在并行超低功耗微控制器上利用增量在线学习进行 sEMG 驱动的手部动态估计。

Marcello Zanghieri;Pierangelo Maria Rapa;Mattia Orlandi;Elisa Donati;Luca Benini;Simone Benatti
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引用次数: 0

摘要

表面肌电图(sEMG)是一种先进的传感模式,可用于消费、工业和康复领域的无创人机界面。目前由 sEMG 驱动的控制策略的主要局限性在于 sEMG 固有的可变性,尤其是由于传感器重新定位而导致的跨时段变化;这限制了负责信号到指令映射的机器/深度学习(ML/DL)的通用性。机器/深度学习(ML/DL)在 sEMG 驱动控制方面的另一个热点是,从固定手部位置分类转向手部运动学和动力学回归,从而有望实现更加灵活和流畅的控制。我们提出了一种基于 sEMG 的多指同时受力估计的增量在线训练策略,使用的是适合嵌入式设备学习的小型时序卷积网络。我们在 HYSER 数据集上跨天验证了我们的方法。我们的增量在线训练在 HYSER 的随机数据集上达到了最大自主收缩的跨天平均绝对误差(MAE)为 (9.58 ± 3.89)%,该数据集为即兴、非预定义力序列,最具挑战性且最接近真实场景。这一 MAE 与利用更多历时进行的以准确性为导向的非嵌入式离线训练相当。此外,我们还证明了我们的在线训练方法可以部署在 GAP9 超低功耗微控制器上,延迟时间为 1.49 ms,每个前向-后向-更新步骤的能耗仅为 40.4 uJ。这些结果表明,我们的解决方案符合在设备上进行精确和实时增量训练的要求。
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sEMG-Driven Hand Dynamics Estimation With Incremental Online Learning on a Parallel Ultra-Low-Power Microcontroller
Surface electromyography (sEMG) is a State-of-the-Art (SoA) sensing modality for non-invasive human-machine interfaces for consumer, industrial, and rehabilitation use cases. The main limitation of the current sEMG-driven control policies is the sEMG's inherent variability, especially cross-session due to sensor repositioning; this limits the generalization of the Machine/Deep Learning (ML/DL) in charge of the signal-to-command mapping. The other hot front on the ML/DL side of sEMG-driven control is the shift from the classification of fixed hand positions to the regression of hand kinematics and dynamics, promising a more versatile and fluid control. We present an incremental online-training strategy for sEMG-based estimation of simultaneous multi-finger forces, using a small Temporal Convolutional Network suitable for embedded learning-on-device. We validate our method on the HYSER dataset, cross-day. Our incremental online training reaches a cross-day Mean Absolute Error (MAE) of (9.58 ± 3.89)% of the Maximum Voluntary Contraction on HYSER's RANDOM dataset of improvised, non-predefined force sequences, which is the most challenging and closest to real scenarios. This MAE is on par with an accuracy-oriented, non-embeddable offline training exploiting more epochs. Further, we demonstrate that our online training approach can be deployed on the GAP9 ultra-low power microcontroller, obtaining a latency of 1.49 ms and an energy draw of just 40.4 uJ per forward-backward-update step. These results show that our solution fits the requirements for accurate and real-time incremental training-on-device.
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