t. kiryu , k. shibai , y. hayashibsp.eng.niigata-u.ac.jp/.../presentation/2002/ieee2002_p.pdf ·...

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.

Keywords - cycle ergometer, heart rate, muscle activity, ratings of perceived exertion, artificialneural network


Purpose

Health Promotion byExercise

“Wellness”

Social Background- progression of aged society- high motivation for health

Howeverphysical work capacity

- risk for overuse- degeneration of functions- large individual differences

Web-based Healthcare System

IT systemto support Exercise for Wellness


analyzed results

Active 4-bar electrode

Ref. electrode

measured data

control info.

Sports Gymnasium

Network server

PC for maintenance& service

MonitoringIf necessary

Communication portmeasured data

Wellness Bikepaddle sensor

HR sensorInternet

Zoom Up

Home

Health Care Center

��

amp. network

support center

� ��

measurement & control unit

�� fuzzy parameters

Internet

Browsing by Java made programs

Exercise at any time and at any location

PC for maintenance& service

Continuously Supportingat any time and at anylocation

Internet-based Cycle Ergometer Systemfor Serving Personally Fitted Workload


Estimation of Total fatigue index δδδδ

SEMG, HR

WLWLWL n1n ∆δ ⋅−=+

Recursive Workload Control

WLn : workload at n-th frame

∆WL: incremental step of workload

δ: fatigue indexWorkload control

-Fuzzy Rules-Membership Functions

Surface EMG(SEMG)- amplitude info- frequency-related info.

Heart Rate (HR)

Fuzzy control

Control by Objective Indices

Workload Control for Cycle Ergometer

time

wor

kloa

d

controlled workload

AT


Objective Indices

Amplitude indx

Frequency index

Correlation coefficient betweenARV and MPF

Heart Rate

Surface Myoelectric Signals

m : frame number

emg : samples of ME signals

P(m, f) : power spectrum of ME signal at m-th frame

fH : high-frequency limit for estimation

N : number of samples in a frame

Muscular fatigue index

HR

ARV mN

emg kk m

m N

( ) ( )==

+ −

∑1 1

MPF m

f P m f

P m f

f f

f

f f

fL

H

L

H( )

( , )

( , )

=

⋅=

=

∑

∑

γγγγARV-MPF

fL : low-frequency limit for estimation

1 frame = 5 sec


Segmentation in HR-γγγγARV-MPF Scatter Graph

-1

0

1

60 70 80 90 100 110

seg-1

seg-2

seg-3

µ2-σ2 µ2 µ2+σ2

last

early

µ2-σ2

µ2

µ2+σ2

middle

µ1, µ2, µ3 : mean at each segment σ1, σ2, σ3 : s.d. at each segment

membership functions

heavy

light

γγγγARV-MPFγγγγ

ARV-MPF

Heart Rate

1) Weigh the random values for eachsample,

2) Estimate the center of samples by fuzzymeans, and tentatively segment the samples,

3) Change the weight based on the distancesbetween the center and each sample,

4) Repeat steps 1) - 3) until the centerposition reaches a steady point, and then

5) Divide the samples.


Changes in HR-γγγγARV-MPF Scatter Graph

HR

γARV-MPF

XXXXXXXXXXXXXX

XXXXXXXXXX

XXX

XXXXXX

XXX

X

XXXXXXXXXX

XXXXXXXXXXXXXXXXXXXXX

XXXXXXXXX

XXXXX

XXXXXXXXXXXX

XXX

XXXXXXXXXXXXXXXXX

XXXXX

XXXX

XX

X

XXXXXXXXXXXX-0.8

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

60 80 100 120 140 160 180

middle phase

last phase

early phase

Type A

XXXXXXXXXXXXXX

XX

XXXXXXXXXXXXXX

XXXXXXXXXX

XXXXXX

XXXXXXX

XX

XX

X

XX

XXXXXXXX

XXXXXXXX

X

X

X

XXXXXXXXXXXXXXXXX

XXX

XXXXXXXXXXXXXXXXXXXXX

X

XXXXXXXXXX

-0.8

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

60 80 100 120 140 160 180

γARV-MPF

(a) first set

[bpm]HR [bpm]

(b) fourth set

progressively increasing workload

seven monthsApril, 2000 November, 2000

subject TH (a 70-year-old man)


Objective / Subjective Representation of Fatigue

RPE-Scale

myoelectric signals

electrocardiogram

7 Very, very light 8 9 Very light1011 Fairly light1213 Somewhat light1415 Hard1617 Very hard1819 Very, very hard20

RPE (Ratings of Perceived Exertion)by Borg (Med. Sci. Sports Exerc. 1973)

Objective DataObjective Data

tightSubjective RepresentationSubjective

Representation


Workload Adjustment Procedure

PIWCW1

CW2CW3

PIW

Design of Control Parameters- Fuzzy Rules- Membership Functions

�CW Controlled Workload

�PIW Progressively Increasing Workload

Updating Control Parameters

Key points• classification• segmentation

conventionalapproach

Key point• Personal Fitting

next trial

trial


Evaluation of Personal Fitting Procedure

WL

∆WL

HR

RPE

ANNγARV-MPF

εEstimated RPE

Objective indicesSubjective indices

Estimation errorEstimation

ε( ) ( ) ˆ ( )n RPE n RPE n= −

Estimation of RPEby

Artificial Neural Network

Comparison


Estimation of RPE by ANN

Selection of a suitable ANN

- HR- γARV-MPF

- WL- ∆WL

Estimated RPE

ε( ) ( ) ˆ ( )n RPE n RPE n= −minimum squared error

Numbers of cells at each layer were 4 at the input,4 - 20 at the middle, and 1 at the output (the totalnumber of ANNs checked was 25).

