Validation of temporal gait metrics from three IMU locations to the gold standard force plate

Patterson, M.R.; O’Mahony, Niamh; O'Mahony, Sam; Nolan, Eimear; Caulfield, Brian

doi:10.1109/embc.2016.7590790

Cited by 13 publications

(15 citation statements)

References 34 publications

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“…The ankle-positioned IMUs were valid for the assessment of the capacity-based number of steps, walking time, gait speed, and stride length when tested during a standardized measurement protocol, including turns, slaloms, and short walking bouts. The validity of the IMU-based assessment of capacity-based gait speed, stride length, and number of steps during regular walking is in line with previous studies using ankle-worn sensors [ 29 , 33 , 34 , 35 ]. A limitation of the algorithm was that one sensor sometimes missed stride detection, which occurred in 8% during standardized testing and in 16% beyond standardized testing.…”

Section: Discussionsupporting

confidence: 85%

“…However, possible (slight) deviations from actual therapy time were not registered for reasons of feasibility and privacy. Although the IMU-based gait algorithms have shown to be valid [ 25 , 34 , 35 ], it still remains challenging to validly measure impaired gait. We, therefore, validated the gait algorithm during the gait capacity assessment.…”

Section: Discussionmentioning

confidence: 99%

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Using Sensor Technology to Measure Gait Capacity and Gait Performance in Rehabilitation Inpatients with Neurological Disorders

Hendriks

Hulst

Weijs

et al. 2022

Sensors

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The aim of this study was to objectively assess and compare gait capacity and gait performance in rehabilitation inpatients with stroke or incomplete spinal cord injury (iSCI) using inertial measurement units (IMUs). We investigated how gait capacity (what someone can do) is related to gait performance (what someone does). Twenty-two inpatients (11 strokes, 11 iSCI) wore ankle positioned IMUs during the daytime to assess gait. Participants completed two circuits to assess gait capacity. These were videotaped to certify the validity of the IMU algorithm. Regression analyses were used to investigate if gait capacity was associated with gait performance (i.e., walking activity and spontaneous gait characteristics beyond therapy time). The ankle positioned IMUs validly assessed the number of steps, walking time, gait speed, and stride length (r ≥ 0.81). The walking activity was strongly (r ≥ 0.76) related to capacity-based gait speed. Maximum spontaneous gait speed and stride length were similar to gait capacity. However, the average spontaneous gait speed was half the capacity-based gait speed. Gait capacity can validly be assessed using IMUs and is strongly related to gait performance in rehabilitation inpatients with neurological disorders. Measuring gait performance with IMUs provides valuable additional information about walking activity and spontaneous gait characteristics to inform about functional recovery.

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Section: Discussionsupporting

confidence: 85%

Section: Discussionmentioning

confidence: 99%

Using Sensor Technology to Measure Gait Capacity and Gait Performance in Rehabilitation Inpatients with Neurological Disorders

Hendriks

Hulst

Weijs

et al. 2022

Sensors

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“…These datasets have then been used to validate the methods. One study has presented the validation of temporal gait metrics from three IMU locations to the GA using Bland-Altman diagrams [25,26]. We have chosen to use a structured methodology that adopts a robust approach to complete the relative task for the validation protocol, as required by the general methodology defined in [24], to validate the use of the new system.…”

Section: Introductionmentioning

confidence: 99%

Validation of a Wearable IMU System for Gait Analysis: Protocol and Application to a New System

et al. 2018

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Miniaturized wearable Inertial Measurement Units (IMU) offer new opportunities for the functional assessment of motor functions for medicine, sport, and ergonomics. Sparse reliability validation studies have been conducted without a common specific approach and protocol. A set of guidelines to design validation protocol for these systems is proposed hereafter. They are based on the comparison between video analysis and the gold standard optoelectronic motion capture system for Gait Analysis (GA). A setup of the protocol has been applied to a wearable device implementing an inertial measurement unit and a dedicated harmonic oscillator kinematic model of the center of mass. In total, 10 healthy volunteers took part in the study, and four trials of walking at a self-selected speed and step length have been simultaneously recorded by the two systems, analyzed, and compared blindly (40 datasets). The model detects the steps and the foot which supports body weight. The stride time and the cadence have a mean absolute percentage error of 5.7% and 4.9%, respectively. The mean absolute percentage error in the measurement of step’s length and step’s speed is 5.6% and 13.5%, respectively. Results confirm that the proposed methodology is complete and effective. It is demonstrated that the developed wearable system allows for a reliable assessment of human gait spatio-temporal parameters. Therefore, the goal of this paper is threefold. The first goal is to present and define structured Protocol Design Guidelines, where the related setup is implemented for the validation of wearable IMU systems particularly dedicated to GA and gait monitoring. The second goal is to apply these Protocol Design Guidelines to a case study in order to verify their feasibility, user-friendliness, and efficacy. The third goal is the validation of our biomechanical kinematic model with the gold standard reference.

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“…Some works exist regarding validation analysis of wearable systems for gait analysis based on processing of kinematic signals. These studies address comparative analyses with clinical instruments, such as instrumented treadmill [ 49 ], force platform [ 50 ] or pressure sensitive walkway (GAITRite

) [ 35 , 51 , 52 ]. No works presenting a comparative analysis with the gold standard (stereophotogrammetry system) has been found.…”

Section: Discussionmentioning

confidence: 99%

Design and Validation of an E-Textile-Based Wearable Sock for Remote Gait and Postural Assessment

Amitrano

Coccia

Ricciardi

et al. 2020

Sensors

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This paper presents a new wearable e-textile based system, named SWEET Sock, for biomedical signals remote monitoring. The system includes a textile sensing sock, an electronic unit for data transmission, a custom-made Android application for real-time signal visualization, and a software desktop for advanced digital signal processing. The device allows the acquisition of angular velocities of the lower limbs and plantar pressure signals, which are postprocessed to have a complete and schematic overview of patient’s clinical status, regarding gait and postural assessment. In this work, device performances are validated by evaluating the agreement between the prototype and an optoelectronic system for gait analysis on a set of free walk acquisitions. Results show good agreement between the systems in the assessment of gait cycle time and cadence, while the presence of systematic and proportional errors are pointed out for swing and stance time parameters. Worse results were obtained in the comparison of spatial metrics. The “wearability” of the system and its comfortable use make it suitable to be used in domestic environment for the continuous remote health monitoring of de-hospitalized patients but also in the ergonomic assessment of health workers, thanks to its low invasiveness.

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Validation of temporal gait metrics from three IMU locations to the gold standard force plate

Cited by 13 publications

References 34 publications

Using Sensor Technology to Measure Gait Capacity and Gait Performance in Rehabilitation Inpatients with Neurological Disorders

Using Sensor Technology to Measure Gait Capacity and Gait Performance in Rehabilitation Inpatients with Neurological Disorders

Validation of a Wearable IMU System for Gait Analysis: Protocol and Application to a New System

Design and Validation of an E-Textile-Based Wearable Sock for Remote Gait and Postural Assessment

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