A Low-Cost Wearable Device to Estimate Body Temperature Based on Wrist Temperature.

The remote monitoring of vital signs and healthcare provision has become an urgent necessity due to the impact of the COVID-19 pandemic on the world. Blood oxygen level, heart rate, and body temperature data are crucial for managing the disease and ensuring timely medical care. This study proposes a low-cost wearable device employing non-contact sensors to monitor, process, and visualize critical variables, focusing on body temperature measurement as a key health indicator. The wearable device developed offers a non-invasive and continuous method to gather wrist and forehead temperature data. However, since there is a discrepancy between wrist and actual forehead temperature, this study incorporates statistical methods and machine learning to estimate the core forehead temperature from the wrist. This research collects 2130 samples from 30 volunteers, and both the statistical least squares method and machine learning via linear regression are applied to analyze these data. It is observed that all models achieve a significant fit, but the third-degree polynomial model stands out in both approaches. It achieves an R2 value of 0.9769 in the statistical analysis and 0.9791 in machine learning.

Novel near E-Field Topography Sensor for Human-Machine Interfacing in Robotic Applications.

This work investigates a new sensing technology for use in robotic human-machine interface (HMI) applications. The proposed method uses near E-field sensing to measure small changes in the limb surface topography due to muscle actuation over time. The sensors introduced in this work provide a non-contact, low-computational-cost, and low-noise method for sensing muscle activity. By evaluating the key sensor characteristics, such as accuracy, hysteresis, and resolution, the performance of this sensor is validated. Then, to understand the potential performance in intention detection, the unmodified digital output of the sensor is analysed against movements of the hand and fingers. This is done to demonstrate the worst-case scenario and to show that the sensor provides highly targeted and relevant data on muscle activation before any further processing. Finally, a convolutional neural network is used to perform joint angle prediction over nine degrees of freedom, achieving high-level regression performance with an RMSE value of less than six degrees for thumb and wrist movements and 11 degrees for finger movements. This work demonstrates the promising performance of this novel approach to sensing for use in human-machine interfaces.

An OpenSim-Based Closed-Loop Biomechanical Wrist Model for Subject-Specific Pathological Tremor Simulation.

OBJECTIVE: A pathological tremor (PT) is an involuntary rhythmic movement of varying frequency and amplitude that affects voluntary motion, thus compromising individuals' independence. A comprehensive model incorporating PT's physiological and biomechanical aspects can enhance our understanding of the disorder and provide valuable insights for therapeutic approaches. This study aims to build a biomechanical model of pathological tremors using OpenSim's realistic musculoskeletal representation of the human wrist with two degrees of freedom. METHODS: We implemented a Matlab/OpenSim interface for a forward dynamics simulation, which allows for the modeling, simulation, and design of a physiological H∞ closed-loop control. This system replicates pathological tremors similar to those observed in patients when their arm is extended forward, the wrist is pronated, and the hand is subject to gravity forces. The model was individually tuned to five subjects (four Parkinson's disease patients and one diagnosed with essential tremor), each exhibiting distinct tremor characteristics measured by an inertial sensor and surface EMG electrodes. Simulation agreement with the experiments for EMGs, central frequency, joint angles, and angular velocities were evaluated by Jensen-Shannon divergence, histogram centroid error, and histogram intersection. RESULTS: The model emulated individual tremor statistical characteristics, including muscle activations, frequency, variability, and wrist kinematics, with greater accuracy for the four Parkinson's patients than the essential tremor. CONCLUSION: The proposed model replicated the main statistical features of subject-specific wrist tremor kinematics. SIGNIFICANCE: Our methodology may facilitate the design of patient-specific rehabilitation devices for tremor suppression, such as neural prostheses and electromechanical orthoses.

Exploration of outcome measures for assessing orthotic intervention in upper extremity peripheral nerve injuries: 2 case reports.

