Head Movements Research Articles

Multi-Characteristic Opsin Therapy to Functionalize Retina, Attenuate Retinal Degeneration, and Restore Vision in Mouse Models of Retinitis Pigmentosa.

Retinal degeneration 1 and 10 (rd1 and rd10) mice are useful animal models of retinitis pigmentosa (RP) with rapidly and slowly progressive pathologies, respectively. Our study aims were to determine the effect of adeno-associated viral vector 2 (AAV2)-delivered multi-characteristic opsin (MCO-010; under the control of a metabotropic glutamate receptor-6 promoter enhancer) on the morphological and functional characteristics of vision in both rd1 and rd10 mice. Various retinal measures of MCO-010 transduction and electrophysiological, behavioral, and other routine blood analyses were performed in the rd1 and/or rd10 mice after intravitreal injection of 1µL of MCO-010 or AAV2 vehicle. Functional tests included electroretinogram, visually evoked potential, and behavior assay (optomotor and water maze). Retinal thickness, intraocular pressure, and plasma cytokine levels were also determined. Following intravitreal MCO-010 injection, approximately 80% of bipolar cells were transduced in the retina, and no alterations in retinal thickness were observed at 4months post-injection. However, retinal thickness significantly decreased in control mice. MCO-010 treatment increased head movements and induced faster navigation of mice to the platform in a water-maze test. The MCO-010 gene therapy helped preserve visually evoked electrical response in the retina and visual cortex. No ocular toxicity, immunotoxicity, or phototoxicity was observed in the MCO-010-treated mice, even under chronic intense light conditions. Intravitreal MCO-010 was well tolerated in rd1 and rd10 mice models of RP, and it appeared to attenuate retinal photoreceptor degeneration based on retinal structure and functional outcome measures. As reported here, optogenetic treatment of the inner retina attenuates further retinal degeneration in addition to photosensitizing higher order neurons, and this disease-modifying aspect should be evaluated in optogenetic clinical trials.

Impact of proprioceptive cervical dizziness in chronic neck pain syndromes on gait and stance during active head-turn challenges.

An earlier observational study described selected patients with acute neck pain syndrome, who experienced short bursts of cervical vertigo elicited by rapid head movements. The current study on a larger cohort of 20 patients with chronic or frequently recurring neck pain syndrome and age-matched controls focused on two major questions: (1) Can head movements in subjects with exacerbations of neck pain and restrictions of neck mobility also elicit bursts of vertigo? (2) What is the impact on postural balance measured by analysis of body sway and locomotion? A detailed questionnaire was applied, posture and gait were evaluated by use of instrumented posturography-and gait analysis with and without slow or rapid horizontal head rotations in the yaw plane with and without sight/visual input. All patients reported some or frequent episodes of dizziness in the range of seconds only elicited by rapid, not by slow head movements. Postural sway in patients was unremarkable in undisturbed conditions without head movements, but specifically increased by rapid but not slow head turns. The latter is best explained by the lack of continuous control of velocity and amplitude of saccadic head movements. Gait analysis revealed a slowed and cautious gait pattern already at undisturbed condition that was even exaggerated during rapid head turns. These observations demonstrate that chronic or recurrent neck pain is associated with episodic experiences of dizziness and above results in both chronic and episodic alterations of stance and gait that resemble those described for patients with phobic postural vertigo/persistent postural perceptual dizziness, a functional gait disorder.

Estimation of Interferences in Magnetoencephalography (MEG) Brain Data Using Intelligent Methods for BCI-based Neurorehabilitation Applications

