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| Zdroj: |
Drug Week; 2024, p2383-2383, 1p |
| Abstrakt: |
A recent study conducted at Michigan State University has developed an on-device semi-supervised human activity detection system for personalized health monitoring. The system uses wearable devices with Inertial Measurement Unit sensors to monitor and detect unhealthy sedentary lifestyles. The proposed learning model preserves data privacy while providing personalized activity detection services. The study found that the system is highly accurate and computationally efficient, with a maximum accuracy of 90% and 100% classification rates. This research contributes to the field of personalized medicine and therapy. [Extracted from the article] |
| Databáze: |
Complementary Index |
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