In upper extremity peripheral nerve injuries, orthotic intervention has been used as a valuable device to restore function. However, there is lacking evidence to support it. The purpose of this study was to explore the application of body function's outcome measures for orthotic intervention evaluation in patients with peripheral nerve injury. Two participants sustaining a peripheral nerve injury who underwent orthotic intervention were assessed: subject 1 was a 25-year-old man with ulnar and median nerve injury presenting with a composite claw; subject 2, a 28-year-old man with radial nerve injury presenting with a dropped wrist. Strength, range of motion, and electromyography were measured in 2 conditions: wearing the orthosis and without it. The Jamar, Pinch Gauge, a 3D motion capture system (Optitrack-NaturalPoint), and surface electromyography (Trigno Wireless System, Delsys) were the chosen instruments. Both subjects presented differences in grip and pinch strength. In both tasks, subject 1 reached higher wrist extension while wearing the orthosis. Subject 2 reached higher wrist extension and radial deviation while wearing the orthosis. There were marked differences in both tasks for subject 2, especially the maintenance of wrist extension when wearing the orthosis. Electromyographic assessment showed higher root-mean-square values for all muscles, in both tasks for subject 1. For subject 2, a higher root-mean-square value was found for flexor carpi ulnaris during the execution of task 1 wearing the orthosis. Outcome measures of body function can quantify the impact of orthotic intervention in patients sustaining peripheral nerve injury, and therefore, they are feasible for evaluating it.

ExoMechHand prototype development and testing with EMG signals for hand rehabilitation.

Rehabilitation is a major requirement to improve the quality of life and mobility of patients with disabilities. The use of rehabilitative devices without continuous supervision of medical experts is increasing manifold, mainly due to prolonged therapy costs and advancements in robotics. Due to ExoMechHand's inexpensive cost, high robustness, and efficacy for participants with median and ulnar neuropathies, we have recommended it as a rehabilitation tool in this study. ExoMechHand is coupled with three different resistive plates for hand impairment. For efficacy, ten unhealthy subjects with median or ulnar nerve neuropathies are considered. After twenty days of continuous exercise, three subjects showed improvement in their hand grip, range of motion of the wrist, or range of motion of metacarpophalangeal joints. The condition of the hand is assessed by features of surface-electromyography signals. A Machine-learning model based on these features of fifteen subjects is used for staging the condition of the hand. Machine-learning algorithms are trained to indicate the type of resistive plate to be used by the subject without the need for examination by the therapist. The extra-trees classifier came out to be the most effective algorithm with 98% accuracy on test data for indicating the type of resistive plate, followed by random-forest and gradient-boosting with accuracies of 95% and 93%, respectively. Results showed that the staging of hand condition could be analyzed by sEMG signal obtained from the flexor-carpi-ulnaris and flexor-carpi-radialis muscles in subjects with median and ulnar neuropathies.

Pathophysiology of sex difference in refractoriness in lateral epicondylitis: Biomechanical study of wrist torque.

Eccentric contractions of the wrist extensors worsen lateral epicondylitis (LE), whose pathophysiology may involve sex differences in wrist torque. This study aimed to investigate sex differences in wrist torque in patients with LE. The wrist extension and flexion torques of 22 patients with LE (11 males and 11 females) were measured. Maximum muscle output over time was measured for 20 s, initial torque was defined as muscle strength, and the degree of eccentric contraction was quantified and defined as the eccentric contraction index (ECI). The affected/unaffected side ratio of the wrist extensor, extensor/flexor ratio of muscle strength, and affected/unaffected side difference of ECI between sexes were statistically analyzed. Furthermore, correlations between wrist extensor torque, ECI, and Visual Analog Scale of pain during the examination were evaluated. Females were found to display lower affected/unaffected side ratios of the wrist extensor and wrist extension/flexion ratios for the affected side, compared with males; however, no differences were found in the wrist extension/flexion ratios for the unaffected side in both sexes. Additionally, females presented with larger differences between the affected and unaffected sides in the ECI. Based on correlations between wrist torques, ECI, and pain, females tended to suppress muscle output to prevent pain from eccentric contraction of wrist extensors more than males, which would induce an imbalance in muscle strength of the wrist extensors and flexors. This imbalance may result in chronic eccentric contraction of the wrist extensors with gripping, exacerbating LE.

