Monitoring and alerting system to determine muscle strength for fall risk assessment : cloud computing
The objective of this project is to develop a wireless continuous monitoring system for fall risk assessment by using an Electromyography (EMG) muscle sensor. The muscle sensor records the user’s real-time EMG data and communicates with a single board computer Raspberry Pi 4 (RP-4) for analysis on t...
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| Format: | Final Year Project |
| Language: | English |
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Nanyang Technological University
2021
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| Online Access: | https://hdl.handle.net/10356/148835 |
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| Institution: | Nanyang Technological University |
| Language: | English |
| Summary: | The objective of this project is to develop a wireless continuous monitoring system for fall risk assessment by using an Electromyography (EMG) muscle sensor. The muscle sensor records the user’s real-time EMG data and communicates with a single board computer Raspberry Pi 4 (RP-4) for analysis on the user’s muscle strength. This information is used to give feedback to the user and send alerts to user/caregivers whenever an unbalanced state is detected. This information may also be used with the Electroencephalogram (EEG) extracted from the same user to increase the reliability of fall assessment.
This project utilizes Machine Learning (ML) method to analyze extracted features from real-time EMG data to determine the user’s real-time physical condition. Two different ML models (K-nearest neighbors and logistic regression) were tested in this study to obtain the optimal performance, Confusion Matrix was used for result evaluation for both ML models. |
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