daily activity and movement complexity in health, aging...
TRANSCRIPT
Daily activity and movement complexity in health, aging and disease Kamiar Aminian
lmam.epfl.ch
1st EU Fall Festival Stuttgart, 24-25 March 2015
Physical Activity Side Back
Lying
Tilting Leaning
Sitting Standing
Walking
Transition
Tilting
slow
fast
low activity
high activity
Activities of daily living 2
Main techniques for activity monitoring:
¨ Epoch detection ¨ Event detection ¨ Expert systems ¨ Machine learning ¨ Single sensor ¨ Multiple sensors ¨ Activity classification
Bussmann et al.,1995, Veltink et al., 1996, Aminian et al., 1999, Ng J, et al., 2000, Bao et al., 2004, Ganea, Aminian et al, 2012, Godfrey et al., 2011,Salarian, Aminian et al., 2007, Paraschiv-Ionescu, Aminian et al., 2004, Nyan et al., 2006
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acc=1 acc=0
Trunk tilt
epoch event
Smartphone
Main daily activities
Lying 36% Sitting
48%
Walking 7%
Standing 9%
Lying36%
Time, min
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• duration
• frequency
• gait velocity/cadence
• sit-stand smoothness
• gait symmetry
• gait variability
• walking distribution
• power-law
• short activity
• long rest Changes with aging and disease
• sit-stand duration
• number of transfer
Transfer Activity Pattern
Multi-scale Analysis Possible fall risk related activity metrics 5
Ganea, Aminian et al. (2012) IEEE TMBE, Ganea, Aminian et al. (2011), Med Phys&Eng. A. Paraschiv-Ionescu, Aminian et al., Scientific Reports, 2013
Global index?
Complexity concept 6
[Physiological complexity, aging, and the path to frailty LA Lipsitz - Science's SAGE KE, 2004]
Reduction in physiological inputs and their connections over time
Loss of complexity in the output signal
Loss of functional ability
frailty
Interaction of a myriad of structural units enabling the organism to adapt to the stresses of daily life
Physiological complexity Fractals behavior
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A.L. Golberger, PNAS, 2002, A.L. Golberger, Lancet, 1996
Self-similar structure Self-similar dynamics
Movement complexity: Fractal behavior of stride intervals 8
scaling index (α) is 0.56 for the elderly subject and 1.04 for the young subject.
Hausdorff et al. / Physica A 302 (2001)
α= 0.5à the signal is random 0.5 < α ≤ 1àpresence of long-range (fractal like) correlations
fractal
random
1000 episodes (5 days)
200 episodes
50 episodes
Movement complexity: Fractal behavior of walking episodes
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Walking episode
duration, sec
A. Paraschiv-Ionescu et al., Physical Review E, 2008
A. Paraschiv-Ionescu and K. Aminian (2009) in A. Na¨ıt-Ali (ed.), Advanced Biosignal Processing,
healthy
chronic pain
Movement complexity: Fractal behavior of walking episodes (5 days) 10
Using barcode as complexity measure
Lying 36% Sitting
48%
Walking 7%
Standing 9%
Lying36%
Time, min
barcode?
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Mapping physical activity onto states
Paraschiv-Ionescu et al. (2012) , PLoS ONE
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Entropy measures:
• Entropy • Lempel-Ziv • Weighted Permutation Entropy
Seconds
Painfull
Pain free
Physical activity barcode 13
Physical behavior and Complexity in aging
¨ Subjects: N=100 ¨ Age: 41-98 y.o ¨ Smartphone recording:
7 days, 9hours/day ¨ Activity states & Barcodes:
n type: lying/sedentary, active, gait n intensity: activity counts, cadence n duration: walking (gait) bouts n 18 states barcodes
Waist case belt used for wearning the smartphone
Movement complexity Fit, Pre-frail and Frail subjects
¨ Frailty evaluation based on Fried criteria ¨ Fit (N=20), Pre-Frail (N=19), Frail (N=29) ¨ Activity recording: 2 days, 7h30min/day ¨ Fear of Falling evaluation based on questionnaire ¨ Number of falls
Fried criteria § Sarcopenia or weight loss § Reduced muscular strength § Slow walking speed § Exhaustion § Low activity level
Fit: 0 criteria
Pre-frail: 1-2 criteria
Frail: 3+ criteria
Conclusions
¨ Today wearable technologies provide: ¤ long-term monitoring of activity
behavior ¤ useful and interpretable metrics to
assess decline in motor function
¨ Movement complexity can provide a global index of activity behavior
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Thank you for your attention