Design, Fabrication and Evaluation of a Stretchable High-Density Electromyography Array.

The adoption of high-density electrode systems for human-machine interfaces in real-life applications has been impeded by practical and technical challenges, including noise interference, motion artefacts and the lack of compact electrode interfaces. To overcome some of these challenges, we introduce a wearable and stretchable electromyography (EMG) array, and present its design, fabrication methodology, characterisation, and comprehensive evaluation. Our proposed solution comprises dry-electrodes on flexible printed circuit board (PCB) substrates, eliminating the need for time-consuming skin preparation. The proposed fabrication method allows the manufacturing of stretchable sleeves, with consistent and standardised coverage across subjects. We thoroughly tested our developed prototype, evaluating its potential for application in both research and real-world environments. The results of our study showed that the developed stretchable array matches or outperforms traditional EMG grids and holds promise in furthering the real-world translation of high-density EMG for human-machine interfaces.

The control of the arm's equilibrium position.

To generate a force, the brain activates muscles that act like springs to pull the arm toward a new equilibrium position. The equilibrium position (EP) is central to our understanding of the biological control of viscoelastic muscles. Although there is evidence of the EP during the control of limb posture, EPs have not been directly identified when the limb exerts a force against the environment. Here, we asked participants to apply a constant force in one of eight directions against a point-like constraint. This constraint was released abruptly to observe the final position to which the arm converged. Importantly, the same force magnitude was maintained while changing the arm's stiffness by modulating the strength of the hand's power grasp. The final position moved further away from the constraint as the arm became less stiff and was inversely proportional to the arm's stiffness, thereby confirming that the final position was the arm's EP. These results demonstrate how the EP changes with the arm's stiffness to produce a desired force in different directions.NEW & NOTEWORTHY According to numerous theories, the brain controls posture and movement by activating muscles that attract the limb toward a so-called equilibrium position, but the universality of this mechanism has not been shown for different motor behaviors. Here, we show that even when pushing or pulling against the environment, the brain achieves the desired force through an equilibrium position that lies beyond the physical constraint.

GripAble: Interrater reliability and normative grip strength of UK population.

BACKGROUND: Hand grip strength is an established indicator of individual health status and is used as a biomarker for predicting mortality, disability, and disease risks. GripAble hand grip dynamometer offers a modernized approach to measuring grip strength with its digital and high-accuracy measurement system. PURPOSE: This study aimed to (1) assess the interrater reliability of maximum grip strength (MGS) measurement and (2) establish GripAble's own gender-, age group- and hand-stratified normative MGS reference values of the adult UK population. STUDY DESIGN: Cross-sectional study design. METHODS: Interrater reliability among three raters assessing 30 participants across diverse age groups was measured using the intraclass correlation. In the second study, 11 investigators gathered MGS data from 907 participants across diverse age groups and gender. The average, standard deviation, minimum, median, maximum, and percentiles of MGS were computed for each gender, age group, and hand (L/R). The relationship between MGS and age was examined using quantile regression analysis. Additionally, generalized linear model regression analysis was conducted to explore the influence of participants' demographics (gender, hand [L/R], hand length, hand circumference, age, weight, and height) on MGS. RESULTS: MGS measurements between raters showed excellent agreement (ICC(2,1) = 0.991, 95% confidence interval [0.98, 1.0]). The MGS and age relationship follows a curvilinear pattern, reaching a peak median MGS values of up to 20 kg between 30 and 49 years for females and up to 35 kg between 30 and 59 years for males. Subsequently, MGS declined as age advanced. Gender and hand (L/R) emerged as the primary factors influencing MGS, followed by hand length, hand circumference, age, weight, and height. CONCLUSIONS: The presented normative MGS reference values can be used for interpreting MGS measurements obtained from adults in the United Kingdom using GripAble. This study, along with previous studies on GripAble devices, confirms GripAble as a reliable and valid tool for measuring MGS.

Dissociating haptic feedback from physical assistance does not improve motor performance.

In robots for motor rehabilitation and sports training, haptic assistance typically provides both mechanical guidance and task-relevant information. With the natural human tendency to minimise metabolic cost, mechanical guidance may however prevent efficient short term learning and retention. In this work, we explore the effect of providing haptic feedback to the not active hand during a tracking task. We test four types of haptic feedback: task- or error-related information, no information and irrelevant information. The results show that feedback provided to the hand not carrying out the tracking task did not improve task performance. However, irrelevant information to the task worsened performance, and negatively influenced the participants' perception of helpfulness, assistance, likability and predictability.

A virtual reality platform to evaluate the effects of supernumerary limbs' appearance.

