Hand Movements Research Articles

Hand Gesture Controller

Abstract: The rapid evolution of human-computer interaction has spurred significant progress in gesture recognition technologies, placing a specific emphasis on diverse applications. This paper highlights key advancements in machine learning algorithms tailored for gesture recognition, including deep learning approaches that have notably improved the accuracy and robustness of hand tracking systems. Furthermore, the integration of hand gesture control into wearable devices and its implications for everyday technology usage are thoroughly examined. This paper relies on the formidable capabilities of Deep Learning, specifically Convolutional Neural Networks and Recurrent Neural Networks, to decipher and translate hand gestures into actionable directives. The implementation of the Hand Gesture Controller necessitates the integration of a camera or sensor module capable of capturing intricate hand movements. Real-time image processing and feature extraction are essential components, facilitating the provision of input data to the Deep Learning model. As technology progresses, this interface emerges as a versatile tool capable of enhancing productivity and inclusivity across various domains. The paper concludes with a discussion on future directions in hand gesture controller development, exploring anticipated technological advancements, novel use cases, and the potential for increased accessibility. In summary, the "Hand Gesture Controller using Deep Learning" project signifies a substantive stride forward in human-computer interaction, introducing an innovative interface that promises a future where devices seamlessly respond to innate gestures, thereby rendering technology more accessible and user-focused.

Work procedure action recognition based on skeleton and video features

In the field of industrial engineering, traditional methods for analyzing manufacturing process actions have limitations such as time-consuming, labor-intensive, and experience-dependent. To address these challenges in action analysis, we propose an intelligent action recognition method based on both skeleton and video features, aiming to replace manual decomposition of action elements. The MediaPipe framework is used for human posture estimation to obtain the skeleton sequence, and the CNN-GRU model is constructed action recognition based on skeleton features. For hand movements involving the use of industrial gloves, an enhanced TimeSformer video understanding model is introduced for action recognition based on video features. This improvement incorporates uniform attention and external attention mechanisms, resulting in enhanced model performance. The final experimental validation for the self-constructed process action dataset shows that the online detection speed of the skeleton action recognition model reaches 25 FPS, and the accuracy of the end-to-end video action recognition model is improved by 10.5 percentage points compared to the base model.

Mechanism for High-Precision Control of Movement at Maximum Output in the Vertical Jump Task.

Human movements are governed by a tradeoff between speed and accuracy. Previous studies that have investigated the tradeoff relationship in sports movements involving whole-body movements have been limited to examining the relationship from the perspective of competition-specific movements, and the findings on whether the relationship is valid have not been unified. Therefore, this study incorporated a vertical jump task with the introduction of a condition in which landing position control was added to evaluate the essence of a sports movement that requires both speed and accuracy. Accuracy was examined using a method that quantifies the coordinates of the landing and takeoff positions using entropy. The mechanism of that tradeoff was then examined by confirming the phenomenon and analyzing the 3D vector trajectories. An increase in accuracy and a decrease in speed were observed when the landing position was the control target, even in the vertical jumping task normally performed at maximum effort, and the 3D velocity vector was characterized by the following: a reduced scalar and a more vertical direction. While the entropy from the takeoff to the landing position seemed to decrease when the accuracy of the landing position improved, the following noteworthy results were obtained given the characteristics of the vertical jump. Unlike traditional feedback control in the entropy reduction in hand movements, the trajectory is predetermined in a feedforward-like manner by controlling the initial velocity vector at takeoff, which allows the landing point to be adjusted.

Anticholinergic Toxidrome due to Thorn Apple Seed Ingestion in an Elderly Couple.

