Simulating worm feeding patterns with computational models.

Worms create complex paths when moving through sediment to feed. This research applies computer simulation models to provide a unique approach to visualise and quantify the process by which complex worm paths can emerge from simple local movement decisions. A grid environment is proposed in which worms can move with choice of up to 8 directions at each step. This uses a square grid with diagonal paths which has not been investigated before and the resulting number of complex paths is increased compared to triangular grids. Results identify many novel worm paths. Some of the resulting paths are symmetrical, others produce repetitive looping paths, others return to the origin. Interesting worm paths are identified with chaotic movement. Some include oscillating between chaotic and ordered movement for which the outcome is still unknown after millions of steps. A conclusion that may be extrapolated to other creatures is that local movement decisions of a species substantially determine the overall global search strategy that emerges.

Post-task responses following working memory and movement are driven by transient spectral bursts with similar characteristics.

The post-movement beta rebound has been studied extensively using magnetoencephalography (MEG) and is reliably modulated by various task parameters as well as illness. Our recent study showed that rebounds, which we generalise as "post-task responses" (PTRs), are a ubiquitous phenomenon in the brain, occurring across the cortex in theta, alpha, and beta bands. Currently, it is unknown whether PTRs following working memory are driven by transient bursts, which are moments of short-lived high amplitude activity, similar to those that drive the post-movement beta rebound. Here, we use three-state univariate hidden Markov models (HMMs), which can identify bursts without a priori knowledge of frequency content or response timings, to compare bursts that drive PTRs in working memory and visuomotor MEG datasets. Our results show that PTRs across working memory and visuomotor tasks are driven by pan-spectral transient bursts. These bursts have very similar spectral content variation over the cortex, correlating strongly between the two tasks in the alpha (R2 = .89) and beta (R2 = .53) bands. Bursts also have similar variation in duration over the cortex (e.g., long duration bursts occur in the motor cortex for both tasks), strongly correlating over cortical regions between tasks (R2 = .56), with a mean over all regions of around 300 ms in both datasets. Finally, we demonstrate the ability of HMMs to isolate signals of interest in MEG data, such that the HMM probability timecourse correlates more strongly with reaction times than frequency filtered power envelopes from the same brain regions. Overall, we show that induced PTRs across different tasks are driven by bursts with similar characteristics, which can be identified using HMMs. Given the similarity between bursts across tasks, we suggest that PTRs across the cortex may be driven by a common underlying neural phenomenon.

BACK-to-MOVE: Machine learning and computer vision model automating clinical classification of non-specific low back pain for personalised management.

BACKGROUND: Low back pain (LBP) is a major global disability contributor with profound health and socio-economic implications. The predominant form is non-specific LBP (NSLBP), lacking treatable pathology. Active physical interventions tailored to individual needs and capabilities are crucial for its management. However, the intricate nature of NSLBP and complexity of clinical classification systems necessitating extensive clinical training, hinder customised treatment access. Recent advancements in machine learning and computer vision demonstrate promise in characterising NSLBP altered movement patters through wearable sensors and optical motion capture. This study aimed to develop and evaluate a machine learning model (i.e., 'BACK-to-MOVE') for NSLBP classification trained with expert clinical classification, spinal motion data from a standard video alongside patient-reported outcome measures (PROMs). METHODS: Synchronised video and three-dimensional (3D) motion data was collected during forward spinal flexion from 83 NSLBP patients. Two physiotherapists independently classified them as motor control impairment (MCI) or movement impairment (MI), with conflicts resolved by a third expert. The Convolutional Neural Networks (CNNs) architecture, HigherHRNet, was chosen for effective pose estimation from video data. The model was validated against 3D motion data (subset of 62) and trained on the freely available MS-COCO dataset for feature extraction. The Back-to-Move classifier underwent fine-tuning through feed-forward neural networks using labelled examples from the training dataset. Evaluation utilised 5-fold cross-validation to assess accuracy, specificity, sensitivity, and F1 measure. RESULTS: Pose estimation's Mean Square Error of 0.35 degrees against 3D motion data demonstrated strong criterion validity. Back-to-Move proficiently differentiated MI and MCI classes, yielding 93.98% accuracy, 96.49% sensitivity (MI detection), 88.46% specificity (MCI detection), and an F1 measure of .957. Incorporating PROMs curtailed classifier performance (accuracy: 68.67%, sensitivity: 91.23%, specificity: 18.52%, F1: .800). CONCLUSION: This study is the first to demonstrate automated clinical classification of NSLBP using computer vision and machine learning with standard video data, achieving accuracy comparable to expert consensus. Automated classification of NSLBP based on altered movement patters video-recorded during routine clinical examination could expedite personalised NSLBP rehabilitation management, circumventing existing healthcare constraints. This advancement holds significant promise for patients and healthcare services alike.