Supernumerary robot limbs (SL) can expand the ability of users by increasing the number of degrees of freedom that they control. While several SLs have been designed and tested on human participants, the effect of the limb's appearance on the user's acceptance, embodiment and device usage is not yet understood. We developed a virtual reality platform with a three-arm avatar that enabled us to systematically investigate the effect of the supernumerary limb's appearance on their perception and motion control performance. A pilot study with 14 participants exhibited similar performance, workload and preference in human-like or robot-like appearance with a trend of preference for the robotic appearance.

Redundancy Resolution in Trimanual vs. Bimanual Tracking Tasks.

Supernumerary limbs promise to allow users to perform complex tasks that would otherwise require the actions of teams. However, how the user's capability for multimanual coordination compares to bimanual coordination, and how the motor system decides to configure its limb contributions given task redundancy is unclear. We conducted bimanual and trimanual (with the foot as a third-hand controller) virtual reality visuomotor tracking experiments to study how 32 healthy participants changed their limb coordination in response to uninstructed cursor mapping changes. This used a shared cursor mapped to the average limbs' position for different limb combinations. The results show that most participants correctly identified the different mappings during bimanual tracking, and accordingly minimized task-irrelevant motion. Instead during trimanual coordination, participants consistently moved all three limbs concurrently, showing weaker ipsilateral hand-foot coordination. These findings show how redundancy resolution and the resulting coordination patterns differ between similar bimanual and trimanual tasks. Further research is needed to consider the effect of learning on coordination behaviour.

Explicit learning based on reward prediction error facilitates agile motor adaptations.

Error based motor learning can be driven by both sensory prediction error and reward prediction error. Learning based on sensory prediction error is termed sensorimotor adaptation, while learning based on reward prediction error is termed reward learning. To investigate the characteristics and differences between sensorimotor adaptation and reward learning, we adapted a visuomotor paradigm where subjects performed arm movements while presented with either the sensory prediction error, signed end-point error, or binary reward. Before each trial, perturbation indicators in the form of visual cues were presented to inform the subjects of the presence and direction of the perturbation. To analyse the interconnection between sensorimotor adaptation and reward learning, we designed a computational model that distinguishes between the two prediction errors. Our results indicate that subjects adapted to novel perturbations irrespective of the type of prediction error they received during learning, and they converged towards the same movement patterns. Sensorimotor adaptations led to a pronounced aftereffect, while adaptation based on reward consequences produced smaller aftereffects suggesting that reward learning does not alter the internal model to the same degree as sensorimotor adaptation. Even though all subjects had learned to counteract two different perturbations separately, only those who relied on explicit learning using reward prediction error could timely adapt to the randomly changing perturbation. The results from the computational model suggest that sensorimotor and reward learning operate through distinct adaptation processes and that only sensorimotor adaptation changes the internal model, whereas reward learning employs explicit strategies that do not result in aftereffects. Additionally, we demonstrate that when humans learn motor tasks, they utilize both learning processes to successfully adapt to the new environments.

Hybrid Functional Electrical Stimulation and Robotic Assistance for Wrist Motion Training After Stroke: Preliminary Results.

This work presents preliminary results of a clinical study with sub-acute stroke patients using a hybrid system for wrist rehabilitation. The patients trained their wrist flexion/extension motion through a target tracking task, where electrical stimulation and robotic torque assisted them proportionally to their tracking error. Five sub-acute stroke patients have completed the training for 3 sessions on separate days. The preliminary results show hybrid assistance improves tracking performance and motion smoothness in most participants. In each session, patients' tracking performances before and after training were evaluated in unassisted tracking trials, without assistance. Their unassisted performance was compared across sessions and the results suggest that moderately to severely impaired patients might benefit more from hybrid training with our system than mildly impaired patients. Subjective assessments from all sessions show that the patients found the use of the device very comfortable and the training enjoyable. More data is being collected and future work will aim at verifying these trends.

Increasing the Motivation to Train Through Haptic Social Interaction - Pilot study.

Motivation is crucial in stroke rehabilitation, as it enhances patient engagement, adherence, and recovery. Robots can be employed to improve motivation through multiplayer rehabilitation games, which allow patients to collaborate and interact in a virtual environment through multimodal sensory cues. This social interaction can provide social support and increase motivation, resulting in better therapy engagement. A hand rehabilitation robot (PLUTO) was used to investigate the potential of social interaction to implement haptic multiplayer games. Twelve unimpaired participants (6 dyads) played in solo, collaborative, and competitive game modes. Surprisingly, no difference was found in self-reported engagement, tension, or competence between solo and multiplayer games. However, the IMI scale indicated that engagement for multiplayer games was rated higher than for solo games. The collaborative game was preferred by 10 out of 12 participants, highlighting its potential for promoting behavioural involvement and engagement. This study indicates that using PLUTO with multiplayer game modes can enhance therapy engagement. This can potentially improve rehabilitation outcomes if translated to the patient population.

