370 results found
Hu ZJ, Wang Z, Huang Y, et al., 2023, Towards Human-Robot Collaborative Surgery: Trajectory and Strategy Learning in Bimanual Peg Transfer, IEEE Robotics and Automation Letters, Vol: 8, Pages: 4553-4560
While the traditional control of surgical robots relies on fully manual teleoperations, human-robot collaborative systems promise to address issues such as workspace constrains and laborious tasks. In particular, shared control can reduce the surgeon's workload and improve the overall surgical performance by supporting the surgeon effort during movements while keeping them in charge of complex control phases. In this letter, we propose a segmentation of the bimanual peg transfer task that alternates manual and autonomous control correspondingly. The authority allocation in this shared control framework considers both the limitation of learning-based methods and the higher dexterity of humans during physical interaction. The motion and strategies are transferred from an expert human to a da Vinci Research Kit (dVRK) using an epsilon-greedy on a maximum entropy inverse reinforcement learning algorithm. The model generated enables to train an intelligent agent that can skillfully collaborate with the human operator during the surgical task. The proposed shared control framework is verified both on a virtual platform and then on a real dVRK to assess its usability and robustness. The results show that, compared to traditional teleoperation, our method can achieve faster and more consistent peg transfers. An analysis of the participants' effort also reveals a significantly lower perception of the workload.
Wang Z, Lam HK, Guo Y, et al., 2023, Adaptive Event-Triggered Control for Nonlinear Systems With Asymmetric State Constraints: A Prescribed-Time Approach, IEEE Transactions on Automatic Control, Vol: 68, Pages: 3625-3632, ISSN: 0018-9286
Finite/fixed-time control yields a promising tool to optimize a system's settling time, but lacks the ability to separately define the settling time and the convergence domain (known as practically prescribed-time stability, PPTS). We provide a sufficient condition for PPTS based on a new piecewise exponential function, which decouples the settling time and convergence domain into separately user-defined parameters. We propose an adaptive event-triggered prescribed-time control scheme for nonlinear systems with asymmetric output constraints, using an exponential-type barrier Lyapunov function. We show that this PPTS control scheme can guarantee tracking error convergence performance, while restricting the output state according to the prescribed asymmetric constraints. Compared with traditional finite/fixed-time control, the proposed methodology yields separately user-defined settling time and convergence domain without the prior information on disturbance. Moreover, asymmetric state constraints can be handled in the control structure through bias state transformation, which offers an intuitive analysis technique for general constraint issues. Simulation and experiment results on a heterogeneous teleoperation system demonstrate the merits of the proposed control scheme.
Pena-Perez N, Mutalib SA, Eden J, et al., 2023, The Impact of Stiffness in Bimanual Versus Dyadic Interactions Requiring Force Exchange., IEEE Trans Haptics, Vol: PP
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 twenty 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's 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.
Mastria G, Scaliti E, Mehring C, et al., 2023, Morphology, connectivity, and encoding features of tactile and motor representations of the fingers in the human precentral and postcentral gyrus, The Journal of Neuroscience, Vol: 43, Pages: 1572-1589, ISSN: 0270-6474
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
Farina D, Burdet E, Mehring C, et al., 2023, Roboticists Want to Give You a Third Arm: Unused Bandwidth in Neurons Can be Tapped to Control Extra Limbs, IEEE Spectrum, Vol: 60, ISSN: 0018-9235
Börner H, Carboni G, Cheng X, et al., 2023, Physically interacting humans regulate muscle coactivation to improve visuo-haptic perception., Journal of Neurophysiology, Vol: 129, Pages: 494-499, ISSN: 0022-3077
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.
