What is the impact of robotic rehabilitation on balance and gait outcomes in people with multiple sclerosis? A systematic review of randomized control trials.

INTRODUCTION: In recent years, robot-assisted gait training (RAGT) has been proposed as therapy for balance and gait dysfunctions in people with multiple sclerosis (PwMS). Through this systematic review, we aimed to discuss the impact of RAGT on balance and gait outcomes. Furthermore, characteristics of the training in terms of robots used, participants characteristics, protocols and combined therapeutic approaches have been described. EVIDENCE ACQUISITION: As part of the Italian Consensus on robotic rehabilitation "CICERONE" a systematic search was provided in PubMed, the Cochrane Library and PEDro to identify relevant studies published before December 2019. Only randomized control trials (RCT) involving RAGT for PwMS were included. PEDro scale was used to assess the risk of bias and the Oxford Center for Evidence-Based Medicine (OCEBM) was used to assess level of evidence of included studies. EVIDENCE SYNTHESIS: The search on databases resulted in 336 records and, finally, 12 studies were included. RAGT was provided with Exoskeleton in ten studies (6-40 session, 2-5 per week) and with end-effector in two studies (12 sessions, 2-3 per week) with large variability in terms of participants' disability. All the exoskeletons were combined with bodyweight support treadmill and movement assistance varied from 0% to 100% depending on participants' disability, two studies combined exoskeleton with virtual reality. The end-effector speed ranged between 1.3 and 1.8 km/h, with bodyweight support starting from 50% and progressively reduced. In seven out of twelve studies RAGT was provided in a multimodal rehabilitation program or in combination with standard physical therapy. There is level 2 evidence that RAGT has positive impact in PwMS, reaching the minimally clinically importance difference in Berg Balance Scale, six-minute walking test and gait speed. CONCLUSIONS: In available RCT, RAGT is mostly provided with exoskeleton devices and improves balance and gait outcomes in a clinically meaningful way. Considering several advantages in terms of safety, motor assistance and intensity of training provided, RAGT should be promoted for PwMS with severe disability in a multimodal rehabilitation context as an opportunity to maximize recovery.

Learning tactile skills through curious exploration.

We present curiosity-driven, autonomous acquisition of tactile exploratory skills on a biomimetic robot finger equipped with an array of microelectromechanical touch sensors. Instead of building tailored algorithms for solving a specific tactile task, we employ a more general curiosity-driven reinforcement learning approach that autonomously learns a set of motor skills in absence of an explicit teacher signal. In this approach, the acquisition of skills is driven by the information content of the sensory input signals relative to a learner that aims at representing sensory inputs using fewer and fewer computational resources. We show that, from initially random exploration of its environment, the robotic system autonomously develops a small set of basic motor skills that lead to different kinds of tactile input. Next, the system learns how to exploit the learned motor skills to solve supervised texture classification tasks. Our approach demonstrates the feasibility of autonomous acquisition of tactile skills on physical robotic platforms through curiosity-driven reinforcement learning, overcomes typical difficulties of engineered solutions for active tactile exploration and underactuated control, and provides a basis for studying developmental learning through intrinsic motivation in robots.

Decoding of grasping information from neural signals recorded using peripheral intrafascicular interfaces.

BACKGROUND: The restoration of complex hand functions by creating a novel bidirectional link between the nervous system and a dexterous hand prosthesis is currently pursued by several research groups. This connection must be fast, intuitive, with a high success rate and quite natural to allow an effective bidirectional flow of information between the user's nervous system and the smart artificial device. This goal can be achieved with several approaches and among them, the use of implantable interfaces connected with the peripheral nervous system, namely intrafascicular electrodes, is considered particularly interesting. METHODS: Thin-film longitudinal intra-fascicular electrodes were implanted in the median and ulnar nerves of an amputee's stump during a four-week trial. The possibility of decoding motor commands suitable to control a dexterous hand prosthesis was investigated for the first time in this research field by implementing a spike sorting and classification algorithm. RESULTS: The results showed that motor information (e.g., grip types and single finger movements) could be extracted with classification accuracy around 85% (for three classes plus rest) and that the user could improve his ability to govern motor commands over time as shown by the improved discrimination ability of our classification algorithm. CONCLUSIONS: These results open up new and promising possibilities for the development of a neuro-controlled hand prosthesis.

Cognitive vision system for control of dexterous prosthetic hands: experimental evaluation.

BACKGROUND: Dexterous prosthetic hands that were developed recently, such as SmartHand and i-LIMB, are highly sophisticated; they have individually controllable fingers and the thumb that is able to abduct/adduct. This flexibility allows implementation of many different grasping strategies, but also requires new control algorithms that can exploit the many degrees of freedom available. The current study presents and tests the operation of a new control method for dexterous prosthetic hands. METHODS: The central component of the proposed method is an autonomous controller comprising a vision system with rule-based reasoning mounted on a dexterous hand (CyberHand). The controller, termed cognitive vision system (CVS), mimics biological control and generates commands for prehension. The CVS was integrated into a hierarchical control structure: 1) the user triggers the system and controls the orientation of the hand; 2) a high-level controller automatically selects the grasp type and size; and 3) an embedded hand controller implements the selected grasp using closed-loop position/force control. The operation of the control system was tested in 13 healthy subjects who used Cyberhand, attached to the forearm, to grasp and transport 18 objects placed at two different distances. RESULTS: The system correctly estimated grasp type and size (nine commands in total) in about 84% of the trials. In an additional 6% of the trials, the grasp type and/or size were different from the optimal ones, but they were still good enough for the grasp to be successful. If the control task was simplified by decreasing the number of possible commands, the classification accuracy increased (e.g., 93% for guessing the grasp type only). CONCLUSIONS: The original outcome of this research is a novel controller empowered by vision and reasoning and capable of high-level analysis (i.e., determining object properties) and autonomous decision making (i.e., selecting the grasp type and size). The automatic control eases the burden from the user and, as a result, the user can concentrate on what he/she does, not on how he/she should do it. The tests showed that the performance of the controller was satisfactory and that the users were able to operate the system with minimal prior training.

Humans can integrate force feedback to toes in their sensorimotor control of a robotic hand.

Tactile sensory feedback is essential for dexterous object manipulation. Users of hand myoelectric prostheses without tactile feedback must depend essentially on vision to control their device. Indeed, improved tactile feedback is one of their main priorities. Previous research has provided evidence that conveying tactile feedback can improve prostheses control, although additional effort is required to solve problems related to pattern recognition learning, unpleasant sensations, sensory adaptation, and low spatiotemporal resolution. Still, these studies have mainly focused on providing stimulation to hairy skin regions close to the amputation site, i.e., usually to the upper arm. Here, we explored the possibility to provide tactile feedback to the glabrous skin of toes, which have mechanical and neurophysiological properties similar to the fingertips. We explored this paradigm in a grasp-and-lift task, in which healthy participants controlled two opposing digits of a robotic hand by changing the spacing of their index finger and thumb. The normal forces applied by the robotic fingertips to a test object were fed back to the right big and second toe. We show that within a few lifting trials, all the participants incorporated the force feedback received by the foot in their sensorimotor control of the robotic hand.