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Blockchain is receiving increasing attention from academy and industry, since it is considered a breakthrough technology that could bring huge benefits to many different sectors. In 2017, Gartner positioned blockchain close to the peak of inflated expectations, acknowledging the enthusiasm for this technology that is now largely discussed by media. In this scenario, the risk to adopt it in the wake of enthusiasm, without objectively judging its actual added value is rather high. Insurance is one the sectors that, among others, started to carefully investigate the possibilities of blockchain. For this specific sector, however, the hype cycle shows that the technology is still in the innovation trigger phase, meaning that the spectrum of possible applications has not been fully explored yet. Insurers, as with many other companies not necessarily active only in the financial sector, are currently requested to make a hard decision, that is, whether to adopt blockchain or not, and they will only know if they were right in 3–5 years. The objective of this paper is to support actors involved in this decision process by illustrating what a blockchain is, analyzing its advantages and disadvantages, as well as discussing several use cases taken from the insurance sector, which could easily be extended to other domains.

Activity-based training and lumbosacral spinal cord epidural stimulation (scES) have the potential to restore standing and walking with self-balance assistance after motor complete spinal cord injury (SCI). However, improvements in upright postural control have not previously been addressed in this population. Here, we implemented a novel robotic postural training with scES, performed with free hands, to restore upright postural control in individuals with chronic, cervical (n?=?5) or high-thoracic (n?=?1) motor complete SCI, who had previously undergone stand training with scES using a walker or a standing frame for self-balance assistance. Robotic postural training re-enabled and/or largely improved the participants' ability to control steady standing, self-initiated trunk movements and upper limb reaching movements while standing with free hands, receiving only external assistance for pelvic control. These improvements were associated with neuromuscular activation pattern adaptations above and below the lesion. These findings suggest that the human spinal cord below the level of injury can generate meaningful postural responses when its excitability is modulated by scES, and can learn to improve these responses. Upright postural control improvements can enhance functional motor recovery promoted by scES after severe SCI.

Extended reality (XR) technology combines physical reality with computer synthetic virtuality to deliver immersive experience to users. Virtual reality (VR) and augmented reality (AR) are two subdomains within XR with different immersion levels. Both of these have the potential to be combined with robot-assisted training protocols to maximize postural control improvement. In this study, we conducted a randomized control experiment with sixty-three healthy subjects to compare the effectiveness of robot-assisted posture training combined with VR or AR against robotic training alone. A robotic Trunk Support Trainer (TruST) was employed to deliver assistive force at the trunk as subjects moved beyond the stability limits during training. Our results showed that both VR and AR significantly enhanced the training outcomes of the TruST intervention. However, the VR group experienced higher simulator sickness compared to the AR group, suggesting that AR is better suited for sitting posture training in conjunction with TruST intervention. Our findings highlight the added value of XR to robot-assisted training and provide novel insights into the differences between AR and VR when integrated into a robotic training protocol. In addition, we developed a custom XR application that suited well for TruST intervention requirements. Our approach can be extended to other studies to develop novel XR-enhanced robotic training platforms.

The boundary-based assist-as-needed (BAAN) force field is widely used in robotic rehabilitation and has shown promising results in improving trunk control and postural stability. However, the fundamental understanding of how the BAAN force field affects the neuromuscular control remains unclear. In this study, we investigate how the BAAN force field impacts muscle synergy in the lower limbs during standing posture training. We integrated virtual reality (VR) into a cable-driven Robotic Upright Stand Trainer (RobUST) to define a complex standing task that requires both reactive and voluntary dynamic postural control. Ten healthy subjects were randomly assigned to two groups. Each subject performed 100 trials of the standing task with or without assistance from the BAAN force field provided by RobUST. The BAAN force field significantly improved balance control and motor task performance. Our results also indicate that the BAAN force field reduced the total number of lower limb muscle synergies while concurrently increasing the synergy density (i.e., number of muscles recruited in each synergy) during both reactive and voluntary dynamic posture training. This pilot study provides fundamental insights into understanding the neuromuscular basis of the BAAN robotic rehabilitation strategy and its potential for clinical applications. In addition, we expanded the repertoire of training with RobUST that integrates both perturbation training and goal-oriented functional motor training within a single task. This approach can be extended to other rehabilitation robots and training approaches with them.

