GripAble: An accurate, sensitive and robust digital device for measuring grip strength

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 (95^th 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; 95^th 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.

Development of functional organization within the sensorimotor network across the perinatal period

In the mature human brain, the neural processing related to different body parts is reflected in patterns of functional connectivity, which is strongest between functional homologs in opposite cortical hemispheres. To understand how this organization is first established, we investigated functional connectivity between limb regions in the sensorimotor cortex in 400 preterm and term infants aged across the equivalent period to the third trimester of gestation (32–45 weeks postmenstrual age). Masks were obtained from empirically derived functional responses in neonates from an independent data set. We demonstrate the early presence of a crude but spatially organized functional connectivity, that rapidly matures across the preterm period to achieve an adult‐like configuration by the normal time of birth. Specifically, connectivity was strongest between homolog regions, followed by connectivity between adjacent regions (different limbs but same hemisphere) already in the preterm brain, and increased with age. These changes were specific to the sensorimotor network. Crucially, these trajectories were strongly dependent on age more than age of birth. This demonstrates that during the perinatal period the sensorimotor cortex undergoes preprogrammed changes determining the functional movement organization that are not altered by preterm birth in absence of brain injury.

Modernising grip dynamometry: Inter-instrument reliability between GripAble and Jamar

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.

Adapting the visuo-haptic perception through muscle coactivation

While the nervous system can coordinate muscles’ activation to shape the mechanical interaction with the environment, it is unclear if and how the arm’s coactivation influences visuo-haptic perception and motion planning. Here we show that the nervous system can voluntarily coactivate muscles to improve the quality of the haptic percept. Subjects tracked a randomly moving visual target they were physically coupled to through a virtual elastic band, where the stiffness of the coupling increased with wrist coactivation. Subjects initially relied on vision alone to track the target, but with practice they learned to combine the visual and haptic percepts in a Bayesian manner to improve their tracking performance. This improvement cannot be explained by the stronger mechanical guidance from the elastic band. These results suggest that with practice the nervous system can learn to integrate a novel haptic percept with vision in an optimal fashion.

Perception and Performance of Electrical Stimulation for Proprioception

Proprioception, yielding awareness of the body's position and motion in space, is typically lacking in prostheses and supernumerary limbs. Electrical stimulation is one technique that may provide these devices with proprioception. This paper first investigates how the modalities of electrotactile cues, such as frequency and intensity, are perceived. Using the results, we designed and compared several comfortable and perceptible feedback mappings for spatial cues. Two experiments were conducted using a 16-electrode bracelet worn above the elbow to provide electrical stimuli. We found that subjects could localize the stimulating electrode with a precision of ±1 electrode (110 mm) in all feedback conditions. Moreover, within the range of pulse intensities perceived as comfortable, the participants' performance was more sensitive to changes in frequency than in intensity. The highest performance was obtained for the condition which increased both intensity and frequency with radial distance. These results suggest that electrical stimulation can be used for artificial proprioceptive feedback, which can ensure a comfortable and intuitive interaction and provides high spatial accuracy.

Proof-of-Concept of a Sensor-Based Evaluation Method for Better Sensitivity of Upper-Extremity Motor Function Assessment

In rehabilitation, the Fugl-Meyer assessment (FMA) is a typical clinical instrument to assess upper-extremity motor function of stroke patients, but it cannot measure fine changes of motor function (both in recovery and deterioration) due to its limited sensitivity. This paper introduces a sensor-based automated FMA system that addresses this limitation with a continuous rating algorithm. The system consists of a depth sensor (Kinect V2) and an algorithm to rate the continuous FM scale based on fuzzy inference. Using a binary logic based classification method developed from a linguistic scoring guideline of FMA, we designed fuzzy input/output variables, fuzzy rules, membership functions, and a defuzzification method for several representative FMA tests. A pilot trial with nine stroke patients was performed to test the feasibility of the proposed approach. The continuous FM scale from the proposed algorithm exhibited a high correlation with the clinician rated scores and the results showed the possibility of more sensitive upper-extremity motor function assessment.

