A novel method for in-vivo evaluation of finger kinematics including definition of healthy motion patterns

Coupier, Jérôme; Hamoudi, Samir; Telese-Izzi, Sonia; Feipel, Véronique; Rooze, Marcel; Jan, Serge Van Sint

doi:10.1016/j.clinbiomech.2015.10.002

Cited by 33 publications

(31 citation statements)

References 26 publications

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“…The frequency of each kinematic synergy, and the relationship with the muscle synergies obtained in previous works 29,33,34 could be used to achieve a more intuitive control of hand prostheses, but also in hand kinematic dysfunction evaluation, going beyond the current analysis of range of motion 35 . A recent study proposed to define healthy motion patterns 36 for a future clinical application. According to the results of this study, one important indicator of dysfunction may be the absence of the main finger synergies (CS1 or CS2), as this would imply the patient's inability to perform a coordination present in all healthy subjects.…”

Section: Discussionmentioning

confidence: 99%

Sharing of hand kinematic synergies across subjects in daily living activities

Gracia-Ibáñez

Sancho-Bru

Jarque-Bou

et al. 2020

Sci Rep

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Roda-Sales the motor system is hypothesised to use kinematic synergies to simplify hand control. Recent studies suggest that there is a large set of synergies, sparse in degrees of freedom, shared across subjects, so that each subject performs each action with a sparse combination of synergies. identifying how synergies are shared across subjects can help in prostheses design, in clinical decision-making or in rehabilitation. Subject-specific synergies of healthy subjects performing a wide number of representative daily living activities were obtained through principal component analysis. to make synergies comparable between subjects and tasks, the hand kinematics data were scaled using normative range of motion data. to obtain synergies sparse in degrees of freedom a rotation method that maximizes the sum of the variances of the squared loadings was applied. Resulting synergies were clustered and each cluster was characterized by a core synergy and different indexes (prevalence, relevance for function and within-cluster synergy similarity), substantiating the sparsity of synergies. The first two core synergies represent finger flexion and were present in all subjects. The remaining core synergies represent coordination of the thumb joints, thumb-index joints, palmar arching or fingers adduction, and were employed by subjects in different combinations, thus revealing different subjectspecific strategies.

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Section: Discussionmentioning

confidence: 99%

Sharing of hand kinematic synergies across subjects in daily living activities

Gracia-Ibáñez

Sancho-Bru

Jarque-Bou

et al. 2020

Sci Rep

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show abstract

“…While the manual goniometer is commonly used in clinical practice as a tool to measure joint angles, 3D motion capture systems are increasingly applied in research to measure hand motion [1–5]. Moreover, 3D motion capture systems allow the dynamic evaluation of all hand joints simultaneously [6–8]. They determine the position of skin markers highly accurate.…”

Section: Introductionmentioning

confidence: 99%

Minimal detectable difference of the finger and wrist range of motion: comparison of goniometry and 3D motion analysis

et al. 2019

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Background The measurement of finger and wrist range of motion (ROM) is of great importance to clinicians when assessing functional outcomes of therapeutic interventions and surgical procedures. The purpose of the study was to assess the repeatability of ROM measurements of the hand joints with manual goniometer and 3D motion capture system and to calculate the minimal detectable difference for both methods. Methods Active finger and wrist joints ROM of 20 healthy volunteers were assessed using a manual goniometer and 3D motion capture system. Minimal detectable difference (MDD) and standard error of measurement (SEM) were calculated for both measurement systems and compared within the same task. Maximal ROM of all joints was registered twice on two different days to evaluate the test-retest repeatability. The intraclass correlation coefficients (ICC) was calculated and examined to determine if reliability ≥ 0.70 existed. Results MDD for the 3D motion capture was between 5 and 12° except for the metacarpophalangeal joint (MCP) 1, interphalangeal joint (IP), and MCP5. SEM values lay between 2 and 4° for all joints except for the MCP5, IP, and MCP1. For the goniometric measurements, MDD and SEM were between 12–30° and 4–11°, respectively. The reliability criterion (ICC > 0.7) was achieved for the ROM measurement with the 3D motion capture system for 94% of the joints and in only 65% of the joints with the manual goniometer. Conclusions Joint ROM assessed with 3D motion analysis showed higher test-retest agreement demonstrating overall better repeatability for this method. Because of the smaller measurement error, the 3D motion capture system has a smaller MDD. Only individual test-rest differences bigger than the MDD can be considered as real changes, and therefore, in an experimental situation, the use of a more precise measurement method can greatly reduce the number of subjects needed for a statistical significance. Goniometer measurements of some joints should be carefully interpreted, due to a low repeatability and reliability. Trial registration This study is approved by the Ethical Committee Zurich ( Kek-ZH-Nr: 2015-0395 ).

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“…31 Other researchers have investigated methods of defining local frames of reference for hand kinematic measurement, and acknowledge the need for validation against bone movements from medical imaging. 18 Alternative validation approaches include goniometry 13 and planar fluoroscopy, applied to the hand 10 and the foot. 35 This study is limited by the small sample size.…”

Section: Discussionmentioning

confidence: 99%

Quantifying Soft Tissue Artefacts and Imaging Variability in Motion Capture of the Fingers

et al. 2020

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This study assessed the accuracy of marker-based kinematic analysis of the fingers, considering soft tissue artefacts (STA) and marker imaging uncertainty. We collected CT images of the hand from healthy volunteers with fingers in full extension, mid-and full-flexion, including motion capture markers. Bones and markers were segmented and meshed. The bone meshes for each volunteer's scans were aligned using the proximal phalanx to study the proximal interphalangeal joint (PIP), and using the middle phalanx to study the distal interphalangeal joint (DIP). The angle changes between positions were extracted. The HAWK protocol was used to calculate PIP and DIP joint flexion angles in each position based on the marker centroids. Finally the marker locations were 'corrected' relative to the underlying bones, and the flexion angles recalculated. Static and dynamic marker imaging uncertainty was evaluated using a wand. A strong positive correlation was observed between marker-and CT-based joint angle changes with 0.980 and 0.892 regression slopes for PIP and DIP, respectively, and Root Mean Squared Errors below 4°. Notably for the PIP joint, correlation was worsened by STA correction. The 95% imaging uncertainty interval was < ± 1°for joints, and < ± 0.25 mm for segment lengths. In summary, the HAWK marker set's accuracy was characterised for finger joint flexion angle changes in a small group of healthy individuals and static poses, and was found to benefit from skin movements during flexion.

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A novel method for in-vivo evaluation of finger kinematics including definition of healthy motion patterns

Cited by 33 publications

References 26 publications

Sharing of hand kinematic synergies across subjects in daily living activities

Sharing of hand kinematic synergies across subjects in daily living activities

Minimal detectable difference of the finger and wrist range of motion: comparison of goniometry and 3D motion analysis

Quantifying Soft Tissue Artefacts and Imaging Variability in Motion Capture of the Fingers

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