Cheng Y, Huang Y, Wang Z, et al., 2023, Foot gestures to control the grasping of a surgical robot, Pages: 6844-6850, ISSN: 1050-4729
Many surgical tasks require three or more tools working together, where a hands-free interface could extend a surgeon's actions to control a third surgical tool. However, most current interfaces do not allow skilled control of grasping critical to robotic manipulation. Here we first present a systematic study to identify efficient and intuitive interaction strategies to control grasping of a surgical tool. A series of experiments were conducted to evaluate six foot pressure-based gestures. Based on the results, three modular novel foot-machine interfaces were developed, which can be integrated with other motion control interfaces. The identified interaction strategies were implemented to control a laparoscopic tool in a surgical simulator, and evaluated in a user study. The results illustrate how naive participants can operate grasping yielding smooth and pick & place operation.
Pena-Perez N, Eden J, Ivanova E, et al., 2023, How virtual and mechanical coupling impact bimanual tracking, Journal of Neurophysiology, Vol: 129, Pages: 102-114, ISSN: 0022-3077
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.
Wang Z, Fei H, Huang Y, et al., 2023, Learning to Assist Bimanual Teleoperation using Interval Type-2 Polynomial Fuzzy Inference, IEEE Transactions on Cognitive and Developmental Systems, ISSN: 2379-8920
Assisting humans in collaborative tasks is a promising application for robots, however effective assistance remains challenging. In this paper, we propose a method for providing intuitive robotic assistance based on learning from human natural limb coordination. To encode coupling between multiple-limb motions, we use a novel interval type-2 (IT2) polynomial fuzzy inference for modeling trajectory adaptation. The associated polynomial coefficients are estimated using a modified recursive least-square with a dynamic forgetting factor. We propose to employ a Gaussian process to produce robust human motion predictions, and thus address the uncertainty and measurement noise of the system caused by interactive environments. Experimental results on two types of interaction tasks demonstrate the effectiveness of this approach, which achieves high accuracy in predicting assistive limb motion and enables humans to perform bimanual tasks using only one limb.
Xing X, Burdet E, Si W, et al., 2023, Impedance Learning for Human-Guided Robots in Contact With Unknown Environments, IEEE Transactions on Robotics, ISSN: 1552-3098
Previous works have developed impedance control to increase safety and improve performance in contact tasks, where the robot is in physical interaction with either an environment or a human user. This article investigates impedance learning for a robot guided by a human user while interacting with an unknown environment. We develop automatic adaptation of robot impedance parameters to reduce the effort required to guide the robot through the environment, while guaranteeing interaction stability. For nonrepetitive tasks, this novel adaptive controller can attenuate disturbances by learning appropriate robot impedance. Implemented as an iterative learning controller, it can compensate for position dependent disturbances in repeated movements. Experiments demonstrate that the robot controller can, in both repetitive and nonrepetitive tasks: first, identify and compensate for the interaction, second, ensure both contact stability (with reduced tracking error) and maneuverability (with less driving effort of the human user) in contact with real environments, and third, is superior to previous velocity-based impedance adaptation control methods.
Huang Y, Eden J, Ivanova E, et al., 2023, Can Training Make Three Arms Better than Two Heads for Trimanual Coordination?, IEEE Open Journal of Engineering in Medicine and Biology
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.
Takagi A, Bagnato C, Melendez-Calderon A, et al., 2023, Competition increases the effort put into a physical interaction task, IEEE Transactions on Haptics, ISSN: 1939-1412
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 improve 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.
Uttayopas P, Cheng X, Eden J, et al., 2023, Object Recognition Using Mechanical Impact, Viscoelasticity, and Surface Friction During Interaction., IEEE Trans Haptics, Vol: 16, Pages: 251-260
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.
