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HARMamba: Efficient Wearable Sensor Human Activity Recognition Based on Bidirectional Selective SSM
Wearable sensor-based human activity recognition (HAR) is a critical research domain in activity perception.
MCformer: Multivariate Time Series Forecasting with Mixed-Channels Transformer
Based on this strategy, we introduce MCformer, a multivariate time-series forecasting model with mixed channel features.
A Multi-Task Deep Learning Approach for Sensor-based Human Activity Recognition and Segmentation
Sensor-based human activity segmentation and recognition are two important and challenging problems in many real-world applications and they have drawn increasing attention from the deep learning community in recent years.
Negative Selection by Clustering for Contrastive Learning in Human Activity Recognition
Compared with SimCLR, it redefines the negative pairs in the contrastive loss function by using unsupervised clustering methods to generate soft labels that mask other samples of the same cluster to avoid regarding them as negative samples.
Sensor Data Augmentation by Resampling for Contrastive Learning in Human Activity Recognition
The experiment results show that the resampling augmentation method outperforms all state-of-the-art methods under a small amount of labeled data, on SimCLRHAR and MoCoHAR, with mean F1-score as the evaluation metric.