Mechanism of the Pulvinus-Driven Leaf Movement: An Overview.

Leaf movement is a manifestation of plant response to the changing internal and external environment, aiming to optimize plant growth and development. Leaf movement is usually driven by a specialized motor organ, the pulvinus, and this movement is associated with different changes in volume and expansion on the two sides of the pulvinus. Blue light, auxin, GA, H+-ATPase, K+, Cl-, Ca2+, actin, and aquaporin collectively influence the changes in water flux in the tissue of the extensor and flexor of the pulvinus to establish a turgor pressure difference, thereby controlling leaf movement. However, how these factors regulate the multicellular motility of the pulvinus tissues in a species remains obscure. In addition, model plants such as Medicago truncatula, Mimosa pudica, and Samanea saman have been used to study pulvinus-driven leaf movement, showing a similarity in their pulvinus movement mechanisms. In this review, we summarize past research findings from the three model plants, and using Medicago truncatula as an example, suggest that genes regulating pulvinus movement are also involved in regulating plant growth and development. We also propose a model in which the variation of ion flux and water flux are critical steps to pulvinus movement and highlight questions for future research.

Auditory neuroscience: Sounds make the face move.

Animals including humans often react to sounds by involuntarily moving their face and body. A new study shows that facial movements provide a simple and reliable readout of a mouse's hearing ability that is more sensitive than traditional measurements.

Motor learning in multijoint virtual arm movements with novel kinematics.

Humans move their hands toward precise positions, a skill supported by the coordination of multiple joint movements, even in the presence of inherent redundancy. However, it remains unclear how the central nervous system learns the relationship between redundant joint movements and hand positions when starting from scratch. To address this question, a virtual-arm reaching task was performed in which participants were required to move a cursor corresponding to the hand of a virtual arm to a target. The joint angles of the virtual arm were determined by the heights of the participants' fingers. The results demonstrated that the participants moved the cursor to the target straighter and faster in the late phase than they did in the initial phase of learning. This improvement was accompanied by a reduction in the amount of angular changes in the virtual limb joint, predominantly characterized by an increased reliance on the virtual shoulder joint as opposed to the virtual wrist joint. These findings suggest that the central nervous system selects a combination of multijoint movements that minimize motor effort while learning novel upper-limb kinematics.

De novo motor learning creates structure in neural activity that shapes adaptation.

Animals can quickly adapt learned movements to external perturbations, and their existing motor repertoire likely influences their ease of adaptation. Long-term learning causes lasting changes in neural connectivity, which shapes the activity patterns that can be produced during adaptation. Here, we examined how a neural population's existing activity patterns, acquired through de novo learning, affect subsequent adaptation by modeling motor cortical neural population dynamics with recurrent neural networks. We trained networks on different motor repertoires comprising varying numbers of movements, which they acquired following various learning experiences. Networks with multiple movements had more constrained and robust dynamics, which were associated with more defined neural 'structure'-organization in the available population activity patterns. This structure facilitated adaptation, but only when the changes imposed by the perturbation were congruent with the organization of the inputs and the structure in neural activity acquired during de novo learning. These results highlight trade-offs in skill acquisition and demonstrate how different learning experiences can shape the geometrical properties of neural population activity and subsequent adaptation.

Accuracy of Video-Based Hand Tracking for People With Upper-Body Disabilities.