Muscle redistribution technique for expressing motion intention in patients with wrist-level amputation.

This study reports the preliminary results of a technique for redistributing muscles at the wrist in the stump of hand amputees by suturing the tendons to the dermis. The technique has the potential to improve control of hand prostheses by detecting movement intentions.

Investigating the relationship between pain level and grip and wrist muscles strength in individuals with lateral epicondylitis: is pain a barrier to strength assessment?

PURPOSE: This study was carried out to examine the relationship between rest, activity, and nighttime pain and grip and isokinetic muscle strength of the wrist muscles in individuals with lateral epicondylitis. METHODS: Fifty-six sedentary individuals aged between 18 and 65 years diagnosed with unilateral lateral epicondylitis volunteered to participate in the study. The level of rest, activity, and nighttime pain was evaluated with visual analog scale (VAS). The grip strengths of both arms were evaluated by averaging a maximum of three grip strength measurements using a hand dynamometer. The strength of both wrist flexor and extensor muscles were evaluated with isokinetic dynamometer at angular velocities of 60 and 180°/s with five and 15 concentric repetitions respectively. RESULTS: There was no significant relationship found between the affected side's grip strength and isokinetic muscle strength with rest, activity and nighttime pain (all P > 0.05). However, there was a difference observed between the affected and unaffected side in grip strength and isokinetic strength measurements of all wrist muscles (all P < 0.05); the unaffected side values were found to be higher. CONCLUSION: The result of this study found no correlation between the stated level of pain and the true muscle strength in the affected hand. In line with these findings, we think that assessments involving strength can be made in other musculoskeletal problems where pain is present. However, the findings may not reflect the true muscle strength which will tend to be underrated.

Hand and distal joint tremor are most coherent with the activity of elbow flexors and wrist extensors in persons with essential tremor.

Essential tremor (ET) affects millions of people. Although frontline treatment options (medication, deep brain stimulation, and focused ultrasound ablation) have provided significant relief, many patients are unsatisfied with the outcomes. Peripheral suppression techniques, such as injections of botulinum toxin or sensory electrical stimulation of muscles, are gaining popularity, but could be optimized if the muscles most responsible for a patient's tremor were identified. The purpose of this study was to quantify the relationship between the activity in various upper limb muscles and the resulting tremor in patients with ET. Surface electromyogram (sEMG) from the 15 major superficial muscles of the upper limb and displacement of the hand and upper limb joints were recorded from 22 persons with ET while they performed kinetic and postural tasks representative of activities of daily living. We calculated the peak coherence (frequency-dependent correlation) in the tremor band (4-8 Hz) between the sEMG of each muscle and the displacement in each major degree of freedom (DOF). Averaged across subjects with ET, the highest coherence was found between elbow flexors (particularly biceps brachii and brachioradialis) and the distal DOF (forearm, wrist, and hand motion), and between wrist extensors (extensor carpi radialis and ulnaris) and the same distal DOF. These coherence values represent the upper bound on the proportion of the tremor caused by each muscle. We conclude that, without further information, elbow flexors and wrist extensors should be among the first muscles considered for peripheral suppression techniques in persons with ET.NEW & NOTEWORTHY We characterized the relationships between activity in upper limb muscles and tremor in persons with essential tremor using coherence, which provides an upper bound on the proportion of the tremor due to each muscle. Averaged across subjects and various tasks, tremor in the hand and distal joints was most coherent with elbow flexors and wrist extensors. We conclude that, without further information, these muscle groups should be among the first considered for peripheral suppression techniques.

Cortical effects of wrist tendon vibration during an arm tracking task in chronic stroke survivors: An EEG study.

