Human activity recognition using earable device
Author:
Affiliation:
1. Applied Science for Integrated System Eng. and Kyushu Institute of Technology, Japan
2. Kyushu Institute of Technology, Japan
3. University of Dhaka, Bangladesh and Osaka University, Japan
Publisher
ACM
Link
https://dl.acm.org/doi/pdf/10.1145/3341162.3343822
Reference13 articles.
1. Analysis of Time and Frequency Domain Features of Accelerometer Measurements
2. L. Gao A. Bourke J. Nelson "Evaluation of Accelerometer Based Multi-sensor versus Single-sensor Activity Recognition Systems " Medical Engineering and Physics 2014. L. Gao A. Bourke J. Nelson "Evaluation of Accelerometer Based Multi-sensor versus Single-sensor Activity Recognition Systems " Medical Engineering and Physics 2014.
3. Detection of abnormal sensor patterns in eldercare
4. Activity Recognition by Using LoRaWAN Sensor
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1. Evaluating the Correlation between Eyeglass-Type Wearable Device Measurements and Subjective Physical Activity Assessments;Cureus;2024-08-26
2. EarDA: Towards Accurate and Data-Efficient Earable Activity Sensing;2024 IEEE Coupling of Sensing & Computing in AIoT Systems (CSCAIoT);2024-05-13
3. A CNN Model for Physical Activity Recognition and Energy Expenditure Estimation from an Eyeglass-Mounted Wearable Sensor;Sensors;2024-05-11
4. Exploring Uni-manual Around Ear Off-Device Gestures for Earables;Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies;2024-03-06
5. Unobtrusive Air Leakage Estimation for Earables with In-ear Microphones;Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies;2023-12-19
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