Hand Movements Research Articles

Hand Motion Analysis Using Accelerometer-Based Sensors and Sheep’s Head Model for Basic Training in Functional Endoscopic Sinus Surgery

Hand Motion Analysis Using Accelerometer-Based Sensors and Sheep’s Head Model for Basic Training in Functional Endoscopic Sinus Surgery

EEG source imaging of hand movement-related areas: an evaluation of the reconstruction and classification accuracy with optimized channels

The hand motor activity can be identified and converted into commands for controlling machines through a brain-computer interface (BCI) system. Electroencephalography (EEG) based BCI systems employ electrodes to measure the electrical brain activity projected at the scalp and discern patterns. However, the volume conduction problem attenuates the electric potential from the brain to the scalp and introduces spatial mixing to the signals. EEG source imaging (ESI) techniques can be applied to alleviate these issues and enhance the spatial segregation of information. Despite this potential solution, the use of ESI has not been extensively applied in BCI systems, largely due to accuracy concerns over reconstruction accuracy when using low-density EEG (ldEEG), which is commonly used in BCIs. To overcome these accuracy issues in low channel counts, recent studies have proposed reducing the number of EEG channels based on optimized channel selection. This work presents an evaluation of the spatial and temporal accuracy of ESI when applying optimized channel selection towards ldEEG number of channels. For this, a simulation study of source activity related to hand movement has been performed using as a starting point an EEG system with 339 channels. The results obtained after optimization show that the activity in the concerned areas can be retrieved with a spatial accuracy of 3.99, 10.69, and 14.29 mm (localization error) when using 32, 16, and 8 channel counts respectively. In addition, the use of optimally selected electrodes has been validated in a motor imagery classification task, obtaining a higher classification performance when using 16 optimally selected channels than 32 typical electrode distributions under 10–10 system, and obtaining higher classification performance when combining ESI methods with the optimal selected channels.

How to use one surface electromyography sensor to recognize six hand movements for a mechanical hand in real time: a method based on Morse code.

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

Metastasis of small cell lung cancer to bilateral extraocular muscles: a case report

BackgroundOrbital metastasis is a possible complication of small cell lung cancer and a pattern of bilateral invasion of the extraocular muscles has rarely been reported in literature.Case presentationA 46-year-old white male with a past medical history of smoking and stage IV small cell lung carcinoma presented with loss of vision and pain in the left eye. Examination revealed bilateral proptosis and left afferent pupillary defect, and visual acuity was hand motion on the left eye and 4/10 on the right eye. An orbital computed tomography scan showed a compression of the left optic nerve between the extraocular muscles at the apex, and a lateral canthotomy was performed for a new-onset compressive optic neuropathy, with residual visual improvement. There was also significant enlargement of the extraocular muscles in the right orbit. The patient was maintained in palliative treatment with both chemotherapy and local medical and surgical (amniotic membrane cover for exposure keratopathy) ophthalmological treatments until he eventually died 5 months after.ConclusionBilateral metastasis to the extraocular muscles is a very rare manifestation of small cell lung cancer and the palliative treatment in these cases is challenging.

Reliability of the Hand-Eye Coordination Test among Adults Using a Software: A Longitudinal Follow-up Study

 This study aims to determine the reliability of the hand-eye coordination Software. The integrated control of hand and eye movements, as well as the processing of visual information to support attainment and acquisition and the use of hand proprioception to guide the eyes, are known as hand-eye coordination. Thus, this study aims to determine the reliability of the tests, so the research was conducted thrice with an interval of 6-7 days (test-retest). 28 Young Adults (18- 35 years of age) have participated in the study. The analysis includes finger-pointing tasks performed only with the use of Hand-eye coordination software prepared by the Software engineer.In this Software, Visual targets in the form of a ball then appeared on the screen on the left, on the right, or in the center in random order., and participants were asked to point an index finger of the leading hand as rapidly and precisely as possible at all targets on the screen. After touching the target (indicated by its disappearance), the participant immediately placed his/her hand back in the designated starting position (touchpad).Each participant was required to repeat this finger-pointing task. The accuracy value after 1 minute was recorded and the average accuracy value was calculated and used in the analysis A smaller value indicates greater touch accuracy. In addition, the average reaction time (in milliseconds) was also measured. Based on the data analysis, the reliability score is 0.806 (Cronbach’s Alpha), which indicates a high level of internal Consistency.