Hybrid Robotic and Electrical Stimulation Assistance Can Enhance Performance and Reduce Mental Demand.

Combining functional electrical stimulation (FES) and robotics may enhance recovery after stroke, by providing neural feedback with the former while improving quality of motion and minimizing muscular fatigue with the latter. Here, we explored whether and how FES, robot assistance and their combination, affect users' performance, effort, fatigue and user experience. 15 healthy participants performed a wrist flexion/extension tracking task with FES and/or robotic assistance. Tracking performance improved during the hybrid FES-robot and the robot-only assistance conditions in comparison to no assistance, but no improvement is observed when only FES is used. Fatigue, muscular and voluntary effort are estimated from electromyographic recording. Total muscle contraction and volitional activity are lowest with robotic assistance, whereas fatigue level do not change between the conditions. The NASA-Task Load Index answers indicate that participants found the task less mentally demanding during the hybrid and robot conditions than the FES condition. The addition of robotic assistance to FES training might thus facilitate an increased user engagement compared to robot training and allow longer motor training session than with FES assistance.

Competition Increases the Effort Put Into a Physical Interaction Task.

Physical interaction can enhance motor learning, but it remains unclear what type of interaction is best suited to increasing the active effort put into a task, which should support learning. Here, we used the same interactive tracking task with different instructions to induce three training conditions: competition, collaboration, and self-improvement, where partners improve their own performance while interacting haptically with each other. The effort was gauged by measuring the total normalized muscle activity. Feedback of task performance and the haptic dynamics were identical in all three training conditions, so the effort needed to complete the task was the same. Only the instructions to 'compete with the partner', 'improve your and your partner's accuracy' and 'improve your accuracy' were different among the competition, collaboration, and self-improvement conditions, respectively. Despite having the same goal of maximizing self-performance during competition and self-improvement, participants exerted significantly more effort during competition, and their tracking accuracy was highest during competitive practice. Least effort was put into collaboration but tracking accuracy during collaboration was comparable to self-improvement. Our results suggest that interactive haptic competition can induce higher active drive or effort than either collaborative training or self-focused practice.

The Impact of Stiffness in Bimanual Versus Dyadic Interactions Requiring Force Exchange.

During daily activities, humans routinely manipulate objects bimanually or with the help of a partner. This work explored how bimanual and dyadic coordination modes are impacted by the object's stiffness, which conditions inter-limb haptic communication. For this, we recruited 20 healthy participants who performed a virtual task inspired by object handling, where we looked at the initiation of force exchange and its continued maintenance while tracking. Our findings suggest that while individuals and dyads displayed different motor behaviours, which may stem from the dyad members' need to estimate their partner's actions, they exhibited similar tracking accuracy. For both coordination modes, increased stiffness resulted in better tracking accuracy and more correlated motions, but required a larger effort through increased average torque. These results suggest that stiffness may be a key consideration in applications such as rehabilitation, where bimanual or external physical assistance is often provided.

Object Recognition Using Mechanical Impact, Viscoelasticity, and Surface Friction During Interaction.

Current robotic haptic object recognition relies on statistical measures derived from movement dependent interaction signals such as force, vibration or position. Mechanical properties, which can be estimated from these signals, are intrinsic object properties that may yield a more robust object representation. Therefore, this paper proposes an object recognition framework using multiple representative mechanical properties: stiffness, viscosity and friction coefficient as well as the coefficient of restitution, which has been rarely used to recognise objects. These properties are estimated in real-time using a dual Kalman filter (without tangential force measurements) and then are used for object classification and clustering. The proposed framework was tested on a robot identifying 20 objects through haptic exploration. The results demonstrate the technique's effectiveness and efficiency, and that all four mechanical properties are required for the best recognition rate of 98.18 ± 0.424%. For object clustering, the use of these mechanical properties also results in superior performance when compared to methods based on statistical parameters.

Physically interacting humans regulate muscle coactivation to improve visuo-haptic perception.

