Full Hand Pose Recognition in Performing Daily Activities for Tele-Rehabilitation based on Decision Tree Algorithm
DOI:
https://doi.org/10.15282/mekatronika.v6i1.10187Keywords:
Hand Pose Recognitiion, Decision Tree, Machine Learning, Data Collection, Tele- RehabilitationAbstract
The older population has the highest risk of getting a stroke, leading to a high healthcare cost and a heavy economic burden to the nation. Tele-rehabilitation aids to enhance the life of stroke survivors by allowing them to conduct the therapy from home which helps the patient with low mobility and living far from the medical centers. This work focuses on the development of full hand pose recognition in performing daily activities for tele-rehabilitation treatment using Decision Tree algorithm under the Machine Learning. Force sensor, flexible sensors and MPU6050 Micro Electro-Mechanical system (MEMS) are used for the data collection. The sensors’ resistance and acceleration are the input to the Machine Learning algorithm and the type of hand pose acts as the output. Three hand gesture procedure are chosen in this study, which are grasping a glass, turning the pipe and switching on the plug. The procedure for data collection has been devised. The Decision Tree has been trained and tested using Python programming language on Jupyter Notebook web-based interactive computing platform. At this stage of study, tests are conducted with healthy subjects to validate the effectiveness of the proposed recognition system. An accuracy of 94%. has been achieved. The sensor readings show different patterns of the curves for each activity. This project will assist the medical staffs in delivering a better treatment for the patients and will lead to a faster recovery process.
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