Wearable devices that monitor muscle activity based on surface electromyography could be of use in the development of hand gesture recognition applications. Such devices typically use machine-learning models, either locally or externally, for gesture classification. However, most devices with local processing cannot offer training and updating of the machine-learning model during use, resulting in suboptimal performance under practical conditions. Here we report a wearable surface electromyography biosensing system that is based on a screen-printed, conformal electrode array and has in-sensor adaptive learning capabilities. Our system implements a neuro-inspired hyperdimensional computing algorithm locally for real-time gesture classification, as well as model training and updating under variable conditions such as different arm positions and sensor replacement. The system can classify 13 hand gestures with 97.12% accuracy for two participants when training with a single trial per gesture. A high accuracy (92.87%) is preserved on expanding to 21 gestures, and accuracy is recovered by 9.5% by implementing model updates in response to varying conditions, without additional computation on an external device.

基于表面肌电图监测肌肉活动的可穿戴设备可用于手势识别应用程序的开发。这样的设备通常在本地或外部使用机器学习模型来进行手势分类。但是，大多数具有本地处理功能的设备在使用过程中无法提供机器学习模型的训练和更新，从而导致在实际条件下性能欠佳。在这里，我们报告了一种可穿戴的表面肌电图生物传感系统，该系统基于丝网印刷的保形电极阵列，并具有传感器内自适应学习功能。我们的系统在本地实现了神经启发的超维计算算法，用于实时手势分类，以及在各种条件下（例如不同的手臂位置和传感器更换）进行模型训练和更新。当每个手势进行一次试验时，系统可以为两个参与者对13个手势进行分类，准确率达到97.12％。通过扩展到21个手势，可以保留较高的精度（92.87％），并且可以通过响应变化的条件实施模型更新来恢复9.5％的精度，而无需在外部设备上进行额外的计算。