Movement Complexities Kamiar Arminian
1st EU Fall Festival Stuttgart, 24-25 March 2015 Daily activity and movement complexity in health, aging and disease Kamiar Aminian lmam.epfl.ch Activities of daily living 2 Side Back Physical Activity Tilting g Lyin low activity ion t i s Tran high activity Sittin g Tilting Leaning Standing slow Walking fast Main techniques for activity monitoring: 3 Epoch detection Trunk tilt ¨ Event detection ¨ Expert systems event ¨ Machine learning ¨ Single sensor ¨ Multiple sensors ¨ Activity classification ¨ acc=1 acc=0 epoch Smartphone 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 Main daily activities 4 Lying 36% Lying 36% Walking 7% Time, min Standing 9% Sitting 48% Multi-scale Analysis Possible fall risk related activity metrics 5 Transfer • sit-stand duration • number of transfer Activity Pattern • duration • frequency • sit-stand smoothness • gait symmetry • gait velocity/cadence • gait variability • walking distribution Global index? Changes with aging and disease • power-law • short activity • long rest Ganea, Aminian et al. (2012) IEEE TMBE, Ganea, Aminian et al. (2011), Med Phys&Eng. A. Paraschiv-Ionescu, Aminian et al., Scientific Reports, 2013 Complexity concept 6 Interaction of a myriad of structural units enabling the organism to adapt to the stresses of daily life Reduction in physiological inputs and their connections over time Loss of complexity in the output signal Loss of functional ability frailty [Physiological complexity, aging, and the path to frailty LA Lipsitz - Science's SAGE KE, 2004] Physiological complexity 7 Fractals behavior Self-similar structure A.L. Golberger, PNAS, 2002, A.L. Golberger, Lancet, 1996 Self-similar dynamics Movement complexity: Fractal behavior of stride intervals 8 fractal scaling index (α) is 0.56 forrandom the elderly subject and 1.04 for the young subject. α= 0.5à the signal is random 0.5 < α ≤ 1àpresence of long-range (fractal like) correlations Hausdorff et al. / Physica A 302 (2001) Movement complexity: Fractal behavior of walking episodes 9 Walking episode duration, sec 1000 episodes (5 days) 200 episodes 50 episodes A. Paraschiv-Ionescu et al., Physical Review E, 2008 Movement complexity: Fractal behavior of walking episodes (5 days) 10 healthy chronic pain A. Paraschiv-Ionescu and K. Aminian (2009) in A. Na¨ıt-Ali (ed.), Advanced Biosignal Processing, Using barcode as complexity measure 11 Lying 36% Lying 36% barcode? Time, min Walking 7% Standing 9% Sitting 48% Mapping physical activity onto states 12 Entropy measures: • Entropy • Lempel-Ziv • Weighted Permutation Entropy Paraschiv-Ionescu et al. (2012) , PLoS ONE Physical activity barcode 13 Painfull Pain free Seconds Physical behavior and Complexity in aging Subjects: N=100 ¨ Age: 41-98 y.o ¨ Smartphone recording: 7 days, 9hours/day ¨ Activity states & Barcodes: ¨ n n n n type: lying/sedentary, active, gait intensity: activity counts, cadence duration: walking (gait) bouts 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 eria riteria t i r c Fit: 0 il: 1-2 c § Slow walking speed ra § Exhaustion Pre-f + criteria 3 § Low activity level Frail: Conclusions 16 ¨ 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 Thank you for your attention 17
© Copyright 2024