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(1) Background: As digital health technology evolves, the role of accurate medical-gloved hand tracking is becoming more important for the assessment and training of practitioners to reduce procedural errors in clinical settings. (2) Method: This study utilized computer vision for hand pose estimation to model skeletal hand movements during in situ aseptic drug compounding procedures. High-definition video cameras recorded hand movements while practitioners wore medical gloves of different colors. Hand poses were manually annotated, and machine learning models were developed and trained using the DeepLabCut interface via an 80/20 training/testing split. (3) Results: The developed model achieved an average root mean square error (RMSE) of 5.89 pixels across the training data set and 10.06 pixels across the test set. When excluding keypoints with a confidence value below 60%, the test set RMSE improved to 7.48 pixels, reflecting high accuracy in hand pose tracking. (4) Conclusions: The developed hand pose estimation model effectively tracks hand movements across both controlled and in situ drug compounding contexts, offering a first-of-its-kind medical glove hand tracking method. This model holds potential for enhancing clinical training and ensuring procedural safety, particularly in tasks requiring high precision such as drug compounding.

Movement intentions of motor impaired individuals can be detected in laboratory settings via electroencephalography Brain-Computer Interfaces (EEG-BCIs) and used for motor rehabilitation and external system control. The real-world BCI use is limited by the costly, time-consuming, obtrusive, and uncomfortable setup of scalp EEG. Ear-EEG offers a faster, more convenient, and more aesthetic setup for recording EEG, but previous work using expensive amplifiers detected motor intentions at chance level. This study investigates the feasibility of a low-cost ear-EEG BCI for the detection of tongue and hand movements for rehabilitation and control purposes. In this study, ten able-bodied participants performed 100 right wrist extensions and 100 tongue-palate movements while three channels of EEG were recorded around the left ear. Offline movement vs. idle activity classification of ear-EEG was performed using temporal and spectral features classified with Random Forest, Support Vector Machine, K-Nearest Neighbours, and Linear Discriminant Analysis in three scenarios: Hand (rehabilitation purpose), hand (control purpose), and tongue (control purpose). The classification accuracies reached 70%, 73%, and 83%, respectively, which was significantly higher than chance level. These results suggest that a low-cost ear-EEG BCI can detect movement intentions for rehabilitation and control purposes. Future studies should include online BCI use with the intended user group in real-life settings.

Background: Hand function assessment heavily relies on specific task scenarios, making it challenging to ensure validity and reliability. In addition, the wide range of assessment tools, limited and expensive data recording, and analysis systems further aggravate the issue. However, smartphones provide a promising opportunity to address these challenges. Thus, the built-in, high-efficiency sensors in smartphones can be used as effective tools for hand function assessment., Objective: This review aims to evaluate existing studies on hand function evaluation using smartphones., Methods: An information specialist searched 8 databases on June 8, 2023. The search criteria included two major concepts: (1) smartphone or mobile phone or mHealth and (2) hand function or function assessment. Searches were limited to human studies in the English language and excluded conference proceedings and trial register records. Two reviewers independently screened all studies, with a third reviewer involved in resolving discrepancies. The included studies were rated according to the Mixed Methods Appraisal Tool. One reviewer extracted data on publication, demographics, hand function types, sensors used for hand function assessment, and statistical or machine learning (ML) methods. Accuracy was checked by another reviewer. The data were synthesized and tabulated based on each of the research questions., Results: In total, 46 studies were included. Overall, 11 types of hand dysfunction-related problems were identified, such as Parkinson disease, wrist injury, stroke, and hand injury, and 6 types of hand dysfunctions were found, namely an abnormal range of motion, tremors, bradykinesia, the decline of fine motor skills, hypokinesia, and nonspecific dysfunction related to hand arthritis. Among all built-in smartphone sensors, the accelerometer was the most used, followed by the smartphone camera. Most studies used statistical methods for data processing, whereas ML algorithms were applied for disease detection, disease severity evaluation, disease prediction, and feature aggregation., Conclusions: This systematic review highlights the potential of smartphone-based hand function assessment. The review suggests that a smartphone is a promising tool for hand function evaluation. ML is a conducive method to classify levels of hand dysfunction. Future research could (1) explore a gold standard for smartphone-based hand function assessment and (2) take advantage of smartphones' multiple built-in sensors to assess hand function comprehensively, focus on developing ML methods for processing collected smartphone data, and focus on real-time assessment during rehabilitation training. The limitations of the research are 2-fold. First, the nascent nature of smartphone-based hand function assessment led to limited relevant literature, affecting the evidence's completeness and comprehensiveness. This can hinder supporting viewpoints and drawing conclusions. Second, literature quality varies due to the exploratory nature of the topic, with potential inconsistencies and a lack of high-quality reference studies and meta-analyses., (©Yan Fu, Yuxin Zhang, Bing Ye, Jessica Babineau, Yan Zhao, Zhengke Gao, Alex Mihailidis. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 16.09.2024.)

Intercepting moving targets is a fundamental skill in human behavior, influencing various domains such as sports, gaming, and other activities. In these contexts, precise visual processing and motor control are crucial for adapting and navigating effectively. Nevertheless, there are still some gaps in our understanding of how these elements interact while intercepting a moving target. This study explored the dynamic interplay among eye movements, pupil size, and interceptive hand movements, with visual and motion uncertainty factors. We developed a simple visuomotor task in which participants used a joystick to interact with a computer-controlled dot that moved along two-dimensional trajectories. This virtual system provided the flexibility to manipulate the target's speed and directional uncertainty during chase trials. We then conducted a geometric analysis based on optimal angles for each behavior, enabling us to distinguish between simple tracking and predictive trajectories that anticipate future positions of the moving target. Our results revealed the adoption of a strong interception strategy as participants approached the target. Notably, the onset and amount of optimal interception strategy depended on task parameters, such as the target's speed and frequency of directional changes. Furthermore, eye-tracking data showed that participants continually adjusted their gaze speed and position, continuously adapting to the target's movements. Finally, in successful trials, pupillary responses predicted the amount of optimal interception strategy while exhibiting an inverse relationship in trials without collisions. These findings reveal key interactions among visuomotor parameters that are crucial for solving complex interception tasks., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Márquez, Treviño. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

The body tracking systems on the current market offer a wide range of options for tracking the movements of objects, people, or extremities. The precision of this technology is often limited and determines its field of application. This work aimed to identify relevant technical and environmental factors that influence the performance of body tracking in industrial environments. The influence of light intensity, range of motion, speed of movement and direction of hand movement was analyzed individually and in combination. The hand movement of a test person was recorded with an Azure Kinect at a distance of 1.3 m. The joints in the center of the hand showed the highest accuracy compared to other joints. The best results were achieved at a luminous intensity of 500 lx, and movements in the x-axis direction were more precise than in the other directions. The greatest inaccuracy was found in the z-axis direction. A larger range of motion resulted in higher inaccuracy, with the lowest data scatter at a 100 mm range of motion. No significant difference was found at hand velocity of 370 mm/s, 670 mm/s and 1140 mm/s. This study emphasizes the potential of RGB-D camera technology for gesture control of industrial robots in industrial environments to increase efficiency and ease of use.

