Modern smartphones contain multiple sensors and long lasting batteries, making them ideal platforms for mobility monitoring. Mobility monitoring can provide rehabilitation professionals with an objective portrait of a patient’s daily mobility habits outside of a clinical setting.
The objective of this thesis was to improve the performance of the human activity recognition within a custom Wearable Mobility Measurement System (WMMS). Performance of a current WMMS was evaluated on able-bodied and stroke participants to identify areas in need of improvement and differences between populations. Signal features for the waist-worn smartphone WMMS were selected using classifier-independent methods to identify features that were useful across populations. The newly selected features and a transition state recognition method were then implemented before evaluating the improved WMMS system’s activity recognition performance.
This thesis demonstrated: 1) diverse population data is important for WMMS system design; 2) certain signal features are useful for human activity recognition across diverse populations; 3) the use of carefully selected features and transition state identification can provide accurate human activity recognition results without computationally complex methods.
| Identifer | oai:union.ndltd.org:uottawa.ca/oai:ruor.uottawa.ca:10393/32793 |
| Date | January 2015 |
| Creators | Capela, Nicole Alexandra |
| Contributors | Lemaire, Edward, Baddour, Natalie |
| Publisher | Université d'Ottawa / University of Ottawa |
| Source Sets | Université d’Ottawa |
| Language | English |
| Detected Language | English |
| Type | Thesis |
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