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[1]: A Review of AIoT-based Human Activity Recognition: From Submission to … South China University of Technology; … (aiot) driven human Activity Recognition (HAR) field by systematically extrapolating from various application domains to deduce potential techniques and algorithms. … A review of AIoT-based Human Activity Recognition: From Application to Technique. / Qi, Wen; Xu, Xiangmin; Qian, Kun 等. 在: IEEE …
URL: [https://pure.bit.edu.cn/zh/publications/a-review-of-aiot-based-human-activity-recognition-from-applicatio](https://pure.bit.edu.cn/zh/publications/a-review-of-aiot-based-human-activity-recognition-from-applicatio)
[2]: Incheon National University Scientists Enhance Smart home Security with …In smart home AIoT technology, accurate human activity recognition is crucial. It helps smart devices identify various tasks,such as cooking and exercising. based on this facts, the AIoT system can tweak lighting or switch music automatically, thus improving user experience while also ensuring energy efficiency.
URL: [https://www.eejournal.com/industry_news/incheon-national-university-scientists-enhance-smart-home-security-with-aiot-and-wifi/](https://www.eejournal.com/industry_news/incheon-national-university-scientists-enhance-smart-home-security-with-aiot-and-wifi/)
[3]: Indoor Human Activity recognition using Multiple Dynamic Nonlinear … Activity recognition is essential in computer vision applications such as smart homes and healthcare services.While RGB images have been widely used in this area, they pose challenges related to privacy invasion and environmental…ucture comprising short-time Fourier transform along with discrete wavelet transform, a transformer, and an attention-based fusion branch. While the dual-stream structure pinpoints abnormal information in CSI, the transformer extracts high-level features from the data efficiently. Lastly, the fusion branch boosts cross-model fusion.
The researchers performed experiments to validate the performance of their framework, finding that it achieves remarkable Cohen’s Kappa scores of 91.82%,69.76%, 85.91%,and 75.66% on SignFi, Widar3.0, UT-HAR, and NTU-HAR datasets, respectively.These values highlight the superior performance of MSF-Net compared to state-of-the-art techniques for wifi data-based coarse and fine activity recognition.
“The multimodal frequency fusion technique has significantly improved accuracy and efficiency compared to existing technologies, increasing the possibility of practical applications. This research can be used in various fields such as smart homes,rehabilitation medicine,and care for the elderly. As an example, it can prevent falls by analyzing the user’s movements and contribute to improving the quality of life by establishing a non-face-to-face health monitoring system,” concludes Prof. Jeon.
activity recognition using WiFi, the convergence technology of iot and AI proposed in this work, is expected to greatly improve people’s lives through everyday convenience and safety!
