Walking speed measurement with an Ambient Measurement System (AMS) in patients with multiple sclerosis and walking impairment

Béthoux, François; Varsanik, Jonathan S.; Chevalier, Tomas; Halpern, Elkan F.; Stough, Darlene; Kimmel, Z

doi:10.1016/j.gaitpost.2018.01.033

Cited by 17 publications

(16 citation statements)

References 22 publications

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“…We did not utilise an automated reference standard such as a pressure mat, as the ambulation activity the AMS used in this study has been validated separately against such a reference standard in an adult population [11]. No automated reference standard was used for the arm raising/lowering or rise-to-stand activities because we did not identify any such standards for these activities.…”

Section: Discussionmentioning

confidence: 99%

“…Each Echo5D AMS device used in this study consisted of a Microsoft Kinect (Version 2) spatial sensor [20][21][22] (Microsoft, Seattle, WA) connected to a computer running custom software (Atlas5D, Cambridge, MA), as previously described [9][10][11]. One Echo5D device was used in each of the rise-to-stand and arm-raise data acquisitions, and five Echo5D devices were used in the ambulation data acquisitions.…”

Section: Amsmentioning

confidence: 99%

“…The distances and times from frame to frame from all five AMS devices were added to obtain a total distance and total time, from which average speed was calculated. The measurements that were acquired when the participant was measured by more than one sensor at the same time were stitched together as described previously [11]. The total distance measured by each AMS was not necessarily equal to 10 m due to variations in the time taken for each AMS to detect the presence of the participant and in the timing of the human observer pressing the start and stop buttons.…”

Section: Ambulationmentioning

confidence: 99%

“…In this manner, it is possible to gather repeated assessments of function at home without requiring active involvement by the subject. The AMS used in this study was previously validated in adult populations, including healthy subjects and multiple sclerosis (MS) patients, in both clinic and home settings [9][10][11]. This study presents the first validation results for a paediatric population.…”

Section: Introductionmentioning

confidence: 99%

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Validation of an ambient measurement system (AMS) for physical activities in a paediatric population

Varsanik

Kimmel

Laforet

et al. 2019

Journal of Medical Engineering & Technology

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Ambient measurement systems (AMSs) can enable continuous assessment of functional performance at home, increasing the availability of data for monitoring of neuromuscular disease. An AMS passively measures movement whenever someone is in range of the sensor, without the need for any wearable sensors. The current study evaluates the performance of an AMS for three metrics associated with functional assessments in Duchenne muscular dystrophy (DMD): ambulation speed, rise-to-stand speed and arm-raise speed. Healthy paediatric subjects performed a series of functional tasks and were graded by both a human rater and an AMS. Linear mixed-effect models were fit to calculate agreement between the two measurement methods. For all activities, the AMS and human rater supplied similar measurements of average speed, with correlation coefficients of 0.76-0.92 and systematic differences ranging in magnitude from 0 to 0.48 m per second. The largest systematic difference was for the 10-m run, which was likely due to human rater reaction time. Systematic differences in arm-raise measurements were due to incomplete execution of movements by test participants. These results are consistent with previous studies comparing automated and manual measurements of movement. This study demonstrates that an AMS device is able to measure ambulation speed, rise-to-stand speed and arm-raise speed in a paediatric population in a controlled setting without the need for complicated installation, calibration or worn sensors.

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Section: Discussionmentioning

confidence: 99%

Section: Amsmentioning

confidence: 99%

Section: Ambulationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Validation of an ambient measurement system (AMS) for physical activities in a paediatric population

Varsanik

Kimmel

Laforet

et al. 2019

Journal of Medical Engineering & Technology

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“…Dobkin and Dorsch [64] also introduce two other sensors, the DynaPort Minimod [65] and IDDEA Lifegate sensor [66] to analyze different activities for gait analysis, including walking, sitting and standing. The GAITRite CIRFace (GC) sensor was also configured as a straight path for electronic gait analysis by Bethoux et al [67].…”

Section: Multimodal Gait and Balance Monitoringmentioning

confidence: 99%

A Survey of Assistive Technologies for Assessment and Rehabilitation of Motor Impairments in Multiple Sclerosis

Rajavenkatanarayanan

Kanal

Tsiakas

et al. 2019

MTI

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Multiple sclerosis (MS) is a disease that affects the central nervous system, which consists of the brain and spinal cord. Although this condition cannot be cured, proper treatment of persons with MS (PwMS) can help control and manage the relapses of several symptoms. In this survey article, we focus on the different technologies used for the assessment and rehabilitation of motor impairments for PwMS. We discuss sensor-based and robot-based solutions for monitoring, assessment and rehabilitation. Among MS symptoms, fatigue is one of the most disabling features, since PwMS may need to put significantly more intense effort toward achieving simple everyday tasks. While fatigue is a common symptom across several neurological chronic diseases, it remains poorly understood for various reasons, including subjectivity and variability among individuals. To this end, we also investigate recent methods for fatigue detection and monitoring. The result of this survey will provide both clinicians and researchers with valuable information on assessment and rehabilitation technologies for PwMS, as well as providing insights regarding fatigue and its effect on performance in daily activities for PwMS.

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Remote-Sensoring – neue Optionen des Progressionsmonitorings bei Multipler Sklerose

et al. 2019

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Walking speed measurement with an Ambient Measurement System (AMS) in patients with multiple sclerosis and walking impairment

Abstract: Among MS patients with walking impairments, the Echo5D AMS acquired walking speeds which were closely correlated with the standard measures of GC and SW. The strong agreement supports the use of Echo5D to assess in-home, real-world walking performance in MS.

Cited by 17 publications

References 22 publications

Validation of an ambient measurement system (AMS) for physical activities in a paediatric population

Validation of an ambient measurement system (AMS) for physical activities in a paediatric population

A Survey of Assistive Technologies for Assessment and Rehabilitation of Motor Impairments in Multiple Sclerosis

Remote-Sensoring – neue Optionen des Progressionsmonitorings bei Multipler Sklerose

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