Deeperedge

Movements are complex and can be difficult to describe and compare. In doctors’ offices, the responses of patients to movement disorder therapies are assessed qualitatively by observation in clinic, while assessment of the vast remainder of movements in the world at large relies on the patient’s ability to properly narrate their ailments. In sports, injuries are prevented and managed according to an athlete’s reported pain levels, which may provide little insight into the stability of the body part in question. In figure skating and other performance arts, the technical elements in the programs are assessed on a purely qualitative level, subjecting the artists to the vagaries of judging. Classifying individual movement components enables apples-to-apples comparison of complex movements to determine quality and change in quality. A quantitative rather than a qualitative measurement of movement could allow for more precise assessment and management, or even prediction of future movement performance. We are developing movement classification algorithms which identify, assess, and compare movements in health and in disease. The finished product will consist of a few wearable sensors (similar to fitbits) that patients and athletes alike can use in conjunction with mobile devices and/or laptops to follow a quantitative analysis of their movements. For patients with injuries, movement disorders, or even just normal aging, the quantitative review could be used to see how a treatment is working, or simply to know if they truly are getting better/worse, providing insight into their perception of what’s happening throughout the day. For skaters, the quantitative review could tell them if their movements suggest that they might be getting injured soon, act as a progress report on their movements versus those of an elite athlete, or add a more objective metric to augment judging for performance arts.