| Abstrakt: |
In smart cities, pervasive sensing and wearable computing techniques are increasingly being employed to monitor and recognize human activities through body sensor networks, which have been widely used in urban safety, healthcare, and manufacturing. However, most researchers regard human activity recognition (HAR) as a high-cost research task requiring a large amount of labeled data, which is often unrealistic in real-world applications. To address this problem, we propose a self-supervised learning framework for HAR (SS-HAR). SS-HAR initially takes the unlabeled data generated by data augmentation as the input of the network, mines the supervised information of the unlabeled data under the effect of self-supervised learning, and uses the obtained backbone network as a feature extractor to extract activity features for subsequent classification. After that, we use part of the labeled data as the training set and extract the activity features using the backbone network for training and fitting the classifier. Then we utilize the rest of the data to verify the feasibility and effectiveness of the proposed self-supervised learning method. We have conducted multiple experiments on three publicly available datasets and one self-collected basketball activity dataset SZU_HAD_Basketball. The experimental results show that the SS-HAR method is able to achieve higher classification accuracy and stability than supervised and semi-supervised methods. Specifically, on the UCI dataset, SS-HAR achieves better classification performance compared to other approaches which improve the classification accuracy by 1% over the supervised method and by 5%–6% over the semi-supervised method, respectively. |
