Machine Learning Architectures to Classify Activities of Daily Living and Fall Types From Wearable Accelerometer Data
Author:
Affiliation:
1. Politecnico di Milano,Department of Electronics, Information and Bioengineering,Milano,Italy
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10372541/10372594/10372728.pdf?arnumber=10372728
Reference20 articles.
1. Activities of daily living with motion: A dataset with accelerometer, magnetometer and gyroscope data from mobile devices
2. A Comprehensive Analysis on Wearable Acceleration Sensors in Human Activity Recognition
3. Evaluation of Accelerometer-Based Fall Detection Algorithms on Real-World Falls
4. Optimal Placement of Accelerometers for the Detection of Everyday Activities
5. Hip and Wrist Accelerometer Algorithms for Free-Living Behavior Classification
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