Carboni G, Nanayakkara T, Takagi A, et al., 2022, Author Correction: Adapting the visuo-haptic perception through muscle coactivation., Sci Rep, Vol: 12
Yurkewich A, Ortega S, Sanchez J, et al., 2022, Integrating hand exoskeletons into goal-oriented clinic and home stroke and spinal cord injury rehabilitation, Journal of Rehabilitation and Assistive Technologies Engineering, Vol: 9, Pages: 1-11, ISSN: 2055-6683
IntroductionRobotic exoskeletons are emerging as rehabilitation and assistive technologies that simultaneously restore function and enable independence for people with disabilities.AimWe 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).MethodA 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.ResultsFour 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 re
Ivanova E, Eden J, Carboni G, et al., 2022, Interaction with a reactive partner improves learning in contrast to passive guidance, SCIENTIFIC REPORTS, Vol: 12, ISSN: 2045-2322
Takagi A, Gomi H, Burdet E, et al., 2022, A model predictive control strategy to regulate movements and interactions
<jats:title>Abstract</jats:title><jats:p>Humans are adept at moving the arm to interact with objects and surfaces. The brain is thought to regulate motion and interactions using two different controllers, one specialized for movements and the other for force regulation. However, it remains unclear whether different control mechanisms are necessary. Here we show that the brain can employ a single high-level control strategy for both movement and interaction control. The Model Predictive Control (MPC) strategy introduced in this paper uses an internal model of the environment to plan the arm’s muscle activity whilst updating its predictions using periodic feedback. Computer simulations demonstrate MPC’s ability to produce human-like movements and after-effects in free and force field environments. It can simultaneously regulate both force and stiffness during interactions, and can accomplish motor tasks demanding transitions between motion and interaction control. Model Predictive Control promises to be an important tool to test ideas of motor control as it can handle nonlinear dynamics with changing environments and goals without having to specify the movement duration.</jats:p>
Li Y, Sena A, Wang Z, et al., 2022, A review on interaction control for contact robots through intent detection, Progress in Biomedical Engineering, Vol: 4, Pages: 1-21, ISSN: 2516-1091
Interaction control presents opportunities for contact robots physically interacting with their human user, such as assistance targeted to each human user, communication of goals to enable effective teamwork, and task-directed motion resistance in physical training and rehabilitation contexts. Here we review the burgeoning field of interaction control in the control theory and machine learning communities, by analysing the exchange of haptic information between the robot and its human user, and how they share the task effort. We first review the estimation and learning methods to predict the human user intent with the large uncertainty, variability and noise and limited observation of human motion. Based on this motion intent core, typical interaction control strategies are described using a homotopy of shared control parameters. Recent methods of haptic communication and game theory are then presented to consider the co-adaptation of human and robot control and yield versatile interactive control as observed between humans. Finally, the limitations of the presented state of the art are discussed and directions for future research are outlined.
Perez NP, Eden J, Ivanova E, et al., 2022, Is a Robot Needed to Modify Human Effort in Bimanual Tracking?, IEEE ROBOTICS AND AUTOMATION LETTERS, Vol: 7, Pages: 8069-8075, ISSN: 2377-3766
Huang Y, Eden J, Ivanova E, et al., 2022, Human Performance of Three Hands in Unimanual, Bimanual and Trimanual Tasks., Annu Int Conf IEEE Eng Med Biol Soc, Vol: 2022, Pages: 1493-1497
Trimanual operation using a robotic supernumerary limb is a new and challenging mechanism for human operators that could enable a single user to perform tasks requiring more than two hands. Foot-controlled interfaces have previously proven able to be intuitively controlled, enabling simple tasks to be performed. However, the effect of going from unimanual to bimanual and then to trimanual tasks on subjects performance and coordination is not well understood. In this paper, unimanual, bimanual and trimanual teleoperation tasks were performed in a virtual reality scene to evaluate the impact of extending to trimanual actions. 15 participants were required to move their limbs together in a coordinated reaching activity. The results show that the addition of another hand resulted in an increase in operating time, where the time increased in going from unimanual to bimanual operation and then increased further when going from bimanual to trimanual. Moreover, the success rate for performing bimanual and trimanual tasks was strongly influenced by the subject's performance in ipsilateral hand-foot activities, where the ipsilateral combination had a lower success rate than contralateral limbs. The addition of a hand did not affect any two-hand coordination rate and even in some cases reduced coordination deviations. Clinical relevance - This work can contribute to build efficient training and learning framework on human multiple limbs motion control and coordination for both rehabilitation and augmentation.
