The reserve of joint torque determines movement coordination

Beyond Arm Capacity in Chronic Stroke: Evaluating Paretic Arm Non-Use Through Arm Efficiency-A Cross-Sectional Study

BACKGROUND

After a stroke, the use of the paretic arm is determined by its capacity (what it can or cannot do). When both arms have capacity to perform a task, the choice of which arm to use must be based on another criterion, probably by comparing the efficiency of each arm. Two numerical models account for this: the capacity model (the paretic arm is chosen in preference) and the efficiency model (the most efficient arm is chosen).

OBJECTIVE

To numerically determine whether capacity or efficiency best predict the use of the paretic arm in activities of daily living.

METHODS

We performed numerical simulations to predict paretic arm use with either the capacity model or the efficiency model. We used the Bayesian Information Criterion (BIC) to compare the adequacy of the 2 models in predicting clinical and accelerometric data collected from 30 patients with chronic stroke.

RESULTS

The efficiency model predicted arm use in activities of daily living better than the capacity model (BIC = -66.95 vs -5.89; root mean square error = 0.26 vs 0.72).

CONCLUSIONS

The study highlights the importance of considering efficiency when assessing paretic arm non-use. Assessing individuals' arm efficiency should help personalize rehabilitation strategies after stroke.

Force reserve predicts compensation in reaching movement with induced shoulder strength deficit

Following events such as fatigue or stroke, individuals often move their trunks forward during reaching, leveraging a broader muscle group even when only arm movement would suffice. In previous work, we showed the existence of a "force reserve": a phenomenon where individuals, when challenged with a heavy weight, adjusted their motor coordination to preserve approximately 40% of their shoulder's force. Here, we investigated if such reserve can predict hip, shoulder, and elbow movements and torques resulting from an induced shoulder strength deficit. We engaged 20 healthy participants in a reaching task with incrementally heavier dumbbells, analyzing arm and trunk movements via motion capture and joint torques through inverse dynamics. We simulated these movements using an optimal control model of a 3-degree-of-freedom upper body, contrasting three cost functions: traditional sum of squared torques, a force reserve function incorporating a nonlinear penalty, and a normalized torque function. Our results demonstrate a clear increase in trunk movement correlated with heavier dumbbell weights, with participants employing compensatory movements to maintain a shoulder force reserve of approximately 40% of maximum torque. Simulations showed that while traditional and reserve functions accurately predicted trunk compensation, only the reserve function effectively predicted joint torques under heavier weights. These findings suggest that compensatory movements are strategically employed to minimize shoulder effort and distribute load across multiple joints in response to weakness. We discuss the implications of the force reserve cost function in the context of optimal control of human movements and its relevance for understanding compensatory movements poststroke.NEW & NOTEWORTHY Our study reveals key findings on compensatory movements during upper limb reaching tasks under shoulder strength deficits, as observed poststroke. Using heavy dumbbells with healthy volunteers, we demonstrate how forward trunk displacement conserves around 40% of shoulder strength reserve during reaching. We show that an optimal controller employing a cost function combining squared motor torque and a nonlinear penalty for excessive muscle activation outperforms traditional controllers in predicting torques and compensatory movements in these scenarios.

Upper limb joint coordination preserves hand kinematics after a traumatic brachial plexus injury

Background

Traumatic brachial plexus injury (TBPI) causes a sensorimotor deficit in upper limb (UL) movements.

Objective

Our aim was to investigate the arm–forearm coordination of both the injured and uninjured UL of TBPI subjects.

Methods

TBPI participants (n = 13) and controls (n = 10) matched in age, gender, and anthropometric characteristics were recruited. Kinematics from the shoulder, elbow, wrist, and index finger markers were collected, while upstanding participants transported a cup to their mouth and returned the UL to a starting position. The UL coordination was measured through the relative phase (RP) between arm and forearm phase angles and analyzed as a function of the hand kinematics.

Results

For all participants, the hand transport had a shorter time to peak velocity (p < 0.01) compared to the return. Also, for the control and the uninjured TBPI UL, the RP showed a coordination pattern that favored forearm movements in the peak velocity of the transport phase (p < 0.001). TBPI participants' injured UL showed a longer movement duration in comparison to controls (p < 0.05), but no differences in peak velocity, time to peak velocity, and trajectory length, indicating preserved hand kinematics. The RP of the injured UL revealed altered coordination in favor of arm movements compared to controls and the uninjured UL (p < 0.001). Finally, TBPI participants' uninjured UL showed altered control of arm and forearm phase angles during the deceleration of hand movements compared to controls (p < 0.05).

Conclusion

These results suggest that UL coordination is reorganized after a TBPI so as to preserve hand kinematics.

Validity and Reliability of Kinect v2 for Quantifying Upper Body Kinematics during Seated Reaching

Kinematic analysis of the upper limbs is a good way to assess and monitor recovery in individuals with stroke, but it remains little used in clinical routine due to its low feasibility. The aim of this study is to assess the validity and reliability of the Kinect v2 for the analysis of upper limb reaching kinematics. Twenty-six healthy participants performed seated hand-reaching tasks while holding a dumbbell to induce behaviour similar to that of stroke survivors. With the Kinect v2 and with the VICON, 3D upper limb and trunk motions were simultaneously recorded. The Kinect assesses trunk compensations, hand range of motion, movement time and mean velocity with a moderate to excellent reliability. In contrast, elbow and shoulder range of motion, time to peak velocity and path length ratio have a poor to moderate reliability. Finally, instantaneous hand and elbow tracking are not precise enough to reliably assess the number of velocity peaks and the peak hand velocity. Thanks to its ease of use and markerless properties, the Kinect can be used in clinical routine for semi-automated quantitative diagnostics guiding individualised rehabilitation of the upper limb. However, engineers and therapists must bear in mind the tracking limitations of the Kinect.