Hand Movement Recognition (HMR) with sEMG is crucial for artificial hand prostheses. HMR performance mostly depends on the feature information that is fed to the classifiers. However, sEMG often captures noise like power line interference (PLI) and motion artifacts. This may extract redundant and insignificant feature information, which can degrade HMR performance and increase computational complexity. This study aims to address these issues by proposing a novel procedure for automatically removing PLI and motion artifacts from experimental sEMG signals. This will make it possible to extract better features from the signal and improve the categorization of various hand movements. Empirical mode decomposition and energy entropy thresholding are utilized to select relevant mode components for artifact removal. Time domain features are then used to train classifiers (kNN, LDA, SVM) for hand movement categorization, achieving average accuracies of 92.36%, 93.63%, and 98.12%, respectively, across subjects. Additionally, muscle contraction efforts are classified into low, medium, and high categories using this technique. Validation is performed on data from ten subjects performing eight hand movement classes and three muscle contraction efforts with three surface electrode channels. Results indicate that the proposed preprocessing improves average accuracy by 9.55% with the SVM classifier, significantly reducing computational time., (© 2024 IOP Publishing Ltd. All rights, including for text and data mining, AI training, and similar technologies, are reserved.)

The loss of a hand disrupts the sophisticated neural pathways between the brain and the hand, severely affecting the level of independence of the patient and the ability to carry out daily work and social activities. Recent years have witnessed a rapid evolution of surgical techniques and technologies aimed at restoring dexterous motor functions akin to those of the human hand through bionic solutions, mainly relying on probing of electrical signals from the residual nerves and muscles. Here, we report the clinical implementation of an interface aimed at achieving this goal by exploiting muscle deformation, sensed through passive magnetic implants: the myokinetic interface. One participant with a transradial amputation received an implantation of six permanent magnets in three muscles of the residual limb. A truly self-contained myokinetic prosthetic arm embedding all hardware components and the battery within the prosthetic socket was developed. By retrieving muscle deformation caused by voluntary contraction through magnet localization, we were able to control in real time a dexterous robotic hand following both a direct control strategy and a pattern recognition approach. In just 6 weeks, the participant successfully completed a series of functional tests, achieving scores similar to those achieved when using myoelectric controllers, a standard-of-care solution, with comparable physical and mental workloads. This experience raised conceptual and technical limits of the interface, which nevertheless pave the way for further investigations in a partially unexplored field. This study also demonstrates a viable possibility for intuitively interfacing humans with robotic technologies.

This single-participant case study examines the feasibility of using custom virtual reality (VR) gaming software in the home environment for low-dose Hand Arm Bimanual Intensive Training (HABIT). A 10-year-old with right unilateral cerebral palsy participated in this trial. Fine and gross motor skills as well as personal goals for motor outcomes were assessed before and after the intervention using the Box and Blocks Test, Nine-Hole Peg Test, and Canadian Occupational Performance Measure. Movement intensities collected via the VR hardware accelerometers, VR game scores, and task accuracy were recorded via the HABIT-VR software as indices of motor performance. The child and family were instructed to use the HABIT-VR games twice daily for 30 minutes over a 14-day period and asked to record when they used the system. The child used the system and completed the 14-hour, low-dose HABIT-VR intervention across 22 days. There was no change in Box and Blocks Test and Nine-Hole Peg Test scores before and after the intervention. Canadian Occupational Performance Measure scores increased but did not reach the clinically relevant threshold, due to high scores at baseline. Changes in motor task intensities during the use of VR and mastery of the VR bimanual tasks suggested improved motor efficiency. This case study provides preliminary evidence that HABIT-VR is useful for promoting adherence to HABIT activities and for the maintenance of upper extremity motor skills in the home setting., (©James E Gehringer, Anne Woodruff Jameson, Hailey Boyer, Jennifer Konieczny, Ryan Thomas, James Pierce III, Andrea B Cunha, Sandra Willett. Originally published in JMIR Formative Research (https://formative.jmir.org), 06.09.2024.)

This study addresses the need for highly sensitive tools to evaluate hand strength, particularly grasp and pinch strength, which are vital for diagnosing and rehabilitating conditions affecting hand function. Current devices like the Jamar dynamometer and Martin Vigorimeter, although reliable, fail to measure extremely low force or pressure values required for individuals with severe hand impairments. This research introduces a novel device, a modified Martin Vigorimeter, utilizing an ultra-soft latex chamber and differential pressure measurement to detect minute pressure changes, thus significantly enhancing sensitivity. The device offers a cost-effective solution, making advanced hand strength evaluation more accessible for clinical and research applications. Future research should validate its accuracy across diverse populations and settings, exploring its broader implications for hand rehabilitation and occupational health.

Background: Previous studies have reported that the sense of "self" is associated with specific brain regions and neural network activities. In addition, the mirror system, which functions when executing or observing an action, might contribute to differentiating the self from others and form the basis of the sense of self as a fundamental physical representation. This study investigated whether differences in mu suppression, an indicator of mirror system activity, reflect cognitions related to self-other discrimination., Methods: The participants were 30 of healthy college students. The participants observed short video clips of hand movements performed by themselves or actors from two perspectives (i.e., first-person and third-person). The electroencephalogram (EEG) mu rhythm (8-13 Hz) was measured during video observation as an index of mirror neuron system activity. EEG activity related to self-detection was analyzed using participants' hand movements as self-relevant stimuli., Results: The results showed that mu suppression in the 8-13-Hz range exhibited perspective-dependent responses to self/other stimuli. There was a significant self-oriented mu suppression response in the first-person perspective. However, the study found no significant response orientation in the third-person perspective. The results suggest that mirror system activity may involve self-other discrimination differently depending on the perspective., Conclusions: In summary, this study examined the mirror system's activity for self and others using the EEG's mu suppression. As a result, it was suggested that differences in self and others or perspectives may influence mu suppression., (© 2024. The Author(s).)

The concept of forward models in the brain, classically applied to describing on-line motor control, can in principle be extended to action planning, i.e. assuming forward sensory predictions are issued during the mere preparation of movements. To test this idea, we combined a delayed movement task with a virtual reality based manipulation of visuomotor congruence during functional magnetic resonance imaging. Participants executed simple hand movements after a delay. During the delay, two aspects of the upcoming movement could be cued: the movement type and the visuomotor mapping (i.e. congruence of executed hand movements and visual movement feedback by a glove-controlled virtual hand). Frontoparietal areas showed increased delay period activity when preparing pre-specified movements (cued > uncued). The cerebellum showed increased activity during the preparation for incongruent > congruent visuomotor mappings. The left anterior intraparietal sulcus showed an interaction effect, responding most strongly when a pre-specified (cued) movement was prepared under expected visuomotor incongruence. These results suggest that motor planning entails a forward prediction of visual body movement feedback, which can be adjusted in anticipation of nonstandard visuomotor mappings, and which is likely computed by the cerebellum and integrated with state estimates for (planned) control in the anterior intraparietal sulcus., (© The Author(s) 2024. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Introduction: Several studies have assessed and validated the impact of exoskeletons on back and shoulder muscle activation; however, limited research has explored the role that exoskeletons could play in mitigating lower arm-related disorders. This study assessed the impact of Ironhand, an active hand exoskeleton (H-EXO) designed to reduce grip force exertion, on worker exertion levels using a two-phase experimental design., Method: Ten male participants performed a controlled, simulated drilling activity, while three male participants completed an uncontrolled concrete demolition activity. The impact of the exoskeleton was assessed in terms of muscle activity across three different muscles using electromyography (EMG), perceived exertion, and perceived effectiveness., Results: Results indicate that peak muscle activation decreased across the target muscle group when the H-EXO was used, with the greatest reduction (27%) observed in the Extensor Carpi Radialis (ECR). Using the exoskeleton in controlled conditions did not significantly influence perceived exertion levels. Users indicated that the H-EXO was a valuable technology and expressed willingness to use it for future tasks., Practical Applications: This study showcases how glove-based exoskeletons can potentially reduce wrist-related disorders, thereby improving safety and productivity among workers. Future work should assess the impact of the H-EXO in various tasks, different work environments and configurations, and among diverse user groups., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 National Safety Council and Elsevier Ltd. All rights reserved.)

