Hand Movements Research Articles

Added cognitive demand from increased sensorimotor complexity resulted in better goal-directed movement following stroke: a novel finding in post-stroke recovery

BACKGROUND: Stroke often causes impairments in goal-directed movements, which are commonly assessed using goal-directed reaching tasks. Here we present two individuals post-stroke who performed two robotic reaching tasks: 1. Visually Guided Reaching (VGR); reaching a cursor representing the fingertip to virtual targets, 2. Reverse Visually Guided Reaching (RVGR); where the cursor moves in the opposite direction of hand motion, requiring a novel cognitive rule. Participants are typically more successful at reaching targets in the simple VGR task compared to the more complex RVGR task (~92% of our database). The two cases are notable as they performed better on RVGR compared to VGR. CASE DESCRIPTIONS: Case 1: 80-year-old patient with left hemorrhagic thalamic stroke, with interventricular extension, presenting with right hemiparesis/reduced motor control. They were unable to complete reaches to any targets in VGR (0/40 targets), but were able to reach ~30% of the targets (15/48) in RVGR. Case 2: 76-year-old patient with a left ischemic pontine stroke, presenting with right hemiparesis/reduced motor control. They were unable to complete reaches to any targets in VGR (0/40) but were able to reach ~60% of the targets in RVGR (29/48). INTERPRETATION: Better performance in RVGR compared to VGR may be associated with a “dual-task benefit” and has potential clinical implications, including informing neuro-rehabilitative strategies, potentially by including tasks with added complexity or cognitive components. These findings also highlight the utility of robotic tools to provide novel environments within which to identify unique patterns of impairments and abilities.

Augmented Virtual Mouse System with Enhanced Gesture Recognition

This paper presents the development of a contactless system designed to serve as an alternative to the traditional physical input device, such as a computer mouse or touchpad, utilized in human-computer interaction. The proposed system aims to offer a more convenient and hygienic way of interacting with computer devices by using hand gestures instead of physical contact with the mouse system or machine interface. The system has potential applications in various fields including healthcare, public interfaces, and gaming. Existing gesture-controlled input systems lack a comprehensive touchless and accurate system capable of performing almost every operation of a physical mouse. The proposed approach involves recognising the hand and its gestures. Hand tracking and dynamic gesture recognition are incorporated to ensure a smooth and hassle-free user experience during interaction. The efficient Mediapipe framework is used to detect and track hand movements from video frames captured through a computing device Camera.Computer vision algorithms are employed for gesture recognition within each frame, along with Python libraries for processing gestures. Sufficient gestures are added into the system, enabling it to execute a wide range of mouse functions with substantial precision metrics. This paper provides details regarding the development and overall performance of the system. Key Words: hands, mouse, contactless, gesture, OpenCV, mediapipe

Function of the hand as a predictor of early diagnosis and progression of Alzheimer's dementia: A systematic review.

The dominant feature of Alzheimer's dementia (AD) is gradual cognitive decline, which can be reflected by reduced finger dexterity. This review analyzed reports on hand function in AD patients to determine the possibility of using it for an early diagnosis and for monitoring the disease progression of AD. PubMed, Web of Science, EMBASE, and Cochrane library were searched systematically (search dates: 2000-2022), and relevant articles were cross-checked for related and relevant publications. Seventeen studies assessed the association of the handgrip strength or dexterity with cognitive performance. The hand dexterity was strongly correlated with the cognitive function in all studies. In the hand dexterity test using the pegboard, there was little difference in the degree of decline in hand function between the healthy elderly (HE) group and the mild cognitive impairment (MCI) group. On the other hand, there was a difference in the hand function between the HE group and the AD group. In addition, the decline in hand dexterity is likely to develop from moderate to severe dementia. In complex hand movements, movement speed variations were greater in the AD than in the HE group, and the automaticity, regularity, and rhythm were reduced. HE and AD can be identified by a simple hand motion test using a pegboard. The data can be used to predict dementia progression from moderate dementia to severe dementia. An evaluation of complex hand movements can help predict the transition from MCI to AD and the progression from moderate to severe dementia.

