Design and Development of an Affordable Haptic Robot with Force-Feedback and Compliant Actuation to Improve Therapy for Patients with Severe Hemiparesis

The study describes the design and development of a single degree-of-freedom haptic robot, Haptic Theradrive, for post-stroke arm rehabilitation for in-home and clinical use. The robot overcomes many of the weaknesses of its predecessor, the TheraDrive system, that used a Logitech steering wheel as the haptic interface for rehabilitation. Although the original TheraDrive system showed success in a pilot study, its wheel was not able to withstand the rigors of use. A new haptic robot was developed that functions as a drop-in replacement for the Logitech wheel. The new robot can apply larger forces in interacting with the patient, thereby extending the functionality of the system to accommodate low-functioning patients. A new software suite offers appreciably more options for tailored and tuned rehabilitation therapies. In addition to describing the design of the hardware and software, the paper presents the results of simulation and experimental case studies examining the system\u27s performance and usability

Haptic human-human interaction does not improve individual visuomotor adaptation

Haptic interaction between two humans, for example, parents physically supporting their child while it learns to keep balance on a bicycle, likely facilitates motor skill acquisition. Haptic human-human interaction has been shown to enhance individual motor improvement in a tracking task with a visuomotor rotation perturbation. These results are remarkable given that haptically assisting or guiding an individual rarely improves their motor improvement when the assistance is removed. We, therefore, replicated a study that reported benefits of haptic interaction between humans on individual motor improvement for tracking a target in a visuomotor rotation. Also, we tested the effect of more interaction time and stronger haptic coupling between the partners on individual performance improvement in the same task. We found no benefits of haptic interaction on individual motor improvement compared to individuals who practised the task alone, independent of interaction time or interaction strength. We also found no effect of the interaction partner's skill level on individual motor improvement

Virtual Physical Coupling of Two Lower-Limb Exoskeletons

Physical interaction between individuals plays an important role in human motor learning and performance during shared tasks. Using robotic devices, researchers have studied the effects of dyadic haptic interaction mostly focusing on the upper-limb. Developing infrastructure that enables physical interactions between multiple individuals' lower limbs can extend the previous work and facilitate investigation of new dyadic lower-limb rehabilitation schemes. We designed a system to render haptic interactions between two users while they walk in multi-joint lower-limb exoskeletons. Specifically, we developed an infrastructure where desired interaction torques are commanded to the individual lower-limb exoskeletons based on the users' kinematics and the properties of the virtual coupling. In this pilot study, we demonstrated the capacity of the platform to render different haptic properties (e.g., soft and hard), different haptic connection types (e.g., bidirectional and unidirectional), and connections expressed in joint space and in task space. With haptic connection, dyads generated synchronized movement, and the difference between joint angles decreased as the virtual stiffness increased. This is the first study where multi-joint dyadic haptic interactions are created between lower-limb exoskeletons. This platform will be used to investigate effects of haptic interaction on motor learning and task performance during walking, a complex and meaningful task for gait rehabilitation.Comment: 6 pages, 9 figures, accepted at 2023 IEEE International Conference on Rehabilitation Robotics (ICORR

How virtual and mechanical coupling impact bimanual tracking.

Bilateral training systems look to promote the paretic hand's use in individuals with hemiplegia. Although this is normally achieved using mechanical coupling (i.e., a physical connection between the hands), a virtual reality system relying on virtual coupling (i.e., through a shared virtual object) would be simpler to use and prevent slacking. However, it is not clear whether different coupling modes differently impact task performance and effort distribution between the hands. We explored how 18 healthy right-handed participants changed their motor behaviors in response to the uninstructed addition of mechanical coupling, and virtual coupling using a shared cursor mapped to the average hands' position. In a second experiment, we then studied the impact of connection stiffness on performance, perception, and effort imbalance. The results indicated that both coupling types can induce the hands to actively contribute to the task. However, the task asymmetry introduced by using a cursor mapped to either the left or right hand only modulated the hands' contribution when not mechanically coupled. The tracking performance was similar for all coupling types, independent of the connection stiffness, although the mechanical coupling was preferred and induced the hands to move with greater correlation. These findings suggest that virtual coupling can induce the hands to actively contribute to a task in healthy participants without hindering their performance. Further investigation on the coupling types' impact on the performance and hands' effort distribution in patients with hemiplegia could allow for the design of simpler training systems that promote the affected hand's use.NEW & NOTEWORTHY We showed that the uninstructed addition of a virtual and/or a mechanical coupling can induce both hands to actively contribute in a continuous redundant bimanual tracking task without impacting performance. In addition, we showed that the task asymmetry can only alter the effort distribution when the hands are not connected, independent of the connection stiffness. Our findings suggest that virtual coupling could be used in the development of simpler VR-based training devices

A series elastic brake pedal for improving driving performance under regenerative braking