Class demonstrations where the lecturer's hand motor actions are observable generally have a positive effect on the learning of motor skills because they trigger an effortless process of embodied simulations. Whether the learning of cognitive skills is likewise affected by such visualisations is yet to be investigated and might depend on the learning content as well as other factors. This study aimed to investigate whether showing the lecturer's hand via a document camera during an introductory financial accounting class affects student learning (transfer performance), cognitive load responses, and note-taking behaviour compared to a writing pad where the lecturer's hand is not visible. The study utilised a quasi-experimental design in an in-person setting, with a pre-test and post-test comparison of two groups of participants: one group that viewed a lecture video without the lecturer's hand being visible (n = 509), and another group that viewed the same lecture with the lecturer's hand being visible (n = 571). The results showed that the with-hand group had a significantly higher increase in test scores compared to the without-hand group. However, the visibility of the hand did not significantly impact cognitive load or note-taking behaviour. The findings have important practical implications for education, as incorporating non-verbal cues such as the lecturer's hand may effectively enhance learning cognitive skills., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this article., (Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)

The human hand, with its intricate sensory capabilities, plays a pivotal role in our daily interactions with the world. This remarkable organ possesses a wide range of natural sensors that enrich our experiences, enabling us to perceive touch, position, and temperature. These natural sensors work in concert to provide us with a rich sensory experience, enabling us to distinguish between various textures, gauge the force of our grip, determine the position of our fingers without needing to see them, perceive the temperature of objects we come into contact with or detect if a cloth is wet or dry. This complex sensory system is fundamental to our ability to manipulate objects, explore our surroundings, and interact with the world and people around us. In this article, we summarize the research performed in our laboratories over the years and our findings to restore both touch, position, and temperature modalities. The combination of intraneural stimulation, sensory substitution, and wearable technology opens new possibilities for enhancing sensory feedback in prosthetic hands, promising improved functionality and a closer approximation to natural sensory experiences for individuals with limb differences., (© 2024 The Author(s). Artificial Organs published by International Center for Artificial Organ and Transplantation (ICAOT) and Wiley Periodicals LLC.)

We used an armband with embedded surface electromyography (sEMG) electrodes, together with machine-learning (ML) models, to automatically detect lifting-lowering activities and classify hand loads. Nine healthy participants (4 male and 5 female) completed simulated lifting-lowering tasks in various conditions and with two different hand loads (2.3 and 6.8 kg). We compared three sEMG signal feature sets (i.e., time, frequency, and a combination of both domains) and three ML classifiers (i.e., Random Forest, Support Vector Machine, and Logistic Regression). Both Random Forest and Support Vector Machine models, using either time-domain or time- and frequency-domain features, yielded the best performance in detecting lifts, with respective accuracies of 79.2% (start) and 86.7% (end). Similarly, both ML models yielded the highest accuracy (80.9%) in classifying the two hand loads, regardless of the sEMG features used, emphasizing the potential of sEMG armbands for assessing exposure and risks in occupational lifting tasks., (Copyright © 2024. Published by Elsevier Ltd.)

The cold-induced vasodilation (CIVD) response of the human body to Arctic-like environments helps delay or prevent cold injuries to peripheral regions, such as the hands and feet. To more comprehensively predict the thermal responses of these body regions to cold stress, here we extended our previously developed and validated anatomically accurate three-dimensional whole-body thermoregulatory human model by incorporating a new phenomenological formulation of the CIVD mechanism. In this formulation, we modulated the cyclic vasodilation and vasoconstriction flow of warm blood from the body core to the peripheral regions solely by determining the heat-transfer exchange between the skin and the surrounding environment, and deactivated it when the core body temperature decreased to 36.5 °C. In total, we calibrated and validated the model using eight distinct studies involving 153 unique male subjects exposed to 10 diverse experimental conditions, including cold-air exposure of the whole body as well as air exposure and cold-water immersion of the hand or the foot. With CIVD incorporated, the model predictions generally yielded root mean square errors (RMSEs) of <3.0 °C for skin temperature, which represented a reduction of up to 3.6 °C compared to when we did not consider CIVD. Similarly, the incorporation of CIVD increased the fraction of predictions within two standard errors of the measured data by up to 63 %. The model predictions yielded RMSEs for core body temperature of <0.2 °C. The model can be used to provide guidelines to reduce the risk of cold-related injuries during prolonged exposures to very-cold environments., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could appear to have influenced the work reported in this paper., (Published by Elsevier Ltd.)

Background: The rubber hand illusion (RHI) is a well-established method for studying body ownership: Given adequate concordance of visual, sensory, and proprioceptive stimuli, the individual experiences a rubber hand as his or her own., Objective: To study the effects of a threat to the rubber hand., Methods: We created a typical RHI paradigm but added threatening pain: Both the real hand of an individual and the rubber hand were stroked with a brush, either synchronously (RHI-inducing condition) or asynchronously (control condition), but only the rubber hand was then pricked with a needle to create a threatening pain condition. Event-related potentials (ERPs) were recorded in a group of 23 typically-developed individuals. ERP effects were source-localized using low-resolution electromagnetic tomography., Results: The individuals consistently reported experience of the RHI during the experiment when the brush strokes were applied synchronously to both the real hand and the rubber hand. ERP analysis revealed that synchronous brush stroking gave rise to higher amplitude of frontal ERPs in the 100-200 ms range than asynchronous brush stroking, which was interpreted as reflecting the RHI. In the threatening pain condition, ERPs showed a greater positivity at frontocentral electrodes, source localized in the supplementary motor area (SMA)., Conclusion: SMA activation could reflect a control mechanism over reflexive motor activity, facilitating a possible threat-related response. Further studies should address ERP effects and the extent of the RHI to standard and threat stimuli in a correlative fashion to further elucidate the functional significance of the neurophysiological findings., Competing Interests: The authors declare no conflicts of interest., (Copyright © 2024 The Author(s). Published by Wolters Kluwer Health, Inc.)

Hand preference is the preferential use of one hand for a single task. Its study provides insight into the neural mechanisms underlying motor skills, perception, and cognitive functions. From a comparative perspective, it also offers a window into evolutionary history, shedding light on whether manual preferences stem from genetics, environmental influences, or a combination of both. However, there is a paucity of information on preferential hand use for several primate taxa. Here we examine hand preference for the first time in mantled howler monkeys (Alouatta palliata) to determine if there is preferential hand use at the individual and population level as well as sex differences in hand use. We followed 17 wild adult individuals for 10 months and used focal animal sampling (506 focal samples) to record hand use in two types of self-directed behaviors, touching (1246 events) and scratching (1115 events). According to the binomial tests, four individuals were right-hand-preferent, two were left-hand-preferent, and 11 were ambilateral during touching, whereas for scratching seven individuals were right-hand-preferent, two were left-hand-preferent, and eight were ambilateral. At the population level, there was ambilaterality in both behaviors. At the individual level, according to the HI index, hand preference in touching and scratching were not associated and did not vary between sexes. These findings concur with previous studies with howler monkeys and other taxa suggesting that population-level hand preference is not a universal trait across primates., (© 2024. The Author(s), under exclusive licence to Japan Monkey Centre.)

