Personalized Rehabilitation Recognition Model upon ANFIS


  • Yao-Chiang Kan Department of Communication Engineering, Yuan Ze University, Taoyuan, Taiwan
  • Yu-Chieh Kuo Institute of Health Service Administrations, China Medical University, Taoyuan, Taiwan
  • Hsueh-Chun Lin Institute of Health Service Administrations, China Medical University, Taoyuan, Taiwan



ANFIS, physiotherapy, rehabilitation recognition, sensor-enabled wristband, ubiquitous healthcare


This study applied the Adaptive Neuro-Fuzzy Inference System (ANFIS) to design a recognition model of personalized rehabilitation. In the model, the user may take a wearable sensor and follow the assigned joint-relax exercise to measure the motions of the upper limbs. The sensor that is embedded with the chips of accelerometer, gyroscope, and inclinometer produced the sample datasets due to the exercise schedule of physiotherapy assignment. All motion samples were labeled by arbitrary numbers, which can be identified to the specific motion, for the data training process. A Fuzzy Inference System (FIS) was initially designed by the steps of data pre-processing, featuring, fuzzifying, and ruling Fuzzy logics according to the sample datasets. The FIS was then trained by the ANFIS for optimization by tuning parameters of the features. In testing, the accomplished FIS could recognize the motion features by the defuzzifier that infers the label corresponding to the motion. As a result, the average recognition rate was higher than 90% when the testing motions followed the sampling schedule of the physiotherapy assignment. The model can be applied in the ubiquitous healthcare measurement for health services. The professionals can assess whether the subject obeyed the assigned program or not based on detail motions of the exercise. This approach can be enabled on the trackable interface for the physiatrists to screen the motions of routine rehabilitation.


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How to Cite

Y.-C. Kan, Y.-C. Kuo, and H.-C. Lin, “Personalized Rehabilitation Recognition Model upon ANFIS”, Proc. eng. technol. innov., vol. 14, pp. 22–28, Jan. 2020.