Wearable thumb sleeves enabled by self-supervised learning with few stretchable sensors and few-shot data for switchable finger tasks

Human fingers exhibiting remarkable dexterity are ideal for natural human-machine interaction. Traditional meth ods require at least one device per finger and extensive labeled data, often limiting models to a single user and task. Here, we propose a wearable thumb sleeve integrated with self-supervised learning, which exhibits user indepen dence and data efficiency, enabling recognition of various finger-related tasks. The thumb sleeve is equipped with only two stretchable sensors at the thumb joints and learns latent features from unlabeled random thumb move ment data. By using fine-tuning with five-shot labeled data, it can rapidly adapt to new users and tasks, including eight directional commands and 10 knuckle key inputs. It allows free switching between tasks without the need to reconstruct or retrain the model. The proposed approach demonstrates strong potential for real-world applications, serving as a substitute for a mouse and keyboard to enable tasks such as online shopping.