Assessment of Surgeons’ Stress Levels with Digital Sensors during Robot-Assisted Surgery: An Experimental Study

Robot-Assisted Minimally Invasive Surgery (RAMIS) marks a paradigm shift in surgical procedures, enhancing precision and ergonomics. Concurrently it introduces complex stress dynamics and ergonomic challenges regarding the human–robot interface and interaction. This study explores the stress-related aspects of RAMIS, using the da Vinci XI Surgical System and the Sea Spikes model as a standard skill training phantom to establish a link between technological advancement and human factors in RAMIS environments. By employing different physiological and kinematic sensors for heart rate variability, hand movement tracking, and posture analysis, this research aims to develop a framework for quantifying the stress and ergonomic loads applied to surgeons. Preliminary findings reveal significant correlations between stress levels and several of the skill-related metrics measured by external sensors or the SURG-TLX questionnaire. Furthermore, early analysis of this preliminary dataset suggests the potential benefits of applying machine learning for surgeon skill classification and stress analysis. This paper presents the initial findings, identified correlations, and the lessons learned from the clinical setup, aiming to lay down the cornerstones for wider studies in the fields of clinical situation awareness and attention computing.

Age-related differences in finger interdependence during complex hand movements.

The well-known decrease in finger dexterity during healthy aging leads to a significant reduction in quality of life. Still, the exact patterns of altered finger kinematics of older adults in daily life are fairly unexplored. Finger interdependence is the unintentional co-movement of fingers that are not intended to move, and it is known to vary across the lifespan. Nevertheless, the magnitude and direction of age-related differences in finger interdependence are ambiguous across studies and tasks and have not been explored in the context of daily life finger movements. We investigated five different free and daily-life-inspired finger movements of the right, dominant hand as well as a sequential finger tapping task of the thumb against the other fingers, in 17 younger (22 to 37 years) and 17 older (62 to 80 years) adults using an exoskeleton data glove for data recording. Using inferential statistics, we found that the unintentional co-movement of fingers generally decreases with age in all performed daily-life-inspired movements. Finger tapping, however, showed a trend towards higher finger interdependence for older compared to younger adults. Using machine learning, we predicted the age group of a person from finger interdependence features of single movement trials significantly better than chance level for the daily-life-inspired movements, but not for finger tapping. Taken together, we show that for specific tasks, decreased finger interdependence (i.e., less co-movement) could potentially act as a marker of human aging that specifically characterizes older adults' complex finger movements in daily life.

Single-Session Cerebellar Transcranial Direct Current Stimulation Improves Postural Stability and Reduces Ataxia Symptoms in Spinocerebellar Ataxia.

Spinocerebellar ataxia (SCA) results in balance and coordination impairment, and current treatments have limited efficacy. Recent evidence suggests that combining postural training with cerebellar transcranial direct current stimulation (ctDCS) can improve these symptoms. However, the combined effects of ctDCS and postural training on individuals with spinocerebellar ataxia remain underexplored. Ten volunteers with (SCA type 3) participated in a triple-blind, randomized, crossover study to receive a single session of ctDCS (2mA for 20min) and a sham ctDCS session separated by at least one week. The Biodex Balance System was used to assess balance at each session, measuring overall stability index, anteroposterior stability index, and medial-lateral stability index. As secondary outcomes, cerebellar ataxia symptoms were evaluated using the 8-item Scale for Assessment and Rating of Ataxia. The assessments were conducted before and after each session. The results indicated that ctDCS enhanced the overall stability index when compared to sham ctDCS (Z = -2.10, p = 0.03), although it did not significantly affect the anteroposterior or medial-lateral stability indices. Compared to the baseline, a single session of ctDCS reduced appendicular symptoms related to cerebellar ataxia, as evidenced by improvements in the nose-finger test (Z = -2.07, p = 0.04), fast alternating hand movements (Z = -2.15, p = 0.03), and heel-to-shin slide (Z = -1.91, p = 0.05). In conclusion, our study suggests that a single session of ctDCS, in combination with postural training, can enhance balance and alleviate ataxia symptoms in individuals with cerebellar ataxia. This study was approved by the local research ethics committee (No. 2.877.813) and registered on clinicaltrials.org (NCT04039048 - https://www.clinicaltrials.gov/study/NCT04039048 ) on 2019-07-28.