Seated postural limit defines the boundary of a region such that for any excursions made outside this boundary a subject cannot return the trunk to the neutral position without additional external support. The seated postural limits can be used as a reference to provide assistive support to the torso by the Trunk Support Trainer (TruST). However, fixed boundary representations of seated postural limits are inadequate to capture dynamically changing seated postural limits during training. In this study, we propose a conceptual model of dynamic boundary of the trunk center by assigning a vector that tracks the postural-goal direction and trunk movement amplitude during a sitting task. We experimented with 20 healthy subjects. The results support our hypothesis that TruST intervention with an assist-as-needed force controller based on dynamic boundary representation could achieve more significant sitting postural control improvements than a fixed boundary representation. The second contribution of this paper is that we provide an effective approach to embed deep learning into TruST’s real-time controller design. We have compiled a 3D trunk movement dataset which is currently the largest in the literature. We designed a loss function capable of solving the gate-controlled regression problem. We have proposed a novel deep-learning roadmap for the exploration study. Following the roadmap, we developed a deep learning architecture, modified the widely used Inception module, and then obtained a deep learning model capable of accurately predicting the dynamic boundary in real-time. We believe that this approach can be extended to other rehabilitation robots towards designing intelligent dynamic boundary-based assist-as-needed controllers.

Introduction Children with cerebral palsy (CP) classified as gross motor function classification system (GMFCS) levels III–IV demonstrate impaired sitting and reaching control abilities that hamper their overall functional performance. Yet, efficacious interventions for improving sitting-related activities are scarce. We recently designed a motor learning-based intervention delivered with a robotic Trunk-Support-Trainer (TruST-intervention), in which we apply force field technology to individualise sitting balance support. We propose a randomised controlled trial to test the efficacy of the motor intervention delivered with robotic TruST compared with a static trunk support system.Methods and analysis We will recruit 82 participants with CP, GMFCS III–IV, and aged 6–17 years. Randomisation using concealed allocation to either the TruST-support or static trunk-support intervention will be conducted using opaque-sealed envelopes prepared by someone unrelated to the study. We will apply an intention-to-treat protocol. The interventions will consist of 2 hours/sessions, 3/week, for 4 weeks. Participants will start both interventions with pelvic strapping. In the TruST-intervention, postural task progression will be implemented by a progressive increase of the force field boundaries and then by removing the pelvic straps. In the static trunk support-intervention, we will progressively lower the trunk support and remove pelvic strapping. Outcomes will be assessed at baseline, training midpoint, 1-week postintervention, and 3-month follow-up. Primary outcomes will include the modified functional reach test, a kinematic evaluation of sitting workspace, and the Box and Block test. Secondary outcomes will include The Segmental Assessment of Trunk Control test, Seated Postural & Reaching Control test, Gross Motor Function Measure-Item Set, Canadian Occupational Performance Outcome, The Participation and Environment Measure and Youth, and postural and reaching kinematics.Ethics and dissemination The study was approved by the Columbia University Institutional Review Board (AAAS7804). This study is funded by the National Institutes of Health (1R01HD101903-01) and is registered at clinicaltrials.gov.Trial registration number NCT04897347; clinicaltrials.gov.

This study characterizes the effects of a postural training program on balance and muscle control strategies in a virtual reality (VR) environment. The Robotic Upright Stand Trainer (RobUST), which applies perturbative forces on the trunk and assistive forces on the pelvis, was used to deliver perturbation-based balance training (PBT) in a sample of 10 healthy participants. The VR task consisted of catching, aiming, and throwing a ball at a target. All participants received trunk perturbations during the VR task with forces tailored to the participant’s maximum tolerance. A subgroup of these participants additionally received assistive forces at the pelvis during training. Postural kinematics were calculated before and after RobUST training, including (i) maximum perturbation force tolerated, (ii) center of pressure (COP) and pelvic excursions, (iii) postural muscle activations (EMG), and (iv) postural control strategies (the ankle and hip strategies). We observed an improvement in the maximum perturbation force and postural stability area in both groups and decreases in muscle activity. The behavior of the two groups differed for perturbations in the posterior direction where the unassisted group moved towards greater use of the hip strategy. In addition, the assisted group changed towards a lower margin of stability and higher pelvic excursion. We show that training with force assistance leads to a reactive balance strategy that permits pelvic excursion but that is efficient at restoring balance from displaced positions while training without assistance leads to reactive balance strategies that restrain pelvic excursion. Patient populations can benefit from a platform that encourages greater use of their range of motion.

Hand exoskeleton potential applications reach further than grasping or assistance during manipulation. In this paper, we present a preliminary study of how this technology can be applied in order to improve performance during standing to help the user to keep balance under perturbations. Non-impaired users wearing a hand exoskeleton gripping a hand rail were pushed by a cable-driven robot, so that their standing equilibrium was perturbed. The center of pressure, surface electromyography, and interaction force data were recorded in order to assess the performance of users and their postural strategy. The results showed that users could keep their balance with the same outcomes using their bare hands and the hand exoskeleton. However, when wearing the exoskeleton, a higher muscular activity was registered in hand flexor muscles. This is also supported by the grasping force, which shows that users stretched their hand more than expected when wearing the hand exoskeleton. This paper concludes that it is possible that the lack of tactile feedback could lead to over compensation in the grasping. Therefore, the next studies will aim to check whether this effect can be reversed by training users to wear the exoskeleton.