The importance of inertial measurement unit placement in assessing upper limb motion

Höglund, Gustav; Grip, Helena; Öhberg, Fredrik

doi:10.1016/j.medengphy.2021.03.010

Cited by 32 publications

(42 citation statements)

References 25 publications

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“…A total of 53 studies were excluded in full-text screening as follows: WIST studies without feedback ( n = 14) [ 55 , 81 , 82 , 83 , 84 , 85 , 86 , 87 , 88 , 89 , 90 , 91 , 92 , 93 ]; feedback without an inertial sensor (s) [ 94 ]; sensors integrated into equipment ( n = 5), e.g., seat sensors and robotic devices rather than those worn by an individual [ 95 , 96 , 97 , 98 , 99 ]; standing balance and/or lower body sway ( n = 5) [ 100 , 101 , 102 , 103 , 104 ]; abstracts ( n = 7) [ 105 , 106 , 107 , 108 , 109 , 110 , 111 ]; stroke/other neurological rehabilitation studies ( n = 4) [ 112 , 113 , 114 , 115 ]; a non-work setting ( n = 3) [ 116 , 117 , 118 ]; no evaluation of WIST feedback effectiveness ( n = 6) [ 119 , 120 , 121 , 122 , 123 , 124 ]; research proposal ( n = 1) [ 125 ]; and validity and reliability s...…”

Section: Resultsmentioning

confidence: 99%

Evidence for the Effectiveness of Feedback from Wearable Inertial Sensors during Work-Related Activities: A Scoping Review

Lee

James

Edwards

et al. 2021

Sensors

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Background: Wearable inertial sensor technology (WIST) systems provide feedback, aiming to modify aberrant postures and movements. The literature on the effects of feedback from WIST during work or work-related activities has not been previously summarised. This review examines the effectiveness of feedback on upper body kinematics during work or work-related activities, along with the wearability and a quantification of the kinematics of the related device. Methods: The Cinahl, Cochrane, Embase, Medline, Scopus, Sportdiscus and Google Scholar databases were searched, including reports from January 2005 to July 2021. The included studies were summarised descriptively and the evidence was assessed. Results: Fourteen included studies demonstrated a ‘limited’ level of evidence supporting posture and/or movement behaviour improvements using WIST feedback, with no improvements in pain. One study assessed wearability and another two investigated comfort. Studies used tri-axial accelerometers or IMU integration (n = 5 studies). Visual and/or vibrotactile feedback was mostly used. Most studies had a risk of bias, lacked detail for methodological reproducibility and displayed inconsistent reporting of sensor technology, with validation provided only in one study. Thus, we have proposed a minimum ‘Technology and Design Checklist’ for reporting. Conclusions: Our findings suggest that WIST may improve posture, though not pain; however, the quality of the studies limits the strength of this conclusion. Wearability evaluations are needed for the translation of WIST outcomes. Minimum reporting standards for WIST should be followed to ensure methodological reproducibility.

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Section: Resultsmentioning

confidence: 99%

Evidence for the Effectiveness of Feedback from Wearable Inertial Sensors during Work-Related Activities: A Scoping Review

Lee

James

Edwards

et al. 2021

Sensors

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“…As suggested in the MVN Xsens manual, the ZXY Euler rotation sequence is used to present the joint angles, where flexion/extension is defined around the Z-axis, abduction/adduction is defined around the X-axis and internal/external rotation is defined around the Y-axis. However, an exception is made for shoulder abduction/adduction angle, which is recommended to be read out by the Euler rotation sequence XZY, to reduce estimation errors due to gimbal lock [ 43 , 44 ]. Shoulder flexion/extension is defined as the angle along the sagittal plane, determined by rotation of the upper arm around the z-axis of the Euler sequence ZXY along the sagittal plane.…”

Section: Methodsmentioning

confidence: 99%

Characterization of stroke-related upper limb motor impairments across various upper limb activities by use of kinematic core set measures

