Colleagues: Serim Park, Utthara Shankar, and Xiaxiao Chen
Objective: The objective for this project is to gather multi-dimensional vector data through the use of a combination of open source wearable technology and/or embedded systems, apply signal analysis to create classifiers from such data, and utilize these classifiers to guide the form of users to better prevent plausible injuries, such as tennis elbow, by identifying such warnings before it is too late.
Methods: We are researching and testing combinations of wearable technology and embedded systems to accurately gather multi dimensional vector data that are efficient and non-disruptive to the sports motion. After gathering such data, we are applying certain signal processing and informatics algorithms to identify data trends that we can classify as good vs bad form. As the device is used more, we want it to learn as it performs and better calibrate such classifiers due to the primitive factors of the user, and signal the user to correct his or her form if not done properly (good form)
Results: The results from this research is the preliminary scope of data acquisition and implementation of algorithms to identify trends using our algorithms. We hope to provide datasets and basic classifiers to other individuals who may want to improve on our implementation in the near future.