Isolated ectopia lentis with partial anterior dislocation and pupillary block: a case report

BackgroundEctopia lentis is the dislocation of the natural crystalline lens and usually presents in the setting of trauma or other systemic diseases. Herein, we describe a case of an otherwise healthy four-year-old boy with isolated ectopia lentis whose partial lens dislocation was captured on a smartphone by the patient’s father several days prior.Case presentationA four-year-old boy with no past medical, developmental, or trauma history presented with bilateral partial anterior lens dislocation with pupillary block. Initial ophthalmic evaluation two months prior was notable for uncorrected visual acuity at 20/100 OD, 20/250 OS, bilateral iridodenesis, and partially dislocated lenses inferonasally OD and inferiorly OS on slit lamp. Genetic testing found no abnormalities. Ten months later, the patient developed sudden onset of left eye pain. A dislocated lens and temporarily dilated left pupil were captured on a smartphone by the patient’s father. He was evaluated 3 days later after a second episode and found to have hand motion vision OS, a fixed 8 mm left pupil with the crystalline lens subluxed into the pupil space and accompanying intraocular pressure OS of 40 mmHg. The lens was surgically removed with a limited anterior vitrectomy. Four and a half years after surgery, visual acuity was 20/125 OS with aphakic correction. The right eye eventually underwent prophylactic lensectomy and was 20/30 in aphakic correction.ConclusionsThis report presents a unique presentation of isolated ectopia lentis with anterior lens dislocation and pupillary block and illustrates the role of smartphone photography in assisting in the triage of eye emergencies.

GESTURE CONTROLLED SMART GLOVE WITH WEB APPLICATION

his paper presents a gesture-controlled smart glove system integrated with a web application for interactive user interface and control. The glove utilizes an array of sensors, such as accelerometers, gyroscopes, and flex sensors, to detect hand movements and gestures with high precision. These sensors capture real-time data, which is processed by an embedded microcontroller unit (MCU) onboard the glove. The processed data is then transmitted wirelessly to a web application via Bluetooth or Wi-Fi connectivity. The web application, accessible through a browser on any device, provides a user-friendly interface for controlling various applications and devices remotely. Users can customize gesture mappings to specific actions, enabling seamless interaction with IoT devices, virtual reality environments, and computer applications. The system's architecture emphasizes modularity and scalability, allowing for easy integration of additional sensors or functionalities. Moreover, the web application employs responsive design principles, ensuring compatibility with different screen sizes and devices. In conclusion, the proposed gesture-controlled smart glove system offers a versatile and intuitive interface for interacting with digital environments. Its integration with a web application enhances accessibility and usability, making it suitable for a wide range of applications, including gaming, rehabilitation, and virtual control interfaces. Key Words: MCU, Flex sensors, Web Application, Gesture Control, IOT

Assessment of periodontal health status in patients with Parkinson's disease-A systematic review and meta-analysis of cross-sectional studies.

Parkinson's disease (PKD) is neurodegenerative disorder marked by tremors, bradykinesia, muscle rigidity and reduction in precise hand movements which could lead to improper oral hygiene and Periodontal disease. Current systematic review aims to review existing literature and provide assessment of periodontal health in PKD patients through a meta-analysis METHODS: Review was performed in accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines and registered in PROSPERO-CRD42023451700. Databases were searched for studies having sufficient data on periodontal health in patients with PKD compared to healthy controls and reporting outcomes in terms of various periodontal parameters of probing depth (PD), plaque index (PI), clinical attachment level (CAL), presence of bleeding on probing and gingivitis. Quality assessment of included was evaluated using Newcastle Ottawa Scale (NOS). Eleven studies fulfilled the eligibility criteria, of which ten studies were suitable for meta-analysis. Pooled estimate through the SMD showed that all periodontal parameters were altered and significantly deteriorated in PKD compared to controls but this group differences were statistically insignificant (p>0.05). Publication bias through the funnel plot showed symmetric distribution with absence of systematic heterogeneity. Parkinson's disease could be a possible factor in deterioration of periodontal health.

