用低成本芯片实现实时步态诊断，助力假肢与外骨骼普及
A Cost-effective, Stand-alone, and Real-time TinyML-Based Gait Diagnosis Unit Aimed at Lower-limb Robotic Prostheses and Exoskeletons
- 基于TinyML在ESP32上部署双量化模型，实现轻量实时分析
- 92%准确率识别五种步态，95-96ms内完成推理，仅需3秒数据
- 成本仅7.30美元，可独立运行，适合嵌入各类康复设备
相比传统假肢，机器人假肢与外骨骼具有显著优势。然而，在医疗成本高昂的背景下，每10名患者中仅有1人能获得常规假肢，更高级别设备的普及受限于其高昂成本，其中诊断与控制单元占比较大。为解决成本与性能之间的权衡难题，本文提出一种低成本、可穿戴、实时的步态诊断单元（GDU），专用于机器人假肢与外骨骼。作为概念验证，开发了基于ESP32 NodeMCU开发板（7.30美元）的GDU原型，利用TinyML在两个并行量化int8模型上运行，通过两个惯性测量单元（IMUs）采集的加速度数据，有效分类五种步态（静止、步行、跑步、单脚跳、跳跃）并生成异常评分。该可穿戴式独立诊断单元可适配任意假肢或外骨骼，以92%的整体准确率实现实时步态分类，仅需3秒数据即可在95-96毫秒内完成推理。
原文摘要 · Abstract (English)
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.
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