on multi-dimensions data. Meanwhile, an AI-based algorithm running on the cloud server (online), named Cascade-AdaBoosting-CART (CACT), is proposed to further enhance the reliability and accuracy of MPR. We recruit 20 volunteers for real-life experiments to evaluate the effectiveness, and the results show our solution is significantly outstanding in terms of accuracy and reliability while comparing with other typical algorithms. (C) 2021 Published by Elsevier B.V.

Automated summary

The paper presents a collaborative AI-IoT-based solution for human posture recognition, utilizing multi-posture recognition and Cascade-AdaBoosting-CART algorithm to enhance accuracy and reliability.