Comparison of Different Telemedicine Services to Pre-Evaluate Their Use in a New "Computational Hospital".

Future healthcare is transitioning toward a decentralization of patient care, in which personal care is increasingly moved at the patient home and surrounding areas, while hospitals concentrate primarily on procedures that cannot be performed elsewhere, such as surgeries or outpatient examinations. The healthcare system in the Liguria region (Italy) is currently developing a new Center for Computational and Technological Medicine (CMCT), which is intended to facilitate and support this transition. As a component of the strategic planning and design process, this study examines the development and organization of telemedicine services across a range of chosen Italian and European institutions that share similarities with CMCT in terms of scope and scale. We specifically focus on telemedicine services - how they are governed, their main fields of application. The analysis confirmed the growing importance of telemedicine as part of the new vision of medicine, in which the patient is at the center.

Assessment methodology for human-exoskeleton interactions: Kinetic analysis based on muscle activation.

During the development and assessment of an exoskeleton, many different analyzes need to be performed. The most frequently used evaluate the changes in muscle activations, metabolic consumption, kinematics, and kinetics. Since human-exoskeleton interactions are based on the exchange of forces and torques, the latter of these, kinetic analyzes, are essential and provide indispensable evaluation indices. Kinetic analyzes, however, require access to, and use of, complex experimental apparatus, involving many instruments and implicating lengthy data analysis processes. The proposed methodology in this paper, which is based on data collected via EMG and motion capture systems, considerably reduces this burden by calculating kinetic parameters, such as torque and power, without needing ground reaction force measurements. This considerably reduces the number of instruments used, allows the calculation of kinetic parameters even when the use of force sensors is problematic, does not need any dedicated software, and will be shown to have high statistical validity. The method, in fact, combines data found in the literature with those collected in the laboratory, allowing the analysis to be carried out over a much greater number of cycles than would normally be collected with force plates, thus enabling easy access to statistical analysis. This new approach evaluates the kinetic effects of the exoskeleton with respect to changes induced in the user's kinematics and muscular activation patterns and provides indices that quantify the assistance in terms of torque (AMI) and power (API). Following the User-Center Design approach, which requires driving the development process as feedback from the assessment process, this aspect is critical. Therefore, by enabling easy access to the assessment process, the development of exoskeletons could be positively affected.

Artificial Partners to Understand Joint Action: Representing Others to Develop Effective Coordination.

In the last years, artificial partners have been proposed as tools to study joint action, as they would allow to address joint behaviors in more controlled experimental conditions. Here we present an artificial partner architecture which is capable of integrating all the available information about its human counterpart and to develop efficient and natural forms of coordination. The model uses an extended state observer which combines prior information, motor commands and sensory observations to infer the partner's ongoing actions (partner model). Over trials, these estimates are gradually incorporated into action selection. Using a joint planar task in which the partners are required to perform reaching movements while mechanically coupled, we demonstrate that the artificial partner develops an internal representation of its human counterpart, whose accuracy depends on the degree of mechanical coupling and on the reliability of the sensory information. We also show that human-artificial dyads develop coordination strategies which closely resemble those observed in human-human dyads and can be interpreted as Nash equilibria. The proposed approach may provide insights for the understanding of the mechanisms underlying human-human interaction. Further, it may inform the development of novel neuro-rehabilitative solutions and more efficient human-machine interfaces.

Self-operated stimuli improve subsequent visual motion integration.

Evidences of perceptual changes that accompany motor activity have been limited primarily to audition and somatosensation. Here we asked whether motor learning results in changes to visual motion perception. We designed a reaching task in which participants were trained to make movements along several directions, while the visual feedback was provided by an intrinsically ambiguous moving stimulus directly tied to hand motion. We find that training improves coherent motion perception and that changes in movement are correlated with perceptual changes. No perceptual changes are observed in passive training even when observers were provided with an explicit strategy to facilitate single motion perception. A Bayesian model suggests that movement training promotes the fine-tuning of the internal representation of stimulus geometry. These results emphasize the role of sensorimotor interaction in determining the persistent properties in space and time that define a percept.

Versatile and non-versatile occupational back-support exoskeletons: A comparison in laboratory and field studies.

