PERSONAL FITTING PROCEDURE FOR CYCLE ERGOMETER WORKLOAD CONTROL BY ARTIFICIAL NEURAL NETWORKS T. Kiryu et al., IEEE/EMBS October 24, 2002 PERSONAL FITTING PROCEDURE FOR CYCLE ERGOMETER WORKLOAD CONTROL BY ARTIFICIAL NEURAL NETWORKS T. Kiryu 1 , K. Shibai 1 , Y. Hayashi 2 , and K. Tanaka 2 1 Graduated School of Science and Technology, Niigata University, 8050 Ikarashi-2, Niigata 950-2181, Japan 2 Institute of Health and Sport Sciences, University of Tsukuba, 1-1-1 Tennodai, Tsukuba 305-8574, Japan Abstract-The Internet technology was introduced to provide on-demand support for fitting the cycle ergometer workload personally depending on each individual’s physical work capacity. It was useful to combine the ratings of perceived exertion (RPE) with objective physiological indices during the exercise. We estimated the RPE from objective indices (i.e., muscular fatigue-related indices and the heart rate), using a feed-forward type artificial neural network. Interpreting the estimation errors was useful to design an appropriate workload for an individual elderly person. Keywords - cycle ergometer, heart rate, muscle activity, ratings of perceived exertion, artificial neural network
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PERSONAL FITTING PROCEDURE FOR CYCLE ERGOMETER WORKLOAD CONTROL BY ARTIFICIAL NEURAL NETWORKS T. Kiryu et al., IEEE/EMBS October 24, 2002
PERSONAL FITTING PROCEDURE FOR CYCLEERGOMETER WORKLOAD CONTROL BY
ARTIFICIAL NEURAL NETWORKS
T. Kiryu1, K. Shibai1, Y. Hayashi2, and K. Tanaka2
1Graduated School of Science and Technology, Niigata University, 8050 Ikarashi-2, Niigata 950-2181, Japan2Institute of Health and Sport Sciences, University of Tsukuba, 1-1-1 Tennodai, Tsukuba 305-8574, Japan
Abstract-The Internet technology was introduced to provide on-demand support for fitting thecycle ergometer workload personally depending on each individual’s physical work capacity. Itwas useful to combine the ratings of perceived exertion (RPE) with objective physiological indicesduring the exercise. We estimated the RPE from objective indices (i.e., muscular fatigue-relatedindices and the heart rate), using a feed-forward type artificial neural network. Interpreting theestimation errors was useful to design an appropriate workload for an individual elderly person.
PERSONAL FITTING PROCEDURE FOR CYCLE ERGOMETER WORKLOAD CONTROL BY ARTIFICIAL NEURAL NETWORKS T. Kiryu et al., IEEE/EMBS October 24, 2002
1. The fuzzy rules and membership functions weredesigned based on the individual’s physical workcapacity.
2. Using a feed-forward type artificial neural network, weestimated the RPE (i.e., a subjective index) from theHR and muscular fatigue, and studied the balancebetween the objective and subjective representationsof fatigue.
3. The gap was suitable to determine the appropriatelevels and the timing of temporally inserting workloadsin a basic workload variation.
Conclusions
PERSONAL FITTING PROCEDURE FOR CYCLE ERGOMETER WORKLOAD CONTROL BY ARTIFICIAL NEURAL NETWORKS T. Kiryu et al., IEEE/EMBS October 24, 2002
Conclusions (continued)
1. Evaluation of basic data under progressivelyincreasing workload.
2. Continuously changing the intensity and timingof temporary increasing workloads.
3. Comparison between subjective index (RPE)and objective indices by ANN.
- small error between RPE and estimated RPE.
- RPE greater than 13 and no progression of muscular fatigue.