Open-Set Sensor Human Activity Recognition Based on Reciprocal Time Series

Human activity recognition (HAR) using wearable sensors has witnessed significant advancements in recent years. However, the traditional closed-set assumption restricts models to predicting only known activity classes. This limitation can be overcome by building models under the open-set recognition paradigm. Most existing methods mitigate the open-set risk by adjusting known class boundaries, but this approach overlooks the potential correlations between unknown classes and can lead to over-generalization, requiring more and higher-quality training data. This paper introduces the concept of reciprocal time series, which serves as the latent representation of the unknown class space for each known class. By comparing samples to these reciprocal time series, the model can classify them as either known or unknown. We propose a novel metric to measure temporal similarity within the embedding space. The constructed boundary space, formed by the reciprocal time series, facilitates the effective learning of inherent generalization features from a large number of unknown samples through multi-class interaction, ultimately reducing the open-set risk. Extensive experiments on three public sensor datasets demonstrate that our model surpasses existing methods on the open-set recognition task for sensor-based HAR, particularly excelling in recognizing unknown class instances.