Sudden onset of reduced consciousness, psychomotor agitation and mydriasis are all indicative of an anticholinergic toxidrome. It is important to note that numerous drugs, as well as certain herbs and plants, possess anticholinergic properties. An 84-year-old female patient had sudden nocturnal onset of uncoordinated hand movements and altered mental status. Shortly after, the patient's 83-year-old husband developed symptoms of dysarthria, gait ataxia, vertigo, and delirium. Anticholinergic syndrome consists of a combination of central and peripheral anticholinergic symptoms. Physostigmine given intravenously resulted in rapid reversal of symptoms. Thorn apple seeds had been accidentally ingested and were identified as the cause. The clinical presentation of an anticholinergic toxidrome includes both central and peripheral symptoms such as altered consciousness, mydriasis, dry mucous membranes and skin, and tachycardia.Prompt recognition of anticholinergic toxidrome and the administration of physostigmine can lead to rapid reversal of symptoms and improved patient outcomes.Treatment with physostigmine is indicated when a patient with an agitated delirium does not respond adequately to titrated benzodiazepines.

Encoding manual dexterity through modulation of intrinsic alpha band connectivity.

The human hand possesses both consolidated motor skills and remarkable flexibility in adapting to ongoing task demands. However, the underlying mechanisms by which the brain balances stability and flexibility remain unknown. In the absence of external input or behavior, spontaneous (intrinsic) brain connectivity is thought to represent a prior of stored memories. In this study, we investigated how manual dexterity modulates spontaneous functional connectivity in the motor cortex during hand movement. Using magnetoencephalography (MEG), in 47 human participants (both sexes) we examined connectivity modulations in the alpha and beta frequency bands at rest and during two motor tasks (i.e., finger tapping or toe squeezing). The flexibility and stability of such modulations allowed us to identify two groups of participants with different levels of performance (High and Low performers) on the 9-hole peg test, a test of manual dexterity. In the alpha band, participants with higher manual dexterity showed distributed decreases of connectivity, specifically in the motor cortex, increased segregation, and reduced nodal centrality. Participants with lower manual dexterity showed an opposite pattern. Notably, these patterns from the brain to behavior are mirrored by results from behavior to the brain. Indeed, when participants were divided using the median split of the dexterity score, we found the same connectivity patterns. In summary, this experiment shows that a long-term motor skill -manual dexterity- influences the way the motor systems respond during movements.Significance statement Using hands efficiently is central to our daily life. Importantly, however, individuals differ in manual dexterity. We study whether the brain's functional organization encodes variability in manual behavior. Using a large set of MEG data acquired during rest and a finger-tapping task, we investigated how hand movements change the intrinsic functional connectivity and network architecture. Specifically in the alpha band, we demonstrate that higher dexterity is associated with decreased connectivity, specifically in the motor cortex, increased segregation, and reduced nodal centrality. Low-dexterous individuals show opposite patterns. We concluded that manual dexterity influences how the motor system responds during movements. These findings yield high potential to understand how intrinsic connectivity retains relevant behavior and to develop neural biomarkers of pathological behavior.

Gesture Detection System using OpenCV

In the rapidly evolving landscape of human-computer interaction, our project delves into the innovative realm of gesture detection technology. This study presents the development of a sophisticated Gesture Detection System, a responsive interface bridging the gap between human hand movements and digital actions. With the invaluable support of our project supervisor, Mrs. V. M. Khanapure, our project not only explores the intricate nuances of gesture recognition but also showcases the collaborative spirit of our team. Utilizing OpenCV and MediaPipe libraries, our system employs advanced computer vision algorithms for real-time hand tracking and precise gesture recognition. The project’s success is not only attributed to the technical acumen of our team members but also to the seamless cooperation and encourageme Among them. Furthermore, the availability of essential resources provided by Mr.V. Nitnaware Principal, PLGPL, significantly bolstered our project development process. This abstract encapsulates a journey of collaborative innovation, highlighting the symbiotic relationship between technology and teamwork. Through this Gesture Detection System, we aim to redefine user experiences, offering an intuitive and interactive interface that responds to human gestures with precision and fluidity

Glove versus controller: the effect of VR gloves and controllers on presence, embodiment, and cognitive absorption

