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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.

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Hand function assessments in a clinical setting are critical for upper limb rehabilitation after spinal cord injury (SCI) but may not accurately reflect performance in an individual's home environment. When paired with computer vision models, egocentric videos from wearable cameras provide an opportunity for remote hand function assessment during real activities of daily living (ADLs). This study demonstrates the use of computer vision models to predict clinical hand function assessment scores from egocentric video. SlowFast, MViT, and MaskFeat models were trained and validated on a custom SCI dataset, which contained a variety of ADLs carried out in a simulated home environment. The dataset was annotated with clinical hand function assessment scores using an adapted scale applicable to a wide range of object interactions. An accuracy of 0.551±0.139, mean absolute error (MAE) of 0.517±0.184, and F1 score of 0.547±0.151 was achieved on the 5-class classification task. An accuracy of 0.724±0.135, MAE of 0.290±0.140, and F1 score of 0.733±0.144 was achieved on a consolidated 3-class classification task. This novel approach, for the first time, demonstrates the prediction of hand function assessment scores from egocentric video after SCI.

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BACKGROUND: Mixed reality (MR) is helpful in hand training for patients with stroke, allowing them to fully submerge in a virtual space while interacting with real objects. The recognition of individual finger movements is required for MR rehabilitation. This study aimed to assess the effectiveness of updated MR-board 2, adding finger training for patients with stroke. METHODS: Twenty-one participants with hemiplegic stroke (10 with left hemiplegia and 11 with right hemiplegia; nine female patients; 56.7 ± 14.2 years of age; and onset of stroke 32.7 ± 34.8 months) participated in this study. MR-board 2 comprised a board plate, a depth camera, plastic-shaped objects, a monitor, a palm-worn camera, and seven gamified training programs. All participants performed 20 self-training sessions involving 30-min training using MR-board 2. The outcome measurements for upper extremity function were the Fugl-Meyer assessment (FMA) upper extremity score, repeated number of finger flexion and extension (Repeat-FE), the thumb opposition test (TOT), Box and Block Test score (BBT), Wolf Motor Function Test score (WMFT), and Stroke Impact Scale (SIS). One-way repeated measures analysis of variance and the post hoc test were applied for the measurements. MR-board 2 recorded the fingers' active range of motion (AROM) and Dunnett's test was used for pairwise comparisons. RESULTS: Except for the FMA-proximal score (p = 0.617) and TOT (p = 0.005), other FMA scores, BBT score, Repeat-FE, WMFT score, and SIS stroke recovery improved significantly (p < 0.001) during MR-board 2 training and were maintained until follow-up. All AROM values of the finger joints changed significantly during training (p < 0.001). CONCLUSIONS: MR-board 2 self-training, which includes natural interactions between humans and computers using a tangible user interface and real-time tracking of the fingers, improved upper limb function across impairment, activity, and participation. MR-board 2 could be used as a self-training tool for patients with stroke, improving their quality of life. TRIAL REGISTRATION NUMBER: This study was registered with the Clinical Research Information Service (CRIS: KCT0004167).

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Upper-limb spasticity, frequent after central nervous system lesions, is typically treated with botulinum neurotoxin type A (BoNT-A) injections to reduce muscle tone and increase range of motion. However, performing adjunct physical therapy post-BoNT-A can be challenging due to residual weakness or spasticity. This study evaluates the feasibility of hand therapy using a robotic hand orthosis (RELab tenoexo) with a mobile phone application as an adjunct to BoNT-A injections. Five chronic spastic patients participated in a two-session pilot study. Functional (Box and Block Test (BBT), Action Research Arm Test (ARAT)), and muscle tone (Modified Ashworth Scale (MAS)) assessments were conducted to assess functional abilities and impairment, along with usability evaluations. In the first session, subjects received BoNT-A injections, and then they performed a simulated unsupervised therapy session with the RELab tenoexo in a second session a month later. Results showed that BoNT-A reduced muscle tone (from 12.2 to 7.4 MAS points). The addition of RELab tenoexo therapy was safe, led to functional improvements in four subjects (two-cube increase in BBT as well as 2.8 points in grasp and 1.3 points in grip on ARAT). Usability results indicate that, with minor improvements, adjunct RELab tenoexo therapy could enhance therapy doses and, potentially, long-term outcomes.

