Towards optimization of wearable sensor technology to measure human movement in real world settings

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The market for wearable sensors to measure and track human function has exploded in recent years. More and more people are using such devices to monitor many different aspects of everyday life. This explosion has been brought on by advances in technology such as reduced size and cost of such sensors, as well as increased battery life and storage capabilities, and has been facilitated by an ever-growing public desire to better understand how one's body is working. Unfortunately, this growth in availability and demand for these devices has far outgrown the science of the data in which they create. Very little information exists to inform decisions related to best sensor configuration, appropriateness of certain variables, and which activities are best measured. The proposed research will directly address these gaps, and will lead to a novel classification algorithm designed to detect changes in movement parameters in the real world using wearable technology. These are necessary steps to achieve my long-term vision of a better understanding of fundamental human movement through the development of innovative detection algorithms and best practice policies for the collection of movement data using wearable technologies.******The immediate short-term objectives of the research will be to improve the quantification of human movement patterns using wearable sensor technology during common activities of daily living. I will lead a research program over the next five years that will advance our ability to understand human movement in real world settings. We will improve sensor configuration and key parameters related to the collection of human movement data from wearable sensors using sophisticated analysis techniques. Our hypotheses and advances will first be informed and tested using current gold-standard motion capture systems to ensure accuracy and validity of our new sensor configurations. We will then translate our findings to the real world by developing a novel detection algorithm aiming to classify movement changes in real-world settings. ******Our novel contributions from this research will provide researchers with better knowledge for how to best incorporate wearable movement technology into their studies. This knowledge will help facilitate a transition from reliance on expensive laboratory-based movement assessment that may not accurately depict real world functioning. Industry will use the contributions from this research to refine the development of new and existing commercially-available wearable movement sensors that will result in more accurate and reliable products used in the general population. Finally, the general public will benefit from this work. Users ranging from athletes and coaches who use these devices to assess movement to improve athletic performance, to everyday individuals who are interested in simply tracking their movement performance for overall well-being, will stand to benefit.