Utilization of hand-tracking cameras, such as Leap, for hand rehabilitation and functional assessments is an innovative approach to providing affordable alternatives for people with disabilities. However, prior to deploying these commercially-available tools, a thorough evaluation of their performance for disabled populations is necessary. In this study, we provide an in-depth analysis of the accuracy of Leap's hand-tracking feature for both individuals with and without upper-body disabilities for common dynamic tasks used in rehabilitation. Leap is compared against motion capture with conventional techniques such as signal correlations, mean absolute errors, and digit segment length estimation. We also propose the use of dimensionality reduction techniques, such as Principal Component Analysis (PCA), to capture the complex, high-dimensional signal spaces of the hand. We found that Leap's hand-tracking performance did not differ between individuals with and without disabilities, yielding average signal correlations between 0.7-0.9. Both low and high mean absolute errors (between 10-80mm) were observed across participants. Overall, Leap did well with general hand posture tracking, with the largest errors associated with the tracking of the index finger. Leap's hand model was found to be most inaccurate in the proximal digit segment, underestimating digit lengths with errors as high as 18mm. Using PCA to quantify differences between the high-dimensional spaces of Leap and motion capture showed that high correlations between latent space projections were associated with high accuracy in the original signal space. These results point to the potential of low-dimensional representations of complex hand movements to support hand rehabilitation and assessment.

A multifaceted suite of metrics for comparative myoelectric prosthesis controller research.

Upper limb robotic (myoelectric) prostheses are technologically advanced, but challenging to use. In response, substantial research is being done to develop person-specific prosthesis controllers that can predict a user's intended movements. Most studies that test and compare new controllers rely on simple assessment measures such as task scores (e.g., number of objects moved across a barrier) or duration-based measures (e.g., overall task completion time). These assessment measures, however, fail to capture valuable details about: the quality of device arm movements; whether these movements match users' intentions; the timing of specific wrist and hand control functions; and users' opinions regarding overall device reliability and controller training requirements. In this work, we present a comprehensive and novel suite of myoelectric prosthesis control evaluation metrics that better facilitates analysis of device movement details-spanning measures of task performance, control characteristics, and user experience. As a case example of their use and research viability, we applied these metrics in real-time control experimentation. Here, eight participants without upper limb impairment compared device control offered by a deep learning-based controller (recurrent convolutional neural network-based classification with transfer learning, or RCNN-TL) to that of a commonly used controller (linear discriminant analysis, or LDA). The participants wore a simulated prosthesis and performed complex functional tasks across multiple limb positions. Analysis resulting from our suite of metrics identified 16 instances of a user-facing problem known as the "limb position effect". We determined that RCNN-TL performed the same as or significantly better than LDA in four such problem instances. We also confirmed that transfer learning can minimize user training burden. Overall, this study contributes a multifaceted new suite of control evaluation metrics, along with a guide to their application, for use in research and testing of myoelectric controllers today, and potentially for use in broader rehabilitation technologies of the future.

Prototyping a wearable and stretchable graphene-on-PDMS sensor for strain detection on human body physiological and joint movements.

In the era of wearable electronic devices, which are quite popular nowadays, our research is focused on flexible as well as stretchable strain sensors, which are gaining humongous popularity because of recent advances in nanocomposites and their microstructures. Sensors that are stretchable and flexible based on graphene can be a prospective 'gateway' over the considerable biomedical speciality. The scientific community still faces a great problem in developing versatile and user-friendly graphene-based wearable strain sensors that satisfy the prerequisites of susceptible, ample range of sensing, and recoverable structural deformations. In this paper, we report the fabrication, development, detailed experimental analysis and electronic interfacing of a robust but simple PDMS/graphene/PDMS (PGP) multilayer strain sensor by drop casting conductive graphene ink as the sensing material onto a PDMS substrate. Electrochemical exfoliation of graphite leads to the production of abundant, fast and economical graphene. The PGP sensor selective to strain has a broad strain range of ⁓60%, with a maximum gauge factor of 850, detection of human physiological motion and personalized health monitoring, and the versatility to detect stretching with great sensitivity, recovery and repeatability. Additionally, recoverable structural deformation is demonstrated by the PGP strain sensors, and the sensor response is quite rapid for various ranges of frequency disturbances. The structural designation of graphene's overlap and crack structure is responsible for the resistance variations that give rise to the remarkable strain detection properties of this sensor. The comprehensive detection of resistance change resulting from different human body joints and physiological movements demonstrates that the PGP strain sensor is an effective choice for advanced biomedical and therapeutic electronic device utility.