Assistive strategies for occupational back-support exoskeletons have focused, mostly, on lifting tasks. However, in occupational scenarios, it is important to account not only for lifting but also for other activities. This can be done exploiting human activity recognition algorithms that can identify which task the user is performing and trigger the appropriate assistive strategy. We refer to this ability as exoskeleton versatility. To evaluate versatility, we propose to focus both on the ability of the device to reduce muscle activation (efficacy) and on its interaction with the user (dynamic fit). To this end, we performed an experimental study involving healthy subjects replicating the working activities of a manufacturing plant. To compare versatile and non-versatile exoskeletons, our device, XoTrunk, was controlled with two different strategies. Correspondingly, we collected muscle activity, kinematic variables and users' subjective feedbacks. Also, we evaluated the task recognition performance of the device. The results show that XoTrunk is capable of reducing muscle activation by up to in lifting and in carrying. However, the non-versatile control strategy hindered the users' natural gait (e.g., reduction of hip flexion), which could potentially lower the exoskeleton acceptance. Detecting carrying activities and adapting the control strategy, resulted in a more natural gait (e.g., increase of hip flexion). The classifier analyzed in this work, showed promising performance (online accuracy > 91%). Finally, we conducted 9 hours of field testing, involving four users. Initial subjective feedbacks on the exoskeleton versatility, are presented at the end of this work.

Haptic vs sensorimotor training in the treatment of upper limb dysfunction in multiple sclerosis: A multi-center, randomised controlled trial.

BACKGROUND: In multiple sclerosis (MS) exercise improves upper limb functions, but it is unclear what training types are more effective. OBJECTIVE: This study compares robot-assisted training based on haptic or sensorimotor exercise. METHODS: 41clinically definite MS subjects with upper limb impairment were randomised into two groups: (i) Haptic and (ii) Sensorimotor. Subjects in the Haptic performed a robot-assisted training protocol designed to counteract incoordination and weakness. The task -interaction with a virtual mass-spring system against a resistive load- requires coordination skills. Task difficulty and magnitude of resistive load were automatically adjusted to the individual impairment. Subjects in the Sensorimotor performed reaching movements under visual control; the robot generated no forces. Both groups underwent eight training sessions (40 min/session, 2 sessions/week). Treatment outcome were 9HPT and ARAT scores. RESULTS: The average 9HPT score decreased from 74±9 s to 61±8 s for the Haptic and from 49±6 s to 44±6 s. We found a significant Treatment (p=.0453) and Time differences (p=.005), but no significant Treatment×Time interactions although we found that the absolute change was only significant in the Haptic group (p=.011). We observed no significant changes in the ARAT score. Participants tolerated treatments well with a low drop-out rate. In the subjects evaluated at after 12 week (11 subject in sensory-motor and 17 in haptic group) no retention of the effect was found. CONCLUSIONS: Task oriented training may improve upper limb function in persons with MS especially in prevalent pyramidal impaired subjects without maintain the effects after three months. CLINICAL TRIAL REGISTRATION NUMBER: NCT02711566 (clinicaltrial.gov).

Incomplete information about the partner affects the development of collaborative strategies in joint action.

Physical interaction with a partner plays an essential role in our life experience and is the basis of many daily activities. When two physically coupled humans have different and partly conflicting goals, they face the challenge of negotiating some type of collaboration. This requires that both participants understand their partner's state and current actions. But, how would the collaboration be affected if information about their partner were unreliable or incomplete? We designed an experiment in which two players (a dyad) are mechanically connected through a virtual spring, but cannot see each other. They were instructed to perform reaching movements with the same start and end position, but through different via-points. In different groups of dyads we varied the amount of information provided to each player about his/her partner: haptic only (the interaction force perceived through the virtual spring), visuo-haptic (the interaction force is also displayed on the screen), and partner visible (in addition to interaction force, partner position is continuously displayed on the screen). We found that incomplete information about the partner affects not only the speed at which collaboration is achieved (less information, slower learning), but also the actual collaboration strategy. In particular, incomplete or unreliable information leads to an interaction strategy characterized by alternating leader-follower roles. Conversely, more reliable information leads to more synchronous behaviors, in which no specific roles can be identified. Simulations based on a combination of game theory and Bayesian estimation suggested that synchronous behaviors correspond to optimal interaction (Nash equilibrium). Roles emerge as sub-optimal forms of interaction, which minimize the need to account for the partner. These findings suggest that collaborative strategies in joint action are shaped by the trade-off between the task requirements and the uncertainty of the information available about the partner.

A dynamic model of hand movements for proportional myoelectric control of a hand prosthesis.

