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Socially assistive robots (SARs) have been suggested as a platform for post-stroke training. It is not yet known whether long-term interaction with a SAR can lead to an improvement in the functional ability of individuals post-stroke. The aim of this pilot study was to compare the changes in motor ability and quality of life following a long-term intervention for upper-limb rehabilitation of post-stroke individuals using three approaches: 1) training with a SAR in addition to usual care; 2) training with a computer in addition to usual care; and 3) usual care with no additional intervention. Thirty-three post-stroke patients with moderate-severe to mild impairment were randomly allocated into three groups: two intervention groups – one with a SAR (ROBOT group) and one with a computer (COMPUTER group) – and one control group with no intervention (CONTROL group). The intervention sessions took place three times/week, for a total of 15 sessions/participant; The study was conducted over a period of two years, during which 306 sessions were held. Twenty-six participants completed the study. Participants in the ROBOT group significantly improved in their kinematic and clinical measures which included smoothness of movement, action research arm test (ARAT), and Fugl-Meyer upper-extremity assessment (FMA-UE). No significant improvement in these measures was found in the COMPUTER or the control groups. 100% of the participants in the SAR group gained improvement which reached – or exceeded – the minimal clinically important difference in the ARAT, the gold standard for upper-extremity activity performance post-stroke. This study demonstrates both the feasibility and the clinical benefit of using a SAR for long-term interaction with post-stroke individuals as part of their rehabilitation program. Trial Registration: ClinicalTrials.gov NCT03651063.

Engagement with upper limb rehabilitation post-stroke can improve rehabilitation outcomes. Virtual Reality can be used to make rehabilitation more engaging. In this paper, we propose a multiple case study to determine: (1) whether game design principles (identified in an earlier study as being likely to engage) actually do engage, in practice, a sample of stroke survivors with a Desktop Virtual Reality-based Serious Game designed for upper limb rehabilitation; and (2) what game design factors support the existence of these principles in the game. In this study, we considered 15 principles: awareness, feedback, interactivity, flow, challenge, attention, interest, involvement, psychological absorption, motivation, effort, clear instructions, usability, purpose, and a first-person view. Four stroke survivors used, for a period of 12 weeks, a Virtual Reality-based upper limb rehabilitation system called the Neuromender Rehabilitation System. The stroke survivors were then asked how well each of the 15 principles was supported by the Neuromender Rehabilitation System and how much they felt each principle supported their engagement with the system. All the 15 tested principles had good or reasonable support from the participants as being engaging. Use of feedback was emphasised as an important design factor for supporting the design principles, but there was otherwise little agreement in important design factors among the participants. This indicates that more personalised experiences may be necessary for optimised engagement. The insight gained can be used to inform the design of a larger scale statistical study into what engages stroke survivors with Desktop Virtual Reality-based upper limb rehabilitation.

The rejection rates of upper-limb prosthetic devices in adults are high, currently averaging 26% and 23% for body-powered and electric devices, respectively. While many factors influence acceptance, prosthesis training methods relying on novel virtual reality systems have been cited as a critical factor capable of increasing the likelihood of long-term, full-time use. Despite that, these implementations have not yet garnered widespread traction in the clinical setting, and their use remains immaterial. This review aims to explore the reasons behind this situation by identifying trends in existing research that seek to advance Extended Reality “X-Reality” systems for the sake of upper-limb prosthesis rehabilitation and, secondly, analyzing barriers and presenting potential pathways to deployment for successful adoption in the future. The search yielded 42 research papers that were divided into two categories. The first category included articles that focused on the technical aspect of virtual prosthesis training. Articles in the second category utilize user evaluation procedures to ensure applicability in a clinical environment. The review showed that 75% of articles that conducted whole system testing experimented with non-immersive virtual systems. Furthermore, there is a shortage of experiments performed with amputee subjects. From the large-scale studies analyzed, 71% of those recruited solely non-disabled participants. This paper shows that X-Reality technologies for prosthesis rehabilitation of upper-limb amputees carry significant benefits. Nevertheless, much still must be done so that the technology reaches widespread clinical use.

The rejection rates of upper-limb prosthetic devices in adults are high, currently averaging 26% and 23% for body-powered and electric devices, respectively. While many factors influence acceptance, prosthesis training methods relying on novel virtual reality systems have been cited as a critical factor capable of increasing the likelihood of long-term, full-time use. Despite that, these implementations have not yet garnered widespread traction in the clinical setting, and their use remains immaterial. This review aims to explore the reasons behind this situation by identifying trends in existing research that seek to advance Extended Reality “X-Reality” systems for the sake of upper-limb prosthesis rehabilitation and, secondly, analyzing barriers and presenting potential pathways to deployment for successful adoption in the future. The search yielded 42 research papers that were divided into two categories. The first category included articles that focused on the technical aspect of virtual prosthesis training. Articles in the second category utilize user evaluation procedures to ensure applicability in a clinical environment. The review showed that 75% of articles that conducted whole system testing experimented with non-immersive virtual systems. Furthermore, there is a shortage of experiments performed with amputee subjects. From the large-scale studies analyzed, 71% of those recruited solely non-disabled participants. This paper shows that X-Reality technologies for prosthesis rehabilitation of upper-limb amputees carry significant benefits. Nevertheless, much still must be done so that the technology reaches widespread clinical use. [ABSTRACT FROM AUTHOR]

