Motion Tracking: No Silver Bullet, but a Respectable Arsenal
IEEE Computer Graphics and Applications
MOCA: a low-power, low-cost motion capture system based on integrated accelerometers
Advances in Multimedia
Using FSR based muscule activity monitoring to recognize manipulative arm gestures
ISWC '07 Proceedings of the 2007 11th IEEE International Symposium on Wearable Computers
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
IEEE Transactions on Information Technology in Biomedicine - Special section on affective and pervasive computing for healthcare
Motion capture from body-mounted cameras
ACM SIGGRAPH 2011 papers
A dynamic sliding window approach for activity recognition
UMAP'11 Proceedings of the 19th international conference on User modeling, adaption, and personalization
Detecting and interpreting muscle activity with wearable force sensors
PERVASIVE'06 Proceedings of the 4th international conference on Pervasive Computing
Active capacitive sensing: exploring a new wearable sensing modality for activity recognition
Pervasive'10 Proceedings of the 8th international conference on Pervasive Computing
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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.