In myo-controlled prosthetic hands, surface electromyographic signals are used to operate the hand actuators. A pre-requisite for effective control is that the intended movement is decoded from muscle activity. Simpler approaches use pattern recognition techniques, which assume a finite set of possible actions. However, this leads to unnatural, discontinuous control. Proportional controllers do not require a finite set of actions to be specified in advance but are difficult to use, particularly with dexterous multi-fingered hands. Here we discuss a control module which continuously predicts the intended movements from recorded multi-channel electromyographic activity. The module can be seen as a (simplified) forward model of the dynamics of the intact hand. We describe a procedure for estimating model parameters from hand movement and muscle activity data, and discuss its application to the proportional myoelectric control of a prosthetic hand.

Technological Approaches for Neurorehabilitation: From Robotic Devices to Brain Stimulation and Beyond.

Neurological diseases causing motor/cognitive impairments are among the most common causes of adult-onset disability. More than one billion of people are affected worldwide, and this number is expected to increase in upcoming years, because of the rapidly aging population. The frequent lack of complete recovery makes it desirable to develop novel neurorehabilitative treatments, suited to the patients, and better targeting the specific disability. To date, rehabilitation therapy can be aided by the technological support of robotic-based therapy, non-invasive brain stimulation, and neural interfaces. In this perspective, we will review the above methods by referring to the most recent advances in each field. Then, we propose and discuss current and future approaches based on the combination of the above. As pointed out in the recent literature, by combining traditional rehabilitation techniques with neuromodulation, biofeedback recordings and/or novel robotic and wearable assistive devices, several studies have proven it is possible to sensibly improve the amount of recovery with respect to traditional treatments. We will then discuss the possible applied research directions to maximize the outcome of a neurorehabilitation therapy, which should include the personalization of the therapy based on patient and clinician needs and preferences.

Haptic communication between humans is tuned by the hard or soft mechanics of interaction.

To move a hard table together, humans may coordinate by following the dominant partner's motion [1-4], but this strategy is unsuitable for a soft mattress where the perceived forces are small. How do partners readily coordinate in such differing interaction dynamics? To address this, we investigated how pairs tracked a target using flexion-extension of their wrists, which were coupled by a hard, medium or soft virtual elastic band. Tracking performance monotonically increased with a stiffer band for the worse partner, who had higher tracking error, at the cost of the skilled partner's muscular effort. This suggests that the worse partner followed the skilled one's lead, but simulations show that the results are better explained by a model where partners share movement goals through the forces, whilst the coupling dynamics determine the capacity of communicable information. This model elucidates the versatile mechanism by which humans can coordinate during both hard and soft physical interactions to ensure maximum performance with minimal effort.

Computational rehabilitation of neglect: Using state-space models to understand the recovery mechanisms.

Unilateral spatial neglect is a neuropsychological syndrome often observed in right hemisphere stroke patients. The symptoms differ from subject to subject. A few rehabilitation approaches, e.g. prism adaptation, have demonstrated some effect in reducing the symptoms, but the underlying mechanisms are still largely unclear. Recently, neural models have been proposed to qualitatively describe cortical lesions, the resulting neglect symptoms and the effects of treatment. However, these predictions are qualitative and cannot be used to compare different hypotheses or to interpret symptoms at individual subjects level. Here we propose a computational model of the trial-by-trial dynamics of training-induced recovery from neglect. Neglect is modelled in terms of an impaired internal representation of visual stimuli in the left hemispace. The model assumes that recovery is driven by the mismatch between defective representations of visual stimuli and the corresponding hand positions. The model reproduces the main observations of prism adaptation experiments. Using standard system identification techniques, we fitted the model to data from a rehabilitation trial based on a novel rehabilitation approach based on virtual reality, involving reaching movements within an adaptive environment. Our results suggest that the model can be used to interpret data from individual subjects and to formulate testable hypotheses on the mechanisms of recovery and directions for treatment.

Effect of Position- and Velocity-Dependent Forces on Reaching Movements at Different Speeds.

The speed of voluntary movements is determined by the conflicting needs of maximizing accuracy and minimizing mechanical effort. Dynamic perturbations, e.g., force fields, may be used to manipulate movements in order to investigate these mechanisms. Here, we focus on how the presence of position- and velocity-dependent force fields affects the relation between speed and accuracy during hand reaching movements. Participants were instructed to perform reaching movements under visual control in two directions, corresponding to either low or high arm inertia. The subjects were required to maintain four different movement durations (very slow, slow, fast, very fast). The experimental protocol included three phases: (i) familiarization-the robot generated no force; (ii) force field-the robot generated a force; and (iii) after-effect-again, no force. Participants were randomly assigned to four groups, depending on the type of force that was applied during the "force field" phase. The robot was programmed to generate position-dependent forces-with positive (K+) or negative stiffness (K-)-or velocity-dependent forces, with either positive (B+) or negative viscosity (B-). We focused on path curvature, smoothness, and endpoint error; in the latter we distinguished between bias and variability components. Movements in the high-inertia direction are smoother and less curved; smoothness also increases with movement speed. Endpoint bias and variability are greater in, respectively, the high and low inertia directions. A robust dependence on movement speed was only observed in the longitudinal components of both bias and variability. The strongest and more consistent effects of perturbation were observed with negative viscosity (B-), which resulted in increased variability during force field adaptation and in a reduction of the endpoint bias, which was retained in the subsequent after-effect phase. These findings confirm that training with negative viscosity produces lasting effects in movement accuracy at all speeds.

