This paper presents the design, as well as performance and preliminary usability evaluations of HandyBot, a novel portable end-effector haptic device optimized for unsupervised robot-assisted therapy of hand function after stroke. HandyBot builds on the sensorimotor robot-assisted therapy concept developed on two earlier haptic devices, HapticKnob (HK) and ReHapticKnob (RHK), whose efficacy was successfully validated in supervised clinical environments when used by subjects after stroke [12, 34]. HandyBot strives to provide a similar therapy platform (i.e., end-effector haptic device with user interface and sensorimotor therapy exercises) than the one previously validated on the RHK , and to extend its use to different environments (e.g., start in the clinic and continue at home), to further promote unsupervised use. This promises to complement conventional therapies, increase therapy dose of quality rehabilitation and subject autonomy while decreasing reliance on hospital stays .
HandyBot is compact and demonstrates good technical performance
HandyBot is noticeably more compact and portable than the HK and the RHK, and still allows to actively train grasping and forearm pronosupination, and an additional movement (i.e., wrist flexion extension), following the same validated sensorimotor therapy concept [33, 34].
Excluding wearable devices (e.g., hand gloves, exoskeletons), only few powered portable devices focus on the training of hand function [40–42, 70] and allow to actively assist/resist the patient movements and/or to reproduce sensorimotor therapy tasks. Compared to these portable devices and its non-portable predecessors, HandyBot maintains similar performance in terms of workspace, dynamics and sensing, despite achieving an important cost reduction (approximately − 55%) with respect to earlier concepts, making the system more scalable in view of potential deployment in home settings.
The robot workspace is similar to state-of-the-art rehabilitation devices for PS and FE and slightly smaller for GR, both in minimal and maximal hand aperture (i.e., 10-110mm thumb to index) [40, 42, 67]. This allows simulating fine object manipulation while respecting biomechanical and therapy requirements [12, 46]. Maximum achievable movement/force dynamics and sensor resolution are in the same order of magnitude of other devices except for PS, which achieves slightly lower accelerations and has an increased static friction, which can be attributed to the high weight and inertia of the metal L-shape structure necessary to align the robotic wrist FE axis with the user anatomical axis. Maximum generated grasping forces are in line with other rehabilitation devices [31, 67] and correspond to the force levels needed in therapy exercises and in ADL . While PS can achieve torques and, potentially, maximum impedances higher than average [40, 42, 67], the FE DOF achieves maximum torques that, after overcoming the robot inertia, only allow to assist/resist/perturb the user movements, but cannot passively hold the limb of the user in different positions particularly against gravity (i.e., when the user is in extreme pronation or supination positions).
Through a single low-cost force sensor, HandyBot allows to maintain good haptic control performance in terms of rigid contact rendering fidelity, and span between maximum achievable impedances and transparencies, particularly at the level of hand grasping, which is characterized by the finest sensorimotor control [71, 72]. The transparency rendering performance is better than the RHK, achieving a quarter of the apparent mass (i.e., 0.2 kg compared to 0.8 kg in RHK). However, smaller maximum impedances, but still sufficient for the available therapy exercises, are reached [33, 54].
The differences in impedance rendering between HandyBot and RHK could be explained by the lower quality of the low-cost components of HandyBot (e.g., force sensor in GR), which may negatively affect the control performance, and by the nature of their mechanical transmissions (i.e., transmission type, gear ratio). A geared transmission (e.g., gearboxes and/or timing belts, as in the case of RHK and PS in HandyBot) allows to stably render a wide range of impedances, but significantly increases size, weight and inertia of end-effector designs, proportionally to the number of DOF. Additionally, it can reduce transparency mainly due to backlash and/or high gear ratios. A cable transmission (GR and FE in HandyBot) has instead the potential to reduce the size, weight and inertia of the end-effector, and to improve transmission transparency. However, this type of transmission can reduce the range of stable renderable KB combinations depending on the level of cable tensioning, which alters the friction in idler pulleys, on the mismatch in cable tensioning within a transmission chain, which generates cable vibration/resonance similarly to backlash, and on the elasticity of the mechanical structures, all factors that contribute to the instability of the system [73–75]. Furthermore, in GR, HandyBot has approximately a 3:1 gear ratio, which significantly reduces the maximum range of renderable impedances compared to the 12:1 gear ratio of the RHK with the same actuator.
