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| Content Provider | ACM Digital Library |
|---|---|
| Author | Kuo, Cynthia Jacobson, Quinn Zhang, Pei Mokaya, Frank O. Rowe, Anthony Nguyen, Brian |
| Abstract | Poor posture and incorrect muscle usage are a leading cause of many injuries in sports and fitness. For this reason, non- invasive, fine-grained sensing and monitoring of human motion and muscles is important for mitigating injury and improving fitness efficacy. Current sensing systems either de- pend on invasive techniques or unscalable approaches whose accuracy is highly dependent on body sensor placement. As a result these systems are not suitable for use in active sports or fitness training where sensing needs to be scalable, accurate and un-inhibitive to the activity being performed. We present MARS, a system that detects both body motion and individual muscle group activity during physical human activity by only using unobtrusive, non-invasive in- ertial sensors. MARS not only accurately senses and recreates human motion down to the muscles, but also allows for fast personalized system setup by determining the individual identities of the instrumented muscles, obtained with minimal system training. In a real world human study con- ducted to evaluate MARS, the system achieves greater than 95% accuracy in identifying muscle groups. |
| Starting Page | 191 |
| Ending Page | 202 |
| Page Count | 12 |
| File Format | |
| ISBN | 9781450319591 |
| DOI | 10.1145/2461381.2461406 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2013-04-08 |
| Publisher Place | New York |
| Access Restriction | Subscribed |
| Subject Keyword | Wearable sensing Muscle activity recognition |
| Content Type | Text |
| Resource Type | Article |
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