Schwarz

Bhagubai

Nies

et al. 2022

J NeuroEngineering Rehabil

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Background Upper limb kinematic assessments provide quantifiable information on qualitative movement behavior and limitations after stroke. A comprehensive characterization of spatiotemporal kinematics of stroke subjects during upper limb daily living activities is lacking. Herein, kinematic expressions were investigated with respect to different movement types and impairment levels for the entire task as well as for motion subphases. Method Chronic stroke subjects with upper limb movement impairments and healthy subjects performed a set of daily living activities including gesture and grasp movements. Kinematic measures of trunk displacement, shoulder flexion/extension, shoulder abduction/adduction, elbow flexion/extension, forearm pronation/supination, wrist flexion/extension, movement time, hand peak velocity, number of velocity peaks (NVP), and spectral arc length (SPARC) were extracted for the whole movement as well as the subphases of reaching distally and proximally. The effects of the factors gesture versus grasp movements, and the impairment level on the kinematics of the whole task were tested. Similarities considering the metrics expressions and relations were investigated for the subphases of reaching proximally and distally between tasks and subgroups. Results Data of 26 stroke and 5 healthy subjects were included. Gesture and grasp movements were differently expressed across subjects. Gestures were performed with larger shoulder motions besides higher peak velocity. Grasp movements were expressed by larger trunk, forearm, and wrist motions. Trunk displacement, movement time, and NVP increased and shoulder flexion/extension decreased significantly with increased impairment level. Across tasks, phases of reaching distally were comparable in terms of trunk displacement, shoulder motions and peak velocity, while reaching proximally showed comparable expressions in trunk motions. Consistent metric relations during reaching distally were found between shoulder flexion/extension, elbow flexion/extension, peak velocity, and between movement time, NVP, and SPARC. Reaching proximally revealed reproducible correlations between forearm pronation/supination and wrist flexion/extension, movement time and NVP. Conclusion Spatiotemporal differences between gestures versus grasp movements and between different impairment levels were confirmed. The consistencies of metric expressions during movement subphases across tasks can be useful for linking kinematic assessment standards and daily living measures in future research and performing task and study comparisons. Trial registration: ClinicalTrials.gov Identifier NCT03135093. Registered 26 April 2017, https://clinicaltrials.gov/ct2/show/NCT03135093.

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“…In order to avoid further variability between subjects, the fourth sensor of the Myo Armband was positioned above the Extensor Carpi Ulnaris muscle ( Figure 1 ). This was done so that the IMU sensor, which was located within Sensor 4, was located on the dorsal side of the forearm as recommended by Höglund et al [ 18 ].…”

Section: Materials and Methodsmentioning

confidence: 99%

User-Independent Hand Gesture Recognition Classification Models Using Sensor Fusion

Alfaro

Trejos

2022

Sensors

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Recently, it has been proven that targeting motor impairments as early as possible while using wearable mechatronic devices for assisted therapy can improve rehabilitation outcomes. However, despite the advanced progress on control methods for wearable mechatronic devices, the need for a more natural interface that allows for better control remains. To address this issue, electromyography (EMG)-based gesture recognition systems have been studied as a potential solution for human–machine interface applications. Recent studies have focused on developing user-independent gesture recognition interfaces to reduce calibration times for new users. Unfortunately, given the stochastic nature of EMG signals, the performance of these interfaces is negatively impacted. To address this issue, this work presents a user-independent gesture classification method based on a sensor fusion technique that combines EMG data and inertial measurement unit (IMU) data. The Myo Armband was used to measure muscle activity and motion data from healthy subjects. Participants were asked to perform seven types of gestures in four different arm positions while using the Myo on their dominant limb. Data obtained from 22 participants were used to classify the gestures using three different classification methods. Overall, average classification accuracies in the range of 67.5–84.6% were obtained, with the Adaptive Least-Squares Support Vector Machine model obtaining accuracies as high as 92.9%. These results suggest that by using the proposed sensor fusion approach, it is possible to achieve a more natural interface that allows better control of wearable mechatronic devices during robot assisted therapies.

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The importance of inertial measurement unit placement in assessing upper limb motion

Cited by 32 publications

References 25 publications

Evidence for the Effectiveness of Feedback from Wearable Inertial Sensors during Work-Related Activities: A Scoping Review

Evidence for the Effectiveness of Feedback from Wearable Inertial Sensors during Work-Related Activities: A Scoping Review

Characterization of stroke-related upper limb motor impairments across various upper limb activities by use of kinematic core set measures

User-Independent Hand Gesture Recognition Classification Models Using Sensor Fusion

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