The purpose of this study was to characterize changes in cortical activity and connectivity in stroke survivors when vibration is applied to the wrist flexor tendons during a visuomotor tracking task. Data were collected from 10 chronic stroke participants and 10 neurologically-intact controls while tracking a target through a figure-8 pattern in the horizontal plane. Electroencephalography (EEG) was used to measure cortical activity (beta band desynchronization) and connectivity (beta band task-based coherence) with movement kinematics and performance error also being recorded during the task. All participants came into our lab on two separate days and performed three blocks (16 trials each, 48 total trials) of tracking, with the middle block including vibration or sham applied at the wrist flexor tendons. The order of the sessions (Vibe vs. Sham) was counterbalanced across participants to prevent ordering effects. During the Sham session, cortical activity increased as the tracking task progressed (over blocks). This effect was reduced when vibration was applied to controls. In contrast, vibration increased cortical activity during the vibration period in participants with stroke. Cortical connectivity increased during vibration, with larger effect sizes in participants with stroke. Changes in tracking performance, standard deviation of hand speed, were observed in both control and stroke groups. Overall, EEG measures of brain activity and connectivity provided insight into effects of vibration on brain control of a visuomotor task. The increases in cortical activity and connectivity with vibration improved patterns of activity in people with stroke. These findings suggest that reactivation of normal cortical networks via tendon vibration may be useful during physical rehabilitation of stroke patients.

Predicting Office Workers' Productivity: A Machine Learning Approach Integrating Physiological, Behavioral, and Psychological Indicators.

This research pioneers the application of a machine learning framework to predict the perceived productivity of office workers using physiological, behavioral, and psychological features. Two approaches were compared: the baseline model, predicting productivity based on physiological and behavioral characteristics, and the extended model, incorporating predictions of psychological states such as stress, eustress, distress, and mood. Various machine learning models were utilized and compared to assess their predictive accuracy for psychological states and productivity, with XGBoost emerging as the top performer. The extended model outperformed the baseline model, achieving an R2 of 0.60 and a lower MAE of 10.52, compared to the baseline model's R2 of 0.48 and MAE of 16.62. The extended model's feature importance analysis revealed valuable insights into the key predictors of productivity, shedding light on the role of psychological states in the prediction process. Notably, mood and eustress emerged as significant predictors of productivity. Physiological and behavioral features, including skin temperature, electrodermal activity, facial movements, and wrist acceleration, were also identified. Lastly, a comparative analysis revealed that wearable devices (Empatica E4 and H10 Polar) outperformed workstation addons (Kinect camera and computer-usage monitoring application) in predicting productivity, emphasizing the potential utility of wearable devices as an independent tool for assessment of productivity. Implementing the model within smart workstations allows for adaptable environments that boost productivity and overall well-being among office workers.

Wrist EMG Improves Gesture Classification for Stroke Patients.

Electromyography (EMG) is a popular human-machine interface for hand gesture control of assistive and rehabilitative technology. EMG can be used to estimate motor intent even when an individual cannot physically move due to weakness or paralysis. EMG is traditionally recorded from the extrinsic hand muscles located in the forearm. However, the wrist has become an increasingly attractive recording location for commercial applications as EMG sensors can be integrated into wrist-worn wearables (e.g., watches, bracelets). Here we explored the impact that recording EMG from the wrist, instead of the forearm, has on stroke patients with upper-limb hemiparesis. We show that EMG signal-to-noise ratio is significantly worse at the paretic wrist relative to the paretic forearm and non-paretic wrist. Despite this, we also show that the ability to classify hand gestures from EMG was significantly better at the paretic wrist relative to the paretic forearm. Our results also provide guidance as to the ideal gestures for each recording location. Namely, single-digit gestures appeared easiest to classify from both forearm and wrist EMG on the paretic side. These results suggest commercialization of wrist-worn EMG would benefit stroke patients by providing more accurate EMG control in a more widely adopted wearable formfactor.

Development of the Biomech-Wrist: A 3-DOF Exoskeleton for Rehabilitation and Training of Human Wrist.

