| Autor: |
Sharif H; Department of Mechanical Science and Engineering, Health Care Engineering Systems Center, The Grainger College of Engineering, University of Illinois at Urbana-Champaign, Champaign, IL 61820, USA., Eslaminia A; Department of Mechanical Science and Engineering, Health Care Engineering Systems Center, The Grainger College of Engineering, University of Illinois at Urbana-Champaign, Champaign, IL 61820, USA., Chembrammel P; Department of Mechanical Science and Engineering, Health Care Engineering Systems Center, The Grainger College of Engineering, University of Illinois at Urbana-Champaign, Champaign, IL 61820, USA., Kesavadas T; Department of Mechanical Science and Engineering, Health Care Engineering Systems Center, The Grainger College of Engineering, University of Illinois at Urbana-Champaign, Champaign, IL 61820, USA. |
| Abstrakt: |
Stroke is one of the leading causes of mortality and disability worldwide. Several evaluation methods have been used to assess the effects of stroke on the performance of activities of daily living (ADL). However, these methods are qualitative. A first step toward developing a quantitative evaluation method is to classify different ADL tasks based on the hand grasp. In this paper, a dataset is presented that includes data collected by a leap motion controller on the hand grasps of healthy adults performing eight common ADL tasks. Then, a set of features with time and frequency domains is combined with two well-known classifiers, i.e., the support vector machine and convolutional neural network, to classify the tasks, and a classification accuracy of over 99% is achieved. |
