Open Collections

Classification of body movements using a mattress-based sensor array

University of British Columbia

Movement events during sleep could be used to infer underlying sleep physiologies and disorders based on their motor presentations. Periodic Limb Movement Disorder (PLMD), for instance, mostly occurs in the lower extremities and usually involves the dorsiflexion of the ankle. Evaluation of sleep disorders is typically done through clinical polysomnography (PSG). While PSG remains the most reliable and comprehensive tool for such assessments, the studies are intensive in terms of time, cost and labor. Certain motor indices might be underestimated due to the nature of PSG instrumentation, and for some populations, these studies could be considered intrusive and uncomfortable. In this work, SleepSmart, a mattress-based sensing system composing of an 8x6 array of 3-D accelerometer sensors, was developed to provide data for machine learning algorithms to classify body movements into different levels of granularity (coarse/fine-grained labels). A study with 10 subjects was conducted. A movement protocol was adapted to simulate movements during sleep. Three classification domains were defined for the movements: a) Domain A – 3 classes inferring general movement characteristics, b) Domain B – 8 classes indicating movements at various body locations, and c) Domain C – 22 classes, where each class corresponds to a specific movement descriptor. Four learning algorithms were tested and compared. Random Forest (RF), Support Vector Machines (SVM), Naïve- Bayes (NB), and the k-Nearest Neighbor (k-NN) algorithms were used. The classification accuracies averaged across all domains were 96.91%, 94.10%, 88.91%, 83.88% for subject-dependent models, and 89.87%, 89.45%, 73.95%, 69.21% for subject-independent models for the RF, SVM, NB and k-NN algorithms, respectively. In RF models, averaged recall and precision measures were 96.29% and 96.74% for subject-dependent models, and 89.23% and 89.91% for subject-independent models. The investigation of the effect of different training sizes revealed small sample requirements for training (as low as 3 training samples per class) to attain accuracies higher or comparable to the baseline value (84%) for each domain. In this work, we have proposed a non-invasive sensor system and demonstrated the generalizability and the effectiveness of the system in classifying movements at different label granularities under subject-dependent and subject-independent considerations.

Text

2019-04-23

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/69866

Biomedical Engineering

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-nc-nd/4.0/