This work describes a three-degrees-of-freedom rehabilitation exoskeleton robot for wrist articulation movement: the Biomech-Wrist. The proposed development includes the design requirements based on the biomechanics and anthropometric features of the upper limb, the mechanical design, electronic instrumentation, software design, manufacturing, control algorithm implementation, and the experimental setup to validate the functionality of the system. The design requirements were set to achieve human wrist-like movements: ulnar-radial deviation, flexion-extension, and pronation-supination. Then, the mechanical design considers the human range of motion with proper torques, velocities, and geometry. The manufacturing consists of 3D-printed elements and tubular aluminum sections resulting in lightweight components with modifiable distances. The central aspect of the instrumentation is the actuation system consisting of three brushless motors and a microcontroller for the control implementation. The proposed device was evaluated by considering two control schemes to regulate the trajectory tracking on each joint. The first scheme was the conventional proportional-derivative controller, whereas the second was proposed as a first-order sliding mode. The results show that the Biomech-Wrist exoskeleton can perform trajectory tracking with high precision ( RMSEmax = 0.0556 rad) when implementing the sliding mode controller.

High frequency of carpal tunnel syndrome and associated factors: A cross-sectional study in Peruvian workers from agro-export industry.

Carpal tunnel syndrome (CTS) is a peripheral mononeuropathy caused by compression of the median nerve at the wrist and has been reported in workers who perform repetitive movements that involve actions of sustained grasping of vibrating objects. We carried out a cross-sectional analytical study in March 2018 to identify the factors associated with the CTS among workers of agro-export companies in Ica-Peru. CTS confirmation in our study was based on having at least 1 positive screening test (Tinel or Phalen) and a presumptive result of CTS by the Kamath and Stothard Questionnaire. We carried out 4 Poisson regression models to evaluate the factors associated with CTS based on epidemiological and statistical criteria. We enrolled 112 agro-export workers in production (42.0%), packing (35.7%), and administration (22.3%) working areas. The CTS frequency in Peruvian agro-export workers in production, packing, and administrative working area were 78.7%, 45.0%, and 28.0%, respectively. The bivariate analysis found a relationship between the CTS with age, female sex, sports practice, job seniority in the working area (year), and repetitive wrist movements (hours per day). In the multivariate analysis, only job seniority in the working area (year) and repetitive wrist movements maintained their association with CTS. Occupational factors are significantly associated with a high frequency of CTS, such as job seniority in the working area (year) and repetitive wrist movements in agro-export workers. Surveillance programs should be held to prevent, reduce, and monitor workers' health status.

The Investigation of Bio-impedance Analysis at a Wrist Phantom with Two Pulsatile Arteries.

PURPOSE: Bio-impedance analysis (BIA) has been widely investigated for hemodynamic monitoring. However, previous works rarely modelled two synchronously pulsatile arteries (representing the radial and ulnar arteries) in the wrist/forearm model. This work aims to clarify and quantify the influences of two pulsatile arteries on BIA. METHODS: First, two blood-filled arteries were structured in a 3D wrist segment using the finite element method (FEM). Afterwards, an easy-to-produce two-arteries artificial wrist was fabricated with two components: gelatine-based surrounding tissue phantom and saline blood phantom. A syringe driver was utilised to constrict the arteries, and the impedance signals were measured using a Multi-frequency Impedance Analyser (MFIA). RESULTS: Both simulation and experimental results demonstrated the non-negligible influences of the ulnar artery on the overall BIA, inducing unwanted resistance changes to the acquired signals from the radial artery. The phantom experiments revealed the summation of the individual resistance changes caused by a single pulsatile artery was approximately equal to the measured resistance change caused by two synchronously pulsatile arteries, confirming the measured impedance signal at the wrist contains the pulsatile information from both arteries. CONCLUSION: This work is the first simulation and phantom investigation into two synchronously pulsatile arteries under BIA in the distal forearm, providing a better insight and understanding in the morphology of measured impedance signals. Future research can accordingly select either a small spacing 4-spot electrode configuration for a single artery sensing or a band electrode configuration for overall pulsatile arteries sensing. A more accurate estimation of blood volume change and pulse wave analysis (PWA) could help to develop cuffless blood pressure measurement (BPM).

