A Real-time Human Pose Estimation Approach for Optimal Sensor Placement in Sensor-based Human Activity Recognition-Reference-Cited by-同舟云学术

A Real-time Human Pose Estimation Approach for Optimal Sensor Placement in Sensor-based Human Activity Recognition

Published:2023-09-21 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 8th international Workshop on Sensor-Based Activity Recognition and Artificial Intelligence
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Author:

Konak Orhan¹^ORCID,Wischmann Alexander²^ORCID,van De Water Robin¹^ORCID,Arnrich Bert¹^ORCID

Affiliation:

1. Digital Health - Connected Healthcare, Hasso Plattner Institute, Germany

2. Hasso Plattner Institute, Germany

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3615834.3615848

Reference30 articles.

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2. Ferhat Attal 2015 . Physical Human Activity Recognition Using Wearable Sensors. Sensors 15 , 12 (2015), 31314 – 31338 . Ferhat Attal 2015. Physical Human Activity Recognition Using Wearable Sensors. Sensors 15, 12 (2015), 31314–31338.

3. Z. Cao 2019. OpenPose: Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields . IEEE Transactions on Pattern Analysis and Machine Intelligence ( 2019 ). Z. Cao 2019. OpenPose: Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields. IEEE Transactions on Pattern Analysis and Machine Intelligence (2019).

4. Wenqiang Chen 2021. SenseCollect: We Need Efficient Ways to Collect On-Body Sensor-Based Human Activity Data!Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 5, 3 , Article 91 (sep 2021 ), 27 pages. Wenqiang Chen 2021. SenseCollect: We Need Efficient Ways to Collect On-Body Sensor-Based Human Activity Data!Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 5, 3, Article 91 (sep 2021), 27 pages.

5. MMHAR-EnsemNet: A Multi-Modal Human Activity Recognition Model

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