Metformin Induces MeCP2 in the Hippocampus of Male Mice with Sex-Specific and Brain-Region-Dependent Molecular Impact.

Rett Syndrome (RTT) is a progressive X-linked neurodevelopmental disorder with no cure. RTT patients show disease-associated symptoms within 18 months of age that include developmental regression, progressive loss of useful hand movements, and breathing difficulties, along with neurological impairments, seizures, tremor, and mental disability. Rett Syndrome is also associated with metabolic abnormalities, and the anti-diabetic drug metformin is suggested to be a potential drug of choice with low or no side-effects. Previously, we showed that in vitro exposure of metformin in a human brain cell line induces MECP2E1 transcripts, the dominant isoform of the MECP2 gene in the brain, mutations in which causes RTT. Here, we report the molecular impact of metformin in mice. Protein analysis of specific brain regions in the male and female mice by immunoblotting indicated that metformin induces MeCP2 in the hippocampus, in a sex-dependent manner. Additional experiments confirm that the regulatory role of metformin on the MeCP2 target "BDNF" is brain region-dependent and sex-specific. Measurement of the ribosomal protein S6 (in both phosphorylated and unphosphorylated forms) confirms the sex-dependent role of metformin in the liver. Our results can help foster a better understanding of the molecular impact of metformin in different brain regions of male and female adult mice, while providing some insight towards its potential in therapeutic strategies for the treatment of Rett Syndrome.

ATST-Net: A method to identify early symptoms in the upper and lower extremities of PD

Bradykinesia, a core symptom of motor disorders in Parkinson's disease (PD), is a major criterion for screening early PD patients in clinical practice. Currently, many studies have proposed automatic assessment schemes for bradykinesia in PD. However, existing schemes suffer from problems such as dependence on professional equipment, single evaluation tasks, difficulty in obtaining samples and low accuracy. This paper proposes a manual feature extraction- and neural network-based method to evaluate bradykinesia, effectively solving the problem of a small sample size. This method can automatically assess finger tapping (FT), hand movement (HM), toe tapping (TT) and bilateral foot sensitivity tasks (LA) through a unified model. Data were obtained from 120 individuals, including 93 patients with Parkinson's disease and 27 age- and sex-matched normal controls (NCs). Manual feature extraction and Attention Time Series Two-stream Networks (ATST-Net) were used for classification. Accuracy rates of 0.844, 0.819, 0.728, and 0.768 were achieved for FT, HM, TT, and LA, respectively. To our knowledge, this study is the first to simultaneously evaluate the upper and lower limbs using a unified model that has significant advantages in both model training and transfer learning.

Permainan Edukasi Puzzle Math Untuk Menstimulasi Motorik Siswa

This service examines the necessity and pertinence of adopting a pedagogical approach that incorporates educational game components in order to promote motor development and enhance student engagement in the learning process. On November 18, 2023, this service activity was conducted at UPTD. SD Negeri 014672 Tanjung Alam. This activity is structured into three distinct phases during its execution: preparation, implementation, and practice. Based on empirical findings, engaging in direct interaction with mathematical puzzles has been found to enhance hand-motor coordination, finger dexterity, and precision control during object manipulation. Ninety-five percent of students were extremely interested in this educational game; eighty-five percent felt that math puzzle games improved their fine motor skills (hand and finger movements); and ninety-two percent intend to continue playing math puzzles on other materials in the future, according to the results of the questionnaire assessing the experience of playing with math puzzles. Students' fine motor skills improved significantly as a result of the implementation of this game, it was concluded.

ST-TGR: Spatio-Temporal Representation Learning for Skeleton-Based Teaching Gesture Recognition.