Assessing biomechanics of upper limb movement is essential for guiding targeted therapy to treat conditions such as spasticity and dystonia. Targeted therapy, including injections of medications into specific muscles (e.g., lidocaine, botulinum toxin type A), requires accurate identification (activity) and contribution of as many muscles as possible. Currently, this is achieved by visual clinical assessment or using surface electromyography (sEMG). Although sEMG could provide a reasonable estimate of muscle activity for certain superficial muscles after an intense filtering process, they are unable to provide separated activity and contribution for every superficial and deep muscle. Other proposed musculoskeletal and machine learning models similarly do not provide a detailed and accurate activity of every muscle. The objective of the study is to design a subject-specific musculoskeletal model to predict the activity and contribution of each muscle pertaining to any upper limb movement with improved detail and accuracy over existing methodologies. Performance metrics were calculated for validation by comparing the predicted muscle activity with the normalized sEMG data computed from 8 superficial muscles, while the deeper muscles were not included in the validation as the sEMG is unable to provide a separated activity for those muscles. The results show that the proposed model has a mean R2 value of 0.8190 and also indicated a statistically significant correlation ( $P < 0.0001$ ) between the calculated (normalized sEMG data) and predicted activity value. Additionally, and significantly, compared to earlier studies, the proposed model predicts the individual muscle activity and contribution of deeper muscles.

Modernising the way upper-limb prosthetic sockets are made has seen limited progress. The casting techniques that are employed in clinics today resemble those developed over 50 years ago and there is still a heavy reliance on manual labour. Modern manufacturing methods such as 3D scanning and printing are often presented as ready-to-use solutions for producing low-cost functional devices, with public perceptions being largely shaped by the superficial media representation and advertising. The promise is that modern socket manufacturing methods can improve patient satisfaction, decrease manufacturing times and reduce the workload in the clinic. However, the perception in the clinical community is that total conversion to digital methods in a clinical environment is not straightforward. Anecdotally, there is currently a disconnect between those developing technology to produce prosthetic devices and the actual needs of clinicians and people with limb difference. In this paper, we demonstrate strengths and drawbacks of a fully digitised, low-cost trans-radial diagnostic socket making process, informed by clinical principles. We present volunteer feedback on the digitally created sockets and provide expert commentary on the use of digital tools in upper-limb socket manufacturing. We show that it is possible to utilise 3D scanning and printing, but only if the process is informed by expert knowledge. We bring examples to demonstrate how and why the process may go wrong. Finally, we provide discussion on why progress in modernising the manufacturing of upper-limb sockets has been slow yet it is still too early to rule out digital methods. [ABSTRACT FROM AUTHOR]

This article features a novel sliding mode controller for robotic arms using nonlinear model-based switching functions. The new controller is experimentally validated on a 7-DOF exoskeleton arm used for upper-limb rehabilitation applications. The proposed approach features a novel concept using model-based switching functions in the sliding mode controller, which leads to considerable simplifications on the torque control inputs. Compared to conventional linear switching functions, model-based switching functions show substantial control performance improvements on the torque inputs, such as transient constraints reduction and enhanced robustness, while maintaining a very good tracking performance. Moreover, model-based switching functions design ensures a complete decoupling of chattering effect between joint axes. Furthermore, this approach can be combined with existing chattering reduction techniques to ensure proper control of chattering levels on the torque inputs. These advantages make the practical implementation of the model-based switching functions approach particularly desirable for wearable robotics, where smooth movements and high accuracy are important requirements for patients’ comfort and security. [ABSTRACT FROM AUTHOR]

Reliable control of assistive devices using surface electromyography (sEMG) remains an unsolved task due to the signal’s stochastic behavior that prevents robust pattern recognition for real-time control. Non-representative samples lead to inherent class overlaps that generate classification ripples for which the most common alternatives rely on post-processing and sample discard methods that insert additional delays and often do not offer substantial improvements. In this paper, a resilient classification pipeline based on Extreme Learning Machines (ELM) was used to classify 17 different upper-limb movements through sEMG signals from a total of 99 trials derived from three different databases. The method was compared to a baseline ELM and a sample discarding (DISC) method and proved to generate more stable and consistent classifications. The average accuracy boost of ≈ 10% in all databases lead to average weighted accuracy rates higher as 53,4% for amputees and 89,0% for non-amputee volunteers. The results match or outperform related works even without sample discards. [ABSTRACT FROM AUTHOR]