<p>Brain-Computer Interface (BCI) neurorehabilitation offers the potential to improve recovery and quality of life for stroke survivors. It aims to restore lost physical and mental abilities through motor and cognitive therapies. Magnetoencephalography (MEG) signals are a major advancement in BCI technology as they provide accurate and consistent assessments of brain activity for control and interaction applications. MEG is indispensable for recording the magnetic fields produced in the brain during motor imagery tasks due to its capability to evaluate cerebral activity with remarkable temporal resolution. However, one of the major challenges associated with MEG recording is the loss of signal quality due to physiological artifacts and ambient noise. Additionally, the head movement of the individual during the recording process can result in the introduction of artifacts into the recorded data, which can distort the spatial mapping of brain activity. This, in turn, can jeopardize the reliability and accuracy of the results obtained. This study aims to identify the most effective technique for removing artifacts from MEG signals by conducting a comparative performance analysis of prominent denoising algorithms, such as Infomax, FastICA, SOBI, and SWT. The findings conclude that Infomax is the most effective algorithm for removing physiological artifacts from a signal while maintaining the integrity and essential features of the original data. FastICA was found to be the second most effective algorithm. Infomax outperformed FastICA in Power Spectral Density (PSD) and Percentage Root mean square error Difference (PRD) measurements.</p>

Detection of human drowsiness by the vestibulo-ocular reflex compensating for heartbeat-induced head perturbations

The vestibulo-ocular reflex (VOR) stabilizes vision during head movements by counter-rotating the eyes in the orbits. Although considered one of the simplest reflexes due to its minimal neuronal circuity comprising a 3-neuron arc, previous studies have shown that VOR performance deteriorates in both monkeys and humans when they are drowsy. Given constant head perturbations under dynamic environments, the VOR has been proposed as a viable biomarker for detecting human drowsiness in automobiles and other moving vehicles. However, under stationary environments where exogenous head movements are absent, its applicability has been questioned. In this study, we demonstrate that each heartbeat generates small yet distinctive head movements, and the VOR compensates for these minor head perturbations. Furthermore, we show that the effectiveness of VOR responses varies with the degree of drowsiness, indicating that the VOR can serve as an indicator of drowsiness, even in stationary contexts such as in classrooms and offices.

Exploring consumer acceptability of leafy greens in earth and space immersive environments using biometrics

Novel research on food perception is required for long-term space exploration. There is limited research on food/beverage sensory analysis in space and space-simulated conditions, with many studies presenting biases in sensory and statistical methods. This study used univariate and multivariate analysis on data from pick-and-eat leafy greens to assess self-reported and biometric consumer sensory analysis in simulated microgravity using reclining chairs and space-immersive environments. According to ANOVA (p < 0.05), there were significant differences between interaction room × position for head movements; besides, there were non-significant differences in the interaction samples × environment. On the other hand, there were significant differences in the sample×position interaction for all liking attributes. Results from multivariate analysis showed effects on self-reported, physiological, and emotional responses of samples in space-related positions and environments related to sensory perception changes. Non-invasive biometrics could offer a powerful tool for developing digital twins to assess genetically modified plants and plant-based food/beverages for long-term space exploration.

Visual Anomaly Detection via CNN-BiLSTM Network with Knit Feature Sequence for Floating-Yarn Stacking during the High-Speed Sweater Knitting Process

In order to meet the current expanding market demand for knitwear, high-speed automatic knitting machines with “one-line knit to shape” capability are widely used. However, the frequent emergence of floating-yarn stacking anomalies during the high-speed knitting process will seriously hinder the normal reciprocating motion of the needles and cause a catastrophic fracture of the whole machine needle plate, greatly affecting the efficiency of the knitting machines. To overcome the limitations of the existing physical-probe detection method, in this work, we propose a visual floating-yarn anomaly recognition framework based on a CNN-BiLSTM network with the knit feature sequence (CNN-BiLSTM-KFS), which is a unique sequence of knitting yarn positions depending on the knitting status. The sequence of knitting characteristics contains the head speed, the number of rows, and the head movements of the automatic knitting machine, enabling the model to achieve more accurate and efficient floating-yarn identification in complex knitting structures by utilizing contextual information from knitting programs. Compared to the traditional probe inspection method, the framework is highly versatile as it does not need to be adjusted to the specifics of the automatic knitting machine during the production process. The recognition model is trained at the design and sampling stages, and the resulting model can be applied to different automatic knitting machines to recognize floating yarns occurring in various knitting structures. The experimental results show that the improved network spends 75% less time than the probe-based detection, has a higher overall average detection accuracy of 93% compared to the original network, and responds faster to floating yarn anomalies. The as-proposed CNN-BiLSTM-KFS floating-yarn visual detection method not only enhances the reliability of floating-yarn anomaly detection, but also reduces the time and cost required for production adjustments. The results of this study will bring significant improvements in the field of automatic floating-yarn detection and have the potential to promote the application of smart technologies in the knitting industry.

