Ting Zhang, George D. Fulk, Wenlong Tang, and Edward S. Sazonov, In Proceedings of 35th Annual International Conference of the IEEE EMBS
Osaka, Japan, 3 - 7 July, 2013, pp. 6337-6340.


Improving community mobility is a common goal for persons with stroke. Measuring daily physical activity is helpful to determine the effectiveness of
rehabilitation interventions. In our previous studies, a novel wearable shoe-based sensor system (SmartShoe) was shown to be capable of accurately classify three major postures and activities (sitting, standing, and walking) from individuals with stroke by using Artificial Neural Network (ANN). In this study, we utilized decision tree algorithms to develop individual and group activity classification models for stroke patients. The data was acquired from 12 participants with stroke. For 3-class classification, the average accuracy was 99.1% with individual models and 91.5% with group models. Further, we extended the activities into 8 classes: sitting, standing, walking, cycling, stairs-up, stairs-down, wheel-chair-push, and wheel-chair-propel. The classification
accuracy for individual models was 97.9%, and for group model was 80.2%, demonstrating feasibility of multi-class activity recognition by SmartShoe in stroke patients.