Deep-learning enabled smart insole system aiming for multifunctional foot-healthcare applications

This paper proposes a deep-learning enabled wearable smart insole system. With a highly sensitive capacitive pressure sensing insole and deep-learning enabled data analysis process, it provides long-term data analysis, early prevention indicators, and deep insights into the relationship between plantar pressure and foot issues. Various foot-healthcare applications are proven, including daily statistics, exercise injury avoidance, and diabetic foot ulcer prevention.


Real-time foot pressure monitoring using wearable smart systems, with comprehensive foot health monitoring and analysis, can enhance quality of life and prevent foot-related diseases. However, traditional smart insole solutions that rely on basic data analysis methods of manual feature extraction are limited to real-time plantar pressure mapping and gait analysis, failing to meet the diverse needs of users for comprehensive foot healthcare. To address this, we propose a deep learning-enabled smart insole system comprising a plantar pressure sensing insole, portable circuit board, deep learning and data analysis blocks, and software interface. The capacitive sensing insole can map both static and dynamic plantar pressure with a wide range over 500 kPa and excellent sensitivity. Statistical tools are used to analyze long-term foot pressure usage data, providing indicators for early prevention of foot diseases and key data labels for deep learning algorithms to uncover insights into the relationship between plantar pressure patterns and foot issues. Additionally, a segmentation method assisted deep learning model is implemented for exercise-fatigue recognition as a proof of concept, achieving a high classification accuracy of 95%. The system also demonstrates various foot healthcare applications, including daily activity statistics, exercise injury avoidance, and diabetic foot ulcer prevention.

Author list:

Yu Tian†, Lei Zhang†*, Chi Zhang, Bo Bao, Qingtong Li, Longfei Wang, Zhenqiang Song*, Dachao Li*

How to cite:

Y. Tian, L. Zhang, C. Zhang, B. Bao, Q. Li, L. Wang, Z. Song, D. Li, Exploration 2023, 4, 20230109.