Simultaneous and Proportional Control of Wrist and Hand Movements Based on a Neural-Driven Musculoskeletal Model.

Human-machine interfaces (HMIs) based on electromyography (EMG) signals have been developed for simultaneous and proportional control (SPC) of multiple degrees of freedom (DoFs). The EMG-driven musculoskeletal model (MM) has been used in HMIs to predict human movements in prosthetic and robotic control. However, the neural information extracted from surface EMG signals may be distorted due to their limitations. With the development of high density (HD) EMG decomposition, accurate neural drive signals can be extracted from surface EMG signals. In this study, a neural-driven MM was proposed to predict metacarpophalangeal (MCP) joint flexion/extension and wrist joint flexion/extension. Ten non-disabled subjects (male) were recruited and tested. Four 64-channel electrode grids were attached to four forearm muscles of each subject to record the HD EMG signals. The joint angles were recorded synchronously. The acquired HD EMG signals were decomposed to extract the motor unit (MU) discharge for estimating the neural drive, which was then used as the input to the MM to calculate the muscle activation and predict the joint movements. The Pearson's correlation coefficient (r) and the normalized root mean square error (NRMSE) between the predicted joint angles and the measured joint angles were calculated to quantify the estimation performance. Compared to the EMG-driven MM, the neural-driven MM attained higher r values and lower NRMSE values. Although the results were limited to an offline application and to a limited number of DoFs, they indicated that the neural-driven MM outperforms the EMG-driven MM in prediction accuracy and robustness. The proposed neural-driven MM for HMI can obtain more accurate neural commands and may have great potential for medical rehabilitation and robot control.

Characteristics of multi-channel intermuscular directional coupling based on time-varying partial directional coherence analysis.

The human body transmits directional information between muscles during upper limb movements, and this will be particularly evident when the dominant muscle changes during movement transitions. By capturing the electromyography (EMG) signals of wrist flexion and extension continuous transition movements, we investigated the characteristics of multichannel intermuscular directional coupling and directional information transmission, and consequently explored the control mechanism of Central nervous system (CNS) and the coordination mechanism of motor muscles. Multi-channel EMG was collected from 12 healthy subjects under continuous translational movements of wrist flexion and extension, and the time-varying biased directional coherence analysis (TVPDC) model was constructed using partial directional coherence analysis (PDC) frequency domain directionality to study the directional information transfer characteristics in the time-frequency domain, screen closely related muscle pairs and perform directional coupling significance analysis. Palmaris longus (PL) played a dominant role under wrist flexion movements(WF), Extensor Carpi Radialis (ECR) played a dominant role under wrist extension movements(WE), and the remaining muscles responded to them with information and Biceps Brachii (BB) played a responsive role throughout the movement; flexor pairs had the highest positive coupling values in the beta band during Conversion action1 (MC1) and WF phases, and extensor pairs had the highest positive coupling values in the gamma band during Conversion action2(MC2) phase and the highest coupling values in the beta band during WE phase. TVPDC can effectively analyze the multichannel intermuscular directional coupling and information transmission relationship of surface electromyography under wrist flexion and extension transition movements, providing a reference for exploring the control mechanism of CNS and abnormal control mechanism in patients with motor dysfunction in a new perspective.

Effect of muscle length in a handgrip task on corticomotor excitability of extrinsic and intrinsic hand muscles under resting and submaximal contraction conditions.