Teaching gesture recognition is a technique used to recognize the hand movements of teachers in classroom teaching scenarios. This technology is widely used in education, including for classroom teaching evaluation, enhancing online teaching, and assisting special education. However, current research on gesture recognition in teaching mainly focuses on detecting the static gestures of individual students and analyzing their classroom behavior. To analyze the teacher's gestures and mitigate the difficulty of single-target dynamic gesture recognition in multi-person teaching scenarios, this paper proposes skeleton-based teaching gesture recognition (ST-TGR), which learns through spatio-temporal representation. This method mainly uses the human pose estimation technique RTMPose to extract the coordinates of the keypoints of the teacher's skeleton and then inputs the recognized sequence of the teacher's skeleton into the MoGRU action recognition network for classifying gesture actions. The MoGRU action recognition module mainly learns the spatio-temporal representation of target actions by stacking a multi-scale bidirectional gated recurrent unit (BiGRU) and using improved attention mechanism modules. To validate the generalization of the action recognition network model, we conducted comparative experiments on datasets including NTU RGB+D 60, UT-Kinect Action3D, SBU Kinect Interaction, and Florence 3D. The results indicate that, compared with most existing baseline models, the model proposed in this article exhibits better performance in recognition accuracy and speed.

Integrating Biomarkers From Virtual Reality and Magnetic Resonance Imaging for the Early Detection of Mild Cognitive Impairment Using a Multimodal Learning Approach: Validation Study.

Early detection of mild cognitive impairment (MCI), a transitional stage between normal aging and Alzheimer disease, is crucial for preventing the progression of dementia. Virtual reality (VR) biomarkers have proven to be effective in capturing behaviors associated with subtle deficits in instrumental activities of daily living, such as challenges in using a food-ordering kiosk, for early detection of MCI. On the other hand, magnetic resonance imaging (MRI) biomarkers have demonstrated their efficacy in quantifying observable structural brain changes that can aid in early MCI detection. Nevertheless, the relationship between VR-derived and MRI biomarkers remains an open question. In this context, we explored the integration of VR-derived and MRI biomarkers to enhance early MCI detection through a multimodal learning approach. We aimed to evaluate and compare the efficacy of VR-derived and MRI biomarkers in the classification of MCI while also examining the strengths and weaknesses of each approach. Furthermore, we focused on improving early MCI detection by leveraging multimodal learning to integrate VR-derived and MRI biomarkers. The study encompassed a total of 54 participants, comprising 22 (41%) healthy controls and 32 (59%) patients with MCI. Participants completed a virtual kiosk test to collect 4 VR-derived biomarkers (hand movement speed, scanpath length, time to completion, and the number of errors), and T1-weighted MRI scans were performed to collect 22 MRI biomarkers from both hemispheres. Analyses of covariance were used to compare these biomarkers between healthy controls and patients with MCI, with age considered as a covariate. Subsequently, the biomarkers that exhibited significant differences between the 2 groups were used to train and validate a multimodal learning model aimed at early screening for patients with MCI among healthy controls. The support vector machine (SVM) using only VR-derived biomarkers achieved a sensitivity of 87.5% and specificity of 90%, whereas the MRI biomarkers showed a sensitivity of 90.9% and specificity of 71.4%. Moreover, a correlation analysis revealed a significant association between MRI-observed brain atrophy and impaired performance in instrumental activities of daily living in the VR environment. Notably, the integration of both VR-derived and MRI biomarkers into a multimodal SVM model yielded superior results compared to unimodal SVM models, achieving higher accuracy (94.4%), sensitivity (100%), specificity (90.9%), precision (87.5%), and F1-score (93.3%). The results indicate that VR-derived biomarkers, characterized by their high specificity, can be valuable as a robust, early screening tool for MCI in a broader older adult population. On the other hand, MRI biomarkers, known for their high sensitivity, excel at confirming the presence of MCI. Moreover, the multimodal learning approach introduced in our study provides valuable insights into the improvement of early MCI detection by integrating a diverse set of biomarkers.

Role of the Cerebellum in the Construction of Functional and Geometrical Spaces.