Strategies for motion- and respiration-robust estimation of fMRI intrinsic neural timescales

Abstract Intrinsic neural timescales (INT) reflect the time window of neural integration within a brain region and can be measured via resting-state fMRI (rs-fMRI). Despite the potential relevance of INT to cognition, brain organization, and neuropsychiatric illness, the influences of physiological artifacts on rs-fMRI INT have not been systematically considered. Two artifacts, head motion and respiration, pose serious issues in rs-fMRI studies. Here, we described their impact on INT estimation and tested the ability of two denoising strategies for mitigating these artifacts, high-motion frame censoring and global signal regression (GSR). We used a subset of the HCP Young Adult dataset with runs annotated for breathing patterns (Lynch et al., 2020) and at least one “clean” (reference) run that had minimal head motion and no respiration artifacts; other runs from the same participants (n = 46) were labeled as “non-clean.” We found that non-clean runs exhibited brain-wide increases in INT compared to their respective clean runs and that the magnitude of error in INT between non-clean and clean runs correlated with the amount of head motion. Importantly, effect sizes were comparable to INT effects reported in the clinical literature. GSR and high-motion frame censoring improved the similarity between INT maps from non-clean runs and their respective clean run. Using a pseudo-random frame-censoring approach, we uncovered a relationship between the amount of censored frames and both the mean INT and mean error, suggesting that frame censoring itself biases INT estimation. A group-level correction procedure reduced this bias and improved similarity between non-clean runs and their respective clean run. Based on our findings, we offer recommendations for rs-fMRI INT studies, which include implementing GSR and high-motion frame censoring with Lomb-Scargle interpolation of censored data, and performing group-level correction of the bias introduced by frame censoring.

5155 Benign Paroxysmal Positional Vertigo as a Potential Manifestation of Hypothyroidism, a Case Report

Abstract Disclosure: M. Ahmad: None. M. Herrera: None. D. Deyar: None. P. Gill: None. Introduction: Benign Paroxysmal Positional Vertigo (BPPV) is a vestibular disorder characterized by recurrent episodes of vertigo triggered by specific head movements. The pathophysiology of BPPV involves the displacement of otoconia within the semicircular canals of the inner ear. However, emerging evidence suggests that BPPV may manifest as a consequence of hypothyroidism. In this case report, we present a clinical case of a patient who exhibited symptoms of BPPV and was subsequently diagnosed with hypothyroidism. We aim to elucidate the relationship between these two seemingly distinct conditions and highlight the importance of considering hypothyroidism as a potential cause of BPPV. Case presentation: A 56-year-old female with a past medical history of migraines presented to the hospital for three days of dizziness. She had multiple bouts of vomiting and right-sided headaches similar to her prior migraine headaches. The patient also noted dry skin, constipation, and hair loss for the past few months. The patient’s vital signs were as follows: temperature: 35.6 C, heart rate: 65 beats per minute, blood pressure: 153/66 mmHg, and respiratory rate: 18 breaths per minute. On physical examination, the Dix-Hallpike maneuver was positive on the right side and the patient was noted to have diffusely dry skin. The patient’s labs were significant for the following: sodium: 137 mmol/L, potassium: 3.7 mmol/L, creatinine: 0.88 mg/dL, thyroid stimulating hormone: 31.2 uIU/ml, free T4 ng/dL, hemoglobin: 10.8 g/dL (MCV of 87), and TPO antibody: 2350 IU/ml. The patient underwent CT and MRI imaging of the brain without contrast, which was negative for any acute intracranial abnormalities. The patient was diagnosed with BPPV and hypothyroidism secondary to Hashimoto’s thyroiditis and subsequently underwent vestibular physical therapy with improvement in her vertigo and was started on 112 mcg of levothyroxine daily. She is set to follow up with her primary care provider as an outpatient. Discussion: BPPV generates significant repercussions to an individual’s quality of life, autonomy, and functionality. Hypothyroidism is a common condition characterized by thyroid hormone deficiency that has been linked with a wide array of clinical manifestations, including vestibular disturbances. Autoimmune dysfunction, anomalies of the endolymphatic flow and low perfusion to the inner ear are potential etiological factors that can explain the relationship between thyroid disorders with BPPV. Some studies mention that thyroid autoantibodies could induce autoimmune-complex deposition in the inner ear, which might change the endolymphatic flow and induce BPPV. Raising awareness regarding this potential manifestation of hypothyroidism can guide clinical and diagnostic decisions that improve patient care, especially in individuals under evaluation for BPPV with other symptoms of hypothyroidism. Presentation: 6/3/2024

