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B1127 Physiology and Biomechanics
Exercise testing lecture Accuracy of step detection using a customized mobile phone app David Rowe1, Allan Hewitt1, Campbell Reid2, Arlene McGarty3 1 Physical Activity for Health Research Group, University of Strathclyde 2 Bioengineering Unit, University of Strathclyde 3 Physical Activity Research Center, University of Edinburgh
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B1127 Physiology and Biomechanics
Exercise testing lecture Background Walking – the “perfect exercise” Health benefits Convenience Parameters of walking: Volume (total steps) Intensity (speed, steps per minute) Pattern (gait symmetry, phase timing) Measurement Pedometer vs. accelerometer Research grade vs. applied/commercial
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Dose-response – step “volume”
B1127 Physiology and Biomechanics Exercise testing lecture Dose-response – step “volume” Source: Ewald et al. (in press), Journal of Physical Activity for Health
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B1127 Physiology and Biomechanics
Exercise testing lecture Mobile phone apps Cost Ubiquity Convenience Uses Quality control
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Evolution of the mobile phone
B1127 Physiology and Biomechanics Exercise testing lecture Evolution of the mobile phone
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B1127 Physiology and Biomechanics
Exercise testing lecture The App(s) Developed using Objective C (Apple) Adapting for Android Accelerometer function Step-detection algorithm Music playlist “Activity points” algorithm
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B1127 Physiology and Biomechanics
Exercise testing lecture Protocol 32 adults (53% female; 29±13 yr) Six 90-s treadmill walking trials 53, 67 and 80 m·min-1 (≈ 2.0, 2.5 and 3.0 mph) 0% and 5% gradient All trials video recorded
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B1127 Physiology and Biomechanics
Exercise testing lecture Protocol iPod touches (iTouches) worn in pouches on right midline thigh (waist), mid-back, and in the right pocket 3rd generation, 8GB memory
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B1127 Physiology and Biomechanics
Exercise testing lecture Protocol Parameters of step detection algorithm (signal processing) Sampling rate 50 Hz Smoothing algorithm Peak detection Time censoring window (0.33 s) Equivalent to > 180 steps/min (specific to walking)
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Accelerometer signal
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Measurement and data analysis
B1127 Physiology and Biomechanics Exercise testing lecture Measurement and data analysis Criterion step counts determined by hand-counter using time-stamped video recording iPhone step counts compared to criterion (video count) Repeated measures t-tests Cohen’s d Discrepancy plot (modified Bland-Altman)
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B1127 Physiology and Biomechanics
Exercise testing lecture Results Pocket position: Steps were significantly (p < .05) and meaningfully (d = ) over-counted in all trials Waist and mid-back: Steps were significantly (p < .05) and meaningfully (d = 0.3 and 0.6) under-counted at 53 m·min-1 Accurately counted at 67 and 80 m·min-1, at level and 5% gradient (d = ).
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Discrepancy plot – Waist
54 m·sec-1, 0% grade 67 m·sec-1, 0% grade 80 m·sec-1, 0% grade 54 m·sec-1, 5% grade 67 m·sec-1, 5% grade 80 m·sec-1, 5% grade
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Discrepancy plot – Mid-back
54 m·sec-1, 0% grade 67 m·sec-1, 0% grade 80 m·sec-1, 0% grade 54 m·sec-1, 5% grade 67 m·sec-1, 5% grade 80 m·sec-1, 5% grade
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Discrepancy plot – Pocket
54 m·sec-1, 0% grade 67 m·sec-1, 0% grade 80 m·sec-1, 0% grade 54 m·sec-1, 5% grade 67 m·sec-1, 5% grade 80 m·sec-1, 5% grade
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B1127 Physiology and Biomechanics
Exercise testing lecture Discussion Similar to traditional pedometers, steps are under-counted by a mobile phone app at slow speeds, but accurately counted at moderate speeds and higher, when worn securely. When carried in the pocket, steps are over-counted regardless of speed and gradient. Further analysis of the raw acceleration signal and the time-stamped video recording will help identify reasons for inaccuracy and inform future signal-processing decisions in mobile phone accelerometer uses.
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Limitations/future research
B1127 Physiology and Biomechanics Exercise testing lecture Limitations/future research Processor capability/dual-task processing Apple problem Battery life Slow technology development Distraction (it’s a phone!) Mobile phone habits Non “wear”
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B1127 Physiology and Biomechanics
Exercise testing lecture Thank you!
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