The neurophysiological mechanisms underlying muscle force control for different wrist postures still need to be better understood. To further elucidate these mechanisms, the present study aimed to investigate the effects of wrist posture on the corticospinal excitability by transcranial magnetic stimulation (TMS) of extrinsic (flexor [FCR] and extensor carpi radialis [ECR]) and intrinsic (flexor pollicis brevis (FPB)) muscles at rest and during a submaximal handgrip strength task. Fourteen subjects (24.06 ± 2.28 years) without neurological or motor disorders were included. We assessed how the wrist posture (neutral: 0°; flexed: +45°; extended: -45°) affects maximal handgrip strength (HGSmax ) and the motor evoked potentials (MEP) amplitudes during rest and active muscle contractions. HGSmax was higher at 0° (133%) than at -45° (93.6%; p < 0.001) and +45° (73.9%; p < 0.001). MEP amplitudes were higher for the FCR at +45° (83.6%) than at -45° (45.2%; p = 0.019) and at +45° (156%; p < 0.001) and 0° (146%; p = 0.014) than at -45° (106%) at rest and active condition, respectively. Regarding the ECR, the MEP amplitudes were higher at -45° (113%) than at +45° (60.8%; p < 0.001) and 0° (72.6%; p = 0.008), and at -45° (138%) than +45° (96.7%; p = 0.007) also at rest and active conditions, respectively. In contrast, the FPB did not reveal any difference among wrist postures and conditions. Although extrinsic and intrinsic hand muscles exhibit overlapping cortical representations and partially share the same innervation, they can be modulated differently depending on the biomechanical constraints.

Cumulative Spike Train Estimation for Muscle Excitation Assessment From Surface EMG Using Spatial Spike Detection.

Estimating cumulative spike train (CST) of motor units (MUs) from surface electromyography (sEMG) is essential for the effective control of neural interfaces. However, the limited accuracy of existing estimation methods greatly hinders the further development of neural interface. This paper proposes a simple but effective approach for identifying CST based on spatial spike detection from high-density sEMG. Specifically, we use a spatial sliding window to detect spikes according to the spatial propagation characteristics of the motor unit action potential, focusing on the spikes of activated MUs in a local area rather than those of a specific MU. We validated the effectiveness of our proposed method through an experiment involving wrist flexion/extension and pronation/supination, comparing it with a recognized CST estimation method and an MU decomposition based method. The results demonstrated that the proposed method obtained higher accuracy on multi-DoF wrist torque estimation leveraging the estimated CST compared to the other three methods. On average, the correlation coefficient (R) and the normalized root mean square error (nRMSE) between the estimation results and recorded force were 0.96 ± 0.03 and 10.1% ± 3.7%, respectively. Moreover, there was an extremely high interpretive extent between the CSTs of proposed method and the MU decomposition method. The outcomes reveal the superiority of the proposed method in identifying CSTs and can provide promising driven signals for neural interface.

Effects of wrist posture and stabilization on precision grip force production and muscle activation patterns.

Most of the power for generating forces in the fingers arises from muscles located in the forearm. This configuration maximizes finger joint range of motion while minimizing finger mass and inertia. The resulting multiarticular arrangement of the tendons, however, complicates independent control of the wrist and the digits. Actuating the wrist impacts sensorimotor control of the fingers and vice versa. The goal of this study was to systematically investigate interactions between isometric wrist and digit control. Specifically, we examined how the need to maintain a specified wrist posture influences precision grip. Fifteen healthy adults produced maximum precision grip force at 11 different wrist flexion/extension angles, with the arm supported, under two conditions: 1) the participant maintained the desired wrist angle while performing the precision grip and 2) a robot maintained the specified wrist angle. Wrist flexion/extension posture significantly impacted maximum precision grip force (P < 0.001), with the greatest grip force achieved when the wrist was extended 30° from neutral. External wrist stabilization by the robot led to a 20% increase in precision grip force across wrist postures. Increased force was accompanied by increased muscle activation but with an activation pattern similar to the one used when the participant had to stabilize their wrist. Thus, simultaneous wrist and finger requirements impacted performance of an isometric finger task. External wrist stabilization can promote increased precision grip force resulting from increased muscle activation. These findings have potential clinical significance for individuals with neurologically driven finger weakness, such as stroke survivors.NEW & NOTEWORTHY We explored the interdependence between wrist and fingers by assessing the influence of wrist posture and external stabilization on precision grip force generation. We found that maximum precision grip force occurred at an extended wrist posture and was 20% greater when the wrist was Externally Stabilized. The latter resulted from amplification of muscle activation patterns from the Self-Stabilized condition rather than adoption of new patterns exploiting external wrist stabilization.