Electric and hybrid vehicles are favored to decrease the carbon footprint on the planet. The electric motor in these vehicles serves a dual purpose. The use of electric motor for deceleration, by converting the kinetic energy of the vehicle into electrical energy to be stored in the battery is called regenerative braking. Regenerative braking is commonly employed by electrical vehicles to signi cantly improve energy e ciency and to help to meet emission standards. When the regenerative and friction brakes are simultaneously activated by the driver interacting with the brake pedal, the conventional haptic brake pedal feel is disturbed due to the regenerative braking. In particular, while there exists a physical coupling between the brake pedal and the conventional friction brakes, no such physical coupling exists for the regenerative braking. As a result, no reaction forces are fed back to the brake pedal, resulting in a unilateral power ow between the driver and the vehicle. Consequently, the relationship between the brake pedal force and the vehicle deceleration is strongly in uenced by the regenerative braking. This results in a unfamiliar response of the brake pedal, negatively impacting the driver's performance and posing a safety concern. The reaction forces due to regenerative braking can be fed back to the brake pedal, through actuated pedals that re-establish the bilateral power ow to recover the natural haptic pedal feel. We propose a force-feedback brake pedal with series elastic actuation to preserve the conventional brake pedal feel during regenerative braking. The novelty of the proposed design is due to the deliberate introduction of a compliant element between the actuator and the brake pedal whose de ections are measured to estimate interaction forces and to perform closed-loop force control. Thanks to its series elasticity, the force-feedback brake pedal can utilize robust controllers to achieve high delity force control, possesses favorable output impedance characteristics over the entire frequency spectrum, and can be implemented in a compact package using low-cost components. We introduce pedal feel compensation algorithms to recover the missing regenerative brake forces on the brake pedal. The proposed algorithms are implemented for both two-pedal cooperative braking and one-pedal driving conditions. For those driving conditions, the missing pedal feedback due to the regenerative brake forces are rendered through the active pedal to recover the conventional pedal force mapping. In two-pedal cooperative braking, the regenerative braking is activated by pressing the brake pedal, while in one-pedal driving the activation takes place as soon as the throttle pedal is released. The applicability and e ectiveness of the proposed series elastic brake pedal and haptic pedal feel compensation algorithms in terms of driving safety and performance have been investigated through human subject experiments. The experiments have been conducted using a haptic pedal feel platform that consists of a SEA brake pedal, a torque-controlled dynamometer, and a throttle pedal. The dynamometer renders the pedal forces due to friction braking, while the SEA brake pedal renders the missing pedal forces due to the regenerative braking. The throttle pedal is utilized for the activation of regenerative braking in one-pedal driving. The simulator implements a vehicle pursuit task similar to the CAMP protocol and provides visual feedback to the participant. The e ectiveness of the preservation of the natural brake pedal feel has been studied under two-pedal cooperative braking and one-pedal driving scenarios. The experimental results indicate that pedal feel compensation can signi cantly decrease the number of hard braking instances, improving safety for both two-pedal cooperative braking and one-pedal driving. Volunteers also strongly prefer compensation, while they equally prefer and can e ectively utilize both two-pedal and one-pedal driving conditions. The bene cial e ects of haptic pedal feel compensation on safety is evaluated to be larger for the two-pedal cooperative braking condition, as lack of compensation results in sti ening/softening pedal feel characteristics in this cas

Human adaptive haptic sensing

How do humans physically interact with the environment or with other humans? It is well known that the nervous system can modify the body’s stiffness by selectively cocontracting muscles to shape the mechanical interaction with the environment, but how this influences haptic perception is not known. This thesis examines whether humans can adapt muscles’ activation to influence their perception of the physical interaction with the environment. This question is investigated by conducting behavioural experiments using dedicated robotic interfaces to study sensorimotor interactions in the presence of haptic and visual perturbations. Hypotheses about the underlying mechanism are then tested through mathematical modelling and simulations. Chapter 1 reviews related frameworks and introduces the most relevant questions addressed in this work. Chapter 2 then shows that the central nervous system (CNS) can voluntarily adapt muscle cocontraction to increase haptic sensitivity. In an experiment, participants tracked a randomly moving target with visual noise while being physically guided by a virtual elastic band, where the band’s stiffness was controlled by their muscle coactivation. The results show that participants learned to increase cocontraction with visual noise and decrease it when the guidance is incongruent with the visual target. The adaptation law governing the regulation of the body’s stiffness by the CNS is then derived through computational modelling. This model is designed to maximise visuo-haptic information while minimising metabolic cost, thus trading off sensory information with energy. Further, it is shown in Chapter 3 that when the subjects are coupled via a tuneable connection to a robotic guidance designed to hinder their tracking through perturbations at the turning points (where participants physiologically increase cocontraction), they adapted cocontraction to reduce the impact of perturbations on performance. These results highlight the CNS ability to modify the muscle activation patterns to improve performance with minimal effort. Chapter 4 tests the robustness of human adaptive haptic sensing introduced in the previous chapters for human-human physical interaction. For example, in tango dancing physical contact provides haptic information of the partner’s action required to coordinate the movements. During such physical interactions, should one keep the arms compliant so that the partner can correct the motion, or should one stiffen them to better keep along the planned movement? Using a tracking task in which a dyad is coupled via a rigid connection, subjects readily adapted the compliance of their limb depending on both the accuracy of the partner’s and their own movement. The same computational model introduced in Chapter 2 could explain these results and predict the experimentally observed cocontraction adaptation. This suggests that the minimisation of prediction error and energy is a general principle also holding in interpersonal interactions. Altogether, these findings shed light on how humans can adapt haptic sensing by changing body properties, and propose a novel framework to interpret visuo-haptic perception for interaction with the environment and other humans.Open Acces