Prehension movements in primates have been extensively studied for decades, and hand transport and hand grip adjustment are usually considered as the main components of any object reach-to-grasp action. Evident temporal patterns were found for the velocity of the hand during the transport phase and for the digits kinematics during pre-shaping and enclosing phases. However, such kinematics were always analysed separately in regard to time, and never studied in terms of dependence one from another. Nevertheless, if a reliable one-to-one relationship is proven, it would allow reconstructing the digit velocity (and position) simply by knowing the hand acceleration during reaching motions towards the target object, ceasing the usual dependence seen in literature from time of movement and distance from the target. In this study, the aim was precisely to analyse reach-to-grasp motions to explore if such relationship exists and how it can be formulated. Offline and real-time results not only seem to suggest the existence of a time-independent, one-to-one relationship between hand transport and hand grip adjustment, but also that such relationship is quite resilient to the different intrinsic and extrinsic properties of the target objects such as size, shape and position., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: [Prof. Christian Cipriani (last author) is the founder of Prensilia SRL, a university spin-off that develops multi-articulated and sensorized robotic hands. All authors declare this research was conducted in the absence of any commercial or financial relationships that could constitute a potential conflict of interest]., (Copyright © 2024 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

We frequently interact with textured surfaces with both our feet and hands. Like texture's importance for grasping, texture perception via the foot sole might provide important signals about the stability of a surface, aiding in maintaining balance. However, how textures are perceived by the foot, and especially under the high forces experienced during walking, is unknown. The current study builds on extensive research investigating texture perception at the hand by presenting everyday textures to the foot while stepping onto them, exploring them with the foot while sitting, and exploring them with the hand. Participants rated each texture along three perceptual dimensions: roughness, hardness, and stickiness. Participants also rated how stable their posture felt when standing upon each texture. Results show that perceptual ratings of each textural dimension were highly correlated across conditions. Hardness exhibited the greatest consistency and stickiness the weakest. Moreover, correlations between stepping and exploration with the foot were lower than those between exploration with the foot and exploration with the hand, suggesting that mode of interaction (high vs. low force) impacts perception more than body region used (foot vs. hand). On an individual level, correlations between conditions were higher than those between participants, suggesting that differences are greater between individuals than between mode of interaction or body region. When investigating the relationship to perceived stability, only hardness contributed significantly, with harder surfaces rated as more stable. Overall, tactile perception appears consistent across body regions and interaction modes, although differences in perception are greater during walking. NEW & NOTEWORTHY We frequently interact with textured surfaces using our feet, but little is known about how textures on the foot sole are perceived as compared with the hand. Here, we show that roughness, hardness, and stickiness ratings are broadly consistent when stepping on textures, exploring them with the foot sole, or with the hand. Hardness also contributes to perceived stability.

Surface electromyography (sEMG) signal is a kind of physiological signal reflecting muscle activity and muscle force. At present, the existing methods of recognizing human motion intention need more than two sensors to recognize more than two kinds of movements, the sensor pasting positions are special, and the hardware conditions for execution are high. In this work, a real-time motion intention recognition method based on Morse code is proposed and applied to the mechanical hand. The short-time and long-term muscle contraction signals collected by a single sEMG sensor were extracted and encoded with the Morse code method, and then the developed mapping method from Morse code to six hand movements were used to recognize hand movements. The average recognition accuracy of hand movements was 94.8704 ± 2.3556%, the average adjusting time was 34.89 s for all subjects, and the execution time of a single movement was 381 ms. The corresponding experiment video can be found in the attachment to show the experiment. The method proposed in this work is a method with one sensor to recognize six movements, low hardware conditions, high recognition accuracy, and insensitive to the difference of sensor pasting position., (© 2024. International Federation for Medical and Biological Engineering.)

In real-time human-machine interaction (HMI) applications, hand gesture recognition (HGR) requires high accuracy with low latency. Surface electromyography (sEMG), a physiological electrical signal reflecting muscle activation, is extensively used in HMI. Recently, transient sEMG, generated during the gesture transitions, has been employed in HGR to achieve lower observational latency compared to steady-state sEMG. However, the use of long feature windows (up to 200 ms) still make it less desirable in low-latency HMI. In addition, most studies have relied on remote computing, where remote data processing and large data transfer result in high computation and network latency. In this paper, we proposed a method leveraging transient high density sEMG (HD-sEMG) and in-sensor computing to achieve low-latency HGR. An sEMG contrastive convolution network (sCCN) was proposed for HGR. The mean absolute value and its average integration were used to train the sCCN in a contrastive learning manner. In addition, all signal acquisition, data processing, and pattern recognition processes were deployed within designed sensor for in-sensor computing. Compared to the state-of-the-art study using multi-channel 200-ms transient sEMG, our proposed method achieved a comparable HGR accuracy of 0.963, and a 58% lower observational latency of only 84 ms. In-sensor computing realizes a 4 times lower computation latency of 3 ms, and significantly reduces the network latency to 2 ms. The proposed method offers a promising approach to achieving low-latency HGR without compromising accuracy. This facilitates real-time HMI in biomedical applications such as prostheses, exoskeletons, virtual reality, and video games.

Floor inclination can alter hand force production, and lower limb kinetics, affecting control operations, and threatening operator safety in various domains, such as aviation, naval, construction industry, or agriculture. This study investigates the effects of different floor inclinations, on handle push or pull force production. Participants performed maximal isometric contraction tasks requiring to exert a maximal voluntary force either by pulling or pushing a handle, at different floor inclinations from -30° to +30° about the transverse and longitudinal axes. Maximal hand force and Ground Reaction Forces about both feet were recorded. The results revealed non-equivalent variations in hand and feet responses as a function of inclination angle. Specifically, there was a significant reduction in handle push-pull force production, up to 70% (p < 0.001) for extreme inclinations, around both axes. This study provides critical data for design engineers, highlighting the challenge of production forces at steep angles., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: This work was financially supported by the ANR as part of the France Relance plan of the French government, in collaboration with Naval Group (France). However, the authors do not derive any commercial benefit from this work., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

This study explains the role of muscle coordination in chopstick manipulation and investigates the effects of object width and weight on intrinsic and extrinsic hand muscle activity when picking up objects with chopsticks. Surface electromyography was used to measure the activity of the intrinsic and extrinsic hand muscles when picking up objects of varying widths and weights using chopsticks. The results revealed coordinated muscle activity patterns in the intrinsic and extrinsic hand muscles and coordination between them during chopstick manipulation. Object widths varying between 1 and 3 cm did not significantly affect muscle activity; however, object weight influenced muscle activity during both chopstick closing and object grasping, with greater muscle activity in the 40 g condition than in the 10 g condition. Intrinsic hand muscles were found to be involved in object grasping, regardless of object weight. These findings suggest that object weight should be considered when practicing picking up objects with chopsticks in scenarios resembling daily dining, to prevent excessive muscle activity during rehabilitation., (Copyright © 2024 SFCM. Published by Elsevier Masson SAS. All rights reserved.)