Investigating the after effect of head-tracked movement on externalization for varying sound sources in binaural synthesis.

Binaural audio reproduction aims to create a realistic auditory experience for the listener through headphones. However, front and rear sound sources are often internalized (perceived inside the head) rather than externalized (perceived outside the head). Previous studies have shown that large head movements can significantly improve externalization, and that this improvement can persist even after head movements have stopped. This study examined how head-tracked movements influence the perception of sound externalization depending on the nature of the sound sources. The stimuli consisted of a first ``head movement" part during which the participants had to either perform a head movement or stay still, followed by a "rating" part where they evaluated externalization without moving. The sound sources during the ``rating" part could be similar, slightly different, or very different from the source during the head movement part. Results demonstrated that head-tracked movements effectively heightened externalization perception for all types of sources immediately after the movement. The type of source (female voice, male voice, or pink noise) did not affect perceived externalization. These findings suggest that the externalization enhancement induced by head-tracked movement persists after the movement ends even when encountering a different sound source.

Azimuth perception of virtual sources in automotive environment : speech and musical stimulus

The spatial reproduction of musical media in car environment presents a number of challenges, due to the fact that the loudspeakers are not symmetrical in relation to the listeners and to the unique acoustic characteristics. In particular, the positioning of virtual sources is difficult to apprehend, unlike in conventional stereophonic configurations. In an initial study, it was shown that an azimuth perception model, the extended energy vector, produced results that were consistent with experimental data. As the first experiment was carried out using only a short burst of pink noise, it seemed interesting to compare this model with other data from more natural signals. New experiments were therefore carried out using audio samples such as speech and instruments. The subjects, seated in the driving seat, pointed their noses in the perceived direction of the signal, their head movements being recorded with a headtracker. The model will be compared with these new experimental data.

A chromatic feature detector in the retina signals visual context changes.

The retina transforms patterns of light into visual feature representations supporting behaviour. These representations are distributed across various types of retinal ganglion cells (RGCs), whose spatial and temporal tuning properties have been studied extensively in many model organisms, including the mouse. However, it has been difficult to link the potentially nonlinear retinal transformations of natural visual inputs to specific ethological purposes. Here, we discover a nonlinear selectivity to chromatic contrast in an RGC type that allows the detection of changes in visual context. We trained a convolutional neural network (CNN) model on large-scale functional recordings of RGC responses to natural mouse movies, and then used this model to search in silico for stimuli that maximally excite distinct types of RGCs. This procedure predicted centre colour opponency in transient suppressed-by-contrast (tSbC) RGCs, a cell type whose function is being debated. We confirmed experimentally that these cells indeed responded very selectively to Green-OFF, UV-ON contrasts. This type of chromatic contrast was characteristic of transitions from ground to sky in the visual scene, as might be elicited by head or eye movements across the horizon. Because tSbC cells performed best among all RGC types at reliably detecting these transitions, we suggest a role for this RGC type in providing contextual information (i.e. sky or ground) necessary for the selection of appropriate behavioural responses to other stimuli, such as looming objects. Our work showcases how a combination of experiments with natural stimuli and computational modelling allows discovering novel types of stimulus selectivity and identifying their potential ethological relevance.