Self-Directed Exergaming for Stroke Upper Limb Impairment Increases Exercise Dose Compared to Standard Care

Background. One of the strongest modifiable determinants of rehabilitation outcome is exercise dose. Technologies enabling self-directed exercise offer a pragmatic means to increase dose, but the extent to which they achieve this in unselected cohorts, under real-world constraints, is poorly understood. Objective. Here we quantify the exercise dose achieved by inpatient stroke survivors using an adapted upper limb (UL) exercise gaming (exergaming) device and compare this with conventional (supervised) therapy. Methods. Over 4 months, patients presenting with acute stroke and associated UL impairment were screened at a single stroke centre. Participants were trained in a single session and provided with the device for unsupervised use during their inpatient admission. Results. From 75 patients referred for inpatient UL therapy, we recruited 30 (40%), of whom 26 (35%) were able to use the device meaningfully with their affected UL. Over a median enrolment time of 8 days (IQR: 5–14), self-directed UL exercise duration using the device was 26 minutes per day (median; IQR: 16–31), in addition to 25 minutes daily conventional UL therapy (IQR: 12–34; same cohort plus standard care audit; joint n = 50); thereby doubling total exercise duration (51 minutes; IQR: 32–64) relative to standard care (Z = 4.0, P <.001). The device enabled 104 UL repetitions per day (IQR: 38–393), whereas conventional therapy achieved 15 UL repetitions per day (IQR: 11–23; Z = 4.3, P <.001). Conclusion. Self-directed adapted exergaming enabled participants in our stroke inpatient cohort to increase exercise duration 2-fold, and repetitions 8-fold, compared to standard care, without requiring additional professional supervision.

An eye tracking based virtual reality system for use inside magnetic resonance imaging systems

Patients undergoing Magnetic Resonance Imaging (MRI) often experience anxiety and sometimes distress prior to and during scanning. Here a full MRI compatible virtual reality (VR) system is described and tested with the aim of creating a radically different experience. Potential benefits could accrue from the strong sense of immersion that can be created with VR, which could create sense experiences designed to avoid the perception of being enclosed and could also provide new modes of diversion and interaction that could make even lengthy MRI examinations much less challenging. Most current VR systems rely on head mounted displays combined with head motion tracking to achieve and maintain a visceral sense of a tangible virtual world, but this technology and approach encourages physical motion, which would be unacceptable and could be physically incompatible for MRI. The proposed VR system uses gaze tracking to control and interact with a virtual world. MRI compatible cameras are used to allow real time eye tracking and robust gaze tracking is achieved through an adaptive calibration strategy in which each successive VR interaction initiated by the subject updates the gaze estimation model. A dedicated VR framework has been developed including a rich virtual world and gaze-controlled game content. To aid in achieving immersive experiences physical sensations, including noise, vibration and proprioception associated with patient table movements, have been made congruent with the presented virtual scene. A live video link allows subject-carer interaction, projecting a supportive presence into the virtual world.

Stochastic optimal feedforward-feedback control determines timing and variability of arm movements with or without vision

Human movements with or without vision exhibit timing (i.e. speed and duration) and variability characteristics which are not well captured by existing computational models. Here, we introduce a stochastic optimal feedforward-feedback control (SFFC) model that can predict the nominal timing and trial-by-trial variability of self-paced arm reaching movements carried out with or without online visual feedback of the hand. In SFFC, movement timing results from the minimization of the intrinsic factors of effort and variance due to constant and signal-dependent motor noise, and movement variability depends on the integration of visual feedback. Reaching arm movements data are used to examine the effect of online vision on movement timing and variability, and test the model. This modelling suggests that the central nervous system predicts the effects of sensorimotor noise to generate an optimal feedforward motor command, and triggers optimal feedback corrections to task-related errors based on the available limb state estimate.

Robotic Assisted Upper Limb Training Post Stroke: A Randomized Control Trial Using Combinatory Approach Toward Reducing Workforce Demands

