Validity of accelerometry in step detection and gait speed measurement in orthogeriatric patients

Background Mobile accelerometry is a powerful and promising option to capture long-term changes in gait in both clinical and real-world scenarios. Increasingly, gait parameters have demonstrated their value as clinical outcome parameters, but validation of these parameters in elderly patients is still limited. Objective The aim of this study was to implement a validation framework appropriate for elderly patients and representative of real-world settings, and to use this framework to test and improve algorithms for mobile accelerometry data in an orthogeriatric population. Methods Twenty elderly subjects wearing a 3D-accelerometer completed a parcours imitating a real-world scenario. High-definition video and mobile reference speed capture served to validate different algorithms. Results Particularly at slow gait speeds, relevant improvements in accuracy have been achieved. Compared to the reference the deviation was less than 1% in step detection and less than 0.05 m/s in gait speed measurements, even for slow walking subjects (< 0.8 m/s). Conclusion With the described setup, algorithms for step and gait speed detection have successfully been validated in an elderly population and demonstrated to have improved performance versus previously published algorithms. These results are promising that long-term and/or real-world measurements are possible with an acceptable accuracy even in elderly frail patients with slow gait speeds.

[1]  Marcello Maggio,et al.  Short-Physical Performance Battery (SPPB) score is associated with falls in older outpatients , 2018, Aging Clinical and Experimental Research.

[2]  Scott E. Crouter,et al.  Step Counting: A Review of Measurement Considerations and Health-Related Applications , 2016, Sports Medicine.

[3]  A. Young,et al.  Treadmill Walking in Old Age May Not Reproduce the Real Life Situation , 1993, Journal of the American Geriatrics Society.

[4]  Bernd Grimm,et al.  Evaluating physical function and activity in the elderly patient using wearable motion sensors , 2016, EFORT open reviews.

[5]  Christian Lederer,et al.  Association between Walking Speed and Age in Healthy, Free-Living Individuals Using Mobile Accelerometry—A Cross-Sectional Study , 2011, PloS one.

[6]  Adele Winter,et al.  Validation of an Activity Monitor in Older Inpatients Undergoing Slow Stream Rehabilitation. , 2015, Journal of physical activity & health.

[7]  M. Schimpl,et al.  Development and Validation of a New Method to Measure Walking Speed in Free-Living Environments Using the Actibelt® Platform , 2011, PloS one.

[8]  Tae-Seong Kim,et al.  Accelerometer’s position independent physical activity recognition system for long-term activity monitoring in the elderly , 2010, Medical & Biological Engineering & Computing.

[9]  Yoshifumi Niki,et al.  Relationship between Daily and In-laboratory Gait Speed among Healthy Community-dwelling Older Adults , 2019, Scientific Reports.

[10]  K. Kaufman,et al.  Validity of using tri-axial accelerometers to measure human movement - Part II: Step counts at a wide range of gait velocities. , 2014, Medical engineering & physics.

[11]  Ieuan Clay,et al.  Impact of Digital Technologies on Novel Endpoint Capture in Clinical Trials , 2017, Clinical pharmacology and therapeutics.

[12]  R. Motl,et al.  Possible clinical outcome measures for clinical trials in patients with multiple sclerosis , 2010, Therapeutic advances in neurological disorders.

[13]  ATS statement: guidelines for the six-minute walk test. , 2002, American journal of respiratory and critical care medicine.

[14]  Andrea Mannini,et al.  Fourier-based integration of quasi-periodic gait accelerations for drift-free displacement estimation using inertial sensors , 2015, BioMedical Engineering OnLine.

[15]  Shyamal Patel,et al.  A machine learning approach for gait speed estimation using skin-mounted wearable sensors: From healthy controls to individuals with multiple sclerosis , 2017, PloS one.

[16]  Malcolm Kohler,et al.  Accelerometer- versus questionnaire-based assessment of physical activity and their changes over time in patients with COPD , 2017, International journal of chronic obstructive pulmonary disease.

[17]  Nandini Dendukuri,et al.  Gait speed as an incremental predictor of mortality and major morbidity in elderly patients undergoing cardiac surgery. , 2010, Journal of the American College of Cardiology.

[18]  Henning Tiemeier,et al.  Physical activity derived from questionnaires and wrist-worn accelerometers: comparability and the role of demographic, lifestyle, and health factors among a population-based sample of older adults , 2017, Clinical epidemiology.

[19]  R. Motl,et al.  Accuracy of the actibelt(®) accelerometer for measuring walking speed in a controlled environment among persons with multiple sclerosis. , 2012, Gait & posture.

[20]  Kyue-Nam Park,et al.  Relationship between objectively measured lifestyle factors and health factors in patients with knee osteoarthritis , 2019, Medicine.

[21]  Martin Daumer,et al.  Method to collect ground truth data for walking speed in real-world environments: description and validation , 2019 .

[22]  C. Tudor-Locke,et al.  Pedometer accuracy in nursing home and community-dwelling older adults. , 2004, Medicine and science in sports and exercise.

[23]  Helena M. Mentis,et al.  Comparison of tri-axial accelerometers step-count accuracy in slow walking conditions. , 2017, Gait & posture.

[24]  John Hansen,et al.  Accuracy of a step counter during treadmill and daily life walking by healthy adults and patients with cardiac disease , 2017, BMJ Open.

[25]  Ben J Smith,et al.  Physical activity participation and the risk of chronic diseases among South Asian adults: a systematic review and meta-analysis , 2019, Scientific Reports.

[26]  M. Decramer,et al.  Quantifying physical activity in daily life with questionnaires and motion sensors in COPD , 2006, European Respiratory Journal.

[27]  K. Aminian,et al.  Physical activity monitoring based on accelerometry: validation and comparison with video observation , 1999, Medical & Biological Engineering & Computing.

[28]  Matthew Parsons,et al.  Does functionally based activity make a difference to health status and mobility? A randomised controlled trial in residential care facilities (The Promoting Independent Living Study; PILS). , 2007, Age and ageing.

[29]  Rachel Senden,et al.  Clinical validation of a body-fixed 3D accelerometer and algorithm for activity monitoring in orthopaedic patients , 2017, Journal of orthopaedic translation.

[30]  A. Kriska,et al.  Gait speed and step-count monitor accuracy in community-dwelling older adults. , 2008, Medicine and science in sports and exercise.

[31]  Ieuan Clay,et al.  Continuous Monitoring of Patient Mobility for 18 Months Using Inertial Sensors following Traumatic Knee Injury: A Case Study , 2018, Digital Biomarkers.

[32]  K. Westerterp,et al.  Physical Activity Assessment With Accelerometers: An Evaluation Against Doubly Labeled Water , 2007, Obesity.