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BACKGROUND: Hand dexterity is important with Duchenne muscular dystrophy (DMD). OBJECTIVE: The aim of this study was to compare hand dexterity and hand laterality task assessments in patients with DMD with typically-developing peers. METHODS: The study included 25 DMD with a mean age of 10.2±2.38 and 21 typically-developing peers with a mean age of 10.33±2.26. Functional levels of DMD patients were determined by Brooke Upper Extremity Functional Classification Scale and Brooke Lower Extremity Functional Scale. The ABILHAND-Kids and 9-hole peg test were used to assess the hand dexterity of all participants, and assess the hand laterality task. RESULTS: Patients with DMD had lower ABILHAND-Kids scores than their typically-developing peers (p < 0.001). Patients with DMD had higher 9-hole peg test duration on the dominant and non-dominant extremity compared to typically-developing peers (p < 0.001). Patients with DMD were found to be different from their typically-developing peers (p < 0.001) in lateralization response time and accuracy. CONCLUSION: Patients with DMD were found to have lower manual dexterity and hand laterality task skills compared to their typically-developing peers. It is recommended that hand dexterity and upper extremity recognition capacities should be considered in assessment and intervention programs for physiotherapists and clinicians working in this field.

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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.

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BACKGROUND: Accurate visual information is needed to guide and perform efficient movements in daily life. AIMS: To investigate the relation between visual functions, functional vision, and bimanual function in children with unilateral cerebral palsy (uCP). METHODS AND PROCEDURES: In 49 children with uCP (7-15 y), we investigated the relation between stereoacuity (Titmus Stereo Fly test), visual perception (Test of Visual Perceptual Skills), visuomotor integration (Beery Buktenica Test of Visual-Motor Integration) and functional vision (Flemish cerebral visual impairment questionnaire) with bimanual dexterity (Tyneside Pegboard Test), bimanual coordination (Kinarm exoskeleton robot, Box opening task), and functional hand use (Children's Hand-use Experience Questionnaire; Assisting Hand Assessment) using correlations (rs) and elastic-net regularized regressions (d). OUTCOMES AND RESULTS: Visual perception correlated with bimanual coordination (rs=0.407-0.436) and functional hand use (rs=0.380-0.533). Stereoacuity (rs=-0.404), visual perception (rs=-0.391 to -0.620), and visuomotor integration (rs=-0.377) correlated with bimanual dexterity. Functional vision correlated with functional hand use (rs=-0.441 to -0.458). Visual perception predicted bimanual dexterity (d=0.001-0.315), bimanual coordination (d=0.004-0.176), and functional hand use (d=0.001-0.345), whereas functional vision mainly predicted functional hand use (d=0.001-0.201). CONCLUSIONS AND IMPLICATIONS: Visual functions and functional vision are related to bimanual function in children with uCP highlighting the importance of performing extensive visual assessment to better understand children's difficulties in performing bimanual tasks. WHAT THIS PAPER ADDS: Previous findings showed that up to 62 % of children with unilateral cerebral palsy (uCP) present with visual impairment, which can further compromise their motor performance. However, the relation between visual and motor function has hardly been investigated in this population. This study makes a significant contribution to the literature by comprehensively investigating the multi-level relation between the heterogenous spectrum of visual abilities and bimanual function in children with uCP. We found that mainly decreased visual perception was related to decreased bimanual dexterity, bimanual coordination, and functional hand use while impairments in functional vision were only related to decreased functional hand use. Additionally, elastic-net regression models showed that visual assessments can predict bimanual function in children with uCP, however, effect sizes were only tiny to small. With our study, we demonstrated a relation between visual functions and bimanual function in children with uCP. These findings suggest the relevance of thoroughly examining visual functions in children with uCP to identify the presence of visual impairments that may further compromise their bimanual function.

