INTERNET BASED SYSTEM FOR ADJUSTING CYCLE ERGOMETER WORKLOAD TO MODERATE EXERCISE

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1 INTERNET BASED SYSTEM FOR ADJUSTING CYCLE ERGOMETER WORKLOAD TO MODERATE EXERCISE Tohru Kiryu*, Kenichro Yamaguchi*, Kiyoji Tanaka**, and Akira Shionoya*** *Graduate School of Science and Technology, Niigata University, Niigata, JAPAN **Institute of Health and Sports Sciences, University of Tsukuba, Tsukuba, JAPAN ***Nagaoka University of Technology, Nagaoka, JAPAN A cycle ergometer is used as several fields including wellness, sport training, and rehabilitation. In order to adjust the cycle ergometer workload, heart rate is generally used. The other factors, however, should be considered to establish moderate exercise. We have combined the information of muscle activity to that of heart rate and proposed a total evaluation pattern using multivariate analysis of several indices estimated from heart rate and myoelectric signals. In this paper, we introduced a fuzzy system to accomplish moderate exercise for an individual person and developed an Internet based system for adjusting the cycle ergometer workload. The fuzzy rules were designed based on the different properties between cardiovascular and muscular activities. We used the Internet technology to achieve on demand support for adjusting the cycle ergometer workload because the workload should be fitted to the individual ability of exercise that probably changes depending on training and physical conditions.

2 Purpose Creation of appropriate exercise environment for older adults Development of training system that adjusts workload depending on the exercise ability of individuals Problems - Large differences in exercise ability - possibility of overuse - Low flexibility in exercise ability Approaches - Customization of workload control using Fuzzy Inference - Utilization of computer network

3 Exercise for Older Person Sports Physiology cardiovascular heavy fatigue muscle respiratory tight Wellness Environment 2st Century - Increasing population of older person who need exercise for health - Exercise at anytime and any place Solutions - Remote maintenance via the Internet - PCA and Fuzzy inference for managing functional activities

4 Workload Control Optical Sensor Collection of Blood 4-bar Electrode Trigger Sensor Mask Control Box A/D Respiratory Analyzer Multi-Channel Amplifires Notebook Computer Sirial-Port cardiovascular PCA Surface EMG muscles feedback every 2-min ARV & MPF Fuzzy Inference Membership functions, Fuzzy rules Control coefficient workload at (n+)-frame Info. for work load control WL n + = WL n δ n WL WL n + WL n δ n WL δ δ n workload at n-frame control coefficient at n-frame step-size of workload every 5 cycle

Experimental Conditions - Subjects Seven old man and woman (60.0+-6.65years) - Myoelectric Signals muscles: right leg vastus lateralis gain: 60dB frequency band width: 5.3Hz -.

5 Experimental Conditions - Subjects Seven old man and woman ( years) - Myoelectric Signals muscles: right leg vastus lateralis gain: 60dB frequency band width: 5.3Hz -.2kHz 4-bar active array electrode - Heart Rate LED-photo transistor - Metabolism Lactate in blood every minute respiration gas exchange every minute Ratings of perceived exertion (RPE)

sec User s PC Collection of Blood 4-bar Electrode Results ISDN router 60 kb Trigger

6 Network System Niigata University Doctor s PC Java based system Optical Sensor Mask Box EMG monitor 9. sec User s PC Collection of Blood 4-bar Electrode Results ISDN router 60 kb Trigger Sensor ISDN router 40 sec ISDN line Internet less than sec kb Higashi-Toride Hospital Tsukuba Info. For Control 4.7 sec 20 kb Measured data Communication Center

7 Experimental Protocol progressively increasing workload trial for desining Fuzzy inference controlled workload control by referring cardiovascular and muscular activities 5W/min 50W 0 w/min WL = 0.83 W 0W 2min measured interval cool down 0W 30W 0W preperation 2min measured interval cool down 0W WL n + = WL n δ n WL cycle ergometer exercise at 60 rpm

8 Information for Control Fuzzy Membership Functions Fuzzy Rules b middle low high a 0 negative zero a b positive c + c low middle fatigue heavy - 0 Hi Lo: low : middle Hi: high Po Ze Ne Lo Lo He He Po: positive Ze: zero Ne: negative Hi δ Lo He Lo: low : middle He: heavy

subject:09eh - different from young adults - short endurance time - fat AT: 70 watt, 0 bpm, RPE 2; LT: 90 watt, 28 bpm, RPE: 3

9 hr + Results during Exercise at PW Type-A 2/ Type-B 5/ mpf normalized pattern subject:28mm - same as young adults - relatively long endurance time - slim AT: 80 watt, 05 bpm, RPE: 8 ; LT: 05 watt, 3 bpm, RPE: 0 - arv hr + mpf normalized pattern subject:09eh - different from young adults - short endurance time - fat AT: 70 watt, 0 bpm, RPE 2; LT: 90 watt, 28 bpm, RPE: 3 - arv [mv] [Hz] [bpm] ARV MPF Type-A Type-B Work Load [watts]

