Lightweight HAR Scheme for Rapid Environment Adaption Based on AIoT WiFi Sensing Chips

Human activity recognition (HAR) utilizing WiFi channel state information (CSI) holds profound implications owing to the pervasive WiFi coverage in daily life. Deep learning has enabled the development of many high-precision HAR systems, but also brings the challenges of degrading performance in the new environments and high complexity issues for implementing on a single chip. In this paper, we introduce a lightweight HAR scheme for rapid adaption to the new environments, which can be implemented in a WiFi based artificial intelligence internet of things (AIoT) chip. The proposed scheme consists of two modules, which are change pattern extraction (CPE) and self-attention based adaptive model (SAAM). In CPE, the intricate multi-subcarrier CSI are transformed into unique change patterns closely related to each human activity with low complexity using deep non-negative matrix factorization (DNMF). Then, SAAM facilitates the correlation of change patterns across different environments through learned temporal features, enabling rapid generalization to new environments with only a few new samples, boasting advantages of low training costs, parameter memory usage, and computational time. Experimental results demonstrate that our system achieves not only 94% accuracy in the original environment, but also exhibits promising performance in new environments, requiring only three new training samples for each activity.