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The single-arm feasibility study was planned to evaluate the therapeutic effect of hand arm bimanual intensive training in improving the fine and gross motor functions of hand, and in the reduction of intensity with respect to mirror movement disorder. The sample comprised unilateral spastic cerebral palsy children aged 6-16 years who were having mirror movement disorder and were able to make a gross grip. The hand arm bimanual intensive training was provided to the participants for 6 hours per day for 15 days for a total of 90 hours. Comparison of baseline and post-intervention showed that the functional independence level of children had improved, with improvement in unimanual and bimanual hand performance (p˂0.05). However, there was no improvement seen in the severity of mirror movements (p>0.05). Hence, hand arm bimanual intensive training was found to be effective in increasing the functional independence of cerebral palsy children by improving the hand function, but there was no effect on mirror movement disorder.

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BACKGROUND: Excessive smartphone usage among students can lead to discomfort in their hands and fingers. This study investigates the impact of smartphone holding posture, duration of usage, and the prevalence of wrist and finger pain among university students. METHODS: This cross-sectional study involved 213 university students who were selected based on inclusion criteria. Data was collected through a demographic information questionnaire. Participants self-reported five different postures for holding and interacting with a smartphone. The prevalence, frequency, severity, and interference of wrist and finger discomfort were assessed using the Cornell Hand Discomfort Questionnaires (CHDQ). RESULTS: The study revealed that the average age of participants was 21.3 ± 2.2 years. On average, they had been using smartphones for 7.9 ± 3.1 years and spent an average of 4.9 ± 2.5 h daily holding them in their hands. In terms of discomfort, more than 25% of students reported pain in areas C (thumb finger), E (Palm Pollicis), and F (wrist) of the right hand, which was significantly related to the duration of holding the smartphone in that hand. Additionally, smartphone holding duration significantly affected areas D (palm) and F of the left hand, with over 11% of students experiencing discomfort. The most prevalent posture among students (41% of participants) involved holding the smartphone with the right hand only, with the thumb touching the screen. Notably, areas B (χ2 = 21.7), C (χ2 = 10.27), D (χ2 = 65.54), and E (χ2 = 59.49) of the right hand, as well as areas C (χ2 = 6.58) and E (χ2 = 44.28) of the left hand, exhibited significant associations with the postures of holding the smartphone. CONCLUSIONS: The duration of smartphone use and the postures in which it is held contribute to the prevalence of discomfort in the thumb area and related muscles among right-handed students.

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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 (R2= 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.

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Individuals with untreated, mild-to-moderate recurrent neck pain or stiffness (subclinical neck pain (SCNP)) have been shown to have impairments in upper limb proprioception, and altered cerebellar processing. It is probable that aiming trajectories will be impacted since individuals with SCNP cannot rely on accurate proprioceptive feedback or feedforward processing (body schema) for movement planning and execution, due to altered afferent input from the neck. SCNP participants may thus rely more on visual feedback, to accommodate for impaired cerebellar processing. This quasi-experimental study sought to determine whether upper limb kinematics and oculomotor processes were impacted in those with SCNP. 25 SCNP and 25 control participants who were right-hand dominant performed bidirectional aiming movements using two different weighted styli (light or heavy) while wearing an eye-tracking device. Those with SCNP had a greater time to and time after peak velocity, which corresponded with a longer upper limb movement and reaction time, seen as greater constant error, less undershoot in the upwards direction and greater undershoot in the downwards direction compared to controls. SCNP participants also showed a trend towards a quicker ocular reaction and movement time compared to controls, while the movement distance was fairly similar between groups. This study indicates that SCNP alters aiming performances, with greater reliance on visual feedback, likely due to altered proprioceptive input leading to altered cerebellar processing.

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During role-differentiated bimanual movements (RDBM), an object is typically stabilized with 1 hand and manipulated with the other. RDBM require coupling both hands for coordinated action (achieved through interhemispheric connections), but also inhibition of crosstalk to avoid involuntary movements in the stabilizing hand. We investigated how healthy cognitive aging and mild cognitive impairments (MCI) affect force stabilization during an RDBM in a cohort sequential study design with up to 4 measurement points over 32 months. In total, 132 older adults (>80 years) participated in this study, 77 were cognitively healthy individuals (CHI) and 55 presented with MCI. Participants performed a visuomotor bimanual force-tracking task. They either produced a constant force with both hands (bimanual constant) or a constant force with 1 and an alternating force with the other hand (role-differentiated). We investigated force fluctuations of constant force production using the coefficient of variation (CV), detrended fluctuation analysis (DFA), and sample entropy (SEn). Results showed higher CV and less complex variability structure (higher DFA and lower SEn) during the role-differentiated compared to the bimanual constant task. Furthermore, CHI displayed a more complex variability structure during the bimanual constant, but a less complex structure during the role-differentiated task than MCI. Interestingly, this complexity reduction was more pronounced in CHI than MCI individuals, suggesting different changes in the control mechanisms. Although understanding these changes requires further research, potential causes might be structural deteriorations leading to less efficient (intra- and interhemispheric) networks because of MCI, or an inability to appropriately divert the focus of attention.