10 Results during Exercise at CW Work Load [bpm] [watt] [frame] subject:nm 30-min Work Load [watt] [frame] subject:220mm AT: 80 watt, 05 bpm; LT: 05 watt, 3 bpm, RPE: 9 30-min

11 Energy Metabolism and Exercise AT 5 30 lactate in blood cardiovascular limit due to increase of VCO 2 ( 3 00 ) anaerobic energy Type-A time AT: 80 watt, 05 bpm, RPE: 8 ; LT: 05 watt, 3 bpm, RPE: 0 aerobic energy efficient aerobic exercise AT 4 30 LT 7 00 anaerobic energy outdraw of metabolic byproducts due to blood flow Type-B anaerobic exercise aerobic exercise immediate transition LT accumulation of metabolic byproducts in muscles 7 30 time AT: 70 watt, 0 bpm, RPE 2; LT: 90 watt, 28 bpm, RPE: aerobic energy

12 Conclusions - Workload control for older adults workload control based on cardiovascular and muscular customization of workload for individuals - Internet system for remote maintenance multi-platform network program by Java field experiments using ISDN line Next step - Classifing fuzzy membership functions and rules - Design of appropriate control scheme for type-a and B - Adjusting control timing depending on time-varying behavior of biosignals

13 Key Technology () EMG Measurement duirng Dynamic Contractions 400 µv st channel 2nd channel 4bar electrode st channel 2nd channel 3rd channel 3rd channel frame ( ms) subframe (204.8 ms) m= m=2 m=3 one frame = ms, one subframe = ms Estimation of ARV & MPF at time n arv m = max arv m 3 arv n = arv m 3 m= mpf m = min 3 mpf n = mpf 3 m= ch = ( ch) { } 3 ch = ( ch) ch = { mpf } 3 m ch = m

14 Key Technology (2) Evaluation of Functional Activities during Exercise prop st 2 min trial prop 2nd 24min trial 3rd 2 min trial MPF CV ARV MPF CV ARV r CV-ARV r CV-ARV r MPF-ARV r MPF-ARV work load work load work load work load (a) (b) proportion of z correlation coefficients absolute values of components

15 Key Technology (3) Fuzzy Inference & Internet for Customization -Fuzzy low 0 a c negative 0 zero b middle - a c + b positive high fatigue low heavy middle - 0 Hi Lo: low : middle Hi: high Po Ze Ne Lo Lo He He Po: positive Ze: zero Ne: negative Hi δ Lo He Lo: low : middle He: heavy - Internet Subject's PC Demand by button JSA connection Applet Execution by Java VM on Web Browser access class file download JSA: Java Stand-alone application Communication Center(C.C) Web Server <html> yyy.class <applet>... </applet> </html> JSA server client Connection to JSA on C. Center on demand JSA server client read write data Local disk data

16 References - J. A. Davis, M. H. Frank, B. J. Whipp, and K. Wasserman, Anaerobic threshold alterations caused by endurance training in middle- aged men, J Appl Physiol, vol. 46, 6, pp , G. Borg, G. Ljunggren, and R. Ceci, The increase of perceived exertion, aches and pain in the legs, heart rate and blood lactate during exercise on a bicycle ergometer, Eur J Appl Physiol, vol. 54, 4, pp , T. Moritani, T. Takaishi, T. Matsumoto, Determination of maximal power output at neuromuscular fatigue threshold, J. of Appl. Physiol. Vol. 74, 4, pp , T. Kiryu, K. Takahashi, and K. Ogawa, Multivariate analysis of muscular fatigue during bicycle ergometer exercise, IEEE Trans. on BME, vol. BME44-8, pp , J. L. Chicharro, M. Perez, A. F. Vaquero, A. Lucia, and J. C. Legido, Lactic threshold vs ventilatory threshold during a ramp test on a cycle ergometer, J Sports Med Phys Fitness, vol. 37, 2, pp. 7-2, J. J. Chen, N. Y. Yu, D. G. Huang, B. T. Ann, and G. C. Chang, Applying fuzzy logic to control cycling movement induced by functional electrical stimulation, IEEE Trans Rehabil Eng, vol. 5, 2, pp , 997.

17 Change in Exercise Ability due to Aging - Degeneration of flexibility Narrowing working range for elbow, knee, whist joints - Weakness of dynamic power Degeneration of neuromuscular system - Effects of exercise Improvement of neuromuscular system reactions, but less growth of muscles - Change in muscle fiber composition Fast-twitch fibers are easier to be degenerated than slow-twitch fibers. endurance training

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