UCLA

Assurance of outcome is critical for patients that are afflicted with neurological disorders (for example stroke that introduces hemiparetic gait) and motion is a critical indicator of outcome. However outcome in the community environment is very difficult to determine. Wearable sensing system, due to its low cost, energy efficient, non-intrusive and applicable for large-scale development, is feasible for remote, continuous monitoring of motion and activities and thus provide outcome measures in the community. Towards this goal, this thesis explores the following problems:

1. A precise motion classification system is introduced.

It is applicable for diverse subjects whose motion ability ranges from very disabled to very capable and suitable for complicated community environment. Critical kinematic parameters of motion is modeled, characterized and validated, which provides direct outcome measures in the community. Those methods are applied to a large-scale trial with stroke patients. The problems encountered in large-scale trial and the corresponding engineering solutions are summarized.

2. For effective feedback provision, a PCA based visualization method is proposed, where gait quality evolution path is shown.

It also introduces a gait quality vector and in particular, a metric to measure uncertainty in gait.

3. The individualized motion classification model is extended into a population based analysis. First an indexing method for data dimension reduction is proposed. The indexing method expedites the processing speed and maintains least critical information loss for motion analysis. I also hypothesize and validate a power law exist between the diversity of motion (for example gait pattern) and the largest approximate cluster. Second, hierarchical clustering method is employed to derive the primitive gait patterns from the stroke population. The developed techniques help scale the motion classification system for big data.