Introduction: Using our own hands allows humans to interact with the real world, as we learn from an early age. Virtual reality (VR) gloves allow a similar interaction in VR by transferring hand movements through natural mapping. Research outside the VR domain has already shown that this process creates a high level of presence and perceived naturalness. Research in the VR context, on the other hand, is still in an early stage and lacks explorative qualitative research following a human-centered approach and including the direct perceptions of users on mapped controls in system design. Against that background, this research investigates natural mapping in the VR context qualitatively. The study examines how participants experience VR input devices with different degrees of natural mapping (VR gloves, a Valve index controller, and an HTC Vive controller) on perceived naturalness, spatial presence, embodiment, and cognitive absorption.Methods: A focus group (N = 20 aged 22–36) did the VR experience with the devices and was asked about their experience in semi-structured interviews. Questions focused on perceived naturalness, spatial presence, cognitive absorption, and embodiment with the three devices. Data were analyzed using qualitative content analysis.Results: Feedback on spatial presence was widely positive for the VR glove due to the highly natural perceived movement capabilities. Mapping with the partly mapped index controller was also seen as natural and immersing if the movement matched the operated actions in the system. Participants mainly perceived the highest degree of cognitive absorption and embodiment while doing the task with the VR glove.Discussion: The findings indicate that the natural mapping capabilities of VR input devices strongly influence spatial presence. The VR glove stands out while being able to induce a feeling of embodiment in most cases. Furthermore, participants reported a connection between natural mapping and perceived cognitive absorption in the task.

From lab to life: Evaluating the reliability and validity of psychophysiological data from wearable devices in laboratory and ambulatory settings.

Despite the increasing popularity of ambulatory assessment, the reliability and validity of psychophysiological signals from wearable devices is unproven in daily life settings. We evaluated the reliability and validity of physiological signals (electrocardiogram, ECG; photoplethysmography, PPG; electrodermal activity, EDA) collected from two wearable devices (Movisens EcgMove4 and Empatica E4) in the lab (N = 67) and daily life (N = 20) among adults aged 18-64 with Mindware as the laboratory gold standard. Results revealed that both wearable devices' valid data rates in daily life were lower than in the laboratory (Movisens ECG 82.94 vs. 93.10%, Empatica PPG 8.79 vs. 26.14%, and Empatica EDA 41.16 vs. 42.67%, respectively). The poor valid data rates of Empatica PPG signals in the laboratory could be partially attributed to participants' hand movements (r = - .27, p = .03). In laboratory settings, heart rate (HR) derived from both wearable devices exhibited higher concurrent validity than heart rate variability (HRV) metrics (ICCs 0.98-1.00 vs. 0.75-0.97). The number of skin conductance responses (SCRs) derived from Empatica showed higher concurrent validity than skin conductance level (SCL, ICCs 0.38 vs. 0.09). Movisens EcgMove4 provided more reliable and valid HRV measurements than Empatica E4 in both laboratory (split-half reliability: 0.95-0.99 vs. 0.85-0.98; concurrent validity: 0.95-1.00 vs. 0.75-0.98; valid data rate: 93.10 vs. 26.14%) and ambulatory settings (split-half reliability: 0.99-1.00 vs. 0.89-0.98; valid data rate: 82.94 vs. 8.79%). Although the reliability and validity of wearable devices are improving, findings suggest researchers should select devices that yield consistently robust and valid data for their measures of interest.

Hand-Centric Motion Refinement for 3D Hand-Object Interaction via Hierarchical Spatial-Temporal Modeling

Hands are the main medium when people interact with the world. Generating proper 3D motion for hand-object interaction is vital for applications such as virtual reality and robotics. Although grasp tracking or object manipulation synthesis can produce coarse hand motion, this kind of motion is inevitably noisy and full of jitter. To address this problem, we propose a data-driven method for coarse motion refinement. First, we design a hand-centric representation to describe the dynamic spatial-temporal relation between hands and objects. Compared to the object-centric representation, our hand-centric representation is straightforward and does not require an ambiguous projection process that converts object-based prediction into hand motion. Second, to capture the dynamic clues of hand-object interaction, we propose a new architecture that models the spatial and temporal structure in a hierarchical manner. Extensive experiments demonstrate that our method outperforms previous methods by a noticeable margin.

