Using additional training sensors to improve single-sensor complex activity recognition

Author:

Lago Paula1,Matsuki Moe2,Adachi Kohei3,Inoue Sozo3

Affiliation:

1. Kyushu Institute of Technology, Japan and Universidad Nacional Abierta y a Distancia, Columbia

2. Kyusyu Institute Technology, Japan

3. Kyushu Institute of Technology, Japan

Publisher

ACM

Reference31 articles.

Cited by 8 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. iMove: Exploring Bio-Impedance Sensing for Fitness Activity Recognition;2024 IEEE International Conference on Pervasive Computing and Communications (PerCom);2024-03-11

2. Beyond the Pocket: Preparing a Study on the Variations of Wearable Device Location;Proceedings of the International Conference on the Internet of Things;2023-11-07

3. Learning Using Privileged Information for Wearable-based Human Activity Recognition;2023 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops);2023-03-13

4. Improving Noise Robustness of Single Sensor Data in Human Activity Recognition With UMAP and Additional Data;Proceedings of the 2022 ACM International Symposium on Wearable Computers;2022-09-11

5. Learning from the Best: Contrastive Representations Learning Across Sensor Locations for Wearable Activity Recognition;Proceedings of the 2022 ACM International Symposium on Wearable Computers;2022-09-11

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