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OBJECTIVE: Cervical spondylotic myelopathy (CSM) stands as the most prevalent form of spinal cord injury, frequently prompting various changes in both the brain and spinal cord. However, the precise nature of these changes within the brains and spinal cords of CSM patients experiencing hand clumsiness (HCL) symptoms has remained elusive. The authors aimed to scrutinize these alterations and explore potential links between these changes and the onset of HCL symptoms. METHODS: Using the modified Japanese Orthopaedic Association (mJOA) scale, the authors classified CSM patients into two groups: those without HCL and those with HCL. The authors performed voxel-wise z-score transformation amplitude of low-frequency fluctuations (zALFF) and resting-state functional connectivity (FC) evaluations in the brain. Additionally, they used the Spinal Cord Toolbox to calculate the fractional anisotropy (FA) of spinal cord tracts. The analysis also encompassed an examination of the correlation of these measures with improvements in mJOA scores. RESULTS: Significant disparities in zALFF values surfaced in the right calcarine, right cuneus, right precuneus, right middle occipital gyrus (MOG), right superior occipital gyrus (SOG), and right superior parietal gyrus (SPG) between healthy controls (HC), patients without HCL, and patients with HCL, primarily within the visual cortex. In the patient group, patients with HCL displayed reduced FC between the right calcarine, right MOG, right SOG, right SPG, right SFG, bilateral MFG, and left median cingulate and paracingulate gyri when compared with patients without HCL. Moreover, significant differences in FA values of the corticospinal tract (CST) and reticulospinal tract (REST) at the C2 level emerged among HC, patients without HCL, and patients with HCL. Notably, zALFF, FC, and FA values in specific brain regions and spinal cord tracts exhibited correlations with mJOA upper-extremity scores. Additionally, FA values of the CST and REST correlated with zALFF values in the right calcarine, right MOG, right SOG, and right SPG. CONCLUSIONS: Alterations within brain regions associated with the visual cortex, the fronto-parietal-occipital attention network, and spinal cord pathways appear to play a substantial role in the emergence and progression of HCL symptoms. Furthermore, the existence of a potential connection between the spinal cord and the brain suggests that this link might be related to the clinical symptoms of CSM.

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BACKGROUND: In-home rehabilitation systems are a promising, potential alternative to conventional therapy for stroke survivors. Unfortunately, physiological differences between participants and sensor displacement in wearable sensors pose a significant challenge to classifier performance, particularly for people with stroke who may encounter difficulties repeatedly performing trials. This makes it challenging to create reliable in-home rehabilitation systems that can accurately classify gestures. METHODS: Twenty individuals who suffered a stroke performed seven different gestures (mass flexion, mass extension, wrist volar flexion, wrist dorsiflexion, forearm pronation, forearm supination, and rest) related to activities of daily living. They performed these gestures while wearing EMG sensors on the forearm, as well as FMG sensors and an IMU on the wrist. We developed a model based on prototypical networks for one-shot transfer learning, K-Best feature selection, and increased window size to improve model accuracy. Our model was evaluated against conventional transfer learning with neural networks, as well as subject-dependent and subject-independent classifiers: neural networks, LGBM, LDA, and SVM. RESULTS: Our proposed model achieved 82.2% hand-gesture classification accuracy, which was better (P<0.05) than one-shot transfer learning with neural networks (63.17%), neural networks (59.72%), LGBM (65.09%), LDA (63.35%), and SVM (54.5%). In addition, our model performed similarly to subject-dependent classifiers, slightly lower than SVM (83.84%) but higher than neural networks (81.62%), LGBM (80.79%), and LDA (74.89%). Using K-Best features improved the accuracy in 3 of the 6 classifiers used for evaluation, while not affecting the accuracy in the other classifiers. Increasing the window size improved the accuracy of all the classifiers by an average of 4.28%. CONCLUSION: Our proposed model showed significant improvements in hand-gesture recognition accuracy in individuals who have had a stroke as compared with conventional transfer learning, neural networks and traditional machine learning approaches. In addition, K-Best feature selection and increased window size can further improve the accuracy. This approach could help to alleviate the impact of physiological differences and create a subject-independent model for stroke survivors that improves the classification accuracy of wearable sensors. TRIAL REGISTRATION NUMBER: The study was registered in Chinese Clinical Trial Registry with registration number CHiCTR1800017568 in 2018/08/04.