The perceptual and motor systems appear to have a set of movement primitives that exhibit certain geometric and kinematic invariances. Complex patterns and mental representations can be produced by (re)combining some simple motor elements in various ways using basic operations, transformations, and respecting a set of laws referred to as kinematic laws of motion. For example, point-to-point hand movements are characterized by straight hand paths with single-peaked-bell-shaped velocity profiles, whereas hand speed profiles for curved trajectories are often irregular and more variable, with speed valleys and inflections extrema occurring at the peak curvature. Curvature and speed are generically related by the 2/3 power law. Mathematically, such laws can be deduced from a combination of Euclidean, affine, and equi-affine geometries, whose neural correlates have been partially detected in various brain areas including the cerebellum and the basal ganglia. The cerebellum has been found to play an important role in the control of coordination, balance, posture, and timing over the past years. It is also assumed that the cerebellum computes forward internal models in relationship with specific cortical and subcortical brain regions but its motor relationship with the perceptual space is unclear. A renewed interest in the geometrical and spatial role of the cerebellum may enable a better understanding of its specific contribution to the action-perception loop and behavior's adaptation. In this sense, we complete this overview with an innovative theoretical framework that describes a possible implementation and selection by the cerebellum of geometries adhering to different mathematical laws.

Development of a smartphone screening test for preclinical Alzheimer’s disease and validation across the dementia continuum

BackgroundDementia prevalence is predicted to triple to 152 million globally by 2050. Alzheimer’s disease (AD) constitutes 70% of cases. There is an urgent need to identify individuals with preclinical AD, a 10–20-year period of progressive brain pathology without noticeable cognitive symptoms, for targeted risk reduction. Current tests of AD pathology are either too invasive, specialised or expensive for population-level assessments. Cognitive tests are normal in preclinical AD. Emerging evidence demonstrates that movement analysis is sensitive to AD across the disease continuum, including preclinical AD. Our new smartphone test, TapTalk, combines analysis of hand and speech-like movements to detect AD risk. This study aims to [1] determine which combinations of hand-speech movement data most accurately predict preclinical AD [2], determine usability, reliability, and validity of TapTalk in cognitively asymptomatic older adults and [3], prospectively validate TapTalk in older adults who have cognitive symptoms against cognitive tests and clinical diagnoses of Mild Cognitive Impairment and AD dementia.MethodsAim 1 will be addressed in a cross-sectional study of at least 500 cognitively asymptomatic older adults who will complete computerised tests comprising measures of hand motor control (finger tapping) and oro-motor control (syllabic diadochokinesis). So far, 1382 adults, mean (SD) age 66.20 (7.65) years, range 50–92 (72.07% female) have been recruited. Motor measures will be compared to a blood-based AD biomarker, phosphorylated tau 181 to develop an algorithm that classifies preclinical AD risk. Aim 2 comprises three sub-studies in cognitively asymptomatic adults: (i) a cross-sectional study of 30–40 adults to determine the validity of data collection from different types of smartphones, (ii) a prospective cohort study of 50–100 adults ≥ 50 years old to determine usability and test-retest reliability, and (iii) a prospective cohort study of ~1,000 adults ≥ 50 years old to validate against cognitive measures. Aim 3 will be addressed in a cross-sectional study of ~200 participants with cognitive symptoms to validate TapTalk against Montreal Cognitive Assessment and interdisciplinary consensus diagnosis.DiscussionThis study will establish the precision of TapTalk to identify preclinical AD and estimate risk of cognitive decline. If accurate, this innovative smartphone app will enable low-cost, accessible screening of individuals for AD risk. This will have wide applications in public health initiatives and clinical trials.Trial registrationClinicalTrials.gov identifier: NCT06114914, 29 October 2023. Retrospectively registered.

Lactational optic neuritis - A case series with a review of the literature.

Optic neuritis is the most common optic neuropathy affecting adults, especially women. Lactational optic neuritis is a rare entity occurring during the postpartum period with a high chance of recurrence. Through this study, we evaluated the clinical profile and visual outcome of lactational optic neuritis. This was a retrospective study where patients with optic neuritis presenting during the first year of the postpartum period to the neuro-ophthalmology clinic between January 2016 and December 2022 were included in the study. This study included seven eyes of six patients. The mean age of presentation was 24.6 years, with all patients presenting after the first 3 months of the postpartum period. All patients had unilateral involvement except one, presenting with painful vision loss. Best corrected visual acuity ranged from 6/18 to hand movements. Relative afferent pupillary defect and dyschromatopsia were present in all patients. Disc edema was seen in all but one case. Three patients were treated with steroids along with multivitamins, and the remaining three refused treatment with steroids, receiving multivitamins alone. All patients had complete recovery by the end of 12 weeks. Optic neuritis during lactation is attributed more to post-pregnancy immunological changes rather than lactation itself. The presentation and clinical features are similar to classic optic neuritis and remain an important differential diagnosis for visual dysfunction in the postpartum period.

