Gravity effects are better optimized by older than young adults when reaching with the non-dominant arm

Motor lateralization refers to differences in the neural organization of cerebral hemispheres, resulting in different control specializations between the dominant and the non-dominant motor systems. Multiple studies proposed that the dominant hemisphere is specialized for open-loop optimization-like processes. Recently, comparing arm kinematics between upward and downward movements, we found that the dominant arm outperformed the non-dominant one regarding gravity-related motor optimization in healthy young subjects. The literature about aging effects on motor control presents several neurophysiological and behavioral evidences for an age-related reduction of motor lateralization. Here, we compare the lateralization of a well-known gravity-related optimal motor control process between young and older adults. Thirty healthy young (mean age = 24.1 ± 3 years) and nineteen healthy older adults (mean age = 73.0 ± 8) performed single degree-of-freedom vertical arm movements between two targets (upward and downward). Participants alternatively reached with their dominant and non-dominant arms. We recorded arm kinematics and electromyographic activities of the prime movers (Anterior and Posterior Deltoids) and we analyzed parameters thought to represent the hallmark of the gravity-related optimization process (i.e directional asymmetries and negative epochs on the phasic EMG activity). We found no arm difference in older participants, such that parameters with both arms were similar to those of young participants with their dominant arm. With the non-dominant arm, these results suggest that older adults better optimize gravity effects than young adults.

Estimation of arm kinematics in stroke survivors using wearable sensors

BackgroundStroke is the leading cause of long-term disability in the United States, often resulting in upper extremity (UE) motor impairment. Most existing outcome metrics of UE function in rehabilitation are insensitive to change or subject to observer bias. There is growing interest in using movement kinematics to measure UE motor function, since they can provide high-resolution, quantitative measurements. However, measuring arm kinematics in stroke survivors, particularly in the hospital or clinic, can be challenging for traditional optical tracking systems due to non-ideal environments, expense, and a limited ability to perform required calibration poses. The aim of this study was to develop a general framework for accurate measurements of wrist position during reaching movements in people with stroke using relatively inexpensive wearable sensors.MethodsWe developed and presented two methods, one using inertial measurement units (IMUs) and using virtual reality (Vive) sensors, that practically estimate wrist position with respect to the shoulder. We then assessed the estimation accuracies of each method during a 3D reaching task by using a Vicon motion capture system. We also demonstrated each methods ability to track two kinematic metrics, sweep area and smoothness, in chronic stroke survivors. We computed Pearson correlation coefficients when appropriate.ResultsCompared to a traditional optical system, both systems tracked with high accuracy during 3D reaching, with mean absolute errors of 1.00 ± 0.80 cm and 1.09 ± 0.51 cm for the IMU and Vive, respectively. Furthermore, both methods’ estimated kinematics highly agreed with each other (p < 0.01).ConclusionsThese methods may be useful for developing kinematic metrics to evaluate stroke rehabilitation outcomes in both laboratory and clinical environments.Trial RegistrationThe clinical trial (ClinicalTrial.gov ID: NCT03401762) was registered on January 17, 2018 (https://clinicaltrials.gov/ct2/show/NCT03401762) and posted on January 1, 2018. The trial is scheduled to be completed by August 2023. The trial was updated on December 2, 2020 and is currently recruiting.

Distinct adaptation patterns between grip dynamics and arm kinematics when the body is upside-down

During rhythmic arm movements performed in an upside-down posture, grip control adapted very quickly, but kinematics adaptation was more progressive. Our results suggest that grip control and movement kinematics planning might operate in different reference frames. Moreover, by comparing our results with previous results from parabolic flight studies, we propose that a common mechanism underlies adaptation to unfamiliar body postures and adaptation to altered gravity.

Optimal Control as a Tool for Innovation in Aerial Twisting on a Trampoline

Aerial twisting techniques are preferred by trampoline coaches for their balanced landings. As these techniques are not intuitive, computer simulation has been a relevant tool to explore a variety of techniques. Up to now, twisting somersaults were mainly simulated using arm abduction/adduction only (2D). Our objective was to explore more complex (3D) but still anatomically feasible arm techniques to find innovative and robust twisting techniques. The twist rotation was maximized in a straight backward somersault performed by a model including arm abduction/adduction with and without changes in the plane of elevation. A multi-start approach was used to find a series of locally optimal performances. Six of them were retained and their robustness was assessed by adding noise to the first half of the arm kinematics and then reoptimizing the second half of the skill. We found that aerial twist performance linearly correlates with the complexity of arm trajectory. Optimal techniques share a common strategy consisting of moving the arm in a plane formed by the twisting and angular momentum axes, termed as the best tilting plane. Overall, 3D techniques are simpler and require less effort than 2D techniques for similar twist performances. Three techniques which generate ∼3 aerial twists could be used by athletes because kinematic perturbations do not compromise the performance and the landing.