Triadic interactions, wherein infants coordinate attention between caregivers and objects of shared focus, are believed to facilitate infant learning, and emerge around 9-12 months of age (Carpenter et al., 1998). Sensorimotor decoupling, wherein infants look at one percept while manipulating another, or use each hand for different actions, was hypothesized (de Barbaro et al., 2016) to contribute to triadic skills by allowing infants to smoothly shift attention between objects and social partners. We explored the development of Hand-Hand (H-H) and Gaze-Hand (G-H) decoupling in 38 infants at 4, 6, and 9 months. We also tested contingencies between maternal behaviors and infant decoupling: i.e., whether decoupling events followed maternal object-directed actions. Both overall and contingent infant decoupling increased from 4 to 9 months. Decoupling rates (both G-H and H-H) predicted variance in infants' fine and gross motor scores. Contingent G-H decoupling at 6 months predicted BSID-III communication scores at 18 months. Thus the development of infant sensorimotor skills, including decoupling, allows infants to smoothly shift attention and participate in triadic interactions., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

Transcranial electrical stimulation (tES) often targets the EEG-guided C3/C4 area that may not accurately represent M1 for hand muscles. This study aimed to determine if the neuroanatomy-based scalp acupuncture-guided site (AC) was a more effective spot than the C3 site for neuromodulation. Fifteen healthy subjects received one 20-minute session of high-definition transcranial alternating current stimulation (HD-tACS) intervention (20 Hz at 2 mA) at the AC or C3 sites randomly with a 1-week washout period. Subjects performed ball-squeezing exercises with the dominant hand during the HD-tACS intervention. The AC site was indiscernible from the finger flexor hotspot detected by TMS. At the baseline, the MEP amplitude from finger flexors was greater with less variability at the AC site than at the C3 site. HD-tACS intervention at the AC site significantly increased the MEP amplitude. However, no significant changes were observed after tACS was applied to the C3 site. Our results provide evidence that HD-tACS at the AC site produces better neuromodulation effects on the flexor digitorum superficialis (FDS) muscle compared to the C3 site. The AC localization approach can be used for future tES studies., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Interacting with data visualizations without an instrument or touch surface is typically characterized by the use of mid-air hand gestures. While mid-air expressions can be quite intuitive for interacting with digital content at a distance, they frequently lack precision and necessitate a different way of expressing users' data-related intentions. In this work, we aim to identify new designs for mid-air hand gesture manipulations that can facilitate instrument-free, touch-free, and embedded interactions with visualizations, while utilizing the three-dimensional (3D) interaction space that mid-air gestures afford. We explore mid-air hand gestures for data visualization by searching for natural means to interact with content. We employ three studies-an Elicitation Study, a User Study, and an Expert Study, to provide insight into the users' mental models, explore the design space, and suggest considerations for future mid-air hand gesture design. In addition to forming strong associations with physical manipulations, we discovered that mid-air hand gestures can: promote space-multiplexed interaction, which allows for a greater degree of expression; play a functional role in visual cognition and comprehension; and enhance creativity and engagement. We further highlight the challenges that designers in this field may face to help set the stage for developing effective gestures for a wide range of touchless interactions with visualizations.

Background: Eye tracking technology not only reveals the acquisition of visual information at fixation but also has the potential to unveil underlying cognitive processes involved in learning to use a multifunction prosthetic hand. It also reveals gaze behaviours observed during standardized tasks and self-chosen tasks. The aim of the study was to explore the use of eye tracking to track learning progress of multifunction hands at two different time points in prosthetic rehabilitation., Methods: Three amputees received control training of a multifunction hand with new control strategy. Detailed description of control training was collected first. They wore Tobii Pro2 eye-tracking glasses and performed a set of standardized tasks (required to switch to different grips for each task) after one day of training and at one-year-follow-up (missing data for Subject 3 at the follow up due to socket problem). They also performed a self-chosen task (free to use any grip for any object) and were instructed to perform the task in a way how they would normally do at home. The gaze-overlaid videos were analysed using the Tobii Pro Lab and the following metrics were extracted: fixation duration, saccade amplitude, eye-hand latency, fixation count and time to first fixation., Results: During control training, the subjects learned 3 to 4 grips. Some grips were easier, and others were more difficult because they forgot or were confused with the switching strategies. At the one-year-follow-up, a decrease in performance time, fixation duration, eye-hand latency, and fixation count was observed in Subject 1 and 2, indicating an improvement in the ability to control the multifunction hand and a reduction of cognitive load. An increase in saccade amplitude was observed in both subjects, suggesting a decrease in difficulty to control the prosthetic hand. During the standardized tasks, the first fixation of all three subjects were on the multifunction hand in all objects. During the self-chosen tasks, the first fixations were mostly on the objects first., Conclusion: The qualitative data from control training and the quantitative eye tracking data from clinical standardized tasks provided a rich exploration of cognitive processing in learning to control a multifunction hand. Many prosthesis users prefer multifunction hands and with this study we have demonstrated that a targeted prosthetic training protocol with reliable assessment methods will help to lay the foundation for measuring functional benefits of multifunction hands., (© 2024. The Author(s).)

Long-term electroencephalography (EEG) recordings have primarily been used to study resting-state fluctuations. These recordings provide valuable insights into various phenomena such as sleep stages, cognitive processes, and neurological disorders. However, this study explores a new angle, focusing for the first time on the evolving nature of EEG dynamics over time within the context of movement. Twenty-two healthy individuals were measured six times from 2 p.m. to 12 a.m. with intervals of 2 h while performing four right-hand gestures. Analysis of movement-related cortical potentials (MRCPs) revealed a reduction in amplitude for the motor and post-motor potential during later hours of the day. Evaluation in source space displayed an increase in the activity of M1 of the contralateral hemisphere and the SMA of both hemispheres until 8 p.m. followed by a decline until midnight. Furthermore, we investigated how changes over time in MRCP dynamics affect the ability to decode motor information. This was achieved by developing classification schemes to assess performance across different scenarios. The observed variations in classification accuracies over time strongly indicate the need for adaptive decoders. Such adaptive decoders would be instrumental in delivering robust results, essential for the practical application of BCIs during day and nighttime usage., (© 2024. The Author(s).)

In this paper, the innovative design of a robotic hand with soft jointed structure is carried out and a tendon-driven mechanism, a master-slave motor coordinated drive mechanism, a thumb coupling transmission mechanism and a thumb steering mechanism are proposed. These innovative designs allow for more effective actuation in each finger, enhancing the load capacity of the robotic hand while maintaining key performance indicators such as dexterity and adaptability. A mechanical model of the robotic finger was made to determine the application limitations and load capacity. The robotic hand was then prototyped for a set of experiments. The experimental results showed that the proposed theoretical model were reliable. Also, the fingertip force of the robotic finger could reach up to 10.3 N, and the load force could reach up to 72.8 N. When grasping target objects of different sizes and shapes, the robotic hand was able to perform the various power grasping and precision grasping in the Cutkosky taxonomy. Moreover, the robotic hand had good flexibility and adaptability by means of adjusting the envelope state autonomously., (© 2024 IOP Publishing Ltd. All rights, including for text and data mining, AI training, and similar technologies, are reserved.)

Stroke is an acute cerebrovascular disease in which sudden interruption of blood supply to the brain or rupture of cerebral blood vessels cause damage to brain cells and consequently impair the patient's motor and cognitive abilities. A novel rehabilitation training model integrating brain-computer interface (BCI) and virtual reality (VR) not only promotes the functional activation of brain networks, but also provides immersive and interesting contextual feedback for patients. In this paper, we designed a hand rehabilitation training system integrating multi-sensory stimulation feedback, BCI and VR, which guides patients' motor imaginations through the tasks of the virtual scene, acquires patients' motor intentions, and then carries out human-computer interactions under the virtual scene. At the same time, haptic feedback is incorporated to further increase the patients' proprioceptive sensations, so as to realize the hand function rehabilitation training based on the multi-sensory stimulation feedback of vision, hearing, and haptic senses. In this study, we compared and analyzed the differences in power spectral density of different frequency bands within the EEG signal data before and after the incorporation of haptic feedback, and found that the motor brain area was significantly activated after the incorporation of haptic feedback, and the power spectral density of the motor brain area was significantly increased in the high gamma frequency band. The results of this study indicate that the rehabilitation training of patients with the VR-BCI hand function enhancement rehabilitation system incorporating multi-sensory stimulation can accelerate the two-way facilitation of sensory and motor conduction pathways, thus accelerating the rehabilitation process.