Quality of life, craniomandibular function, and psychosocial factors related to pain and movement in patients with head and neck cancer.

PURPOSE: To describe the characteristics of and the associations between health-related quality of life, pain, craniomandibular function, and psychosocial factors related to pain and fear of movement in patients with head and neck cancer. METHODS: Seventy-eight patients diagnosed with HNC were recruited. Measurements of the maximum mouth opening range and pressure pain thresholds on the masseter muscle and the distal phalanx of the thumb were conducted, as well as a battery of self-report questionnaires were administrated, including the QoL Questionnaire (EORT QLQ-H&N35), Numeric Rating Scale (NRS), Pain Catastrophizing Scale (PCS), the Spanish translation of the Tampa Scale for Kinesiophobia for Temporomandibular Disorders (TSK-TMD), and the short version of the Craniofacial Pain and Disability Inventory (CF-PDI-11). RESULTS: The study sample (66.7% men, mean age 60.12 [11.95] years) experienced a moderate impact on their QoL levels (57.68 [18.25] EORT QLQ-H&N35) and high kinesiophobia values (20.49 [9.11] TSK-TMD). Pain was present in 41% of the patients, but only 3.8% reported severe pain. 26.4% had a restricted mouth opening range, and 34.62% showed significant catastrophism levels. There were strong positive correlations between EORT QLQ-H&N35 and CF-PDI-11 (r = 0.81), between NRS and CF-PDI-11 (r = 0.74), and between PCS and CF-PDI-11 (r = 0.66). CONCLUSION: Patients with HNC experience negative effects in their QoL, related to their impairment in craniomandibular function. Fear of movement, pain intensity, and catastrophism are associated with poorer functionality; relationships that should be considered when attempting to improve health care.

Prefrontal cortex activity and functional organisation in dual-task ocular pursuit is affected by concurrent upper limb movement.

Tracking a moving object with the eyes seems like a simple task but involves areas of prefrontal cortex (PFC) associated with attention, working memory and prediction. Increasing the demand on these processes with secondary tasks can affect eye movements and/or perceptual judgments. This is particularly evident in chronic or acute neurological conditions such as Alzheimer's disease or mild traumatic brain injury. Here, we combined near infrared spectroscopy and video-oculography to examine the effects of concurrent upper limb movement, which provides additional afference and efference that facilitates tracking of a moving object, in a novel dual-task pursuit protocol. We confirmed the expected effects on judgement accuracy in the primary and secondary tasks, as well as a reduction in eye velocity when the moving object was occluded. Although there was limited evidence of oculo-manual facilitation on behavioural measures, performing concurrent upper limb movement did result in lower activity in left medial PFC, as well as a change in PFC network organisation, which was shown by Graph analysis to be locally and globally more efficient. These findings extend upon previous work by showing how PFC is functionally organised to support eye-hand coordination when task demands more closely replicate daily activities.

Identification of pattern mining algorithm for rugby league players positional groups separation based on movement patterns.

The application of pattern mining algorithms to extract movement patterns from sports big data can improve training specificity by facilitating a more granular evaluation of movement. Since movement patterns can only occur as consecutive, non-consecutive, or non-sequential, this study aimed to identify the best set of movement patterns for player movement profiling in professional rugby league and quantify the similarity among distinct movement patterns. Three pattern mining algorithms (l-length Closed Contiguous [LCCspm], Longest Common Subsequence [LCS] and AprioriClose) were used to extract patterns to profile elite rugby football league hookers (n = 22 players) and wingers (n = 28 players) match-games movements across 319 matches. Jaccard similarity score was used to quantify the similarity between algorithms' movement patterns and machine learning classification modelling identified the best algorithm's movement patterns to separate playing positions. LCCspm and LCS movement patterns shared a 0.19 Jaccard similarity score. AprioriClose movement patterns shared no significant Jaccard similarity with LCCspm (0.008) and LCS (0.009) patterns. The closed contiguous movement patterns profiled by LCCspm best-separated players into playing positions. Multi-layered Perceptron classification algorithm achieved the highest accuracy of 91.02% and precision, recall and F1 scores of 0.91 respectively. Therefore, we recommend the extraction of closed contiguous (consecutive) over non-consecutive and non-sequential movement patterns for separating groups of players.

