A Cost-effective, Stand-alone, and Real-time TinyML-Based Gait Diagnosis Unit Aimed at Lower-limb Robotic Prostheses and Exoskeletons

TL;DR

This paper presents a low-cost, real-time gait diagnosis unit using TinyML on an ESP32, capable of classifying gait scenarios with high accuracy for prosthetic and exoskeleton applications.

Contribution

It introduces a cost-effective, standalone gait diagnosis device leveraging TinyML and quantized models, suitable for integration with prostheses and exoskeletons.

Findings

Achieved 92% classification accuracy.

Generated anomaly scores within 95-96 ms.

Operated effectively with only 3 seconds of gait data.

Abstract

Robotic prostheses and exoskeletons can do wonders compared to their non-robotic counterpart. However, in a cost-soaring world where 1 in every 10 patients has access to normal medical prostheses, access to advanced ones is, unfortunately, extremely limited especially due to their high cost, a significant portion of which is contributed to by the diagnosis and controlling units. However, affordability is often not a major concern for developing such devices as with cost reduction, performance is also found to be deducted due to the cost vs. performance trade-off. Considering the gravity of such circumstances, the goal of this research was to propose an affordable wearable real-time gait diagnosis unit (GDU) aimed at robotic prostheses and exoskeletons. As a proof of concept, it has also developed the GDU prototype which leveraged TinyML to run two parallel quantized int8 models into an ESP32 NodeMCU development board (7.30 USD) to effectively classify five gait scenarios (idle, walk, run, hopping, and skip) and generate an anomaly score based on acceleration data received from two attached IMUs. The developed wearable gait diagnosis stand-alone unit could be fitted to any prosthesis or exoskeleton and could effectively classify the gait scenarios with an overall accuracy of 92% and provide anomaly scores within 95-96 ms with only 3 seconds of gait data in real-time.