Few-Shot Learning for Hand-Based Micro Activity Recognition

Sensors- and Sensing-based Human Activity Recognition (HAR) systems play a crucial role in healthcare, assistive technologies, and human-computer interaction. In such a context, recognizing hand-based micro activities remains a significant challenge due to their complexity, variability, and limited availability of labeled data. This paper introduces a novel Few-Shot Learning (FSL) approach for recognizing 24 distinct hand-based Activities of Daily Living (ADLs) using data from a wrist-worn inertial sensor. Our method leverages Prototypical Networks enhanced with hard negative mining to improve class separability and generalization in low-data scenarios. The proposed approach is evaluated on datasets containing data from 43 different subjects, demonstrating its effectiveness in learning from minimal labeled examples (i.e., only 35% of the data). The experimental results demonstrate the robustness of our methodology, achieving competitive accuracy using only four samples × subject × class compared to the entire full dataset for micro-ADL recognition. These findings highlight the potential of FSL in real-world applications where data collection is both costly and time-consuming.