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Human activity recognition (HAR) with wearables is promising research thatcan be widely adopted in many smart healthcare applications. In recent years,the deep learning-based HAR models have achieved impressive recognitionperformance. However, most HAR algorithms are susceptible to the multi-classwindows problem that is essential yet rarely exploited. In this paper, wepropose to relieve this challenging problem by introducing the segmentationtechnology into HAR, yielding joint activity segmentation and recognition.Especially, we introduce the Multi-Stage Temporal Convolutional Network(MS-TCN) architecture for sample-level activity prediction to joint segment andrecognize the activity sequence. Furthermore, to enhance the robustness of HARagainst the inter-class similarity and intra-class heterogeneity, a multi-levelcontrastive loss, containing the sample-level and segment-level contrast, hasbeen proposed to learn a well-structured embedding space for better activitysegmentation and recognition performance. Finally, with comprehensiveexperiments, we verify the effectiveness of the proposed method on two publicHAR datasets, achieving significant improvements in the various evaluationmetrics.

Multi-level Contrast Network for Wearables-based Joint Activity Segmentation and Recognition