Change analysis for gait impairment quantification in smart environments

Visual Sensor Networks (VSNs) open up a new realm of smart autonomous applications based on enhanced three-dimensional sensing and collaborative reasoning. An emerging VSN application domain is pervasive healthcare delivery where gait information computed from distributed vision nodes is used for observing the wellbeing of the elderly, quantifying post-operative patient recovery and monitoring the progression of neurodegenerative diseases such as Parkinson's. The development of patient-specific gait analysis models, however, is challenging since it is unfeasible to obtain normal and impaired gait examples from the same patient before the operation in order to build supervised models for gait classification. This paper presents a novel VSN-based framework for quantification of patient-specific gait impairment and post-operative recovery by using change analysis. Real-time target extraction is first applied to VSN data and a skeletonization procedure is subsequently carried out to quantify the internal motion of moving target and compute two features; spatiotemporal cyclic motion between leg segments and head trajectory for each vision node. Change analysis is then used to measure the change, i.e. difference, between two unlabeled datasets collected pre- and post-operatively and quantify gait changes. The potential value of the proposed framework for patient gait monitoring is demonstrated and the results obtained from practical experiments are described.