NiceMeetVR: facing professional baseball pitchers in the virtual batting cage
Proceedings of the 2002 ACM symposium on Applied computing
Pitching a baseball: tracking high-speed motion with multi-exposure images
ACM SIGGRAPH 2004 Papers
Platform Design for Health-Care Monitoring Applications
HCMDSS-MDPNP '07 Proceedings of the 2007 Joint Workshop on High Confidence Medical Devices, Software, and Systems and Medical Device Plug-and-Play Interoperability
ISWC '09 Proceedings of the 2009 International Symposium on Wearable Computers
Wearable automatic feedback devices for physical activities
BodyNets '09 Proceedings of the Fourth International Conference on Body Area Networks
Wearable coach for sport training: A quantitative model to evaluate wrist-rotation in golf
Journal of Ambient Intelligence and Smart Environments
IEEE Transactions on Information Technology in Biomedicine - Special section on affective and pervasive computing for healthcare
The mobile fitness coach: Towards individualized skill assessment using personalized mobile devices
Pervasive and Mobile Computing
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This paper presents a method to evaluate a person's skill level for metal filing. Metal filing by expert engineers is an important manufacturing skill that supports basic areas of industry, although most sequences are already automated with industrial robots. However, there is no effective training method for the skill; "coaching" has been most weighted. Most coaching has depended on the coaches' personal viewpoints. In addition, skill levels have been assessed subjectively by the coaches. Because of these problems, learners have to spend several hundred hours to acquire the basic manufacturing skill. Therefore, to develop an effective skill training scheme and an objective skill level assessment, we analyzed metal filing and implemented a method to evaluate metal-filing skill. We used wearable hybrid sensors that support an accelerometer and gyroscope, and collected data from 4 expert coaches and 10 learners. The data are analyzed from the viewpoint of the mechanical structure of their bodies during metal filing. Our analysis yielded three effective measures for skill assessment: "Class 2 Lever-like Movement Measure", "Upper Body Rigidity Measure", and "Pre-Acceleration Measure". The weighted total measure succeeded in distinguishing the coach group and the learner group as individual skill level groups at a 95% confidence level. The highest-level learner, the lowest-level learner, and the group of other learners were also able to be distinguished as individual skill level groups at a 95% confidence level; this is the same result as an expert coach's subjective score.