This study evaluates the effects of object weight and hand dominance on the end-point kinematics of the hand-to-mouth (withdrawal) movement in a functional reach-to-drink task for typically developing school-aged children. Using 3D motion capture, speed (average velocity and peak velocity), straightness (ratio), and smoothness (number of velocity peaks and log dimensionless jerk) of hand movements were calculated for the withdrawal motion with three different bottle weights (empty, half-filled, and full). Average velocity (550.4 ± 142.0 versus 512.1 ± 145.6 mm/s) and peak velocity (916.3 ± 234 versus 842.7 ± 198.4 mm/s) were significantly higher with the empty versus half-filled bottle and with the non-dominant (average: 543.5 ± 145.2 mm/s; peak: 896.5 ± 207 mm/s) versus dominant (average: 525.2 ± 40.7 mm/s; peak: 864.2 ± 209.2 mm/s) hand. There were no differences in straightness or smoothness. These findings indicate that increasing weight in reach-to-drink task puts greater constraints on the task. The slower movements with the dominant hand might denote better precision control than the non-dominant hand. The quantitative motion capture results show average values for the kinematic variables for a functional reach-to-drink task in a typically developing population of school-aged children with changing weights of the bottles that are relevant to a real-life scenario. These results could inform the design of individualized therapeutic interventions to improve functional upper-extremity use in children with neurodevelopmental motor disorders.

Whether and how the non-lesional sensorimotor cortex is activated and contributes to post-injury motor recovery is controversial. Here, we investigated the role of interhemispheric pathway from the contralesional to ipsilesional premotor cortex in activating the ipsilesional sensorimotor cortex and promoting recovery after lesioning the lateral corticospinal tract at the cervical cord, by unidirectional chemogenetic blockade in macaques. The blockade impaired dexterous hand movements during the early recovery stage. Electrocorticographical recording showed that the low frequency band activity of the ipsilesional premotor cortex around movement onset was decreased by the blockade during the early recovery stage, while it was increased by blockade during the intact state and late recovery stage. These results demonstrate that action of the interhemispheric pathway changed from inhibition to facilitation, to involve the ipsilesional sensorimotor cortex in hand movements during the early recovery stage. The present study offers insights into the stage-dependent role of the interhemispheric pathway and a therapeutic target in the early recovery stage after lesioning of the corticospinal tract., (© 2024. The Author(s).)

This study delves into decoding hand gestures using surface electromyography (EMG) signals collected via a precision Myo-armband sensor, leveraging machine learning algorithms. The research entails rigorous data preprocessing to extract features and labels from raw EMG data. Following partitioning into training and testing sets, four traditional machine learning models are scrutinized for their efficacy in classifying finger movements across seven distinct gestures. The analysis includes meticulous parameter optimization and five-fold cross-validation to evaluate model performance. Among the models assessed, the Random Forest emerges as the top performer, consistently delivering superior precision, recall, and F1-score values across gesture classes, with ROC-AUC scores surpassing 99%. These findings underscore the Random Forest model as the optimal classifier for our EMG dataset, promising significant advancements in healthcare rehabilitation engineering and enhancing human-computer interaction technologies.

Background: Closing the control loop between users and their prostheses by providing artificial sensory feedback is a fundamental step toward the full restoration of lost sensory-motor functions., Methods: We propose a novel approach to provide artificial proprioceptive feedback about two degrees of freedom using a single array of 8 vibration motors (compact solution). The performance afforded by the novel method during an online closed-loop control task was compared to that achieved using the conventional approach, in which the same information was conveyed using two arrays of 8 and 4 vibromotors (one array per degree of freedom), respectively. The new method employed Gaussian interpolation to modulate the intensity profile across a single array of vibration motors (compact feedback) to convey wrist rotation and hand aperture by adjusting the mean and standard deviation of the Gaussian, respectively. Ten able-bodied participants and four transradial amputees performed a target achievement control test by utilizing pattern recognition with compact and conventional vibrotactile feedback to control the Hannes prosthetic hand (test conditions). A second group of ten able-bodied participants performed the same experiment in control conditions with visual and auditory feedback as well as no-feedback., Results: Conventional and compact approaches resulted in similar positioning accuracy, time and path efficiency, and total trial time. The comparison with control condition revealed that vibrational feedback was intuitive and useful, but also underlined the power of incidental feedback sources. Notably, amputee participants achieved similar performance to that of able-bodied participants., Conclusions: The study therefore shows that the novel feedback strategy conveys useful information about prosthesis movements while reducing the number of motors without compromising performance. This is an important step toward the full integration of such an interface into a prosthesis socket for clinical use., (© 2024. The Author(s).)

Motor contagions refer to implicit effects induced by the observation of actions made by others on one's own actions. A plethora of studies conducted over the last two decades have demonstrated that both observed and predicted actions can induce various kinds of motor contagions in a human observer. However, motor contagions have always been investigated with regard to different features of an observed action, and it remains unclear whether the background environment in which an observed action takes place modulates motor contagions as well. Here, we investigated participant movements in an empirical hand steering task during which the participants were required to move a cursor through a visual channel after being presented with videos of an actor performing the same task. We manipulated the congruency between the actions shown in the video and the background channels and examined whether and how they affected the participants' own movements. We observed a clear interaction between the observed action and its background. The movement time of the participants' actions tended to increase or decrease depending on whether they observed a faster or slower movement, respectively, and these changes were amplified if the background was not congruent with the action contained within it. These results suggest that background information can modulate motor contagions in humans., (© 2024. The Author(s).)

Medication adherence is an essential aspect of healthcare for patients and is important for achieving medical objectives. However, the lack of standard techniques for measuring adherence is a global concern, making it challenging to accurately monitor and measure patient medication regimens. The use of sensor technology for medication adherence monitoring has received much attention lately since it makes it possible to continuously observe patients' medication adherence behavior. Sensor devices or smart wearables utilize state-of-the-art machine learning (ML) methods to analyze intricate data patterns and provide predictions accurately. The key aim of this work is to develop a sensor-based hand gesture recognition model to predict medication activities. In this research, a smart sensor device-based hand gesture prediction model is developed to recognize medication intake activities. The device includes a tri-axial gyroscope, geometric, and accelerometer sensors to sense and gather data from hand gestures. A smartphone application gathers hand gesture data from the sensor device, which is then stored in the cloud database in a .csv format. These data are collected, processed, and classified to recognize the medication intake activity using the proposed novel neural network model called Sea Horse Optimization-Deep Neural Network (SHO-DNN). The SHO technique is implemented to update the biases and weights and the number of hidden layers in the DNN model. By updating these parameters, the DNN model is improved in classifying the samples of hand gestures to identify the medication activities. The research model demonstrates impressive performance, with an accuracy of 98.59%, sensitivity of 97.82%, precision of 98.69%, and an F1 score of 98.48%. Hence, the proposed model outperformed the most available models in all the aforementioned aspects. The results indicate that this model is a promising approach for medication adherence monitoring in healthcare applications, instilling confidence in its effectiveness.