A modular motion compensation pipeline for prospective respiratory motion correction of multi-nuclear MR spectroscopy.

Magnetic resonance (MR) acquisitions of the torso are frequently affected by respiratory motion with detrimental effects on signal quality. The motion of organs inside the body is typically decoupled from surface motion and is best captured using rapid MR imaging (MRI). We propose a pipeline for prospective motion correction of the target organ using MR image navigators providing absolute motion estimates in millimeters. Our method is designed to feature multi-nuclear interleaving for non-proton MR acquisitions and to tolerate local transmit coils with inhomogeneous field and sensitivity distributions. OpenCV object tracking was introduced for rapid estimation of in-plane displacements in 2D MR images. A full three-dimensional translation vector was derived by combining displacements from slices of multiple and arbitrary orientations. The pipeline was implemented on 3 T and 7 T MR scanners and tested in phantoms and volunteers. Fast motion handling was achieved with low-resolution 2D MR image navigators and direct implementation of OpenCV into the MR scanner's reconstruction pipeline. Motion-phantom measurements demonstrate high tracking precision and accuracy with minor processing latency. The feasibility of the pipeline for reliable in-vivo motion extraction was shown on heart and kidney data. Organ motion was manually assessed by independent operators to quantify tracking performance. Object tracking performed convincingly on 7774 navigator images from phantom scans and different organs in volunteers. In particular the kernelized correlation filter (KCF) achieved similar accuracy (74%) as scored from inter-operator comparison (82%) while processing at a rate of over 100 frames per second. We conclude that fast 2D MR navigator images and computer vision object tracking can be used for accurate and rapid prospective motion correction. This and the modular structure of the pipeline allows for the proposed method to be used in imaging of moving organs and in challenging applications like cardiac magnetic resonance spectroscopy (MRS) or magnetic resonance imaging (MRI) guided radiotherapy.

Estimation of Ground Reaction Forces during Sports Movements by Sensor Fusion from Inertial Measurement Units with 3D Forward Dynamics Model.

Rotational jumps are crucial techniques in sports competitions. Estimating ground reaction forces (GRFs), a constituting component of jumps, through a biomechanical model-based approach allows for analysis, even in environments where force plates or machine learning training data would be impossible. In this study, rotational jump movements involving twists on land were measured using inertial measurement units (IMUs), and GRFs and body loads were estimated using a 3D forward dynamics model. Our forward dynamics and optimization calculation-based estimation method generated and optimized body movements using cost functions defined by motion measurements and internal body loads. To reduce the influence of dynamic acceleration in the optimization calculation, we estimated the 3D orientation using sensor fusion, comprising acceleration and angular velocity data from IMUs and an extended Kalman filter. As a result, by generating cost function-based movements, we could calculate biomechanically valid GRFs while following the measured movements, even if not all joints were covered by IMUs. The estimation approach we developed in this study allows for measurement condition- or training data-independent 3D motion analysis.

Mapping Method of Human Arm Motion Based on Surface Electromyography Signals.

This paper investigates a method for precise mapping of human arm movements using sEMG signals. A multi-channel approach captures the sEMG signals, which, combined with the accurately calculated joint angles from an Inertial Measurement Unit, allows for action recognition and mapping through deep learning algorithms. Firstly, signal acquisition and processing were carried out, which involved acquiring data from various movements (hand gestures, single-degree-of-freedom joint movements, and continuous joint actions) and sensor placement. Then, interference signals were filtered out through filters, and the signals were preprocessed using normalization and moving averages to obtain sEMG signals with obvious features. Additionally, this paper constructs a hybrid network model, combining Convolutional Neural Networks and Artificial Neural Networks, and employs a multi-feature fusion algorithm to enhance the accuracy of gesture recognition. Furthermore, a nonlinear fitting between sEMG signals and joint angles was established based on a backpropagation neural network, incorporating momentum term and adaptive learning rate adjustments. Finally, based on the gesture recognition and joint angle prediction model, prosthetic arm control experiments were conducted, achieving highly accurate arm movement prediction and execution. This paper not only validates the potential application of sEMG signals in the precise control of robotic arms but also lays a solid foundation for the development of more intuitive and responsive prostheses and assistive devices.

