Human movement quality assessment
Dr Crina Grosan
Senior Lecturer in Clinical Technology
Florence Nightingale Faculty of Nursing, Midwifery & Palliative Care
King’s College London
Prof Louise Rose
The focus of this research is to design and evaluate advanced, cross-disciplinary artificial intelligence methods able to dynamically recognize, anticipate and interpret patients’ complex gestures and movements as they exercise, as part of a routine recovery scheme. Precisely, the research will concentrate on motion/action quality assessment from a correctness perspective, using machine learning methods. A human action usually lasts from several seconds to a few minutes and is spatio-temporal (a sequence of frames or images in time, segmented from a video).
The data is recorded using motion sensor cameras and/or wearable devices. The final goal will be to design, develop and apply machine learning methods for creating a module that can automatically act as a personalized intelligent (virtual) recommender system for each patient. Temporal Convolutional Neural Networks, Rough Path Theory and Dynamic Time Warping are among the methods tested so far with promising results, but more advanced methods have to be tested and/or adapted to this problem.