Increased Perception of Head Tilt to Galvanic Vestibular Stimulation Correlates to Motion Sickness Susceptibility in Vestibular Migraine.

Vestibular migraine is associated with vertigo, persistent swaying, tilting, and disorientation, which suggests a heightened sensitivity of the neural mechanisms subserving spatial orientation. Whether a heightened sense of motion to vestibular stimulation in vestibular migraine is associated with sensitivity to visual motion (visual dependency) or physical motion (motion sickness susceptibility) is unclear. The aim of this study was to explore whether a heightened sense of self-motion sensitivity in vestibular migraine is associated with visual dependency or motion sickness susceptibility. This is a prospective cross-over study. Fifteen participants with vestibular migraine and 20 healthy controls (all right handed) were included in this study. In the main experiment, participants were asked to align a rod to the perceived head position. Head tilt was generated by DC galvanic vestibular stimulation at 1 mA to produce head tilts to the right (left anodal/right cathodal stimulation, LA/RC) or left (right anodal/left cathodal, RA/LC). The perception of head tilt was measured in a dark room using laptop software that allowed participants to turn an illuminated rod to any angle about the midpoint. Participants were instructed to align the rod to the perceived head position before and during galvanic stimulation and the line angle was saved. Head position was objectively monitored with an ultrasound motion system. After completing the perceptual test, visual dependency was measured with a static and rotating background and the Motion Sickness Susceptibility Questionnaire (MSSQ) was completed. In an upright head position, without stimulation, the perceived head position was 1.1 degrees in controls and -0.69 degrees in vestibular migraine participants with no significant difference between groups. During galvanic vestibular stimulation, participants with vestibular migraine had an increased perception of head tilt compared with controls (RA/LC: controls -4.7 degrees and vestibular migraine -9.29, p = 0.002; and LA/RC: controls 6.5 degrees and vestibular migraine 11.12 degrees, p = 0.017), although the size of head movement was similar between groups. The average perception of head tilt correlated to the MSSQ score, but not to the degree of visual dependency in a static or moving background. A heightened sensitivity of the vestibular system to vestibular stimulation in vestibular migraine is consistent with reports of self-motion sensitivity in vestibular migraine.

Comparing repeated end range movements and Kinesio taping effects on head and neck movement pattern and discomfort in smartphone users

IntroductionUsing smartphones has increased exponentially, especially after the COVID-19 pandemic. However, there is a relationship between neck flexion angle during working with a smartphone and neck musculoskeletal disorders. Therefore, this study compares the effect of Kinesio taping and Repeated End Range Movements (RERM) on head and neck flexion angles changes during texting and discomfort after texting with a smartphone. MethodTwenty-four smartphone users participated in this cross-over study. All of 24 subjects were experimented with the control, RERM and taping conditions. They filled Numeric Rating Scale questionnaire before and after 30 min of texting. In addition, their head and neck flexion angles were measured during the task by photogrammetry. ResultsIn the RERM group, individuals felt significantly less discomfort than the control group after the task (P = 0.032). Although the slope of average head and neck flexion angles linear regression lines of all the three groups was positive before the break time, it became negative in the taping and control groups after the break time. In addition, the linear regression of average head and neck flexion angles of the RERM group was significantly different (P < 0.001). ConclusionRERM can effectively maintain normal head and neck movement pattern during and reduce neck discomfort after texting on smartphone.

Stress Assessment for Augmented Reality Applications Based on Head Movement Features.

Augmented reality is one of the enabling technologies of the upcoming future. Its usage in working and learning scenarios may lead to a better quality of work and training by helping the operators during the most crucial stages of processes. Therefore, the automatic detection of stress during augmented reality experiences can be a valuable support to prevent consequences on people's health and foster the spreading of this technology. In this work, we present the design of a non-invasive stress assessment approach. The proposed system is based on the analysis of the head movements of people wearing a Head Mounted Display while performing stress-inducing tasks. First, we designed a subjective experiment consisting of two stress-related tests for data acquisition. Then, a statistical analysis of head movements has been performed to determine which features are representative of the presence of stress. Finally, a stress classifier based on a combination of Support Vector Machines has been designed and trained. The proposed approach achieved promising performances thus paving the way for further studies in this research direction.