Post stroke upper limb rehabilitation is a challenging problem with poor outcomes as 40% of survivors have functionally useless upper limbs. Robot-aided therapy (RAT) is a potential method to alleviate the effort of intensive, task-specific, repetitive upper limb exercises for both patients and therapists. The present study aims to investigate how a time matched combinatory training scheme that incorporates conventional and RAT, using H-Man, compares with conventional training toward reducing workforce demands. In a randomized control trial (NCT02188628, www.clinicaltrials.gov), 44 subacute to chronic stroke survivors with first-ever clinical stroke and predominant arm motor function deficits were recruited and randomized into two groups of 22 subjects: Robotic Therapy (RT) and Conventional Therapy (CT). Both groups received 18 sessions of 90 min; three sessions per week over 6 weeks. In each session, participants of the CT group received 90 min of 1:1 therapist-supervised conventional therapy while participants of the RT group underwent combinatory training which consisted of 60 min of minimally-supervised H-Man therapy followed by 30 min of conventional therapy. The clinical outcomes [Fugl-Meyer (FMA), Action Research Arm Test and, Grip Strength] and the quantitative measures (smoothness, time efficiency, and task error, derived from two robotic assessment tasks) were independently evaluated prior to therapy intervention (week 0), at mid-training (week 3), at the end of training (week 6), and post therapy (week 12 and 24). Significant differences within group were observed at the end of training for all clinical scales compared with baseline [mean and standard deviation of FMA score changes between baseline and week 6; RT: Δ4.41 (3.46) and CT: Δ3.0 (4.0); p < 0.01]. FMA gains were retained 18 weeks post-training [week 24; RT: Δ5.38 (4.67) and week 24 CT: Δ4.50 (5.35); p < 0.01]. The RT group clinical scores improved similarly when compared to CT group with no significant inter-group at all time points although the conventional therapy time was reduced to one third in RT group. There were no training-related adverse side effects. In conclusion, time matched combinatory training incorporating H-Man RAT produced similar outcomes compared to conventional therapy alone. Hence, this study supports a combinatory approach to improve motor function in post-stroke arm paresis. Clinical Trial Registration: www.ClinicalTrials.gov, identifier: NCT02188628.

Human performance in three-hands tasks

The successful completion of complex tasks like hanging a picture or laparoscopic surgery requires coordinated motion of more than two limbs. User-controlled supernumerary robotic limbs (SL) have been proposed to bypass the need for coordination with a partner in such tasks. However, neither the capability to control multiple limbs alone relative to collaborative control with partners, nor how that capability varies across different tasks, is well understood. In this work, we present an investigation of tasks requiring three-hands where the foot was used as an additional source of motor commands. We considered: (1) how does simultaneous control of three hands compare to a cooperating dyad; (2) how this relative performance was altered by the existence of constraints emanating from real or virtual physical connections (mechanical constraints) or from cognitive limits (cognitive constraints). It was found that a cooperating dyad outperformed a single user in all scenarios in terms of task score, path efficiency and motion smoothness. However, while the participants were able to reach more targets with increasing mechanical constraints/decreasing number of simultaneous goals, the relative difference in performance between a dyad and a participant performing trimanual activities decreased, suggesting further potential for SLs in this class of scenario.

Flexible Assimilation of Human's Target for Versatile Human-Robot Physical Interaction

Recent studies on the physical interaction between humans have revealed their ability to read the partner's motion plan and use it to improve one's own control. Inspired by these results, we develop an intention assimilation controller (IAC) that enables a contact robot to estimate the human's virtual target from the interaction force, and combine it with its own target to plan motion. While the virtual target depends on the control gains assumed for the human, we show that this does not affect the stability of the human-robot system, and our novel scheme covers a continuum of interaction behaviours from cooperation to competition. Simulations and experiments illustrate how the IAC can assist the human or compete with them to prevent collisions. In this article, we demonstrate the IAC's advantages over related methods, such as faster convergence to a target, guidance with less force, safer obstacle avoidance and a wider range of interaction behaviours.

Short Time Delay Does Not Hinder Haptic Communication Benefits

Haptic communication, the exchange of force and tactile information during dancing or moving a table together, has been shown to benefit the performance of human partners. Similarly, it could also be used to improve the performance of robots working in contact with a human operator. As we move to more robot integrated workspaces, how common network features such as delay or jitter impact haptic communication need to be better understood. Here using a human-like interactive robotic controller, that has been found to be indistinguishable by humans to human interaction, we evaluate how subjects' performance and perception is altered by varying levels of transmission delay. We find that subjects are able to recognise haptic delay at very small levels within haptic interaction. However, while they are consciously aware of the delay they can only compensate for it up until a certain point, after which they perceive it as the addition of noise/impedance into the system.