Expressive Forecasting of 3D Whole-Body Human Motions

Human motion forecasting, with the goal of estimating future human behavior over a period of time, is a fundamental task in many real-world applications. However, existing works typically concentrate on foretelling the major joints of the human body without considering the delicate movements of the human hands. In practical applications, hand gesture plays an important role in human communication with the real world, and expresses the primary intention of human beings. In this work, we are the first to formulate whole-body human pose forecasting task, which jointly predicts future both body and gesture activities. Correspondingly, we propose a novel Encoding-Alignment-Interaction (EAI) framework that aims to predict both coarse (body joints) and fine-grained (gestures) activities collaboratively, enabling expressive and cross-facilitated forecasting of 3D whole-body human motions. Specifically, our model involves two key constituents: cross-context alignment (XCA) and cross-context interaction (XCI). Considering the heterogeneous information within the whole-body, XCA aims to align the latent features of various human components, while XCI focuses on effectively capturing the context interaction among the human components. We conduct extensive experiments on a newly-introduced large-scale benchmark and achieve state-of-the-art performance. The code is public for research purposes at https://github.com/Dingpx/EAI.

Keypoint Fusion for RGB-D Based 3D Hand Pose Estimation

Previous 3D hand pose estimation methods primarily rely on a single modality, either RGB or depth, and the comprehensive utilization of the dual modalities has not been extensively explored. RGB and depth data provide complementary information and thus can be fused to enhance the robustness of 3D hand pose estimation. However, there exist two problems for applying existing fusion methods in 3D hand pose estimation: redundancy of dense feature fusion and ambiguity of visual features. First, pixel-wise feature interactions introduce high computational costs and ineffective calculations of invalid pixels. Second, visual features suffer from ambiguity due to color and texture similarities, as well as depth holes and noise caused by frequent hand movements, which interferes with modeling cross-modal correlations. In this paper, we propose Keypoint-Fusion for RGB-D based 3D hand pose estimation, which leverages the unique advantages of dual modalities to mutually eliminate the feature ambiguity, and performs cross-modal feature fusion in a more efficient way. Specifically, we focus cross-modal fusion on sparse yet informative spatial regions (i.e. keypoints). Meanwhile, by explicitly extracting relatively more reliable information as disambiguation evidence, depth modality provides 3D geometric information for RGB feature pixels, and RGB modality complements the precise edge information lost due to the depth noise. Keypoint-Fusion achieves state-of-the-art performance on two challenging hand datasets, significantly decreasing the error compared with previous single-modal methods.

Sign Language Recognition by Image Processing

A prototype sign language interpreter that can converse in American Sign Language (ASL) is presented in this study. The visual stimulation of sign language aids in the development of speech and language. It promotes social contact, fosters the development of cognitive structures, and lessens harmful social behaviors. Early exposure to sign language supports language development in academic, social, and emotional domains. Building a user-hand gesture-only human-computer interface (HCI) is the foundation of the dependable paradigm. Using Python and OpenCV, hand gesture recognition is built using the theories of hand segmentation and the hand detection system. The training photos are labeled with different names based on what is in the database. A binary feature vector is created from each image. Convolutional neural networks (CNNs) and cameras are used to capture images from hand sign videos in real time, which are then used to carry out the feature extraction process. The algorithm generates output based on the projected maximum similarity between these features and the features of database photographs. With the aid of an application, this model provides adequate accuracy even in the absence of a constant or monochromatic background. The person's hand movements are captured on video with the camera; the video is subsequently processed and translated into text. The user hears the speech that has been transformed from the output. The suggested system recognizes letters, numbers, and certain common words in sign language. Raw photos and videos are transformed into corresponding text that can be read and comprehended by using image processing algorithms and neural networks to map the gesture to the right text in the training data. When paired with a sizable source database, this protocol will surely be highly beneficial for closing the communication gap between people who can talk and hear and those who cannot.