Identification and Classification of Myoelectric Signal Features Related to Hand Motions

Identification and Classification of Myoelectric Signal Features Related to Hand Motions

A hybrid brain-muscle-machine interface for stroke rehabilitation: Usability and functionality validation in a 2-week intensive intervention.

Introduction: The primary constraint of non-invasive brain-machine interfaces (BMIs) in stroke rehabilitation lies in the poor spatial resolution of motor intention related neural activity capture. To address this limitation, hybrid brain-muscle-machine interfaces (hBMIs) have been suggested as superior alternatives. These hybrid interfaces incorporate supplementary input data from muscle signals to enhance the accuracy, smoothness and dexterity of rehabilitation device control. Nevertheless, determining the distribution of control between the brain and muscles is a complex task, particularly when applied to exoskeletons with multiple degrees of freedom (DoFs). Here we present a feasibility, usability and functionality study of a bio-inspired hybrid brain-muscle machine interface to continuously control an upper limb exoskeleton with 7 DoFs. Methods: The system implements a hierarchical control strategy that follows the biologically natural motor command pathway from the brain to the muscles. Additionally, it employs an innovative mirror myoelectric decoder, offering patients a reference model to assist them in relearning healthy muscle activation patterns during training. Furthermore, the multi-DoF exoskeleton enables the practice of coordinated arm and hand movements, which may facilitate the early use of the affected arm in daily life activities. In this pilot trial six chronic and severely paralyzed patients controlled the multi-DoF exoskeleton using their brain and muscle activity. The intervention consisted of 2 weeks of hBMI training of functional tasks with the system followed by physiotherapy. Patients' feedback was collected during and after the trial by means of several feedback questionnaires. Assessment sessions comprised clinical scales and neurophysiological measurements, conducted prior to, immediately following the intervention, and at a 2-week follow-up. Results: Patients' feedback indicates a great adoption of the technology and their confidence in its rehabilitation potential. Half of the patients showed improvements in their arm function and 83% improved their hand function. Furthermore, we found improved patterns of muscle activation as well as increased motor evoked potentials after the intervention. Discussion: This underscores the significant potential of bio-inspired interfaces that engage the entire nervous system, spanning from the brain to the muscles, for the rehabilitation of stroke patients, even those who are severely paralyzed and in the chronic phase.

Bilateral anterior capsular phimosis in an 85-year-old female patient after stand-alone cataract extraction with intraocular lens implantation

Introduction: Anterior capsular phimosis is a rare complication after cataract extraction with intraocular lens implantation. Patient and clinical findings: An 85-year-old female underwent manual phacoemulsification with toric intraocular lens implantation in the right eye (OD) without any post-operative complications. Approximately 1.5 weeks later, she had the same procedure in the left eye (OS) with similar success. At post-operative week 6 OD and week 4 OS, distance visual acuity without correction was 20/400 OD and hand motion (HM) OS. Anterior segment examination revealed intraocular lens implants with a significant fibrotic anterior capsule in both eyes. Posterior segment examination showed pigmentary macular changes in both eyes that were unchanged from the pre-operative evaluation. Diagnosis, intervention, and outcomes: The patient was diagnosed with bilateral anterior capsular phimosis. She underwent Nd:YAG laser capsulotomy in the left eye, then in the right eye 1 week later. At Nd: YAG laser capsulotomy post-operative day 4 OD and week 2 OS, distance visual acuity without correction was 20/40 OD, OS with best corrected visual acuity 20/30 OD, OS. Conclusions: Anterior capsular phimosis can occur in both eyes sooner than 4 weeks after cataract extraction with intraocular lens implantation in a patient with no risk factors.

