Monitoring gait in multiple sclerosis with novel wearable motion sensors

Moon, Yaejin; McGinnis, Ryan S.; Seagers, Kirsten; Motl, Robert W.; Sheth, Nirav; Wright, John A.; Ghaffari, Roozbeh; Sosnoff, Jacob J.

doi:10.1371/journal.pone.0171346

Cited by 108 publications

(97 citation statements)

References 49 publications

(77 reference statements)

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“…Other systems commonly detect gait by placing sensors at the lumbar [25–27] or shank [8,27,28] of the subject. Many of these systems are shown to be reasonably accurate [29,30] and repeatable (ICC ranging between 0.75 and 0.90) [31].…”

Section: Discussionmentioning

confidence: 99%

Validity and repeatability of inertial measurement units for measuring gait parameters

et al. 2017

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Inertial measurement units (IMUs) are small wearable sensors that have tremendous potential to be applied to clinical gait analysis. They allow objective evaluation of gait and movement disorders outside the clinic and research laboratory, and permit evaluation on large numbers of steps. However, repeatability and validity data of these systems are sparse for gait metrics. The purpose of this study was to determine the validity and between-day repeatability of spatiotemporal metrics (gait speed, stance percent, swing percent, gait cycle time, stride length, cadence, and step duration) as measured with the APDM Opal IMUs and Mobility Lab system. We collected data on 39 healthy subjects. Subjects were tested over two days while walking on a standard treadmill, split-belt treadmill, or overground, with IMUs placed in two locations: both feet and both ankles. The spatiotemporal measurements taken with the IMU system were validated against data from an instrumented treadmill, or using standard clinical procedures. Repeatability and minimally detectable change (MDC) of the system was calculated between days. IMUs displayed high to moderate validity when measuring most of the gait metrics tested. Additionally, these measurements appear to be repeatable when used on the treadmill and overground. The foot configuration of the IMUs appeared to better measure gait parameters; however, both the foot and ankle configurations demonstrated good repeatability. In conclusion, the IMU system in this study appears to be both accurate and repeatable for measuring spatiotemporal gait parameters in healthy young adults.

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Section: Discussionmentioning

confidence: 99%

Validity and repeatability of inertial measurement units for measuring gait parameters

et al. 2017

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“…Earlier generations (BioStamp RC TM and investigational prototypes) of the technology have been used in academic research, including motor assessments in Parkinson disease [28], gait assessment in multiple sclerosis [13], posture classification in Huntington disease [12], postural sway in multiple sclerosis [29], and periodic leg movements in sleep (unpublished data). Additional independent research with BioStamp technology has looked at ambulatory vectorcardiography [30], spasticity [31], and more granular activities of daily living (unpublished data).…”

Section: Discussionmentioning

confidence: 99%

“…Often their form factor, size, and wear locations impede continuous wear, interfere with daily activities, make them conspicuous, and compromise sleep. Recent studies have shown that lightweight, conformal sensors provide an alternative in studies focused on Huntington disease [12], multiple sclerosis [13], physical rehabilitation [14], and cardiac monitoring [15]. Here, we present a validation study conducted with a novel wireless remote monitoring system (BioStamp nPoint®; MC10 Inc., Lexington, MA, USA) that includes lightweight, conformal, multimodal biosensors to capture continuous physiological data in simulated home and clinical settings.…”

Section: Introductionmentioning

confidence: 99%

A Pivotal Study to Validate the Performance of a Novel Wearable Sensor and System for Biometric Monitoring in Clinical and Remote Environments

