Human hands are complex biomechanical systems that allow for dexterous tasks with many degrees of freedom. Coordination of the fingers is essential for many activities of daily living and involves integrating sensory signals. During this sensory integration, the central nervous system deals with the uncertainty of sensory signals. When handling compliant objects, force and position are related. Interactions with stiff objects result in reduced position changes and increased force changes compared to compliant objects. Literature has shown sensory integration of force and position at the shoulder. Nevertheless, differences in sensory requirements between proximal and distal joints may lead to different proprioceptive representations, hence findings at proximal joints cannot be directly transferred to distal joints, such as the digits. Here, we investigate the sensory integration of force and position during pinching. A haptic manipulator rendered a virtual spring with adjustable stiffness between the index finger and the thumb. Participants had to blindly reproduce a force against the spring. In both visual reference trials and blind reproduction trials, the relation between pinch force and spring compression was constant. However, by covertly changing the spring characteristics in catch trials into an adjusted force-position relation, the participants’ weighting of force and position could be revealed. In agreement with previous studies on the shoulder, participants relied more on force sense in trials with higher stiffness. This study demonstrated stiffness-dependent sensory integration of force and position feedback during pinching.

Motion capture systems are extensively used to track human movement to study healthy and pathological movements, allowing for objective diagnosis and effective therapy of conditions that affect our motor system. Current motion capture systems typically require marker placements which is cumbersome and can lead to contrived movements.Here, we describe and evaluate our developed markerless and modular multi-camera motion capture system to record human movements in 3D. The system consists of several interconnected single-board microcomputers, each coupled to a camera (i.e., the camera modules), and one additional microcomputer, which acts as the controller. The system allows for integration with upcoming machine-learning techniques, such as DeepLabCut and AniPose. These tools convert the video frames into virtual marker trajectories and provide input for further biomechanical analysis.The system obtains a frame rate of 40 Hz with a sub-millisecond synchronization between the camera modules. We evaluated the system by recording index finger movement using six camera modules. The recordings were converted via trajectories of the bony segments into finger joint angles. The retrieved finger joint angles were compared to a marker-based system resulting in a root-mean-square error of 7.5 degrees difference for a full range metacarpophalangeal joint motion.Our system allows for out-of-the-lab motion capture studies while eliminating the need for reflective markers. The setup is modular by design, enabling various configurations for both coarse and fine movement studies, allowing for machine learning integration to automatically label the data. Although we compared our system for a small movement, this method can also be extended to full-body experiments in larger volumes.

Introduction: Sensorimotor control can be disturbed because of pain and trauma. There is scarce comprehension about which component of the sensorimotor system would benefit the most from treatment in distal radius fracture (DRF). Purpose of the Study: The purpose of this study was to determine whether the sensorimotor control of subjects with a history of DRF impaired compared with healthy subjects. If so, which component of the sensorimotor system is most affected. Methods: Nine healthy participants and 11 participants with a DRF history executed posture and reproduction tasks in interaction with a robotic wrist manipulator. A posture task with force perturbations assess sensorimotor control. Position and force reproduction tasks assessed sensory feedback. Electromyography recorded the muscle activity to study the motor part of the sensorimotor system. Study Design: Cross-sectional case-control. Results: The results showed that the motor responses to the perturbations during the posture task did not differ significantly, whereas the position reproduction did significantly differ between the 2 groups. Moreover, participants with a DRF history did not adapt to the changed dynamics of the environment during the posture task, whereas the controls did. Discussion: The results of this study imply that processing of sensory position feedback is impaired in people with a DRF history while sensorimotor control during a posture task is unaffected. A possible explanation for these results is that different neural networks are involved during reproduction and posture tasks. Conclusions: A history of DRF is related to disturbed processing of sensory feedback of the sensorimotor system, especially the Joint Position Sense, which leads to an impairment in detecting a changed environment and adapting to it. Impaired Joint Position Sense and thereby the inability to adapt adequately to a changing environment should be taken into account during the rehabilitation of patients with DRF.