Spatio-temporal modulation of cortical activity during motor deadaptation depends on the feedback of task-related error.

Motor adaptations are responsible for recalibrating actions and facilitating the achievement of goals in an ever-changing environment. Once consolidated, the decay of motor adaptation is a process affected by available sensory information during deadaptation. However, the cortical response to task error feedback during the deadaptation phase has received little attention. Here, we explored changes in brain cortical responses due to feedback of task-related error during deadaptation. Twelve healthy volunteers were recruited for the study. Right hand movement and EEG were recorded during repetitive trials of a hand reaching movement. A visuomotor rotation of 30° was introduced to induce motor adaptation. Volunteers participated in two experimental sessions organized in baseline, adaptation, and deadaptation blocks. In the deadaptation block, the visuomotor rotation was removed, and visual feedback was only provided in one session. Performance was quantified using angle end-point error, averaged speed, and movement onset time. Non-parametric spatiotemporal cluster-level permutation test was used to analyze the EEG recordings. During deadaptation, participants experienced a greater error reduction when feedback of the cursor was provided. The EEG responses showed larger activity in the left centro-frontal parietal areas during the deadaptation block when participants received feedback, as opposed to when they did not receive feedback. Centrally distributed clusters were found for the adaptation and deadaptation blocks in the absence of visual feedback. The results suggest that visual feedback of the task-related error activates cortical areas related to performance monitoring, depending on the accessible sensory information.

The Relativity of Reaching: Motion of the touched surface alters the trajectory of hand movements

The Relativity of Reaching: Motion of the touched surface alters the trajectory of hand movements

GORE-TEX SUTURE-ASSOCIATED ENDOPHTHALMITIS IN A SCLERAL-SUTURED INTRAOCULAR LENS.

To describe a unique case of Gore-Tex (Gore Medical, AZ) suture-associated delayed-onset endophthalmitis in a patient with a history of scleromalacia, pathologic myopia, multiple prior vitrectomies in the setting of a scleral-sutured intraocular lens (IOL). A 69-year-old man complained of blurry vision and eye pain 21 months after implantation of a scleral-sutured IOL and was found to have an exposed Gore-Tex suture with scleromalacia, vision loss to hand motion, a hypopyon, and vitritis. He was diagnosed with culture-positive endophthalmitis. He had a previous history of a rhegmatogenous retinal detachment repair and subsequent dislocated IOL in this eye treated with a scleral-sutured IOL. A vitreous tap and injection of broad-spectrum antibiotics and suture removal at the slit lamp were performed at the time of presentation. One week later, he underwent pars plana vitrectomy and removal of the IOL. After the tap and inject and subsequent lens removal, his vision has returned to his preoperative vision of 20/100 with refraction. Gore-Tex suture is a nonabsorbable and flexible option with great tensile strength that has emerged as a favored alternative to polypropylene for the use in transscleral fixated IOLs. Although endophthalmitis and suture erosion are known complications of this procedure, this report describes one of the first cases of Gore-Tex suture-associated endophthalmitis.

TOWARD AN IMAGING-CENTRIC DEFINITION OF NONPARANEOPLASTIC AUTOIMMUNE RETINOPATHY.