Farkhatdinov I, Garnier A, Arichi T, et al., 2022, Evaluation of a Portable fMRI Compatible Robotic Wrist Interface., Annu Int Conf IEEE Eng Med Biol Soc, Vol: 2022, Pages: 2535-2539
This paper presents evaluation of a portable fMRI compatible haptic interface to study the brain correlates of sensorimotor control during wrist motion. The interface is actuated by a shielded DC motor located more than 2 m away from the 3T MR scanner's bore. The achievable wrist torque of the interface is up to 2 Nm, and the interface provides sufficient bandwidth for human motor control experiments. Ergonomic and fMRI compatibility testing with a 3T MR scanner showed that the interface is MR safe, compatible with a strong static magnetic field and radio frequency emission, and its operation does not affect the quality of the acquired images. Clinical Relevance- We present and evaluate an fMRI compatible robotic interface to study human wrist joint motor function.
Perez NP, Eden J, Burdet E, et al., 2022, Lateralization of Impedance Control in Dynamic Versus Static Bimanual Tasks., Annu Int Conf IEEE Eng Med Biol Soc, Vol: 2022, Pages: 785-789
In activities of daily living that require bimanual coordination, humans often assign a role to each hand. How do task requirements affect this role assignment? To address this question, we investigated how healthy right-handed participants bimanually manipulated a static or dynamic virtual object using wrist flexion/extension while receiving haptic feedback through the interacting object's torque. On selected trials, the object shook strongly to destabilize the bimanual grip. Our results show that participants reacted to the shaking by increasing their wrist co-contraction. Unlike in previous work, handedness was not the determining factor in choosing which wrist to co-contract to stabilize the object. However, each participant preferred to co-contract one hand over the other, a choice that was consistent for both the static and dynamic objects. While role allocation did not seem to be affected by task requirements, it may have resulted in different motor behaviours as indicated by the changes in the object torque. Further investigation is needed to elucidate the factors that determine the preference in stabilizing with either the dominant or non-dominant hand.
Bracklein M, Barsakcioglu DY, Ibanez J, et al., 2022, The control and training of single motor units in isometric tasks are constrained by a common input signal, ELIFE, Vol: 11, ISSN: 2050-084X
Huang Y, Ivanova E, Eden J, et al., 2022, Identification of Multiple Limbs Coordination Strategies in a Three-Goal Independent Task, IEEE TRANSACTIONS ON MEDICAL ROBOTICS AND BIONICS, Vol: 4, Pages: 348-351
Murali PK, Dutta A, Gentner M, et al., 2022, Active Visuo-Tactile Interactive Robotic Perception for Accurate Object Pose Estimation in Dense Clutter, IEEE ROBOTICS AND AUTOMATION LETTERS, Vol: 7, Pages: 4686-4693, ISSN: 2377-3766
This study examines how people learn to perform lower limb control in a novel task with a hoverboard requiring to maintain dynamic balance. We designed an experiment to investigate the learning of hoverboard balance and two control strategies: A hip strategy, which mainly uses hip movements to change the angle of the foot, and an ankle strategy relying more on ankle motion to control the orientation of hoverboard plates controlling the motion. Motor learning was indicated by a significant [Formula: see text]% decrease in the trial completion time (p < 0.001) and a significant 24 ± 11% decrease in total muscle activation (p < 0.001). Furthermore, the participants, who had no prior experience riding a hoverboard, learned an ankle strategy to maintain their balance and control the hoverboard. This is supported by significantly stronger cross-correlation, phase synchrony, lower dynamic time warping distance between the hoverboard plate orientation controlling hoverboard motion, and the ankle angle when compared to the hip angle. The adopted ankle strategy was found to be robust to the foot orientation despite salient changes in muscle group activation patterns. Comparison with results of an experienced hoverboard rider confirmed that the first-time riders adopted an ankle strategy.