Virtual rehabilitation has been used during decades to provide a more personalized, controlled, and enjoyable experience on upper-limb motor rehabilitation. Since novel virtual reality (VR) technologies are now accessible and highly immersive, the challenge for a wide dissemination of virtual rehabilitation in clinical scenarios has shifted from the hardware robustness to the software intelligence. A sophisticated technique that provides physiological intelligence to novel human-computer interaction (HCI) applications is biocybernetic adaptation. The concept emerges from the electrophysiological computing field, and it proposes using body signals to detect human states (e.g. workload or fatigue) and modulate the virtual activity accordingly. This paper evaluates the effects of using biocybernetic adaptation in a virtual rehabilitation game that aims to encourage users to exert at a desirable intensity level while interacting with the virtual environment. The system relies on surface-electromyography (sEMG) signals to detect fatigue levels in real-time and adapt the game challenge dynamically. Perceived fatigue levels, game user experience, and game performance parameters are assessed after playing the game, considering two different visualization modalities: non-immersive (conventional flat screen) and immersive (VR headset). Results revealed how the biocybernetic system in the immersive condition not only produced lower levels of perceived fatigue compared with the non-immersive, but also, created a more enjoyable and positive experience in a controlled experiment with 24 healthy subjects. Moreover, participants in the immersive condition showed a better performance in the virtual game and higher usability levels scored by users compared with the non-immersive condition. To conclude, we highlight the importance of combining novel immersive approaches with physiologically aware systems to enhance the benefits of virtual rehabilitation therapies. [ABSTRACT FROM AUTHOR]

Impaired motor control post-stroke is typically measured using clinical assessments employing categorical and subjective scoring. We investigated quantitative kinematic parameters of a complex movement with therapy in chronic stroke. Tri-axial accelerometry of the more-affected arm of 24 patients was recorded during early- (day 2–3) and late- (days 12–14) therapy, and for 13 patients at 6-month follow-up. Clinical assessments included the classification of motor-function as low, moderate, or high. Kinematic parameters were measured during Wii-baseball swings to assess the effect of time and the level of motor-function. Clinical tests improved over time (all p < 0.01). Increased acceleration magnitude over time was significant only at proximal sensors (p < 0.05), and there was an effect of motor-function at distal sensors (p < 0.05). Normalized velocity decreased (p < 0.05) at all sensors over time. Peak acceleration and peak deceleration increased over time, predominately at proximal sensors. Kinematic parameters provide an objective and quantitative measure of change in motor-function that is not possible with clinical assessments. The complex patterns of change were not consistent between and within levels of motor-function but reflected improved motor control that was sustained over time. These data emphasize the potential for ongoing improvements in motor capacity in chronic stroke with additional rehabilitation. [ABSTRACT FROM AUTHOR]

Goal: Little information is available in the existing literature regarding the influence of the scapular kinematic estimate method on musculoskeletal analysis. This study aimed to assess the propagation of errors due to the method used for scapular kinematics reconstruction in the workflow of musculoskeletal modeling (joint kinematics, joint torques, muscle force, and joint reaction force) in shoulder and upper-limb movements. Methods: Two participants performed functional (arm elevation and rotation), daily life (eating and reaching pants pockets), and sports movements (a simulated throwing maneuver). Shoulder kinematics were obtained with five multibody kinematics methods: intracortical pins (Pins, reference method), International Society of Biomechanics (ISB), Jackson (Jack), Projection (Proj), and Ellipsoid (Ell) methods. For the five kinematics methods, joint torques, muscle forces, and glenohumeral joint reaction forces were computed with the Delft Shoulder and Elbow musculoskeletal model. Results: Differences up to 30° in glenohumeral joint kinematics, compared to the Pins method, resulted in differences less than 3 N.m in joint torque estimation. However, these also resulted in differences up to 50 and 831 N in the muscle force and joint reaction force estimate, respectively, in comparison to the reference method (Pins). No method yielded the worst or best results in comparison to the Pins method but the differences were task-specific. Conclusion: We concluded that shoulder biomechanical studies based on skin markers should be completed with caution when assessing joint angles, muscle forces, and glenohumeral joint reaction forces, while researchers may be more confident with the evaluation of shoulder joint torques. [ABSTRACT FROM PUBLISHER]

Bioelectrical signals are used more frequently in order to provide an alternative way to command devices, such as myoelectrical signals or electroencephalic signals ?the last ones used in Brain-Computer Interface applications-. The goal of this paper is using myoelectrical signals to command a robotic upperlimb prosthesis developed as well. The only requirement to be able to use the prosthesis would be to have a voluntary and accurate muscle control. In that way, a training system for prosthesis' future users has been developed in order to train their muscle control. [ABSTRACT FROM PUBLISHER]