How Much Data Is Enough? A Reliable Methodology to Examine Long-Term Wearable Data Acquisition in Gait and Postural Sway
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Samantha R. Fox | Brett M. Meyer | Nick Cheney | Melissa Ceruolo | P. DePetrillo | A. Solomon | B. Loftness | R. Mcginnis | Bryn C. Loftness | Aisling O'Leary | Reed Gurchiek | Nicole Donahue | Jaime Franco | Maura Buckley | Sau Kuen Ng | Reed D. Gurchiek | Aisling O’Leary
[1] R. Mcginnis,et al. Advancing Digital Medicine with Wearables in the Wild , 2022, Sensors.
[2] Brett M. Meyer,et al. The Sit-to-Stand Transition as a Biomarker for Impairment: Comparison of Instrumented 30-Second Chair Stand Test and Daily Life Transitions in Multiple Sclerosis , 2022, IEEE Transactions on Neural Systems and Rehabilitation Engineering.
[3] Brett M. Meyer,et al. Evaluation of unsupervised 30-second chair stand test performance assessed by wearable sensors to predict fall status in multiple sclerosis. , 2022, Gait & posture.
[4] K. B. Beyer,et al. Feasibility of a continuous, multi-sensor remote health monitoring approach in persons living with neurodegenerative disease , 2021, Journal of Neurology.
[5] D. Cui,et al. Recent developments in sensors for wearable device applications , 2021, Analytical and Bioanalytical Chemistry.
[6] Nicky Baker,et al. Inertial Sensor Reliability and Validity for Static and Dynamic Balance in Healthy Adults: A Systematic Review , 2021, Sensors.
[7] Clint R. Bellenger,et al. Wrist-Based Photoplethysmography Assessment of Heart Rate and Heart Rate Variability: Validation of WHOOP , 2021, Sensors.
[8] Lukas Adamowicz,et al. Wearables and Deep Learning Classify Fall Risk From Gait in Multiple Sclerosis , 2020, IEEE Journal of Biomedical and Health Informatics.
[9] Charmaine Demanuele,et al. Assessment of Sit-to-Stand Transfers during Daily Life Using an Accelerometer on the Lower Back , 2020, Sensors.
[10] J. DeLuca,et al. Tired of not knowing what that fatigue score means? Normative data of the Modified Fatigue Impact Scale (MFIS). , 2020, Multiple sclerosis and related disorders.
[11] Koene R. A. Van Dijk,et al. Age and environment-related differences in gait in healthy adults using wearables , 2020, npj Digital Medicine.
[12] Ryan S McGinnis,et al. Metrics extracted from a single wearable sensor during sit-stand transitions relate to mobility impairment and fall risk in people with multiple sclerosis. , 2020, Gait & posture.
[13] Reed D. Gurchiek,et al. Gait event detection using a thigh-worn accelerometer. , 2020, Gait & posture.
[14] Ariel V. Dowling,et al. Verification, analytical validation, and clinical validation (V3): the foundation of determining fit-for-purpose for Biometric Monitoring Technologies (BioMeTs) , 2020, npj Digital Medicine.
[15] Reed D. Gurchiek,et al. Open-Source Remote Gait Analysis: A Post-Surgery Patient Monitoring Application , 2019, Scientific Reports.
[16] A. Pavic,et al. Wearable inertial sensors to measure gait and posture characteristic differences in older adult fallers and non-fallers: A scoping review. , 2019, Gait & posture.
[17] Amir Muaremi,et al. Continuous Digital Monitoring of Walking Speed in Frail Elderly Patients: Noninterventional Validation Study and Longitudinal Clinical Trial , 2019, JMIR mHealth and uHealth.
[18] Stefano Pastorelli,et al. Wearable Inertial Sensors to Assess Standing Balance: A Systematic Review , 2019, Sensors.
[19] Ryan S. McGinnis,et al. Next Steps in Wearable Technology and Community Ambulation in Multiple Sclerosis , 2019, Current Neurology and Neuroscience Reports.
[20] Ryan S McGinnis,et al. Sprint Assessment Using Machine Learning and a Wearable Accelerometer. , 2019, Journal of applied biomechanics.
[21] Ellen W. McGinnis,et al. Rapid detection of internalizing diagnosis in young children enabled by wearable sensors and machine learning , 2019, PloS one.
