User, device and orientation independent human activity recognition on mobile phones
Author:
Affiliation:
1. Bogazici University, Istanbul, Turkey
2. Galatasaray University, Istanbul, Turkey
Publisher
ACM
Link
https://dl.acm.org/doi/pdf/10.1145/2494091.2496039
Reference10 articles.
1. A Review and Taxonomy of Activity Recognition on Mobile Phones
2. Using mobile phones to determine transportation modes
3. Recognizing Human Activities User-independently on Smartphones Based on Accelerometer Data
4. Enabling large-scale human activity inference on smartphones using community similarity networks (csn)
5. Online pose classification and walking speed estimation using handheld devices
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1. Human activity recognition system using wearable accelerometers for classification of leg movements: a first, detailed approach;Journal of Sensors and Sensor Systems;2024-07-22
2. A Human Activity Recognition Scheme Using Mobile Smartphones Based on Varying Orientations and Positions;IEEE Sensors Journal;2024-05-15
3. Multimodal Daily-Life Logging in Free-living Environment Using Non-Visual Egocentric Sensors on a Smartphone;Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies;2024-03-06
4. Device Position-Independent Human Activity Recognition with Wearable Sensors Using Deep Neural Networks;Applied Sciences;2024-03-03
5. The effectiveness of simple heuristic features in sensor orientation and placement problems in human activity recognition using a single smartphone accelerometer;BioMedical Engineering OnLine;2024-02-17
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