Brain-computer interfaces (BCIs) are pivotal in translating neural activities into control commands for external assistive devices. Non-invasive techniques like electroencephalography (EEG) offer a balance of sensitivity and spatial-temporal resolution for capturing brain signals associated with motor activities. This work introduces MOVING, a Multi-Modal dataset of EEG signals and Virtual Glove Hand Tracking. This dataset comprises neural EEG signals and kinematic data associated with three hand movements-open/close, finger tapping, and wrist rotation-along with a rest period. The dataset, obtained from 11 subjects using a 32-channel dry wireless EEG system, also includes synchronized kinematic data captured by a Virtual Glove (VG) system equipped with two orthogonal Leap Motion Controllers. The use of these two devices allows for fast assembly (∼1 min), although introducing more noise than the gold standard devices for data acquisition. The study investigates which frequency bands in EEG signals are the most informative for motor task classification and the impact of baseline reduction on gesture recognition. Deep learning techniques, particularly EEGnetV4, are applied to analyze and classify movements based on the EEG data. This dataset aims to facilitate advances in BCI research and in the development of assistive devices for people with impaired hand mobility. This study contributes to the repository of EEG datasets, which is continuously increasing with data from other subjects, which is hoped to serve as benchmarks for new BCI approaches and applications.

In tactile sensing, decoding the journey from afferent tactile signals to efferent motor commands is a significant challenge primarily due to the difficulty in capturing population-level afferent nerve signals during active touch. This study integrates a finite element hand model with a neural dynamic model by using microneurography data to predict neural responses based on contact biomechanics and membrane transduction dynamics. This research focuses specifically on tactile sensation and its direct translation into motor actions. Evaluations of muscle synergy during in -vivo experiments revealed transduction functions linking tactile signals and muscle activation. These functions suggest similar sensorimotor strategies for grasping influenced by object size and weight. The decoded transduction mechanism was validated by restoring human-like sensorimotor performance on a tendon-driven biomimetic hand. This research advances our understanding of translating tactile sensation into motor actions, offering valuable insights into prosthetic design, robotics, and the development of next-generation prosthetics with neuromorphic tactile feedback., (© 2024. The Author(s).)

High-density electromyography (HD-EMG) can provide a natural interface to enhance human-computer interaction (HCI). This study aims to demonstrate the capability of a novel HD-EMG forearm sleeve equipped with up to 150 electrodes to capture high-resolution muscle activity, decode complex hand gestures, and estimate continuous hand position via joint angle predictions. Ten able-bodied participants performed 37 hand movements and grasps while EMG was recorded using the HD-EMG sleeve. Simultaneously, an 18-sensor motion capture glove calculated 23 joint angles from the hand and fingers across all movements for training regression models. For classifying across the 37 gestures, our decoding algorithm was able to differentiate between sequential movements with 97.3 ± 0.3 % accuracy calculated on a 100 ms bin-by-bin basis. In a separate mixed dataset consisting of 19 movements randomly interspersed, decoding performance achieved an average bin-wise accuracy of 92.8 ± 0.8 % . When evaluating decoders for use in real-time scenarios, we found that decoders can reliably decode both movements and movement transitions, achieving an average accuracy of 93.3 ± 0.9 % on the sequential set and 88.5 ± 0.9 % on the mixed set. Furthermore, we estimated continuous joint angles from the EMG sleeve data, achieving a R 2 of 0.884 ± 0.003 in the sequential set and 0.750 ± 0.008 in the mixed set. Median absolute error (MAE) was kept below 10° across all joints, with a grand average MAE of 1.8 ± 0 . 04 ∘ and 3.4 ± 0 . 07 ∘ for the sequential and mixed datasets, respectively. We also assessed two algorithm modifications to address specific challenges for EMG-driven HCI applications. To minimize decoder latency, we used a method that accounts for reaction time by dynamically shifting cue labels in time. To reduce training requirements, we show that pretraining models with historical data provided an increase in decoding performance compared with models that were not pretrained when reducing the in-session training data to only one attempt of each movement. The HD-EMG sleeve, combined with sophisticated machine learning algorithms, can be a powerful tool for hand gesture recognition and joint angle estimation. This technology holds significant promise for applications in HCI, such as prosthetics, assistive technology, rehabilitation, and human-robot collaboration., (© 2024. The Author(s).)

Hand visibility affects motor control, perception, and attention, as visual information is integrated into an internal model of somatomotor control. Spontaneous brain activity, i.e., at rest, in the absence of an active task, is correlated among somatomotor regions that are jointly activated during motor tasks. Recent studies suggest that spontaneous activity patterns not only replay task activation patterns but also maintain a model of the body's and environment's statistical regularities (priors), which may be used to predict upcoming behavior. Here, we test whether spontaneous activity in the human somatomotor cortex as measured using fMRI is modulated by visual stimuli that display hands vs. non-hand stimuli and by the use/action they represent. A multivariate pattern analysis was performed to examine the similarity between spontaneous activity patterns and task-evoked patterns to the presentation of natural hands, robot hands, gloves, or control stimuli (food). In the left somatomotor cortex, we observed a stronger (multivoxel) spatial correlation between resting state activity and natural hand picture patterns compared to other stimuli. No task-rest similarity was found in the visual cortex. Spontaneous activity patterns in somatomotor brain regions code for the visual representation of human hands and their use., (© 2024. The Author(s).)

Objective. Non-invasive, high-density electromyography (HD-EMG) has emerged as a useful tool to collect a range of neurophysiological motor information. Recent studies have demonstrated changes in EMG features that occur after stroke, which correlate with functional ability, highlighting their potential use as biomarkers. However, previous studies have largely explored these EMG features in isolation with individual electrodes to assess gross movements, limiting their potential clinical utility. This study aims to predict hand function of stroke survivors by combining interpretable features extracted from a wearable HD-EMG forearm sleeve. Approach. Here, able-bodied ( N = 7) and chronic stroke subjects ( N = 7) performed 12 functional hand and wrist movements while HD-EMG was recorded using a wearable sleeve. A variety of HD-EMG features, or views, were decomposed to assess alterations in motor coordination. Main Results. Stroke subjects, on average, had higher co-contraction and reduced muscle coupling when attempting to open their hand and actuate their thumb. Additionally, muscle synergies decomposed in the stroke population were relatively preserved, with a large spatial overlap in composition of matched synergies. Alterations in synergy composition demonstrated reduced coupling between digit extensors and muscles that actuate the thumb, as well as an increase in flexor activity in the stroke group. Average synergy activations during movements revealed differences in coordination, highlighting overactivation of antagonist muscles and compensatory strategies. When combining co-contraction and muscle synergy features, the first principal component was strongly correlated with upper-extremity Fugl Meyer hand sub-score of stroke participants ( R 2 = 0.86). Principal component embeddings of individual features revealed interpretable measures of motor coordination and muscle coupling alterations. Significance. These results demonstrate the feasibility of predicting motor function through features decomposed from a wearable HD-EMG sleeve, which could be leveraged to improve stroke research and clinical care., (Creative Commons Attribution license.)

