Computational musculoskeletal modeling of compensatory movements in the upper limb

Reilly, Michael; Kontson, Kimberly

doi:10.1016/j.jbiomech.2020.109843

Cited by 9 publications

(4 citation statements)

References 20 publications

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“…The muscle groups were sectioned into force vectors connected at two points in the body to represent the origin and insertion points of the muscle. Moreover, model parameter identification and inverse dynamic analysis were conducted following a previously validated study [ 14 ]. Inverse dynamics is a concept used primarily in biomechanics, robotics, and control engineering to analyze and understand the forces and torques involved in the motion of a system.…”

Section: Methodsmentioning

confidence: 99%

The elbow is the load-bearing joint during arm swing

et al. 2023

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Background: Arm swing plays a role in gait by accommodating forward movement through trunk balance. This study evaluates the biomechanical characteristics of arm swing during gait.Methods: The study performed computational musculoskeletal modeling based on motion tracking in 15 participants without musculoskeletal or gait disorder. A three-dimensional (3D) motion tracking system using three Azure Kinect (Microsoft) modules was used to obtain information in the 3D location of shoulder and elbow joints. Computational modeling using AnyBody Modeling System was performed to calculate the joint moment and range of motion (ROM) during arm swing.Results: The mean ROM of the dominant elbow was 29.7°±10.2° and 14.2°±3.2° in flexion–extension and pronation–supination, respectively. The mean joint moment of the dominant elbow was 56.4±12.7 Nm, 25.6±5.2 Nm, and 19.8±4.6 Nm in flexion–extension, rotation, and abduction–adduction, respectively. Conclusions: The elbow bears the load created by gravity and muscle contracture in dynamic arm swing movement.

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Section: Methodsmentioning

confidence: 99%

The elbow is the load-bearing joint during arm swing

et al. 2023

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“…The compensatory strategies impose increased workload on proximal joints, including trunk and neck, and on the non-amputated side. The repeated use of postures with trunk torsion, anteflexion and lateral inclination, and with abduction-internal rotation of the shoulder are known as risk factors for musculoskeletal disorders [64] which can contribute to the handicap [65][66][67][68][69]. Indeed it is demonstrated that significant shoulder abduction is a source of major shoulder strain that can, in the long-term, cause pain [70] Similarly, prolonged arm elevation may result in shoulder pain and musculoskeletal disorders at various thresholds [71,72].…”

Section: Spatial Organization Of Hand Trajectory and Inter-joint Conf...mentioning

confidence: 99%

Kinematic analysis of impairments and compensatory motor behavior during prosthetic grasping in below-elbow amputees

et al. 2022

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After a major upper limb amputation, the use of myoelectric prosthesis as assistive devices is possible. However, these prostheses remain quite difficult to control for grasping and manipulation of daily life objects. The aim of the present observational case study is to document the kinematics of grasping in a group of 10 below-elbow amputated patients fitted with a myoelectric prosthesis in order to describe and better understand their compensatory strategies. They performed a grasping to lift task toward 3 objects (a mug, a cylinder and a cone) placed at two distances within the reaching area in front of the patients. The kinematics of the trunk and upper-limb on the non-amputated and prosthetic sides were recorded with 3 electromagnetic Polhemus sensors placed on the hand, the forearm (or the corresponding site on the prosthesis) and the ipsilateral acromion. The 3D position of the elbow joint and the shoulder and elbow angles were calculated thanks to a preliminary calibration of the sensor position. We examined first the effect of side, distance and objects with non-parametric statistics. Prosthetic grasping was characterized by severe temporo-spatial impairments consistent with previous clinical or kinematic observations. The grasping phase was prolonged and the reaching and grasping components uncoupled. The 3D hand displacement was symmetrical in average, but with some differences according to the objects. Compensatory strategies involved the trunk and the proximal part of the upper-limb, as shown by a greater 3D displacement of the elbow for close target and a greater forward displacement of the acromion, particularly for far targets. The hand orientation at the time of grasping showed marked side differences with a more frontal azimuth, and a more “thumb-up” roll. The variation of hand orientation with the object on the prosthetic side, suggested that the lack of finger and wrist mobility imposed some adaptation of hand pose relative to the object. The detailed kinematic analysis allows more insight into the mechanisms of the compensatory strategies that could be due to both increased distal or proximal kinematic constraints. A better knowledge of those compensatory strategies is important for the prevention of musculoskeletal disorders and the development of innovative prosthetics.

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“…Between the two environments, VR had lower motion quality scores, which is due to the slow movement of subjects while attempting to complete these tasks and the rushed reactions to objects moving away from them. Compensatory movements are known to put extra strain on the musculoskeletal system (Carey et al, 2009;Hussaini et al, 2017;Reilly and Kontson, 2020;Valevicius et al, 2020). This strain can eventually lead to injuries that could cause an individual to stop using their prosthesis.…”

Section: Effect Of Motion Quality On Completion Ratementioning

confidence: 99%

Comparison of Dexterous Task Performance in Virtual Reality and Real-World Environments

Joyner¹,

Vaughn-Cooke²,

Benz³

2021

Front. Virtual Real.

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Virtual reality is being used to aid in prototyping of advanced limb prostheses with anthropomorphic behavior and user training. A virtual version of a prosthesis and testing environment can be programmed to mimic the appearance and interactions of its real-world counterpart, but little is understood about how task selection and object design impact user performance in virtual reality and how it translates to real-world performance. To bridge this knowledge gap, we performed a study in which able-bodied individuals manipulated a virtual prosthesis and later a real-world version to complete eight activities of daily living. We examined subjects' ability to complete the activities, how long it took to complete the tasks, and number of attempts to complete each task in the two environments. A notable result is that subjects were unable to complete tasks in virtual reality that involved manipulating small objects and objects flush with the table, but were able to complete those tasks in the real world. The results of this study suggest that standardization of virtual task environment design may lead to more accurate simulation of real-world performance.

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Computational musculoskeletal modeling of compensatory movements in the upper limb

Cited by 9 publications

References 20 publications

The elbow is the load-bearing joint during arm swing

The elbow is the load-bearing joint during arm swing

Kinematic analysis of impairments and compensatory motor behavior during prosthetic grasping in below-elbow amputees

Comparison of Dexterous Task Performance in Virtual Reality and Real-World Environments

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