Gait analysis with a custom Instrumented Insole with Embedded Machine Learning: Feasibility Study

This work presents the development and evaluation of a wireless instrumented insole for human gait real-time analysis. Equipped with an embedded processor, the device estimates the Center of Pressure (CoP) and identifies eight gait phases (8GP) in real-time. It can be used in both clinical and non-clinical settings via a simple Wi-Fi-to-USB receiver connected to a host PC.Validation involved three volunteers who walked 64 steps in a straight corridor and completed 16 laps in a room with a force platform. Machine learning models were developed, trained, and embedded in a 32-bit ESP32 microcontroller. Decision trees classified gait phases, while artificial neural networks (ANN) estimated xCoP, yCoP, and vertical ground reaction force (vGRF). Two modeling strategies were tested: subject-specific and cross-subject. The subject-specific models achieved an average F1-score of 0.74, while general models reached 0.61. For CoP and vGRF estimation, the models yielded R² values of 0.94 (xCoP), 0.93 (yCoP), and 0.78 (vGRF). The proposed system estimates gait parameters at a 96 Hz sampling rate with fully embedded processing. Based on low-cost, open-source hardware, the insole offers an accessible solution for gait monitoring and integration with assistive rehabilitation technologies across diverse environments, including daily living contexts.