When moving a piano or dancing tango with a partner, how should I control my arm muscles to sense their movements and follow or guide them smoothly? Here we observe how physically connected pairs tracking a moving target with the arm modify muscle coactivation with their visual acuity and the partner's performance. They coactivate muscles to stiffen the arm when the partner's performance is worse and relax with blurry visual feedback. Computational modeling shows that this adaptive sensing property cannot be explained by the minimization of movement error hypothesis that has previously explained adaptation in dynamic environments. Instead, individuals skillfully control the stiffness to guide the arm toward the planned motion while minimizing effort and extracting useful information from the partner's movement. The central nervous system regulates muscle activation to guide motion with accurate task information from vision and haptics while minimizing the metabolic cost. As a consequence, the partner with the most accurate target information leads the movement.NEW & NOTEWORTHY Our results reveal that interacting humans inconspicuously modulate muscle activation to extract accurate information about the common target while considering their own and the partner's sensorimotor noise. A novel computational model was developed to decipher the underlying mechanism: muscle coactivation is adapted to combine haptic information from the interaction with the partner and own visual information in a stochastically optimal manner. This improves the prediction of the target position with minimal metabolic cost in each partner, resulting in the lead of the partner with the most accurate visual information.

Morphology, Connectivity, and Encoding Features of Tactile and Motor Representations of the Fingers in the Human Precentral and Postcentral Gyrus.

Despite the tight coupling between sensory and motor processing for fine manipulation in humans, it is not yet totally clear which specific properties of the fingers are mapped in the precentral and postcentral gyrus. We used fMRI to compare the morphology, connectivity, and encoding of the motor and tactile finger representations (FRs) in the precentral and postcentral gyrus of 25 5-fingered participants (8 females). Multivoxel pattern and structural and functional connectivity analyses demonstrated the existence of distinct motor and tactile FRs within both the precentral and postcentral gyrus, integrating finger-specific motor and tactile information. Using representational similarity analysis, we found that the motor and tactile FRs in the sensorimotor cortex were described by the perceived structure of the hand better than by the actual hand anatomy or other functional models (finger kinematics, muscles synergies). We then studied a polydactyly individual (i.e., with a congenital 6-fingered hand) showing superior manipulation abilities and divergent anatomic-functional hand properties. The perceived hand model was still the best model for tactile representations in the precentral and postcentral gyrus, while finger kinematics better described motor representations in the precentral gyrus. We suggest that, under normal conditions (i.e., in subjects with a standard hand anatomy), the sensorimotor representations of the 5 fingers in humans converge toward a model of perceived hand anatomy, deviating from the real hand structure, as the best synthesis between functional and structural features of the hand.SIGNIFICANCE STATEMENT Distinct motor and tactile finger representations exist in both the precentral and postcentral gyrus, supported by a finger-specific pattern of anatomic and functional connectivity across modalities. At the representational level, finger representations reflect the perceived structure of the hand, which might result from an adapting process harmonizing (i.e., uniformizing) the encoding of hand function and structure in the precentral and postcentral gyrus. The same analyses performed in an extremely rare polydactyly subject showed that the emergence of such representational geometry is also found in neuromechanical variants with different hand anatomy and function. However, the harmonization process across the precentral and postcentral gyrus might not be possible because of divergent functional-structural properties of the hand and associated superior manipulation abilities.

How virtual and mechanical coupling impact bimanual tracking.

Bilateral training systems look to promote the paretic hand's use in individuals with hemiplegia. Although this is normally achieved using mechanical coupling (i.e., a physical connection between the hands), a virtual reality system relying on virtual coupling (i.e., through a shared virtual object) would be simpler to use and prevent slacking. However, it is not clear whether different coupling modes differently impact task performance and effort distribution between the hands. We explored how 18 healthy right-handed participants changed their motor behaviors in response to the uninstructed addition of mechanical coupling, and virtual coupling using a shared cursor mapped to the average hands' position. In a second experiment, we then studied the impact of connection stiffness on performance, perception, and effort imbalance. The results indicated that both coupling types can induce the hands to actively contribute to the task. However, the task asymmetry introduced by using a cursor mapped to either the left or right hand only modulated the hands' contribution when not mechanically coupled. The tracking performance was similar for all coupling types, independent of the connection stiffness, although the mechanical coupling was preferred and induced the hands to move with greater correlation. These findings suggest that virtual coupling can induce the hands to actively contribute to a task in healthy participants without hindering their performance. Further investigation on the coupling types' impact on the performance and hands' effort distribution in patients with hemiplegia could allow for the design of simpler training systems that promote the affected hand's use.NEW & NOTEWORTHY We showed that the uninstructed addition of a virtual and/or a mechanical coupling can induce both hands to actively contribute in a continuous redundant bimanual tracking task without impacting performance. In addition, we showed that the task asymmetry can only alter the effort distribution when the hands are not connected, independent of the connection stiffness. Our findings suggest that virtual coupling could be used in the development of simpler VR-based training devices.

