Abstract
This paper describes the use of embedded machine learning (ML) on raw accelerometer data to classify three lower-limb exercises. The developed model, which uses supervised ML can accurately classify the exercises. The model performed with an accuracy of 99.41 % on the test data and F1 score of 0.99. The trained model is deployed on the microcontroller board, Arduino Nano 33 BLE Sense (ARM Cortex M4F processer). The system has been developed using open-source platforms. The developed wearable device, PhysioSens has the potential to enhance limb exercise monitoring in home-based physical therapy.
Original language | English |
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Title of host publication | APSCON 2023 Symposium Proceedings |
Editors | S. Gopalakrishnan, Hamida Hallil |
Place of Publication | Piscataway NJ USA |
Publisher | IEEE, Institute of Electrical and Electronics Engineers |
Number of pages | 3 |
ISBN (Electronic) | 9781665461634 |
ISBN (Print) | 9781665461641 |
DOIs | |
Publication status | Published - 2023 |
Event | IEEE Applied Sensing Conference 2023 - Bengaluru, India Duration: 23 Jan 2023 → 25 Jan 2023 |
Conference
Conference | IEEE Applied Sensing Conference 2023 |
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Abbreviated title | APSCON 2023 |
Country/Territory | India |
City | Bengaluru |
Period | 23/01/23 → 25/01/23 |
Keywords
- embedded machine learning
- inertial sensor
- mems accelerometer
- physical therapy