To explore characteristic imaging features of nonparaneoplastic autoimmune retinopathy (npAIR) to augment diagnostic criteria. This is a retrospective cohort study of patients with npAIR evaluated at the Emory Eye Center between 2013 and 2019. Multimodal fundus images were evaluated to characterize the evolution of the disease. Twenty-one eyes of 12 patients were classified as having npAIR. Five patients (42%) were female, with median (range) age of 59 years (45-85 years). Median baseline visual acuity was 20/30 (20/20 to hand motions). Disease was asymmetric in 11 patients (92%). Common imaging findings included absence of bone spicules (86% of affected eyes), presence of attenuated vessels (86%), and speckled hypoautofluorescence in perimacular and perivenular regions. Three eyes were noted to present early with subtle splotchy fundus autofluorescence abnormality, ultimately developing characteristic speckled perimacular hypoautofluorescence. On optical coherence tomography, 18 eyes (86%) had loss of outer retinal bands with relative foveal sparing and a tapered transition zone. Many eyes with npAIR exhibit a subacute, asymmetric, generalized photoreceptor degeneration featuring outer retinal atrophy with relative foveal sparing, retinal vascular attenuation, absence of bone spicules, and speckled hypoautofluorescence often in a perimacular and perivenular distribution. Findings of this study augment diagnostic criteria to improve specificity and accessibility of testing for npAIR.

Anthropometry and Physical Features of Bangladeshi Women National Level Kho-Kho and Football Players: A Frank Comparison

Introduction: In Bangladesh Football is the most popular ball game but Kho-Kho is not that popular like Football. Kho-Kho is a popular Game in India but day by day it is becoming popular in Bangladesh. Major muscle groups are used in these two games because both games involve running, driving, jumping, swinging, and hand motions. Methods: Forty national-level women Football players and forty national-level women Kho-Kho players were studied in the present study.  The average age is between 14 to 21 years and the training age is between 2 to 6 years. All the anthropometric measurements and body mass were carried out with proper guidelines. Results: The average leg length for Female Kho-Kho players is 89.14 ( ±4.44 cm)  and that of Female Football players is 86.92 ( ±8.91) cm. The average leg length of women Kho-Kho players is 2.22 cm higher than the average leg length of women Football players which is statistically significant. There is also a significant difference (p ≤0.05) when average BMI is compared. The average fat percentage of Women Footballers is slightly higher than the average Fat % of women Kho-Kho players where the average fat percentage of women football players is 22.09 % ±4.05 and that of Kho-Kho players is 22.01 ±4.01. Conclusion: The Bangladeshi national-level women Kho-Kho players have better physiques than the national women Football players whereas the body composition of both groups is very close.

Flexible piezoelectric pressure sensor based on BaTiO3-CNT/RTV Composite Film with Filter Paper Microstructures for Monitoring Hand Motions

Abstract Flexible piezoelectric sensors are widely used in various applications such as physiological signal monitoring and human-computer interaction. The present study introduces a BaTiO3-CNT/RTV piezoelectric sensor fabricated using a filter paper template. It incorporates micro-scale fiber stacking and a 1% CNT doping in the microstructure, resulting in a notable enhancement of sensor sensitivity, increasing it from 0.07 V/N to 0.69 V/N, representing an almost tenfold improvement. Furthermore, the study investigates the influence of affecting factors like the flexible substrate of the sensing film, thickness, and mass fractions of various materials on the output voltage. The sensor exhibits superior characteristics such as good repeatability under 5000 cyclic loads, high elongation at break, fast response (80 ms) and recovery times (90 ms), and good linearity. It also demonstrates outstanding sensitivity (12 mV/10°) when monitoring different finger bending states, enabling real-time, sensitive, and reliable hand motion tracking. This sensor holds promising prospects for future developments in the fields of intelligent grasping and sign language translation.

SignoSpeak: Bridging the Gap

Abstract: "SignoSpeak: Bridging the Gap" is an innovative software research aimed at transforming communication for the hearing-impaired. It converts sign language to text in real-time, breaking down communication barriers effectively. Besides text conversion, SignoSpeak interprets various gestures, enabling users to express their thoughts vividly. By translating hand and body movements into text and audio, it enriches communication and information conveyance. This device acts as a crucial link between sign language and written language, benefiting those dependent on sign language. Additionally, its ability to recognize gestures fosters inclusive cross-cultural communication. SignoSpeak signifies a significant milestone in promoting inclusivity and meaningful interaction for the hearing-impaired. Its potential lies in revolutionizing global communication norms and understanding the unique needs of individuals with hearing impairments.

