Research on the Developmental Trend of Wearable Devices from the Perspective of Human-Computer Interaction

Wang, Tuanbu; Huang, Wenzhun

doi:10.2991/emim-16.2016.159

Cited by 2 publications

(1 citation statement)

References 8 publications

(6 reference statements)

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“…In terms of human-computer interaction, users can achieve a more natural and direct human-computer interaction through gestures. However, current human body perception methods based on computer vision [4] and wearable devices [5] have certain limitations. Computer vision methods are easy to disclose privacy and cannot work at night, while wearable devices need to carry additional sensing devices.…”

Section: Introductionmentioning

confidence: 99%

WiFi gesture recognition method based on data enhancement

Li,

Wang,

2024

Fourth International Conference on Machine Learning and Computer Application (ICMLCA 2023)

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In recent years, human motion recognition based on wireless sensing has been widely used in smart home, virtual reality and other fields. Among them, human motion recognition based on WiFi channel state information (CSI) has received great attention. Although the current large-action recognition method based on CSI has good performance, the recognition accuracy is low when performing fine-grained human gesture recognition, which is due to the difficulty of collecting CSI human gesture perception dataset and the small number of high-quality datasets. Therefore, we convert CSI data into images and augment CSI images using super-resolution image generation methods. At the same time, we propose a SCBAM attention mechanism to further enhance the attention of the model to important features. In the experiment, we performed experimental analysis using the publicly available Widar3 dataset. The results show that the recognition accuracy of our mixed attention model in the same scene, across location, direction and scene reaches 99.73%, 97.41%, 94.57% and 95.98%, which is 0.07%, 0.29%, 0.05% and 0.99% higher than before data enhancement.

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Section: Introductionmentioning

confidence: 99%

WiFi gesture recognition method based on data enhancement

Li,

Wang,

2024

Fourth International Conference on Machine Learning and Computer Application (ICMLCA 2023)

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WiTransformer: A Novel Robust Gesture Recognition Sensing Model with WiFi

Yang

Zhu

et al. 2023

Sensors

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The past decade has demonstrated the potential of human activity recognition (HAR) with WiFi signals owing to non-invasiveness and ubiquity. Previous research has largely concentrated on enhancing precision through sophisticated models. However, the complexity of recognition tasks has been largely neglected. Thus, the performance of the HAR system is markedly diminished when tasked with increasing complexities, such as a larger classification number, the confusion of similar actions, and signal distortion To address this issue, we eliminated conventional convolutional and recurrent backbones and proposed WiTransformer, a novel tactic based on pure Transformers. Nevertheless, Transformer-like models are typically suited to large-scale datasets as pretraining models, according to the experience of the Vision Transformer. Therefore, we adopted the Body-coordinate Velocity Profile, a cross-domain WiFi signal feature derived from the channel state information, to reduce the threshold of the Transformers. Based on this, we propose two modified transformer architectures, united spatiotemporal Transformer (UST) and separated spatiotemporal Transformer (SST) to realize WiFi-based human gesture recognition models with task robustness. SST intuitively extracts spatial and temporal data features using two encoders, respectively. By contrast, UST can extract the same three-dimensional features with only a one-dimensional encoder, owing to its well-designed structure. We evaluated SST and UST on four designed task datasets (TDSs) with varying task complexities. The experimental results demonstrate that UST has achieved recognition accuracy of 86.16% on the most complex task dataset TDSs-22, outperforming the other popular backbones. Simultaneously, the accuracy decreases by at most 3.18% when the task complexity increases from TDSs-6 to TDSs-22, which is 0.14–0.2 times that of others. However, as predicted and analyzed, SST fails because of excessive lack of inductive bias and the limited scale of the training data.

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Research on the Developmental Trend of Wearable Devices from the Perspective of Human-Computer Interaction

Cited by 2 publications

References 8 publications

WiFi gesture recognition method based on data enhancement

WiFi gesture recognition method based on data enhancement

WiTransformer: A Novel Robust Gesture Recognition Sensing Model with WiFi

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