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The paper introduces professor WANG Haidong's clinical experience in treatment of wrist rheumatoid arthritis with acupotomy mobilization at the muscle regions (sinews/fascia) of three yang meridians of hand. Professor WANG Haidong believes that wrist rheumatoid arthritis belongs to the disorder of meridian muscle regions and is especially associated with the damage of the muscle regions of three yang meridians of hand running through the wrist. Under the guidance of meridian muscle region theory, on the basis of modern anatomy, and the treatment principle, "needling the affected areas may treat disorders of sinews/fascia and dysfunction of meridians simultaneously", acupotomy mobilization is adopted to balance sinews/fascia and bones, operated directly at the involved meridian muscle regions. Besides the foci (palpable knotted sites) on the distribution of muscle regions, acupoints along the affected meridians are stimulated in combination. With this therapy, after determining the location of illness, both the disorder of sinews/fascia and that of meridians can be treated.

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BACKGROUND: After spinal cord injury (SCI), a large number of survivors suffer from severe motor dysfunction (MD). Although the injury site is in the spinal cord, excitability significantly decreases in the primary motor cortex (M1), especially in the lower extremity (LE) area. Unfortunately, M1 LE area-targeted repetitive transcranial magnetic stimulation (rTMS) has not achieved significant motor improvement in individuals with SCI. A recent study reported that the M1 hand area in individuals with SCl contains a compositional code (the movement-coding component of neural activity) that links matching movements from the upper extremities (UE) and the LE. However, the correlation between bilateral M1 hand area excitability and overall functional recovery is unknown. OBJECTIVE: To clarify the changes in the excitability of the bilateral M1 hand area after SCI and its correlation with motor recovery, we aim to specify the therapeutic parameters of rTMS for SCI motor rehabilitation. METHODS: This study is a 12-month prospective cohort study. The neurophysiological and overall functional status of the participants will be assessed. The primary outcomes included single-pulse and paired-pulse TMS. The second outcome included functional near-infrared spectroscopy (fNIRS) measurements. Overall functional status included total motor score, modified Ashworth scale score, ASIA Impairment Scale grade, spinal cord independence measure and modified Barthel index. The data will be recorded for individuals with SCI at disease durations of 1 month, 2 months, 4 months, 6 months and 12 months. The matched healthy controls will be measured during the same period of time after recruitment. DISCUSSION: The present study is the first to analyze the role of bilateral M1 hand area excitability changes in the evaluation and prediction of overall functional recovery (including motor function and activities of daily living) after SCI, which will further expand the traditional theory of the predominant role of M1, optimize the current rTMS treatment, and explore the brain-computer interface design for individuals with SCI. TRIAL REGISTRATION NUMBER: ChiCTR2300068831.