Adaptive training with full-body movements to reduce bradykinesia in persons with Parkinson's disease: a pilot study.

BACKGROUND: Bradykinesia (slow movements) is a common symptom of Parkinson's disease (PD) and results in reduced mobility and postural instability. The objective of this study is to develop and demonstrate a technology-assisted exercise protocol that is specifically aimed at reducing bradykinesia. METHODS: Seven persons with PD participated in this study. They were required to perform whole body reaching movements toward targets placed in different directions and at different elevations. Movements were recorded by a Microsoft Kinect movement sensor and used to control a human-like avatar, which was continuously displayed on a screen placed in front of the subjects. After completion of each movement, subjects received a 0-100 score that was inversely proportional to movement time. Target distance in the next movements was automatically adjusted in order to keep the score around a pre-specified target value. In this way, subjects always exercised with the largest movement amplitude they could sustain. The training protocol was organised into blocks of 45 movements toward targets placed in three different directions and at three different elevations (a total of nine targets). Each training session included a finite number of blocks, fitted within a fixed 40 minutes duration. The whole protocol included a total of 10 sessions (approximately two sessions/week). As primary outcome measure we took the absolute average acceleration. Various aspects of movement performance were taken as secondary outcome measures, namely accuracy (undershoot error), path curvature, movement time, and average speed. RESULTS: Throughout sessions, we observed an increase of the absolute average acceleration and speed and decreased undershoot error and movement time. Exercise also significantly affected the relationship between target elevation and both speed and acceleration - the improvement was greater at higher elevations. CONCLUSIONS: The device and the protocol were well accepted by subjects and appeared safe and easy to use. Our preliminary results point at a training-induced reduction of bradykinesia.

Natural interfaces and virtual environments for the acquisition of street crossing and path following skills in adults with Autism Spectrum Disorders: a feasibility study.

BACKGROUND: Lack of social skills and/or a reduced ability to determine when to use them are common symptoms of Autism Spectrum Disorder (ASD). Here we examine whether an integrated approach based on virtual environments and natural interfaces is effective in teaching safety skills in adults with ASD. We specifically focus on pedestrian skills, namely street crossing with or without traffic lights, and following road signs. METHODS: Seven adults with ASD explored a virtual environment (VE) representing a city (buildings, sidewalks, streets, squares), which was continuously displayed on a wide screen. A markerless motion capture device recorded the subjects' movements, which were translated into control commands for the VE according to a predefined vocabulary of gestures. The treatment protocol consisted of ten 45-minutes sessions (1 session/week). During a familiarization phase, the participants practiced the vocabulary of gestures. In a subsequent training phase, participants had to follow road signs (to either a police station or a pharmacy) and to cross streets with and without traffic lights. We assessed the performance in both street crossing (number and type of errors) and navigation (walking speed, path length and ability to turn without stopping). To assess their understanding of the practiced skill, before and after treatment subjects had to answer a test questionnaire. To assess transfer of the learned skill to real-life situations, another specific questionnaire was separately administered to both parents/legal guardians and the subjects' personal caregivers. RESULTS: One subject did not complete the familiarization phase because of problems with depth perception. The six subjects who completed the protocol easily learned the simple body gestures required to interact with the VE. Over sessions they significantly improved their navigation performance, but did not significantly reduce the errors made in street crossing. In the test questionnaire they exhibited no significant reduction in the number of errors. However, both parents and caregivers reported a significant improvement in the subjects' street crossing performance. Their answers were also highly consistent, thus pointing at a significant transfer to real-life behaviors. CONCLUSIONS: Rehabilitation of adults with ASD mainly focuses on educational interventions that have an impact in their quality of life, which includes safety skills. Our results confirm that interaction with VEs may be effective in facilitating the acquisition of these skills.