Analogous adaptations in speed, impulse and endpoint stiffness when learning a real and virtual insertion task with haptic feedback

Humans have the ability to use a diverse range of handheld tools. Owing to its versatility, a virtual environment with haptic feedback of the force is ideally suited to investigating motor learning during tool use. However, few simulators exist to recreate the dynamic interactions during real tool use, and no study has compared the correlates of motor learning between a real and virtual tooling task. To this end, we compared two groups of participants who either learned to insert a real or virtual tool into a fixture. The trial duration, the movement speed, the force impulse after insertion and the endpoint stiffness magnitude decreased as a function of trials, but they changed at comparable rates in both environments. A ballistic insertion strategy observed in both environments suggests some interdependence when controlling motion and controlling interaction, contradicting a prominent theory of these two control modalities being independent of one another. Our results suggest that the brain learns real and virtual insertion in a comparable manner, thereby supporting the use of a virtual tooling task with haptic feedback to investigate motor learning during tool use.

Abnormal microscale neuronal connectivity triggered by a proprioceptive stimulus in dystonia

We investigated modulation of functional neuronal connectivity by a proprioceptive stimulus in sixteen young people with dystonia and eight controls. A robotic wrist interface delivered controlled passive wrist extension movements, the onset of which was synchronised with scalp EEG recordings. Data were segmented into epochs around the stimulus and up to 160 epochs per subject were averaged to produce a Stretch Evoked Potential (StretchEP). Event-related network dynamics were estimated using a methodology that features Wavelet Transform Coherency (WTC). Global Microscale Nodal Strength (GMNS) was introduced to estimate overall engagement of areas into short-lived networks related to the StretchEP, and Global Connectedness (GC) estimated the spatial extent of the StretchEP networks. Dynamic Connectivity Maps showed a striking difference between dystonia and controls, with particularly strong theta band event-related connectivity in dystonia. GC also showed a trend towards higher values in dystonia than controls. In summary, we demonstrate the feasibility of this method to investigate event-related neuronal connectivity in relation to a proprioceptive stimulus in a paediatric patient population. Young people with dystonia show an exaggerated network response to a proprioceptive stimulus, displaying both excessive theta-band synchronisation across the sensorimotor network and widespread engagement of cortical regions in the activated network.

Cortical Processing of Multimodal Sensory Learning in Human Neonates

Following birth, infants must immediately process and rapidly adapt to the array of unknown sensory experiences associated with their new ex-utero environment. However, although it is known that unimodal stimuli induce activity in the corresponding primary sensory cortices of the newborn brain, it is unclear how multimodal stimuli are processed and integrated across modalities. The latter is essential for learning and understanding environmental contingencies through encoding relationships between sensory experiences; and ultimately likely subserves development of life-long skills such as speech and language. Here, for the first time, we map the intracerebral processing which underlies auditory-sensorimotor classical conditioning in a group of 13 neonates (median gestational age at birth: 38 weeks + 4 days, range: 32 weeks + 2 days to 41 weeks + 6 days; median postmenstrual age at scan: 40 weeks + 5 days, range: 38 weeks + 3 days to 42 weeks + 1 days) with blood-oxygen-level-dependent (BOLD) functional magnetic resonance imaging (MRI) and magnetic resonance (MR) compatible robotics. We demonstrate that classical conditioning can induce crossmodal changes within putative unimodal sensory cortex even in the absence of its archetypal substrate. Our results also suggest that multimodal learning is associated with network wide activity within the conditioned neural system. These findings suggest that in early life, external multimodal sensory stimulation and integration shapes activity in the developing cortex and may influence its associated functional network architecture.

A Multimodal Intention Detection Sensor Suite for Shared Autonomy of Upper-Limb Robotic Prostheses