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BACKGROUND: Parkinson's Disease (PD) demands early diagnosis and frequent assessment of symptoms. In particular, analysing hand movements is pivotal to understand disease progression. Advancements in hand tracking using Deep Learning (DL) allow for the automatic and objective disease evaluation from video recordings of standardised motor tasks, which are the foundation of neurological examinations. In view of this scenario, this narrative review aims to describe the state of the art and the future perspective of DL frameworks for hand tracking in video-based PD assessment. METHODS: A rigorous search of PubMed, Web of Science, IEEE Explorer, and Scopus until October 2023 using primary keywords such as parkinson, hand tracking, and deep learning was performed to select eligible by focusing on video-based PD assessment through DL-driven hand tracking frameworks RESULTS:: After accurate screening, 23 publications met the selection criteria. These studies used various solutions, from well-established pose estimation frameworks, like OpenPose and MediaPipe, to custom deep architectures designed to accurately track hand and finger movements and extract relevant disease features. Estimated hand tracking data were then used to differentiate PD patients from healthy individuals, characterise symptoms such as tremors and bradykinesia, or regress the Movement Disorder Society-Unified Parkinson's Disease Rating Scale (MDS-UPDRS) by automatically assessing clinical tasks such as finger tapping, hand movements, and pronation-supination. CONCLUSIONS: DL-driven hand tracking holds promise for PD assessment, offering precise, objective measurements for early diagnosis and monitoring, especially in a telemedicine scenario. However, to ensure clinical acceptance, standardisation and validation are crucial. Future research should prioritise large open datasets, rigorous validation on patients, and the investigation of new frontiers such as tracking hand-hand and hand-object interactions for daily-life tasks assessment.

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BACKGROUND: Early detection of cognitive impairment or dementia is essential to reduce the incidence of severe neurodegenerative diseases. However, currently available diagnostic tools for detecting mild cognitive impairment (MCI) or dementia are time-consuming, expensive, or not widely accessible. Hence, exploring more effective methods to assist clinicians in detecting MCI is necessary. OBJECTIVE: In this study, we aimed to explore the feasibility and efficiency of assessing MCI through movement kinetics under tablet-based "drawing and dragging" tasks. METHODS: We iteratively designed "drawing and dragging" tasks by conducting symposiums, programming, and interviews with stakeholders (neurologists, nurses, engineers, patients with MCI, healthy older adults, and caregivers). Subsequently, stroke patterns and movement kinetics were evaluated in healthy control and MCI groups by comparing 5 categories of features related to hand motor function (ie, time, stroke, frequency, score, and sequence). Finally, user experience with the overall cognitive screening system was investigated using structured questionnaires and unstructured interviews, and their suggestions were recorded. RESULTS: The "drawing and dragging" tasks can detect MCI effectively, with an average accuracy of 85% (SD 2%). Using statistical comparison of movement kinetics, we discovered that the time- and score-based features are the most effective among all the features. Specifically, compared with the healthy control group, the MCI group showed a significant increase in the time they took for the hand to switch from one stroke to the next, with longer drawing times, slow dragging, and lower scores. In addition, patients with MCI had poorer decision-making strategies and visual perception of drawing sequence features, as evidenced by adding auxiliary information and losing more local details in the drawing. Feedback from user experience indicates that our system is user-friendly and facilitates screening for deficits in self-perception. CONCLUSIONS: The tablet-based MCI detection system quantitatively assesses hand motor function in older adults and further elucidates the cognitive and behavioral decline phenomenon in patients with MCI. This innovative approach serves to identify and measure digital biomarkers associated with MCI or Alzheimer dementia, enabling the monitoring of changes in patients' executive function and visual perceptual abilities as the disease advances.

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Stroke often causes long-term motor and somatosensory impairments. Motor planning and tactile perception rely on spatial body representations. However, the link between altered spatial body representations, motor deficit and tactile spatial coding remains unclear. This study investigates the relationship between motor deficits and alterations of anatomical (body) and tactile spatial representations of the hand in 20 post-stroke patients with upper limb hemiparesis. Anatomical and tactile spatial representations were assessed from 10 targets (nails and knuckles) respectively cued verbally by their anatomical name or using tactile stimulations. Two distance metrics (hand width and finger length) and two structural measures (relative organization of targets positions and angular deviation of fingers from their physical posture) were computed and compared to clinical assessments, normative data and lesions sites. Over half of the patients had altered anatomical and/or tactile spatial representations. Metrics of tactile and anatomical representations showed common variations, where a wider hand representation was linked to more severe motor deficits. In contrast, alterations in structural measures were not concomitantly observed in tactile and anatomical representations and did not correlate with clinical assessments. Finally, a preliminary analysis showed that specific alterations in tactile structural measures were associated with dorsolateral prefrontal stroke lesions. This study reveals shared and distinct characteristics of anatomical and tactile hand spatial representations, reflecting different mechanisms that can be affected differently after stroke: metrics and location of tactile and anatomical representations were partially shared while the structural measures of tactile and anatomical representations had distinct characteristics.

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