IQCB1 (NPHP5)-Retinopathy: Clinical and Genetic Characterization and Natural History

To describe the clinical and genetic features, and explore the natural history of retinopathy associated with IQCB1 variants in children and adults with retinopathy. Retrospective cohort study at a single tertiary care referral center. The study recruited 19 patients with retinopathy, harboring likely disease-causing variants in IQCB1. Demographic data and clinical presentation, best corrected visual acuity (BCVA), fundus appearance, optical coherence tomography (OCT) and autofluorescence features, electroretinography (ERG) and molecular genetics are reported. Ten patients had BCVA better than 1.0 LogMAR, and BCVA remained stable till the last review. Seven patients had a vision of hand movements or worse in at least one eye at presentation. There was no correlation found between age of onset and severity of vision loss. Nine patients (47.4%) had a diagnosis of end-stage renal failure at presentation. The other 10 patients (52.6%) had a diagnosis of non-syndromic IQCB1-retinopathy and maintained normal renal function until the last follow-up. The mean age at diagnosis of renal failure was 26.3 ±19.8 years. OCT showed ellipsoid zone (EZ) disruption with foveal sparing in 8/13 patients. All patients had stable OCT findings. Full-field ERGs in four adults revealed a severe cone-rod dystrophy and three children had extinguished ERGs. We identified 17 IQCB1 variants, all predicted to cause loss of function. IQCB1-retinopathy is a severe early-onset cone-rod dystrophy. The dissociation between severely decreased retinal function and relative preservation of retinal structure over a wide age window makes the disease a candidate for gene therapy.

Safety and efficacy of trofinetide in Rett syndrome: a systematic review and meta-analysis of randomized controlled trials

IntroductionRett syndrome is a rare genetic neurodevelopmental disorder that predominantly impacts females. It presents with loss of acquired skills, impaired communication, and stereotypic hand movements. Given the limited treatment options for Rett syndrome, there is a dire need for effective interventions.ObjectiveTo evaluate the safety and efficacy of trofinetide in Randomized Controlled Trials (RCTs) that report on Rett syndrome patients.MethodsWe identified 109 articles from four databases (Scopus, PubMed, Web of Science, and Cochrane CENTRAL). After removing the duplicates, we narrowed them down to 59 articles for further assessment. We included RCTs that evaluated the efficacy and safety of trofinetide in patients with Rett syndrome. Three studies were eligible for inclusion. Two independent reviewers evaluated the identified studies’ titles, abstracts, and full texts, extracting pertinent data. We assessed the quality of the studies using the Cochrane Risk of Bias (RoB) 2.0 tool. We then conducted a meta-analysis using the fixed effects model in the case of insignificant heterogeneity; otherwise, we used the random effects model. Based on the nature of the outcome, we analyzed the mean difference or the odds ratio. Analysis was conducted using RevMan version 5.3.ResultsAmong the analyzed outcomes in 181 patients in the trofinetide group and 134 patients in the placebo group, significant improvement in Rett Syndrome Behavior Questionnaire (RSBQ) scores was observed at 200 mg dosage (overall mean difference: -3.53, p = 0.001). Clinical Global Impression-Improvement (CGI-I) scores improved considerably at 200 mg dosage (overall mean difference: -0.34, p < 0.0001). No substantial changes were observed in Motor Behavioral Assessment (MBA) or Top 3 Caregiver Concerns. We evaluated Treatment Emergent Adverse Events (TEAEs) across the various dosages and noted significant associations with diarrhea (200 mg), vomiting (200 mg), and irritability (200 mg). However, we did not find a significant association between any of the dosages and the incidence of decreased appetite.ConclusionTrofinetide demonstrated potential in improving RSBQ and CGI-I scores at 200 mg dosage. Although no substantial changes were found in MBA and top 3 caregiver concerns. Adverse events were linked to specific dosages.