[22] Jacob J. Sosnoff,et al. Fall Risk Prediction in Multiple Sclerosis Using Postural Sway Measures: A Machine Learning Approach , 2018, Scientific Reports.
[23] John A. Wright,et al. A novel adhesive biosensor system for detecting respiration, cardiac, and limb movement signals during sleep: validation with polysomnography , 2018, Nature and science of sleep.
[24] Clint Hansen,et al. Wearables for gait and balance assessment in the neurological ward - study design and first results of a prospective cross-sectional feasibility study with 384 inpatients , 2018, BMC Neurology.
[25] Yike Guo,et al. Remote Monitoring in the Home Validates Clinical Gait Measures for Multiple Sclerosis , 2018, Front. Neurol..
[26] Jill M van der Meulen,et al. Free-living and laboratory gait characteristics in patients with multiple sclerosis , 2018, PloS one.
[27] Shyamal Patel,et al. Assessment of Postural Sway in Individuals with Multiple Sclerosis Using a Novel Wearable Inertial Sensor , 2018, Digital Biomarkers.
[28] Jordan J. Craig,et al. The relationship between trunk and foot acceleration variability during walking shows minor changes in persons with multiple sclerosis , 2017, Clinical biomechanics.
[29] J. V. van Dieën,et al. Predicting falls among patients with multiple sclerosis: Comparison of patient-reported outcomes and performance-based measures of lower extremity functions. , 2017, Multiple sclerosis and related disorders.
[30] 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.
[31] Susan L. Kasser,et al. Symptom variability, affect and physical activity in ambulatory persons with multiple sclerosis: Understanding patterns and time-bound relationships. , 2017, Disability and health journal.
[32] G. Allali,et al. Gait variability in multiple sclerosis: a better falls predictor than EDSS in patients with low disability , 2016, Journal of Neural Transmission.
[33] Yuwen Chen,et al. LSTM Networks for Mobile Human Activity Recognition , 2016 .
[34] Ken Kleinman,et al. Calculating Power by Bootstrap, with an Application to Cluster-Randomized Trials , 2014, EGEMS.
[35] Jaap H van Dieën,et al. Assessing physical activity in older adults: required days of trunk accelerometer measurements for reliable estimation. , 2015, Journal of aging and physical activity.
[36] Minsoo Kang,et al. The minimum number of days required to establish reliable physical activity estimates in children aged 2–15 years , 2014, Physiological measurement.
[37] Robert W Motl,et al. Validation of patient determined disease steps (PDDS) scale scores in persons with multiple sclerosis , 2013, BMC Neurology.
[38] Fay B. Horak,et al. Accelerometry Reveals Differences in Gait Variability Between Patients with Multiple Sclerosis and Healthy Controls , 2012, Annals of Biomedical Engineering.
[39] Lorenzo Chiari,et al. ISway: a sensitive, valid and reliable measure of postural control , 2012, Journal of NeuroEngineering and Rehabilitation.
[40] A. Forsberg,et al. Activities-Specific Balance Confidence in People with Multiple Sclerosis , 2012, Multiple sclerosis international.
[41] Scott J Strath,et al. How many days of monitoring predict physical activity and sedentary behaviour in older adults? , 2011, The international journal of behavioral nutrition and physical activity.
[42] X. Montalban,et al. Does the Modified Fatigue Impact Scale offer a more comprehensive assessment of fatigue in MS? , 2005, Multiple sclerosis.
[43] A J Thompson,et al. Measuring the impact of MS on walking ability , 2003, Neurology.
[44] David R Bassett,et al. Sources of variance in daily physical activity levels as measured by an accelerometer. , 2002, Medicine and science in sports and exercise.
[45] B. Ainsworth,et al. Intra-individual variation and estimates of usual physical activity. , 1999, Annals of epidemiology.
[46] L. E. Powell,et al. The Activities-specific Balance Confidence (ABC) Scale. , 1995, The journals of gerontology. Series A, Biological sciences and medical sciences.
[47] H J Montoye,et al. Variability of some objective measures of physical activity. , 1992, Medicine and science in sports and exercise.
[48] J. Kurtzke. Rating neurologic impairment in multiple sclerosis , 1983, Neurology.