This study evaluates the R3THA™ assessment protocol (R3THA-AP™), a technology-supported testing module for personalized rehabilitation in children with cerebral palsy (CP). It focuses on the reliability and validity of the R3THA-AP in assessing hand and arm function, by comparing kinematic assessments with standard clinical assessments. Conducted during a 4-week summer camp, the study assessed the functional and impairment levels of children with CP aged 3-18. The findings suggest that R3THA is more reliable for children aged 8 and older, indicating that age significantly influences the protocol's effectiveness. The results also showed that the R3THA-AP's kinematic measurements of hand and wrist movements are positively correlated with the Box and Blocks Test Index (BBTI), reflecting hand function and dexterity. Additionally, the R3THA-AP's accuracy metrics for hand and wrist activities align with the Melbourne Assessment 2's Range of Motion (MA2-ROM) scores, suggesting a meaningful relationship between R3THA-AP data and clinical assessments of motor skills. However, no significant correlations were observed between the R3THA-AP and MA2's accuracy and dexterity measurements, indicating areas for further research. These findings validate the R3THA-AP's utility in assessing motor abilities in CP patients, supporting its integration into clinical practice.

Multimodal sensory feedback from upper-limb prostheses can increase their function and usability. Here we show that intuitive thermal perceptions during cold-object grasping with a prosthesis can be restored in a phantom hand through targeted nerve stimulation via a wearable thin-film thermoelectric device with high cooling power density and speed. We found that specific regions of the residual limb, when thermally stimulated, elicited thermal sensations in the phantom hand that remained stable beyond 48 weeks. We also found stimulation sites that selectively elicited sensations of temperature, touch or both, depending on whether the stimulation was thermal or mechanical. In closed-loop functional tasks involving the identification of cold objects by amputees and by non-amputee participants, and compared with traditional bulk thermoelectric devices, the wearable thin-film device reliably elicited cooling sensations that were up to 8 times faster and up to 3 times greater in intensity while using half the energy and 1/600 th the mass of active thermoelectric material. Wearable thin-film thermoelectric devices may allow for the non-invasive restoration of thermal perceptions during touch., (© 2023. The Author(s), under exclusive licence to Springer Nature Limited.)

Tools enable humans to extend their sensing abilities beyond the natural limits of their hands, allowing them to sense objects as if they were using their hands directly. The similarities between direct hand interactions with objects (hand-based sensing) and the ability to extend sensory information processing beyond the hand (tool-mediated sensing) entail the existence of comparable processes for integrating tool- and hand-sensed information with vision, raising the question of whether tools support vision in bimanual object manipulations. Here, we investigated participants' performance while grasping objects either held with a tool or with their hand and compared these conditions with visually guided grasping (Experiment 1). By measuring reaction time, peak velocity, and peak of grip aperture, we found that actions were initiated earlier and performed with a smaller peak grip aperture when the object was seen and held with the tool or the contralateral hand compared to when it was only seen. Thus, tool-mediated sensing effectively supports vision in multisensory grasping and, even more intriguingly, resembles hand-based sensing. We excluded that results were due to the force exerted on the tool's handle (Experiment 2). Additionally, as for hand-based sensing, we found evidence that the tool supports vision by mainly providing object positional information (Experiment 3). Thus, integrating the tool-sensed position of the object with vision is sufficient to promote a multisensory advantage in grasping. Our findings indicate that multisensory integration mechanisms significantly improve grasping actions and fine-tune contralateral hand movements even when object information is only indirectly sensed through a tool. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

According to the predictive coding account, the attenuation of tactile perception on the hand exposed to the visuo-tactile Rubber Hand Illusion (vtRHI) relies on a weight increase of visual information deriving from the fake hand and a weight decrease of tactile information deriving from the individual's hand. To explore if this diametrical modulation persists in the absence of vision when adopting the somatic RHI (sRHI), we recorded tactile acuity measures before and after both RHI paradigms in 31 healthy individuals, hypothesizing a weight decrease for somatosensory information deriving from the hand undergoing the illusion and a weight increase for those deriving from the contralateral hand in the sRHI. Our results showed a significant overall decrease in tactile acuity on the hand undergoing the illusion whilst no changes emerged on the contralateral hand during sRHI. Since the sRHI was not accompanied by the hand spatial remapping, despite the generation of the feeling of ownership toward the fake hand, we hypothesized spatial remapping might play a pivotal role in determining sensory information weight attribution., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Soft robotic grippers excel at achieving conformal and reliable contact with objects without the need for complex control algorithms. However, they still lack in grasp and manipulation abilities compared with human hands. In this study, we present a sensorized multi-fingered soft gripper with bioinspired adhesive fingertips that can provide both fingertip-based adhesion grasping and finger-based form closure grasping modes. The gripper incorporates mushroom-like microstructures on its adhesive fingertips, enabling robust adhesion through uniform load shearing. A single fingertip exhibits a maximum load capacity of 4.18 N against a flat substrate. The soft fingers have multiple joints, and each joint can be independently actuated through pneumatic control. This enables diverse bending motions and stable grasping of various objects, with a maximum load capacity of 28.29 N for three fingers. In addition, the soft gripper is equipped with a kirigami-patterned stretchable sensor for motion monitoring and control. We demonstrate the effectiveness of our design by successfully grasping and manipulating a diverse range of objects with varying shapes, sizes, and curvatures. Moreover, we present the practical application of our sensorized soft gripper for remotely controlled cooking.

Recent studies on the imitation of intransitive gestures suggest that the body part effect relies mainly upon the direct route of the dual-route model through a visuo-transformation mechanism. Here, we test the visuo-constructive hypothesis which posits that the visual complexity may directly potentiate the body part effect for meaningless gestures. We predicted that the difference between imitation of hand and finger gestures would increase with the visuo-spatial complexity of gestures. Second, we aimed to identify some of the visuo-spatial predictors of meaningless finger imitation skills. Thirty-eight participants underwent an imitation task containing three distinct set of gestures, that is, meaningful gestures, meaningless gestures with low visual complexity, and meaningless gestures with higher visual complexity than the first set of meaningless gestures. Our results were in general agreement with the visuo-constructive hypothesis, showing an increase in the difference between hand and finger gestures, but only for meaningless gestures with higher visuo-spatial complexity. Regression analyses confirm that imitation accuracy decreases with resource-demanding visuo-spatial factors. Taken together, our results suggest that the body part effect is highly dependent on the visuo-spatial characteristics of the gestures., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Bilateral coordination is commonly impaired in neurodevelopmental conditions including cerebral palsy, developmental coordination disorder, and autism spectrum disorder. However, we lack objective clinical assessments that can quantify bilateral coordination in a clinically feasible manner and determine age-based norms to identify impairments. The objective of this study was to use augmented reality and computer vision to characterize bilateral reaching abilities in typically developing children. Typically developing children (n = 133) ages 6-17 years completed symmetric and asymmetric bilateral reaching tasks in an augmented reality game environment. We analyzed the number of target pairs they could reach in 50 s as well as the time lag between their hands reaching the targets. We found that performance on both tasks developed in parallel, with development slowing but not plateauing after age 12. Children performed better on the symmetric task than asymmetric, both in targets reached and with shorter hand lags. Variability between children in hand lag decreased with age. We also found gender differences with females outperforming males, which were most pronounced in the 10-11 year olds. Overall, this study demonstrates parallel development through childhood and adolescence of symmetric and asymmetric reaching abilities. Furthermore, it demonstrates the ability to quantify bilateral coordination using computer vision and augmented reality, which can be applied to assess clinical populations., Competing Interests: Declaration of competing interest All authors have approved the final version of this manuscript. This work has not been previously published and it is not under consideration at any other journal. We have no competing interests to declare., (Copyright © 2024 Elsevier B.V. All rights reserved.)