Reverse 4-flanged technique for intrascleral fixation of a foldable hydrophobic IOL in the absence of capsular support through a 2.2 mm corneal incision.

The purpose of this study was to describe a modified technique for sutureless intrascleral intraocular lens fixation in in patients without capsular support: the reverse 4-flanged technique. A 2.2 mm corneal incision was made for aphakic patients. The 6-0 polypropylene sutures were threaded through a 30-gauge needle outside the eye. An MDJ® injector was employed to insert the IOL in the eye. A suture-needle snare with 7-0 polypropylene was used to exteriorize the superior end of the sutures through the sclerotomy. The 6-0 polypropylene was heated with a cautery to create the flanges. Nine eyes of nine patients underwent the reverse 4-flanged technique. The median follow-up time was 182 days (range 174 - 195). Best corrected distance visual acuity (BCVA) improved from 20/400 [hand movement - 20/33] to 20/120 [20/400-20/21]. No vitreous haemorrhage, retinal detachment, endophthalmitis, IOL luxation or flange exposure was recorded during the follow-up. The reverse 4-flanged technique maintains the advantages of the original 4-flanged technique in terms of IOL stability while reducing the incision size and surgical manoeuvres performed inside the eye. This modification promises to be a reliable, safe and relatively simple technique to correct aphakia in the absence of capsular support.

Concurrent Supra-Postural Auditory-Hand Coordination Task Affects Postural Control: Using Sonification to Explore Environmental Unpredictability in Factors Affecting Fall Risk.

Clinical screening tests for balance and mobility often fall short of predicting fall risk. Cognitive distractors and unpredictable external stimuli, common in busy natural environments, contribute to this risk, especially in older adults. Less is known about the effects of upper sensory-motor coordination, such as coordinating one's hand with an external stimulus. We combined movement sonification and affordable inertial motion sensors to develop a task for the precise measurement and manipulation of full-body interaction with stimuli in the environment. In a double-task design, we studied how a supra-postural activity affected quiet stance. The supra-postural task consisted of rhythmic synchronization with a repetitive auditory stimulus. The stimulus was attentionally demanding because it was being modulated continuously. The participant's hand movement was sonified in real time, and their goal was to synchronize their hand movement with the stimulus. In the unpredictable condition, the tempo changed at random points in the trial. A separate sensor recorded postural fluctuations. Young healthy adults were compared to older adult (OA) participants without known risk of falling. The results supported the hypothesis that supra-postural coordination would entrain postural control. The effect was stronger in OAs, supporting the idea that diminished reserve capacities reduce the ability to isolate postural control from sensory-motor and cognitive activity.

Applying LSTM and GRU Methods to Recognize and Interpret Hand Gestures, Poses, and Face-Based Sign Language in Real Time

This research uses a real-time, human-computer interaction application to examine sign language recognition. This work develops a rule-based hand gesture approach for Indonesian sign language in order to interpret some words using a combination of hand movements, mimics, and poses. The main objective in this study is the recognition of sign language that is based on hand movements made in front of the body with one or two hands, movements which may involve switching between the left and right hand or may be combined with mimics and poses. To overcome this problem, a research framework is developed by coordinating hand gestures with poses and mimics to create features by using holistic MediaPipe. To train and test data in real time, the long short time memory (LSTM) and gated recurrent unit (GRU) approaches are used. The research findings presented in this paper show that hand gestures in real-time interactions are reliably recognized, and some words are interpreted with the high accuracy rates of 94% and 96% for the LSTM and GRU methods, respectively.

