Goals of the workshop

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Emory Robertson
5 years ago
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1 Welcome!

2 Goals of the workshop Dissemination of WIISEL outcomes Explore possible synergies & actions after the project end

Quantitative and objective measurement of gait parameters Portable and

3 Falling has serious physical, psychological and economical consequences. Gait analysis is applied in diagnosis and monitoring of fall risk Needs Quantitative and objective measurement of gait parameters Portable and unobtrusive systems to extend measurements during users daily activities and over long time periods

General goals Continuous monitoring of elderly people through sensor insole connected

4 WIISEL: A FLEXIBLE RESEARCH TOOL to collect and analyze gait data from real users and correlate parameters related with the risk of falls from the elderly population. General goals Continuous monitoring of elderly people through sensor insole connected to data analysis system Fall risk assessment through data analysis and Pattern recognition

5 INSOLE CETEMMSA UAB ACREO GEISA DATA MANAGEMENT SPRING ACREO WIISEL SYSTEM MEDICAL VALIDATION NUI GALWAY INRCA TASMC

6 Video

7 Technology developed

8 WIISEL system components

9 Gait Analysis Tool Data processing/ filtering Gait parameter definition Fall Risk Index construction framework FRI / Subject s gait data

10 Gait Analysis Tool

11 Gait Analysis Tool

12 Gait Analysis Tool

13 Validation Studies

14 Target Validation site n = 11 n = 16 n = 12 Total n = 39 Study Design Age (mean) Males (%) Females (%) MMSE Score (mean) Fallers (%) Non-Fallers (%)

15 Fall Risk Clusters from clinical tests Fall Risk Index calculated by WIISEL Fall Risk Index (FRI) Low Medium High High fall risk POMA Medium fall risk Low fall risk Predictive of falls DGI Uncertain Safe ambulatior Freely Mobile TUG Mostly independent Variable mobility Total Fall Risk Index (FRI) Low Fall Risk (n = 24) Medium Fall Risk (n = 8) POMA DGI TUG High Fall Risk (n = 4) Low Medium High Low Adherence between FRI and clusters Cluster Medium High 0 0 4

16 The current system received an average SUS score of which is above the OK mark on the System Usability Scale. How satisfied are you with: Effective Comfortable Ease_Use Durability 3,27 3,58 3,82 4,15 Quebec User Evaluation of Satisfaction with Assistive Technology (QUEST 2.0) Safe Adjusting Weight Dimension 4,52 4,03 4,09 3, QUEST 2.0 total score is corresponding to a moderate acceptance

17 Round Table

18 PROGRESS ON NEW METRICS FOR A FALL RISK INDEX

acceleration (AP, ML) range; V- Higher gait variability; ML- Lower

19 Quantifying Fall Risk in Daily Life using a 3-day long Continuous Accelerometer Group Differences based on Mountain Plots : Lower acceleration (AP, ML) range; V- Higher gait variability; ML- Lower gait variability in fallers, compared to non-fallers A Weiss et al., 2013

20 Quantifying Fall Risk in Daily Life using a 3-day long Continuous Accelerometer

21 Quantifying Fall Risk in Daily Life using a 3-day long Continuous Accelerometer Performance based tests correctly identified 84.2% of the subjects as future fallers or future nonfallers (33.3% sensitivity, 97.8% specificity). With the 3-day acceleration-derived measures, 94.7% of the subjects were correctly classified (75.0% sensitivity, 100% specificity). A Weiss et al., 2013

22 Assessing fall risk in Patients with PD PD fallers (n=42) ; PD non-fallers (n=66) Fall Risk Assessment- subject report- 1yr fall history + follow-up 3-day Acceleration derived Gait Features A Weiss et al., 2014

23 Assessing fall risk in Patients with PD PD faller PD non-faller

24 PROSPECTIVE ASEESMENT OF fall risk in Patients with PD Fall history & func performance: 79.8% classified: 65.2% sensitivity, 91.4% specificity Adding 3-day Acceleration : 87.1% classified: 81.8% sensitivity, 91.2% specificity A Weiss et al., 2014

25 Effects of aging and fall risk on transitions in daily living Groups: Young adults (n=30) ; Older adults (n=38); elderly fallers (n=33); 5,027 Sit-to-walk and Walk-to-sit transitions were analyzed; Temporal, distribution-related features ; Machine learning algorithms T Iuz et al. in press, almost

26 Machine Learning Results Identifying Fallers and Non-Fallers: Conventional Testing vs. Transition-derived Metrics T Iuz et al. in press, almost

27 The local dynamic stability of daily life walking in older adult fallers and non-fallers based on 3 day recordings A comparison study of local dynamic stability measures Rosenstein et al. (1993) reaction distance d(t) State space A 1 d(t2) d(t1) d(0) sensitivity B State space Kantz (1994) reaction distance <di(t)> 0.3 <di(t)> Rosenstein et al. (1993) Kantz (1994) Ihlen et al. (2012a) All <di(0)> d4(0) d5,6(0) d3(0) 0.1 d2(0) d1(0) E Ihlen et al. in prep specificity

28 Comparison of Phase Dependent Local Dynamic Stability Measures and Frequency-based measures Local dynamic stability determined at 0, 20, 40, 60 and 80% of gait cycle Predictor matrix X 1 (46 gait features) Predictor matrix X 2 (38 gait features) Predictor matrix X 3 (8 gait features) gait features of Weiss et al. (2013) 35 gait features of Weiss et al. (2013) AP λ wolf AP λ wolf ML λ wolf ML λ wolf V λ wolf V λ wolf λ diff (phase: 0%, Eq. 1) λ diff (phase: 0%, Eq. 1) λ diff (phase: 0%, Eq. 2) λ diff (phase: 0%, Eq. 2) λ diff (phase: 60%, Eq. 1) λ diff (phase: 60%, Eq. 1) Etc Etc E Ihlen et al. in prep.

29 Comparison of Phase Dependent Local Dynamic Stability Measures and Frequency-based measures in Fallers and Non-Fallers The phase-dependent local dynamic stability λ were all significantly larger (p < 0.01) for the non-fallers at 0% and 60% and discriminated between the fallers and non-fallers (e.g., AUC=0.83). Ihlen et al. (2012) found that healthy young adults had a smaller phasedependent λ compared to healthy older adults in a treadmill study. In contrast, in the present study, the phase-dependent λ were larger in older non-fallers, compared to the fallers. Lab-based measures may not be directly correlated with community ambulation derived measures. Added value for continuous measures. E Ihlen et al. in prep.

30 Potential applications

31 Clinical Assessment of gait in daily activities ADL 1. Fall risk monitoring 2. Foot pressure for diabetic ulcer prevention 3. Assessment and monitoring people with compromised lower limb functions Sports Medicine Gait analysis, biomechanics, performance improvement for athletes or for fitness. Real-time analysis of balance and the effect of weight distribution on feet and movements. Performing Arts In dance, fashion models, circus. To improve gait, elegant walking, or performing difficult balanced movements alone or with partners, without limitations of wires. Potential Applications Orthotics, Prosthetics, Braces and Shoes Design, fit, prescription confirmation. Pre and post evaluation. Rehabilitation Rehabilitation biofeedback tool for people with M.S., M.N.D, Parkinson's, C.V.D. (stroke). Research and Development Development of long term gait info during ADL, both normal and abnormal.

32 Thanks! Be informed, get involved

The Ultimate Biomechanics Lab

myometricslab The Ultimate Biomechanics Lab ASSESSED, QUANTIFIED & VERIFIED Noraxon USA provides market-leading technology for measurement and training devices, such as EMG, gait analysis, biofeedback,