A systematic investigation of detectors for low signal-to-noise ratio EMG signals.

Background: Active participation of stroke survivors during robot-assisted movement therapy is essential for sensorimotor recovery. Robot-assisted therapy contingent on movement intention is an effective way to encourage patients' active engagement. For severely impaired stroke patients with no residual movements, a surface electromyogram (EMG) has been shown to be a viable option for detecting movement intention. Although numerous algorithms for EMG detection exist, the detector with the highest accuracy and lowest latency for low signal-to-noise ratio (SNR) remains unknown. Methods: This study, therefore, investigates the performance of 13 existing EMG detection algorithms on simulated low SNR (0dB and -3dB) EMG signals generated using three different EMG signal models: Gaussian, Laplacian, and biophysical model. The detector performance was quantified using the false positive rate (FPR), false negative rate (FNR), and detection latency. Any detector that consistently showed FPR and FNR of no more than 20%, and latency of no more than 50ms, was considered an appropriate detector for use in robot-assisted therapy. Results: The results indicate that the Modified Hodges detector - a simplified version of the threshold-based Hodges detector introduced in the current study - was the most consistent detector across the different signal models and SNRs. It consistently performed for ~90% and ~40% of the tested trials for 0dB and -3dB SNR, respectively. The two statistical detectors (Gaussian and Laplacian Approximate Generalized Likelihood Ratio) and the Fuzzy Entropy detectors have a slightly lower performance than Modified Hodges. Conclusions: Overall, the Modified Hodges, Gaussian and Laplacian Approximate Generalized Likelihood Ratio, and the Fuzzy Entropy detectors were identified as the potential candidates that warrant further investigation with real surface EMG data since they had consistent detection performance on low SNR EMG data.

Can Training Make Three Arms Better Than Two Heads for Trimanual Coordination?

Supernumerary effectors have been proposed to enable users to perform tasks alone that normally require assistance from a partner. While various supernumerary robotic limbs have been developed in the last decade, the capability of users to operate them effectively has not yet been proven. Here we tested whether users (i) can complete a task that requires simultaneous and fine control of three effectors, and (ii) can be trained to do so with similar or superior performance as through collaboration with a human partner. As in previous studies, initial augmented capability was less than that of working with a partner. However, one hour of dedicated solo trimanual training across three days significantly increased task performance, so that participants became able to perform trimanual control alone as well as or better than they could with a new partner. This shows the viability of augmentation systems for applications such as in robotic surgery or industrial assembly, which can be further validated on real tasks with physical systems.

Integrating hand exoskeletons into goal-oriented clinic and home stroke and spinal cord injury rehabilitation.

Introduction: Robotic exoskeletons are emerging as rehabilitation and assistive technologies that simultaneously restore function and enable independence for people with disabilities. Aim: We investigated the feasibility and orthotic and restorative effects of an exoskeleton-supported goal-directed rehabilitation program for people with hand impairments after stroke or Spinal Cord Injury (SCI). Method: A single-arm case-series feasibility study was conducted using a wearable untethered hand exoskeleton during goal-directed therapy programs with in-clinic and at-home components. Therapists trained stroke and SCI patients to use a hand exoskeleton during rehabilitation exercises, activities of daily living and patient-selected goals. Each patient received a 1-hour in-clinic training session on five consecutive days, then took the exoskeleton home for two consecutive days to perform therapist-recommended tasks. Goal Attainment Scaling (GAS) and the Box and Block Test (BBT) were administered at baseline, after in-clinic therapy and after home use, with and again without wearing the exoskeleton. The System Usability Scale (SUS), Motor Activity Log, and Fugl-Meyer Assessment were also administered to assess the intervention's acceptability, adherence, usability and effectiveness. Results: Four stroke patients (Chedoke McMaster Stage of Hand 2-4) and one SCI patient (ASIA C8 Motor Stage 1) 23 ± 19 months post-injury wore the hand exoskeleton to perform 280 ± 23 exercise repetitions in the clinic and additional goal-oriented tasks at home. The patients performed their own goals and the dexterity task with higher performance following the 7-days therapy program in comparison to baseline for both exoskeleton-assisted (ΔGAS: 18 ± 10, ΔBBT: 1 ± 5) and unassisted (ΔGAS: 14 ± 14, ΔBBT: 3 ± 4) assessments. Therapists and patients provided 'good' SUS ratings of 78 ± 6 and no harmful events were reported. Conclusions: The exoskeleton-supported stroke and SCI therapy program with in-clinic and at-home training components was feasible.