Human Hand Movement Classification based on EMG Signal using different Feature Extractor

Electromyography (EMG) based hand movement classification plays a significant role in various fields, namely in prosthetics, rehabilitation, biomechanics, etc. This paper presents the study of EMG-based hand movement classification of 3 human hand gestures (hand at rest, wrist flexion, and wrist extension). The dataset was officially collected from the University of California, Irvine (UCI) machine learning repository. The dataset contains 8 channels and 3 classes representing 3 human hand gestures, with 15000 rows of EMG data for each class. The dataset obtained was raw and unprocessed, to filter this dataset Notch and Butterworth filters were used. After filtering, the sliding window was performed. Various feature extraction techniques, namely frequency domain features (FD) and discrete wavelet transform (DWT) were applied separately on the window dataset and then accuracy was tested on different classifiers, namely random forest (RF), k- nearest neighbor (KNN), and decision tree (DT). As a novel approach, time domain (TD) and DWT extracted features were fused together and then given to the classifiers to test accuracy. Among all these feature extractors, the features extracted by FD provided the highest accuracy of 81.69 for the RF classifier.

How does a motor or cognitive dual-task affect our sense of upper limb proprioception?

Daily upper limb activities require multitasking and our division of attention. How we allocate our attention can be studied using dual-task interference (DTi). Given the vital role proprioception plays in movement planning and motor control, it is important to investigate how conscious upper limb proprioception is impacted by DTi through cognitive and motor interference. To examine how dual-task interference impacts conscious upper limb proprioception during active joint repositioning tasks (AJRT). Forty-two healthy participants, aged between 18 and 35, took part in this cross-sectional study. Participants completed two AJRT during three conditions: baseline (single task), dual-cognitive task (serial subtractions), and dual-motor task (non-dominant hand movements). The proprioceptive error (PE; difference between their estimation and targeted position) was measured using an AJRT of 75% and 90% of maximum internal rotation using the Biodex System IIITM and the Upper Limb Proprioception Reaching Test (PRO-Reach). To determine if PEs differed during dual-task interference, interference change scores from baseline were used with one sample t-tests and analyses of variance. The overall mean PE with the Biodex was 4.1° ± 1.9 at baseline. Mean change scores from baseline reflect a mean improvement of 1.5° ± 1.0 (p < .001) during dual-cognitive task and of 1.5° ± 1.2 (p < .001) during dual-motor task. The overall mean PE with the PRO-Reach was 4.4cm ± 1.1 at baseline. Mean change scores from baseline reflect a mean worsening of 1.0cm ± 1.1 (p < .001) during dual-cognitive task and improvement of 0.8cm ± 0.6 (p < .001) during dual-motor task. Analysis of variance with the Biodex PEs revealed an interference effect (p < .001), with the cognitive condition causing greater PEs compared to the motor condition and a criterion position effect (p = .006), where 75% of maximum IR produced larger PEs during both interference conditions. An interference effect (p = .022) with the PRO-Reach PEs was found highlighting a difference between the cognitive and motor conditions, with decreased PEs during the contralateral motor task. Interference tasks did impact proprioception. Cognitive interference produced mixed results, whereas improved proprioception was seen during motor interference. Individual task prioritization strategies are possible, where each person may choose their own attention strategy when faced with dual-task interference.

A direct spinal cord-computer interface enables the control of the paralysed hand in spinal cord injury.

The paralysis of the muscles controlling the hand dramatically limits the quality of life of individuals living with spinal cord injury (SCI). Here, with a non-invasive neural interface, we demonstrate that eight motor complete SCI individuals (C5-C6) are still able to task-modulate in real-time the activity of populations of spinal motor neurons with residual neural pathways. In all SCI participants tested, we identified groups of motor units under voluntary control that encoded various hand movements. The motor unit discharges were mapped into more than 10 degrees of freedom, ranging from grasping to individual hand-digit flexion and extension. We then mapped the neural dynamics into a real-time controlled virtual hand. The SCI participants were able to match the cue hand posture by proportionally controlling four degrees of freedom (opening and closing the hand and index flexion/extension). These results demonstrate that wearable muscle sensors provide access to spared motor neurons that are fully under voluntary control in complete cervical SCI individuals. This non-invasive neural interface allows the investigation of motor neuron changes after the injury and has the potential to promote movement restoration when integrated with assistive devices.