The platform is safe and shows positive usability
We achieved promising usability results with our therapy platform, showing that HandyBot is usable without supervision and learnable during a first exposure. After a supervised instruction phase, we simulated unsupervised therapy conditions, but we allowed the therapist to intervene only in case help was strictly needed (e.g., in adverse conditions), as done in unsupervised trainings in clinical settings [18, 24, 25]. Throughout the test, the therapist intervention was only required for one subject that had an increase in hand muscle tone during the experiment, but whose muscle tone and hand active ROM were already altered at baseline. Robotic assessments incorporated into the therapy exercises could allow to monitor hand muscle tone throughout the therapy, to avoid negative consequences such as pain that could affect recovery . The device respects safety norms for medical devices, as well as ergonomics and adaptability design requirements, and did not show safety-related problems. Minimization of issues requiring external intervention and safety are fundamental for the use of the therapy platform in the home environment, where supervision is not always available or would require additional communication channels (e.g., telerehabilitation ). The usability results of HandyBot, graphical user interface and Tunnel exercise are between good and excellent (i.e., approximately between 70 and 90 out of 100), which is aligned with the usability results achieved when using the RHK with minimal supervision , meaning that the change of hardware did not affect the user experience during therapy. The WristGrasp exercise obtained lower but positive usability scores (i.e., above “OK”), probably associated with the difficulty of the exercise, which requires good sensorimotor functions to hold the glass sphere without breaking it or to identify target wrist flexion-extension positions based on haptic cues. The usability results are positively aligned with the few other studies that evaluated the usability of technology-assisted therapy platforms [25, 76, 77], although only one of these assessed the SUS with an average score of 71.8 out of 100 .
Necessary improvements and limitations
Our evaluation allowed to identify important design improvements that will be addressed before testing the device in real unsupervised conditions (e.g., home). Hand/arm supports should be optimized to allow precise control of the limb positioning (e.g., avoid compensatory movements) and prevent misalignments between anatomical and robot joints, which could obtrude the movements of the subject in positions that are at the limit of the robot workspace. Completely eliminating these issues is a challenge, particularly in an end-effector device when patients try to control multiple DOF simultaneously. Therefore, exercises that train a maximum of 2 DOF simultaneously should be considered with an end-effector approach. Furthermore, as recommended in literature , to avoid visual compensation in solving sensorimotor therapy tasks, the hand of the subject should be covered during therapy.
Our preliminary usability results should be interpreted with respect to the small sample size tested, although this size can be considered sufficient to identify the major usability challenges of the platform . However, the results should be further validated over a longer time horizon and in real unsupervised conditions (e.g., in the home environment), to verify the feasibility of this therapy approach, whether subjects could learn to use the system, and if their motivation to use the device would change.
Potential of unsupervised robot-assisted therapy with HandyBot
Our positive performance results in terms of haptic rendering and usability with the same unsupervised therapy framework as the RHK open the door to the use of our compact device, HandyBot, in different unsupervised settings (e.g., clinic or home) after an appropriate supervised learning period in the clinic. Our device, focusing on active training of hand function, could complement existing upper limb robot-assisted therapy devices that have been deployed for home rehabilitation [16, 79]. However, these devices should be selected carefully since, particularly among non-wearable devices, usability without supervision and portability are often lacking. This robot-assisted approach could help increase therapy dose for the patients and reduce limb non-use after discharge, decrease therapy-associated costs (e.g., therapist time during unsupervised use in the clinic) and progressively increase patient independence in daily-life settings.
Future investigations should verify the feasibility and usability of our portable therapy platform within a clinical trial in home settings. These tests will also help understanding for which type of patient population (e.g., impairment type and severity, stage after stroke) this kind of therapy is most suitable. To make the step into the home environment, clear protocols will have to be defined to decide when the patient is ready to perform such training at home and how family members and therapists should be instructed to assist the patient (when needed).