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Research Papers

Fatigue Detection Using Phase-Space Warping

[+] Author and Article Information
Abdullatif Alwasel

Department of Systems Design Engineering,
University of Waterloo,
Waterloo, ON N2L 3G1, Canada
e-mail: aalwasel@uwaterloo.ca

Marcus Yung

Department of Kinesiology,
University of Waterloo,
Waterloo, ON N2L 3G1, Canada
e-mail: m4yung@uwaterloo.ca

Eihab M. Abdel-Rahman

Associate Professor
Department of Systems Design Engineering,
University of Waterloo,
Waterloo, ON N2L 3G1, Canada
e-mail: eihab@uwaterloo.ca

Richard P. Wells

Professor Department of Kinesiology,
University of Waterloo,
Waterloo, ON N2L 3G1, Canada
e-mail: wells@uwaterloo.ca

Carl T. Haas

Professor
Department of Civil and
Environmental Engineering,
University of Waterloo,
Waterloo, ON N2L 3G1, Canada
e-mail: chaas@uwaterloo.ca

1Corresponding author.

Manuscript received April 9, 2015; final manuscript received November 15, 2016; published online January 23, 2017. Assoc. Editor: Silvia Blemker.

J Biomech Eng 139(3), 031001 (Jan 23, 2017) (9 pages) Paper No: BIO-15-1157; doi: 10.1115/1.4035367 History: Received April 09, 2015; Revised November 15, 2016

A novel application of phase-space warping (PSW) method to detect fatigue in the musculoskeletal system is presented. Experimental kinematic, force, and physiological signals are used to produce a fatigue metric. The metric is produced using time-delay embedding and PSW methods. The results showed that by using force and kinematic signals, an overall estimate of the muscle group state can be achieved. Further, when using electromyography (EMG) signals the fatigue metric can be used as a tool to evaluate muscles activation and load sharing patterns for individual muscles. The presented method will allow for fatigue evolution measurement outside a laboratory environment, which open doors to applications such as tracking the physical state of players during competition, workers in a plant, and patients undergoing in-home rehabilitation.

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References

Figures

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Fig. 1

Instrumented knee brace used to measure the knee flexion angle in sagittal plane

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Fig. 2

Schematic diagram for the Iso, 7.5–22.5, 1–29, 0–30, and Sine exercises, respectively, performed in the physiological experiment

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Fig. 3

Elbow extension and EMG data collection from Triceps and Biceps muscles

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Fig. 4

Log scale plot of the fill factor F(t) as a function of the time-delay for state variable vector dimensions in the range of d = 2–10

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Fig. 5

Fatigue indices of the lower limb Ej as function of normalized session time

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Fig. 6

The FFT of the EMG signal obtained in the Iso exercise

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Fig. 7

Fatigue indices obtained from measured force during the four cyclic elbow exercises

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Fig. 8

Fatigue indices obtained from the EMG signal of the lateral triceps during five elbow exercises

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Fig. 9

Fatigue indices obtained from the EMG signal of the medial triceps during five elbow exercises

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Fig. 10

Fatigue indices obtained from the EMG signal of the biceps brachii during five elbow exercises

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Fig. 11

Iso signal versus mean frequency power

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Fig. 12

Average change in fatigue metric for muscles and force in all exercises: (a) lateral, (b) medial, (c) biceps, and (d) force

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Fig. 13

The pseudo phase-portrait of the gait cycle obtained from reconstructed phase-space

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Fig. 14

The phase-portrait of the gait cycle extracted from Winter [37] measurements

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