Sen-Gupta¹,

Wright²,

Caccese³

et al. 2019

Digit Biomark

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Background: Increasingly, drug and device clinical trials are tracking activity levels and other quality of life indices as endpoints for therapeutic efficacy. Trials have traditionally required intermittent subject visits to the clinic that are artificial, activity-intensive, and infrequent, making trend and event detection between visits difficult. Thus, there is an unmet need for wearable sensors that produce clinical quality and medical grade physiological data from subjects in the home. The current study was designed to validate the BioStamp nPoint® system (MC10 Inc., Lexington, MA, USA), a new technology designed to meet this need. Objective: To evaluate the accuracy, performance, and ease of use of an end-to-end system called the BioStamp nPoint. The system consists of an investigator portal for design of trials and data review, conformal, low-profile, wearable biosensors that adhere to the skin, a companion technology for wireless data transfer to a proprietary cloud, and algorithms for analyzing physiological, biometric, and contextual data for clinical research. Methods: A prospective, nonrandomized clinical trial was conducted on 30 healthy adult volunteers over the course of two continuous days and nights. Supervised and unsupervised study activities enabled performance validation in clinical and remote (simulated “at home”) environments. System outputs for heart rate (HR), heart rate variability (HRV) (including root mean square of successive differences [RMSSD] and low frequency/high frequency ratio), activity classification during prescribed activities (lying, sitting, standing, walking, stationary biking, and sleep), step count during walking, posture characterization, and sleep metrics including onset/wake times, sleep duration, and respiration rate (RR) during sleep were evaluated. Outputs were compared to FDA-cleared comparator devices for HR, HRV, and RR and to ground truth investigator observations for activity and posture classifications, step count, and sleep events. Results: Thirty participants (77% male, 23% female; mean age 35.9 ± 10.1 years; mean BMI 28.1 ± 3.6) were enrolled in the study. The BioStamp nPoint system accurately measured HR and HRV (correlations: HR = 0.957, HRV RMSSD = 0.965, HRV ratio = 0.861) when compared to Actiheart^TM. The system accurately monitored RR (mean absolute error [MAE] = 1.3 breaths/min) during sleep when compared to a Capnostream35^TM end-tidal CO₂ monitor. When compared with investigator observations, the system correctly classified activities and posture (agreement = 98.7 and 92.9%, respectively), step count (MAE = 14.7, < 3% of actual steps during a 6-min walk), and sleep events (MAE: sleep onset = 6.8 min, wake = 11.5 min, sleep duration = 13.7 min) with high accuracy. Participants indicated “good” to “excellent” usability (average System Usability Scale score of 81.3) and preferred the BioStamp nPoint system over both the Actiheart (86%) and Capnostream (97%) devices. Conclusions: The present study validated the BioSt...

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“…Two of these devices were the BioStamp RC and the MTx inertial sensor. The research demonstrated remarkably low measurement errors (i.e., high accuracy) for capturing actual steps taken compared with estimates from an ActiGraph GT3X during a 6-minute walk on a motorized treadmill in persons with MS (i.e., 0.8% and 0.9%, respectively, vs. 10.1%) [79]. One potential explanation for the superiority of the BioStampRC and MTx relative to the GT3X might involve the positioning of the BioStampRC and MTx inertial sensor on the shanks of the leg, as this allows for assessment of temporal gait paramenters such as stride time, swing time, and step time.…”

Section: Research From 2013–2017: What Is New?mentioning

confidence: 99%

Motion sensors in multiple sclerosis: Narrative review and update of applications

Sasaki

Sandroff

Bamman

et al. 2017

Expert Review of Medical Devices

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Introduction: The use of motion sensors for measuring physical activity in multiple sclerosis (MS) has evolved with increasing research particularly during the past decade. Areas covered: This manuscript reviews the literature regarding the application of motion sensors for measuring physical activity in MS. We first describe ‘what is known’ about their use in MS by examining the evidence generated between 1997 and 2012, including the psychometric properties of motion sensors in MS and the development of MS-specific accelerometer cut-points. We then evaluate ‘what is new’ based on research conducted between 2013 and 2017. This includes newer research on psychometric properties of motion sensors in MS, development of new MS-specific accelerometer and step-rate cut-points, sedentary behavior assessment, and research on fitness trackers and multisensors in MS. The final part presents a picture of “what is next” for the applications of motion sensors in MS, especially pertaining new opportunities for testing and using fitness trackers in MS, and tracking disease and disability progression based on motion sensor output. Expert commentary: The use of motion sensors in MS has grown substantially over the years; however, a lot more can be done to explore the full potential and utility of these devices in MS.

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Monitoring gait in multiple sclerosis with novel wearable motion sensors

Cited by 108 publications

References 49 publications

Validity and repeatability of inertial measurement units for measuring gait parameters

Validity and repeatability of inertial measurement units for measuring gait parameters

A Pivotal Study to Validate the Performance of a Novel Wearable Sensor and System for Biometric Monitoring in Clinical and Remote Environments

Motion sensors in multiple sclerosis: Narrative review and update of applications

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