Effect of interface type in the VR-based acquisition of pedestrian skills in persons with ASD.

Possession of `social' skills is crucial for persons with autism spectrum disorders (ASD) to maintain a certain independence and a better quality of life, and interaction with virtual environments seems an effective learning aid. In a previous study, we reported that in adults with ASD interaction with a virtual environment (a virtual city) is beneficial to the acquisition of pedestrian skills (street crossing and street navigation). Interaction was based on a gesture-based interface (Microsoft Kinect). Here we compare the learning performance when the same virtual environment is operated by a gamepad interface. We used exactly the same training protocol and data analysis than the original study. We found that both interface types are effective in the acquisition of street crossing and city navigation skills. The gamepad interface seems easier to use (thus leading to faster interaction), but gesture-based interfaces are superior in terms of transfer of the learned skills to real road environments (as reported by parents and caregivers).

Modulation of motor performance by a monetary incentive: A pilot study.

It is commonly acknowledged that movement performance is determined by a trade-off between accuracy requirements and energetic expenditure. However, their relative weights are subjective and depend on the perceived benefit (or cost) associated to successful movement completion. A deeper knowledge on how this trade-off affects motor behavior may suggest ways to manipulate it in pathologies, like Parkinson's disease, in which the mechanisms underlying the selection of motor response are believed to be defective. In this preliminary study, we associate a monetary incentive to successful completion of a full-body reaching task and look at the determinants of motor performance. Our preliminary results suggest that motor performance (measured as the absolute average acceleration of hand movements) increases with movement amplitude/target elevation. Overall, performance also increases with the amount of monetary incentive and with the average reward experienced in previous trials. In addition, subjects with a greater sensitivity to incentive exhibit a low sensitivity to the average reward. In contrast, subjects with a negative sensitivity to incentive exhibit a smaller sensitivity to the average reward. These results suggest that motor performance has a complex relation with its perceived benefits, and this relation is probably subject-dependent.

Reward-based learning of a redundant task.

Motor skill learning has different components. When we acquire a new motor skill we have both to learn a reliable action-value map to select a highly rewarded action (task model) and to develop an internal representation of the novel dynamics of the task environment, in order to execute properly the action previously selected (internal model). Here we focus on a 'pure' motor skill learning task, in which adaptation to a novel dynamical environment is negligible and the problem is reduced to the acquisition of an action-value map, only based on knowledge of results. Subjects performed point-to-point movement, in which start and target positions were fixed and visible, but the score provided at the end of the movement depended on the distance of the trajectory from a hidden viapoint. Subjects did not have clues on the correct movement other than the score value. The task is highly redundant, as infinite trajectories are compatible with the maximum score. Our aim was to capture the strategies subjects use in the exploration of the task space and in the exploitation of the task redundancy during learning. The main findings were that (i) subjects did not converge to a unique solution; rather, their final trajectories are determined by subject-specific history of exploration. (ii) with learning, subjects reduced the trajectory's overall variability, but the point of minimum variability gradually shifted toward the portion of the trajectory closer to the hidden via-point.

Neuromotor recovery from stroke: computational models at central, functional, and muscle synergy level.

Computational models of neuromotor recovery after a stroke might help to unveil the underlying physiological mechanisms and might suggest how to make recovery faster and more effective. At least in principle, these models could serve: (i) To provide testable hypotheses on the nature of recovery; (ii) To predict the recovery of individual patients; (iii) To design patient-specific "optimal" therapy, by setting the treatment variables for maximizing the amount of recovery or for achieving a better generalization of the learned abilities across different tasks. Here we review the state of the art of computational models for neuromotor recovery through exercise, and their implications for treatment. We show that to properly account for the computational mechanisms of neuromotor recovery, multiple levels of description need to be taken into account. The review specifically covers models of recovery at central, functional and muscle synergy level.

Robotic assessment of upper limb motor function after stroke.

Traditional assessment of a stroke subject's motor ability, carried out by a therapist who observes and rates the subject's motor behavior using ordinal measurements scales, is subjective, time consuming and lacks sensitivity. Rehabilitation robots, which have been the subject of intense inquiry over the last decade, are equipped with sensors that are used to develop objective measures of motor behaviors in a semiautomated way during therapy. This article reviews the current contributions of robot-assisted motor assessment of the upper limb. It summarizes the various measures related to movement performance, the models of motor recovery in stroke subjects and the relationship of robotic measures to standard clinical measures. It analyses the possibilities offered by current robotic assessment techniques and the aspects to address to make robotic assessment a mainstream motor assessment method.