Eden J, Bräcklein M, Ibáñez J, et al., 2022, Principles of human movement augmentation and the challenges in making it a reality, Nature Communications, Vol: 13, ISSN: 2041-1723
Augmenting the body with artificial limbs controlled concurrently to one's natural limbs has long appeared in science fiction, but recent technological and neuroscientific advances have begun to make this possible. By allowing individuals to achieve otherwise impossible actions, movement augmentation could revolutionize medical and industrial applications and profoundly change the way humans interact with the environment. Here, we construct a movement augmentation taxonomy through what is augmented and how it is achieved. With this framework, we analyze augmentation that extends the number of degrees-of-freedom, discuss critical features of effective augmentation such as physiological control signals, sensory feedback and learning as well as application scenarios, and propose a vision for the field.
Mace M, Mutalib SA, Ogrinc M, et al., 2022, GripAble: an accurate, sensitive and robust digital device for measuring grip strength, Journal of Rehabilitation and Assistive Technologies Engineering, Vol: 9, Pages: 1-12, ISSN: 2055-6683
Introduction: Grip strength is a reliable biomarker of overall health and physiological well-being. It is widely used in clinical practice as an outcome measure. This paper demonstrates the measurement characteristics of GripAble, a wireless mobile handgrip device that measures grip force both isometrically and elastically-resisted for assessment and training of hand function. Methods: A series of bench tests were performed to evaluate GripAble's grip force measurement accuracy and sensitivity. Measurement robustness was evaluated through repeated drop tests interwoven with error verification test phases. Results: GripAble's absolute measurement error at the central position was under 0.81 and 1.67 kg (95th percentiles; N = 47) when measuring elastically and isometrically, respectively, providing similar or better accuracy than the industry-standard Jamar device. Sensitivity was measured as 0.062 ± 0.015 kg (mean ± std; 95th percentiles: [0.036, 0.089] kg; N = 47), independent of the applied force. There was no significant performance degradation following impact from 30 drops from a height >1.5 m. Conclusion: GripAble is an accurate and reliable grip strength dynamometer. It is highly sensitive and robust, which in combination with other novel features (e.g. portability, telerehabilitation and digital data tracking) enable broad applicability in a range of clinical caseloads and environments.
Dall'Orso S, Arichi T, Fitzgibbon SP, et al., 2022, Development of functional organization within the sensorimotor network across the perinatal period, HUMAN BRAIN MAPPING, Vol: 43, Pages: 2249-2261, ISSN: 1065-9471
Mutalib SA, Mace M, Seager C, et al., 2022, Modernising grip dynamometry: Inter-instrument reliability between GripAble and Jamar, BMC Musculoskeletal Disorders, Vol: 23, ISSN: 1471-2474
Introduction:Maximum grip strength (MGS) is a reliable biomarker of overall health and physiological well-being. Therefore, an accurate and reliable measurement device is vital for ensuring the validity of the MGS assessment. This paper presents GripAble, a mobile hand grip device for the assessment of MGS. GripAble’s performance was evaluated using an inter-instrument reliability test against the widely used Jamar PLUS+ dynamometer.Methods:MGS data from sixty-three participants (N = 63, median (IQR) age = 29.0 (29.5) years, 33 M/30 F) from both hands using GripAble and Jamar PLUS+ were collected and compared. Intraclass correlation (ICC), regression, and Bland and Altman analysis were performed to evaluate the inter-instrument reliability and relationship in MGS measurements between GripAble and Jamar PLUS+ .Results:GripAble demonstrates good-to-excellent inter-instrument reliability to the Jamar PLUS+ with ICC3,1 = 0.906 (95% CI [0.87—0.94]). GripAble’s MGS measurement is equivalent to 69% (95% CI [0.67—0.71]%) of Jamar PLUS+’s measurement. There is a proportional difference in mean MGS between the two devices, with the difference in MGS between GripAble and Jamar PLUS+ increasing with MGS.Conclusion:The GripAble is a reliable tool for measuring grip strength. However, the MGS readings from GripAble and Jamar PLUS+ should not be interchanged for serial measurements of the same patient, nor be translated directly from one device to the other. A new normative MGS data using GripAble will be collected and accessed through the software for immediate comparison to age and gender-matched subpopulations.
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