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

3

acc=1 acc=0

Trunk tilt

epoch event

Smartphone

Main daily activities

Lying 36% Sitting

48%

Walking 7%

Standing 9%

Lying36%

Time, min

4

• 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

7

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

9

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?

11

Mapping physical activity onto states

Paraschiv-Ionescu et al. (2012) , PLoS ONE

12

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

16

17

Thank you for your attention