AI Based Human Computer Interactions for Specially Abled

Abstract: With the rise of technology, computing has been embedded in portable electronics like cellphones and palm tops. The QWERTY keyboard is still the primary input method, though. This project proposes a virtual keyboard programme that generates a visual using image processing. This is a representation of a keyboard. When a camera records hand movements as typing inputs, the simulated keyboard will be operational and available. The creation of a virtual cursor that recognizes fingers is based on the same idea. The motions of the hands that move the mouse will be captured by the camera. Use the camera to take a picture of a physical keyboard or mouse so that you may create a virtual version of them.

Hand Glide: Gesture-Controlled Virtual Mouse with Voice Assistant

Abstract: Hand Glide presents a unified system that uses the MediaPipe library for accurate hand landmark recognition and categorization, enabling smooth integration of gesture-based control (GBC) and voice-controlled assistant (VCA) features. The system allows users to effortlessly translate hand movements into actions such as mouse manipulation, clicking, scrolling, and system parameter adjustments by providing distinct classes for hand gesture recognition (HandRecog) and execution (Controller), as well as a main class orchestrating camera input and gesture control. In addition, Hand Glide also has VCA functionality, which uses speech recognition and synthesis libraries and natural language processing (NLP) techniques. With these tools, Hand Glide can reply to greetings, perform tasks like Wikipedia searches and website openings, and converse with users through the GPT-2 language model. By merging these features, Hand Glide providesusers with an interactive and intuitive means of computer control, with broad applications in user interfaces, accessibility solutions, and immersive experiences

A Deep Learning Approach for Real-Time Sign Language Detection and Response through Avatar Model

Abstract: This paper focuses on the evolution of a real-time sign language detection model using computer vision, machine learning, and deep learning. Its goal is to narrow the poor communication for the speaking and hearing-impaired community by using recurrent neural networks and long short-term memory models. The proposed system utilizes a dataset comprising sign language gestures captured in various contexts and by different signers. Preprocessing techniques are applied to draw out fitting features from the video frames, including hand movements, facial expressions, and body postures. The LSTM neural network architecture is chosen to grab temporal dependencies in sequential data, making it suitable for the robust nature of sign language. The training process involves optimizing the LSTM network on the labeled dataset, incorporating techniques such as transfer learning and data augmentation to enhance model generalization. The resulting model is capable of recognizing a divergent set of sign language actions in real-time

Gesture Controlled Robot for the Disabled using Arduino

Abstract: The gesture-controlled robot project presents a novel approach to human-robot interaction, utilizing hand gestures as an intuitive and natural means of control. This project aims to develop a system where users can seamlessly communicate with a robot using simple hand movements. The project consists of two main components: a handheld device for gesture input and a robot equipped with sensors and actuators for movement. Through wireless communication, the handheld device transmits signals encoding the user's hand gestures to the robot's receiver module, which decodes the signals, processes them, and translates them into commands for controlling the robot's movements. Advantages of this system include intuitive control, wireless operation, real-time interaction, enhanced safety, adaptability, educational value, and versatility. However, challenges such as gesture recognition complexity and limited gesture vocabulary must be addressed to ensure the system's effectiveness. The project has numerous applications, including entertainment, assistive technology, industrial automation, education, home automation, IoT, security, and interactive exhibits. By leveraging gesture-controlled robotics, this project contributes to advancing human-robot interaction technology and fostering innovation in various domains.

Robust discrimination of multiple naturalistic same-hand movements from MEG signals with convolutional neural networks

Abstract Discriminating patterns of brain activity corresponding to multiple hand movements is a challenging problem at the limit of the spatial resolution of magnetoencephalography (MEG). Here, we use the combination of MEG, a novel experimental paradigm, and a recently developed convolutional-neural-network-based classifier to demonstrate that four goal-directed real and imaginary movements— all performed by the same hand— can be detected from the MEG signal with high accuracy: > 70% for real movements, and > 60% for imaginary movements. Additional experiments were used to control for possible confounds and to establish the empirical chance level. Investigation of the patterns informing the classification indicated the primary contribution of signals in the alpha (8–12 Hz) and beta (13–30 Hz) frequency range in the contralateral motor areas for the real movements, and more posterior parieto–occipital sources for the imagined movements. The obtained high accuracy can be exploited in practical applications e.g. in brain–computer interface -based motor rehabilitation.