AirMouse and AirWrite Intuitive Digital Interaction

The primary purpose of this project is to redefine the way people will interact with their systems more easily, and this will be achieved by eliminating the touchpads and keyboards to connect to the computer. This cutting-edge system eliminates the need for a regular mouse or a keyboard, allowing for easier device user-interface adaptation. The implementation of the latest gesture recognition technology lets the system not only pick up and decipher the movements of users' hands but also translate them into wider operations that are similar to those of a mouse and keyboard. Central to the design of the metaphoric system is the "virtual mouse," which enables the cursor movement and the performance of various mouse buttons without touching anything physically. So, it is a virtual mouse with an on-the-screen cursor that reacts to hand gestures in real-time. The system utilizes certain hand gestures enough to enable the effortless execution of actions commonly associated with a traditional mouse including left-clicking, right-clicking, and dragging across the screen. The virtual mouse feature is enhanced by the air writing technique, commonly acknowledged as a milestone in input development. This capability precisely reads and decodes the movements developed in the space by the fingertips while conveying them to the screen as written words. The air writing system would predict the characters from the user's gestures, creating a fresh, secure, and user-friendly method that favors the option of air typing rather than relying on a physical keyboard. This project brings innovation in human-computer interaction when implemented using powerful computer vision and machine learning techniques and promotes accessibility and usability of the system where the traditional inputs become inapplicable. On the other hand, its feasible justifications range from medical uses to pioneering in virtual reality, gaming, and education that drives brilliantly the new options in interaction across various fields.

Investigating the Effectiveness of an IMU Portable Gait Analysis Device: An Application for Parkinson’s Disease Management

As part of two research projects, a small gait analysis device was developed for use inside and outside the home by patients themselves. The project PARMODE aims to record accurate gait measurements in patients with Parkinson’s disease (PD) and proceed with an in-depth analysis of the gait characteristics, while the project CPWATCHER aims to assess the quality of hand movement in cerebral palsy patients. The device was mainly developed to serve the first project with additional offline processing, including machine learning algorithms that could potentially be used for the second aim. A key feature of the device is its small size (36 mm × 46 mm × 16 mm, weight: 14 g), which was designed to meet specific requirements in terms of device consumption restrictions due to the small size of the battery and the need for autonomous operation for more than ten hours. This research work describes, on the one hand, the new device with an emphasis on its functions, and on the other hand, its connection with a web platform for reading and processing data from the devices placed on patients’ feet to record the gait characteristics of patients on a continuous basis.

Optimized k-nearest neighbors for classification of prosthetic hand movements using electromyography signal

Electromyography (EMG) signals are essential, as they are used to measure muscular activity in different parts of the human body. The measurement and analysis of EMG signal lead to various applications of muscle disorders such as muscular dystrophy, myopathy, hand movements, etc. In this paper, an improved and effective hand movement classification model is developed for amputee subjects. It includes: (1) EMG feature extraction using Discrete Wavelet Transform (DWT), (2) EMG feature selection using binary Global Best Guided Gaussian Artificial Bee Colony (BGGABC), (3) Hand movements classification using Optimized k-nearest neighbors (OKNN) classifier. The EMG signal is taken from the DB3 of NinaPro dataset comprising 17 different prosthetic hand movements recorded from 11 amputee subjects. Thereafter, DWT is applied to decompose the EMG signal for extracting features. An improved wrapper-based feature selection technique (BGGABC) is used to select the optimal feature subset for effective classification. The two variants of KNN, i.e. Smallest Modified KNN and Largest Modified KNN are taken in which item strength to a class is optimized for efficient classification. The strength of an item to a class depends on distance and weight of an item to a class. Therefore, a multi-objective Non-dominated sorting genetic algorithm-II (NSGA-II) is used for optimizing these two contradictory parameters (distance and weight) simultaneously to have optimized variants, namely: Optimized Smallest KNN (OSKNN) and Optimized Largest KNN (OLKNN). Extensive results show that the proposed method OKNN achieved the highest classification accuracy of 93.07% (OLKNN) and 89.43% (OSKNN) compared with KNN variants and competitors.