Research on a Calculation Model of Ankle-Joint-Torque-Based sEMG.

The purpose of this article is to establish a prediction model of joint movements and realize the prediction of joint movemenst, and the research results are of reference value for the development of the rehabilitation equipment. This will be carried out by analyzing the impact of surface electromyography (sEMG) on ankle movements and using the Hill model as a framework for calculating ankle joint torque. The table and scheme used in the experiments were based on physiological parameters obtained through the model. Data analysis was performed on ankle joint angle signal, movement signal, and sEMG data from nine subjects during dorsiflexion/flexion, varus, and internal/external rotation. The Hill model was employed to determine 16 physiological parameters which were optimized using a genetic algorithm. Three experiments were carried out to identify the optimal model to calculate torque and root mean square error. The optimized model precisely calculated torque and had a root mean square error of under 1.4 in comparison to the measured torque. Ankle movement models predict torque patterns with accuracy, thereby providing a solid theoretical basis for ankle rehabilitation control. The optimized model provides a theoretical foundation for precise ankle torque forecasts, thereby improving the efficacy of rehabilitation robots for the ankle.

Anthropomorphic Tendon-Based Hands Controlled by Agonist-Antagonist Corticospinal Neural Network.

This article presents a study on the neurobiological control of voluntary movements for anthropomorphic robotic systems. A corticospinal neural network model has been developed to control joint trajectories in multi-fingered robotic hands. The proposed neural network simulates cortical and spinal areas, as well as the connectivity between them, during the execution of voluntary movements similar to those performed by humans or monkeys. Furthermore, this neural connection allows for the interpretation of functional roles in the motor areas of the brain. The proposed neural control system is tested on the fingers of a robotic hand, which is driven by agonist-antagonist tendons and actuators designed to accurately emulate complex muscular functionality. The experimental results show that the corticospinal controller produces key properties of biological movement control, such as bell-shaped asymmetric velocity profiles and the ability to compensate for disturbances. Movements are dynamically compensated for through sensory feedback. Based on the experimental results, it is concluded that the proposed biologically inspired adaptive neural control system is robust, reliable, and adaptable to robotic platforms with diverse biomechanics and degrees of freedom. The corticospinal network successfully integrates biological concepts with engineering control theory for the generation of functional movement. This research significantly contributes to improving our understanding of neuromotor control in both animals and humans, thus paving the way towards a new frontier in the field of neurobiological control of anthropomorphic robotic systems.

Motion Capture Technology in Sports Scenarios: A Survey.

Motion capture technology plays a crucial role in optimizing athletes' skills, techniques, and strategies by providing detailed feedback on motion data. This article presents a comprehensive survey aimed at guiding researchers in selecting the most suitable motion capture technology for sports science investigations. By comparing and analyzing the characters and applications of different motion capture technologies in sports scenarios, it is observed that cinematography motion capture technology remains the gold standard in biomechanical analysis and continues to dominate sports research applications. Wearable sensor-based motion capture technology has gained significant traction in specialized areas such as winter sports, owing to its reliable system performance. Computer vision-based motion capture technology has made significant advancements in recognition accuracy and system reliability, enabling its application in various sports scenarios, from single-person technique analysis to multi-person tactical analysis. Moreover, the emerging field of multimodal motion capture technology, which harmonizes data from various sources with the integration of artificial intelligence, has proven to be a robust research method for complex scenarios. A comprehensive review of the literature from the past 10 years underscores the increasing significance of motion capture technology in sports, with a notable shift from laboratory research to practical training applications on sports fields. Future developments in this field should prioritize research and technological advancements that cater to practical sports scenarios, addressing challenges such as occlusion, outdoor capture, and real-time feedback.

Helping Blind People Grasp: Evaluating a Tactile Bracelet for Remotely Guiding Grasping Movements.

The problem of supporting visually impaired and blind people in meaningful interactions with objects is often neglected. To address this issue, we adapted a tactile belt for enhanced spatial navigation into a bracelet worn on the wrist that allows visually impaired people to grasp target objects. Participants' performance in locating and grasping target items when guided using the bracelet, which provides direction commands via vibrotactile signals, was compared to their performance when receiving auditory instructions. While participants were faster with the auditory commands, they also performed well with the bracelet, encouraging future development of this system and similar systems.