A suitable ANN for personal fitted workload control


Experimental Conditions

18 senior subjects (63.5±7.5 years old)

6 senior subjects (69.3±4.1 years old)

muscles: right leg vastus lateralisgain: 60dBfrequency band width: 5.3Hz - 1.2kHz4-bar active array electrode

LED-photo transistor

lactate in blood every minuterespiration gas exchange every minute

ratings of perceived exertion (RPE)

- Subjects- Subjects

- Myoelectric Signals- Myoelectric Signals

- Heart Rate- Heart Rate

- Metabolism- Metabolism

- Subjective Index- Subjective Index

from November 2001 to December 2001 every 2 weeks

from September 2000 to March 2001 every 2 months


Personal Fitting

temporary increasing workload

Workload Design for Personal Fitting

phase 1phase 2

phase 3phase 4

phase 5phase 6

time

wor

kloa

d

WLmin WLmax

T1 T2 T3

AT

How to determine: - level - timing

Little bit hard exercise andperception of workload changes

Protocol for Exercise

ATmax

ATmin

WL%130WL

WL%70WL

=

=

time

wor

kloa

d

Conventional WL control

AT

AT: Anaerobic Threshold


Sufficient Workload ControlWL

RPE RPE^0

50

100150

510

1520

0 50 100 150 200 250 300 350 400

WL

[W

]

time [frame]

6080

100120

-1-0.500.51

0 50 100 150 200 250 300 350 400

HR

[bp

m]

AR

V-M

PF

time[frame]

HR

ARV-MPFγ γ

-5

0

5

0 50 100 150 200 250 300 350 400time [frame]

ε

Sufficient example for PF

^R

PE, R

PE

Subject KY

2001-Dec-23

-1

-0.5

0

0.5

1

60 80 100 120

AR

V-M

PF

HR [bpm]

γ

33.2% subj.A, Dec 23, 2001

: samples in the middle phase

- HR around AT- No severer muscular fatigue

WLAT= 97 W, WLLT =120 W

Type-A, 73-years-old man


Insufficient Levels and Timing

^

WL

RPE

RPE

^0

50

100

150

5

10

15

20

0 50 100 150 200 250 300 350 400

WL

[W

]

RPE

, RPE

time [frame]

60

80

100

120

-1-0.500.51

0 50 100 150 200 250 300 350 400

HR

[bp

m]

AR

V-M

PF

time[frame]

HR

ARV-MPFγ γ-5

0

5

0 50 100 150 200 250 300 350 400time [frame]

ε

98[W]

106[W]

2001-Nov-11

Subject KY

Insufficient example for PF

6080

100120

-1-0.500.51

0 50 100 150 200 250 300 350 400

HR

[bp

m]

AR

V-M

PF

time[frame]

HR

ARV-MPFγ γ^WL

RPE RPE^0

50100

150

51015

20

0 50 100 150 200 250 300 350 400

WL

[W

]

RPE

, RPE

time [frame]

-5

0

5

0 50 100 150 200 250 300 350 400time [frame]

ε2001-Dec-9


-1

-0.5

0

0.5

1

60 80 100 120

AR

V-M

PF

HR [bpm]

γ

HR-γγγγARV-MPF Scatter Graphs in PF

subj. A

: samples in the middle phase

- HR around AT- No severer muscular fatigue

(b)(a) (c)-1

-0.5

0

0.5

1

60 80 100 120

AR

V-M

PF

HR [bpm]

γ

subj.A, Nov 11, 200120.1%-1

-0.5

0

0.5

1

60 80 100 120

AR

V-M

PF

HR [bpm]

γ

16.6% subj.A, Dec 9, 2001

-1

-0.5

0

0.5

1

60 80 100 120

AR

V-M

PF

HR [bpm]

γ

33.2% subj.A, Dec 23, 2001

Lighter than PIW Relatively light for muscles,but hard for respiration

Appropriate exercise,comfortable

Insufficient timingInsufficient level sufficient workload control


RPE-Workload Scatter Graph atPersonally Fitted Trial

0

2

4

6

8

10

12

14

0 20 40 60 80 100 120

Workload [W]

RPE

WLAT

B B B B

B

B

B

B

BBB

B

B B B

B

B

BB

BB B

B

B

BBB

B

J

J

J

J

J

JJJ

JJJJJJJ

J

J

J

J

JJJJJJJJJJJJ

J

JJJJJJJJJJJJJJ

JJJJJJJJJJJJJJJJJJJJJ

J

JJJJJ

JJJJJJJ

JJJ

JJJJJ

JJJJJ

J

J

JJ

J

JJ

JJ

JJJ

-4-3-2-101234

0 20 40 60 80 100 120

EE

EEEE

EEEEEEEEEEEEEE

EEEEEEEEEEEEEEEEE

EEEEEEEEEEEEEEEEE

EEE

EEEEEEEE

EEEEEEEEEEEEEE

E

E

EE

EEEE

E

E

EE

EE

E

E

EEEEE

E

Workload [W]

RPE estimation error90-194 frame

195-294 frame

WLAT= 97 W, WLLT =120 W

Type-A, 73-years-old manWL

RPE RPE^0

50100

150

51015

20

0 50 100 150 200 250 300 350 400

WL

[W

] ^R

PE, R

PE

ε( ) ( ) ˆ ( )n RPE n RPE n= −

Subject KY2001, Dec. 23


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


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.

Personally Fitting Procedure

Wellness Bike for Home-Based Exercise

t. kiryu , k. shibai , y. hayashibsp.eng.niigata-u.ac.jp/.../presentation/2002/ieee2002_p.pdf ·...

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