Name
Abstract | ||
|---|---|---|
•Overexertion can cause bodily injury in lift/lower/carry, and push/pull activities.•Machine learning helps recognize activities from body-mounted smartphone data.•In this research, activity duration and frequency are linked to overexertion risk.•Best results are achieved when smartphone is placed on the subject’s upper-arm.•The designed algorithm is validated using leave-one-subject-out cross-validation. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1016/j.aei.2018.08.020 | Advanced Engineering Informatics |
Keywords | Field | DocType |
Construction health,Wearable sensors,Ergonomics,Overexertion,Human activity recognition,Machine learning | Data collection,Body of knowledge,Gyroscope,Accelerometer,Data acquisition,Human factors and ergonomics,Artificial intelligence,Engineering,Occupational safety and health,Wearable technology,Machine learning | Journal |
Volume | ISSN | Citations |
38 | 1474-0346 | 0 |
PageRank | References | Authors |
0.34 | 13 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Nipun D. Nath | 1 | 4 | 1.51 |
| Theodora Chaspari | 2 | 38 | 19.43 |
| Amir H. Behzadan | 3 | 122 | 17.55 |