Name
Abstract | ||
|---|---|---|
Background: Manual skills teaching, such as physiotherapy education, requires immediate teacher feedback for the students during the learning process, which to date can only be performed by expert trainers. Objectives: A machine-learning system trained only on correct performances to classify and score performed movements, to identify sources of errors in the movement and give feedback to the learner. Methods: We acquire IMU and sEMG sensor data from a commercial-grade wearable device and construct an HMM-based model for gesture classification, scoring and feedback giving. We evaluate the model on publicly available and self-generated data of an exemplary movement pattern executions. Results: The model achieves an overall accuracy of 90.71% on the public dataset and 98.9% on our dataset. An AUC of 0.99 for the ROC of the scoring method could be achieved to discriminate between correct and untrained incorrect executions. Conclusion: The proposed system demonstrated its suitability for scoring and feedback in manual skills training. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.3233/978-1-61499-858-7-164 | Studies in Health Technology and Informatics |
Keywords | Field | DocType |
Wearable Technology,Gestures,Machine Learning,Feedback,Education,mHealth | Physiotherapy training,Computer science,Error detection and correction,Physical medicine and rehabilitation | Conference |
Volume | ISSN | Citations |
248 | 0926-9630 | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Marko Jovanovic | 1 | 1 | 0.75 |
| Johannes Seiffarth | 2 | 0 | 0.34 |
| Ekaterina Kutafina | 3 | 7 | 3.63 |
| Stephan Jonas | 4 | 35 | 8.92 |