Neurorobotic augmentation (e.g., robotic assist) is now in regular use to support individuals suffering from impaired motor functions. A major unresolved challenge, however, is the excessive cognitive load necessary for the human–machine interface (HMI). Grasp control remains one of the most challenging HMI tasks, demanding simultaneous, agile, and precise control of multiple degrees-of-freedom (DoFs) while following a specific timing pattern in the joint and human–robot task spaces. Most commercially available systems use either an indirect mode-switching configuration or a limited sequential control strategy, limiting activation to one DoF at a time. To address this challenge, we introduce a shared autonomy framework centred around a low-cost multi-modal sensor suite fusing: (a) mechanomyography (MMG) to estimate the intended muscle activation, (b) camera-based visual information for integrated autonomous object recognition, and (c) inertial measurement to enhance intention prediction based on the grasping trajectory. The complete system predicts user intent for grasp based on measured dynamical features during natural motions. A total of 84 motion features were extracted from the sensor suite, and tests were conducted on 10 able-bodied and 1 amputee participants for grasping common household objects with a robotic hand. Real-time grasp classification accuracy using visual and motion features obtained 100%, 82.5%, and 88.9% across all participants for detecting and executing grasping actions for a bottle, lid, and box, respectively. The proposed multimodal sensor suite is a novel approach for predicting different grasp strategies and automating task performance using a commercial upper-limb prosthetic device. The system also shows potential to improve the usability of modern neurorobotic systems due to the intuitive control design.

An fMRI Compatible Smart Device for Measuring Palmar Grasping Actions in Newborns

Grasping is one of the first dominant motor behaviors that enable interaction of a newborn infant with its surroundings. Although atypical grasping patterns are considered predictive of neuromotor disorders and injuries, their clinical assessment suffers from examiner subjectivity, and the neuropathophysiology is poorly understood. Therefore, the combination of technology with functional magnetic resonance imaging (fMRI) may help to precisely map the brain activity associated with grasping and thus provide important insights into how functional outcomes can be improved following cerebral injury. This work introduces an MR-compatible device (i.e., smart graspable device (SGD)) for detecting grasping actions in newborn infants. Electromagnetic interference immunity (EMI) is achieved using a fiber Bragg grating sensor. Its biocompatibility and absence of electrical signals propagating through the fiber make the safety profile of the SGD particularly favorable for use with fragile infants. Firstly, the SGD design, fabrication, and metrological characterization are described, followed by preliminary assessments on a preterm newborn infant and an adult during an fMRI experiment. The results demonstrate that the combination of the SGD and fMRI can safely and precisely identify the brain activity associated with grasping behavior, which may enable early diagnosis of motor impairment and help guide tailored rehabilitation programs.

Cable-Driven Robotic Interface for Lower Limb Neuromechanics Identification

This paper presents a versatile cable-driven robotic interface to investigate the single-joint joint neuromechanics of the hip, knee and ankle in the sagittal plane. This endpoint-based interface offers highly dynamic interaction and accurate position control (as is typically required for neuromechanics identification), and provides measurements of position, interaction force and electromyography (EMG) of leg muscles. It can be used with the subject upright, corresponding to a natural posture during walking or standing, and does not impose kinematic constraints on a joint, in contrast to existing interfaces. Mechanical evaluations demonstrated that the interface yields a rigidity above 500 N/m with low viscosity. Tests with a rigid dummy leg and linear springs show that it can identify the mechanical impedance of a limb accurately. A smooth perturbation is developed and tested with a human subject, which can be used to estimate the hip neuromechanics.

A Clustering-Based Approach to Identify Joint Impedance During Walking

Mechanical impedance, which changes with posture and muscle activations, characterizes how the central nervous system regulates the interaction with the environment. Traditional approaches to impedance estimation, based on averaging of movement kinetics, requires a large number of trials and may introduce bias to the estimation due to the high variability in a repeated or periodic movement. Here, we introduce a data-driven modeling technique to estimate joint impedance considering the large gait variability. The proposed method can be used to estimate impedance in both the stance and swing phases of walking. A 2-pass clustering approach is used to extract groups of unperturbed gait data and estimate candidate baselines. Then patterns of perturbed data are matched with the most similar unperturbed baseline. The kinematic and torque deviations from the baselines are regressed locally to compute joint impedance at different gait phases. Simulations using the trajectory data of a subject's gait at different speeds demonstrate a more accurate estimation of ankle stiffness and damping with the proposed clustering-based method when compared with two methods: i) using average unperturbed baselines, and ii) matching shifted and scaled average unperturbed velocity baselines. Furthermore, the proposed method requires fewer trials than methods based on average unperturbed baselines. The experimental results on human hip impedance estimation show the feasibility of clustering-based technique and verifies that it reduces the estimation variability.