Erika D'ANtonio

Bimanual object manipulation involves using both hands to interact with objects in the environment, and the process requires the central nervous system to process sensory feedback and translate it into motor commands. Although there have been significant advancements in haptics and robotics, the kinematic strategies involved in bimanual coupled tasks are still not fully understood. This study aimed to investigate the dynamic interaction between hands during the manipulation of a shared object using two impedance-controlled exoskeletons programmed to simulate bimanual coupled manipulation of virtual objects. Twenty-six participants (right-handed and left-handed) were asked to use both hands to grab and place simulated objects in specific locations. The virtual objects were rendered with four different dynamic properties, affecting the manipulation strategies used to complete the tasks. The results showed that force asymmetries were related to movement direction and handedness preference, with right-handers exhibiting asymmetries related to movement direction and left-handers showing better control of the force applied between their hands. This is possibly due to their constant exposure to objects designed for right-handed use. Additionally, the haptic properties of the virtual objects influenced task performance in terms of timing and failure for all participants. This study demonstrates the potential of advanced technologies to provide realistic simulations of multi-joint movements involving the entire upper extremities. The findings have implications for the development of training programs for bimanual object manipulation tasks and the design of virtual environments that can enhance the learning process.

Haptic feedback is the sensory modality to enhance the so-called “immersion”, meant as the extent to which senses are engaged by the mediated environment during virtual reality applications. However, it can be challenging to meet this requirement using conventional robotic design approaches that rely on rigid mechanical systems with limited workspace and bandwidth. An alternative solution can be seen in the adoption of lightweight wearable systems equipped with Neuromuscular Electrical Stimulation (NMES): in fact, NMES offers a wide range of different forces and qualities of haptic feedback. In this study, we present an experimental setup able to enrich the virtual reality experience by employing NMES to create in the antagonists’ muscles the haptic sensation of being loaded. We developed a subject-specific biomechanical model that estimated elbow torque during object lifting to deliver suitable electrical muscle stimulations. We experimentally tested our system by exploring the differences between the implemented NMES-based haptic feedback (NMES condition), a physical lifted object (Physical condition), and a condition without haptic feedback (Visual condition) in terms of kinematic response, metabolic effort, and participants’ perception of fatigue. Our results showed that both in terms of metabolic consumption and user fatigue perception, the condition with electrical stimulation and the condition with the real weight differed significantly from the condition without any load: the implemented feedback was able to faithfully reproduce interactions with objects, suggesting its possible application in different areas such as gaming, work risk assessment simulation, and education.

Markerless human motion capture could be a potential tool for studying body biomechanics. Despite the extensive previous studies, the design of an accurate markerless system able to perform a quantitative clinical gait analysis is still an open challenge. Our purpose was to characterize the performance of a low-cost markerless system, consisting of the open-source library OpenPose, two webcams and a linear triangulation algorithm. The system was validated in terms of 3D kinematic gait analysis, by comparison with inertial sensors. Two synchronized videos of six healthy subjects were recorded in three webcam configurations, in walking and running sessions on a treadmill. Sagittal joint angles were compared between the two systems, to evaluate the kinematic performance of the markerless system. Results showed that the angular paths, provided by OpenPose, were close to the ones computed by inertial sensors in all camera positions, but OpenPose inaccuracy affected the computation of joint angles parameters producing absolute errors up to 14.0°± 1.8°. The observed differences depended on OpenPose ability in the recognition of joints centers and on camera configurations. In the optimal condition (joint visible from both cameras during the whole trial) our system accuracy resulted equal to 1.6 °± 0.3 °. This system could be useful in those applications where a real-time body tracking is required, i.e. video games or virtual rehabilitative trials. When a quantitative analysis is needed, the low level of accuracy may be overcome, by both increasing the cameras' number and implementing an optimization triangulation algorithm.

Proprioception—the sense of body segment’s position and movement—plays a crucial role in human motor control, integrating the sensory information necessary for the correct execution of daily life activities. Despite scientific evidence recognizes that several neurological diseases hamper proprioceptive encoding with consequent inability to correctly perform movements, proprioceptive assessment in clinical settings is still limited to standard scales. Literature on physiology of upper limb’s proprioception is mainly focused on experimental approaches involving planar setups, while the present work provides a novel paradigm for assessing proprioception during single—and multi-joint matching tasks in a three-dimensional workspace. To such extent, a six-degrees of freedom exoskeleton, ALEx-RS (Arm Light Exoskeleton Rehab Station), was used to evaluate 18 healthy subjects’ abilities in matching proprioceptive targets during combined single and multi-joint arm’s movements: shoulder abduction/adduction, shoulder flexion/extension, and elbow flexion/extension. Results provided evidence that proprioceptive abilities depend on the number of joints simultaneously involved in the task and on their anatomical location, since muscle spindles work along their preferred direction, modulating the streaming of sensory information accordingly. These findings suggest solutions for clinical sensorimotor evaluation after neurological disease, where assessing proprioceptive deficits can improve the recovery path and complement the rehabilitation outcomes.