Research Methods in Biomechanics

Robertson, D. Gordon E.

doi:10.5040/9781492595809

Cited by 497 publications

(440 citation statements)

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“…The start and end of each movement was defined by a jerk threshold method using wrist and elbow LED markers. The motion capture data were used to derive joint angles using linear algebra (Robertson et al, 2013). The EMG was processed consistent with SENIAM recommendations, it was high-pass filtered at 10 Hz, rectified, and low-pass filtered at 20 Hz.…”

Section: Experimental Design and Human Subjectsmentioning

confidence: 99%

The primary afferent activity cannot capture the dynamical features of muscle activity during reaching movements

Hardesty

Boots

Yakovenko

et al. 2017

Preprint

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The stabilizing role of sensory feedback in relation to movement dynamics remains to be poorly understood in realistic three-dimensional movements of limbs. The objective of this experimental and computational study was to classify the contribution of sensory feedback from muscle spindles to the control of assistive and resistive limb dynamics during human pointing movements. We integrated a human upper-limb musculoskeletal model with a model of Ia primary afferent discharge to analyze motion and muscle activation patterns during reaching movements in virtual reality (VR). The reaching target locations in VR were selected to define movements with varying roles of gravity and interaction torques that created diverse dynamical contexts. Nine healthy human subjects performed the VR task by pointing to the reaching targets with visual feedback of their arm location. Motion capture and electromyography (EMG) were recorded, and joint torques and Ia primary afferent discharge were estimated using the integrated model. The experimental and simulated data were analyzed with hierarchal clustering (Gritsenko et al., 2016). The clustering analysis of EMG and predicted proprioceptive signals showed a divergent relationship between muscle activation and sensory feedback patterns. Even though the Ia models had nonlinear dynamical components, their output was still most related to the anatomical grouping of muscles and less so to the dynamical contexts of each movement, reflected in muscle activations. Altogether, these results suggest that sensory feedback is nonlinearly related to muscle activation profiles and that it may contribute information necessary for coupling between proximal and distal muscle groups.

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Section: Experimental Design and Human Subjectsmentioning

confidence: 99%

The primary afferent activity cannot capture the dynamical features of muscle activity during reaching movements

Hardesty

Boots

Yakovenko

et al. 2017

Preprint

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show abstract

“…The GRF data were collected in AMTI's NetForce software (AMTI Inc., Watertown, MA) and exported to be used in Matlab (v. R2017b, Mathworks, Natick, MA) for processing. Ground reaction force data were smoothed using a 4 th order Butterworth filter with a cutoff frequency set at 20 Hz (16). Peak vertical and antero-posterior GRF were determined for each rep and then averaged across reps for the leg-drive condition.…”

Section: Methodsmentioning

confidence: 99%

Leg-drive Does Not Affect Upper Extremity Muscle Activation during a Bench Press Exercise

Gardner¹,

Chia²,

Miller³

2019

saj

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The purpose of this study was to determine if muscle activity of upper extremity muscles differed depending on the involvement of the legs during a bench press. The study included 15 male and 12 female recreationally trained, college age participants. There were 2 testing sessions. Session 1: Participants performed a 1-repetition maximum in a standard bench press followed by a leg-drive familiarization. For the familiarization, participants were instructed in the leg-drive technique (buttocks remained on the bench) and given ample time to practice until comfortable and which satisfied the researchers. Leg-drive pressing force was verified by measuring anterior-posterior and vertical ground reaction forces measured by a force plate. Session 2: Outfitted with surface electromyography (EMG), participants performed 3 repetitions in the bench press with 75% of the standard bench press 1-repetition maximum (1RM) under three conditions: 1) standard 2) leg-drive, 3) legs-in-the-air. The normalized average of the peaks of the three reps for each muscle were analyzed using a 2 x 3 (gender x condition) ANOVA. The ANOVA revealed no significant differences across groups or conditions (pectoralis major p = 0.405, anterior deltoid p = 0.297, triceps brachii p = 0.092). When comparing a standard bench press to leg-drive, our results indicate that similar amounts of muscle activation are required for the task regardless of the leg involvement. This work indicates that using a leg-drive technique that does not allow the buttocks to rise from the bench, does not result in a change in upper extremity muscle activity compared to a standard bench press. Due to lack of differences across conditions, athletes and strength coaches should base their decision on the use of leg-drive on their personal preference.

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“…Segment-link-models are commonly used in biomechanics to examine the moments acting at joints during certain actions or movements [20,21]. Please refer to Figures 3 and 5 for a depiction.…”

Section: Segment Bmentioning

confidence: 99%

Pose to Seat: Automated design of body-supporting surfaces

Guerrero

Winkler

Ohrhallinger

et al. 2020

Computer Aided Geometric Design

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The design of functional seating furniture is a complicated process which often requires extensive manual design effort and empirical evaluation. We propose a computational design framework for pose-driven automated generation of body-supports which are optimized for comfort of sitting. Given a human body in a specified pose as input, our method computes an approximate pressure distribution that also takes frictional forces and body torques into consideration which serves as an objective measure of comfort. Utilizing this information to find out where the body needs to be supported in order to maintain comfort of sitting, our algorithm can create a supporting mesh suited for a person in that specific pose. This is done in an automated fitting process, using a template model capable of supporting a large variety of sitting poses. The results can be used directly or can be considered as a starting point for further interactive design.

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Research Methods in Biomechanics

Cited by 497 publications

References 0 publications

The primary afferent activity cannot capture the dynamical features of muscle activity during reaching movements

The primary afferent activity cannot capture the dynamical features of muscle activity during reaching movements

Leg-drive Does Not Affect Upper Extremity Muscle Activation during a Bench Press Exercise

Pose to Seat: Automated design of body-supporting surfaces

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