Artificial Intelligence Can Recognize Whether a Job Applicant Is Selling and/or Lying According to Facial Expressions and Head Movements Much More Correctly Than Human Interviewers

Artificial Intelligence Can Recognize Whether a Job Applicant Is Selling and/or Lying According to Facial Expressions and Head Movements Much More Correctly Than Human Interviewers

Motion Artifact Detection for T1-Weighted Brain MR Images Using Convolutional Neural Networks.

Quality assessment (QA) of magnetic resonance imaging (MRI) encompasses several factors such as noise, contrast, homogeneity, and imaging artifacts. Quality evaluation is often not standardized and relies on the expertise, and vigilance of the personnel, posing limitations especially with large datasets. Machine learning based on convolutional neural networks (CNNs) is a promising approach to address these challenges by performing automated inspection of MR images. In this study, a CNN for the detection of random head motion artifacts (RHM) in T1-weighted MRI as one aspect of image quality is proposed. A two-step approach aimed to first identify images exhibiting pronounced motion artifacts, and second to evaluate the feasibility of a more detailed three-class classification. The utilized dataset consisted of 420 T1-weighted whole-brain image volumes with isotropic resolution. Human experts assigned each volume to one of three classes of artifact prominence. Results demonstrate an accuracy of 95% for the identification of images with pronounced artifact load. The addition of an intermediate class retained an accuracy of 76%. The findings highlight the potential of CNN-based approaches to increase the efficiency of post-hoc QAs in large datasets by flagging images with potentially relevant artifact loads for closer inspection.

A novel deep learning‐based technique for driver drowsiness detection

AbstractEvery year, many people lose their lives because of road accidents. It is evident from statistics that drowsiness is one of the main causes of a large number of car accidents. In our research, we wish to solve this major problem by measuring the drowsiness level of the human brain while driving. The study aims to develop a novel technique to detect different alertness levels (i.e., awake, moderately drowsy, and maximally drowsy) of a person while driving. A hybrid model using a stacked autoencoder and hyperbolic tangent Long Short‐Term Memory (TLSTM) network with attention mechanism is designed for this purpose. The designed model uses different biopotential signals, such as electroencephalography (EEG), facial electromyography (EMG), and different biomarkers, such as pulse rate, respiration rate galvanic skin response, and head movement to detect a person's alertness level. Here, the stacked autoencoder model is used for automated feature extraction. TLSTM is used to predict a person's alertness level using stacked autoencoder network‐extracted features. The proposed model can classify awake, moderately drowsy, and maximally drowsy states of a person with accuracies of 99%, 98.3%, and 98.6%, respectively. The novel contributions of the paper includes (i) incorporation of an attention mechanism into the TLSTM network of the proposed hybrid model to focus on the emphatic states to enhance classification accuracy, and (ii) utilization of EEG, facial EMG, pulse rate, respiration rate, galvanic skin reaction, and head movement pattern to assess a person's alertness level.

Effect of a smartphone-based physical intervention on depression, fitness factors and movement characteristics in adults

BackgroundPhysical activity has been shown to correlate with mental health and a reduction in symptoms of depression. However, the majority of research has focused only on the effects of either aerobic or nonaerobic exercise on depressive symptoms, while the use of novel technological innovations such as mobile phone-based activity programs and their effects on movement characteristics are underrepresented. This study had two objectives: (1) to investigate how effectively 4 weeks of mobile phone-based physical activity can affect depressive scores (CES-10-D and PHQ) and fitness levels and (2) to investigate the whether 4 weeks of mobile phone-based physical activity affected participants’ movement characteristics.MethodsA total of 31 participants were included and divided into an exercise group (n = 21) and a control group (n = 10). The exercise group was instructed to use a mobile phone-based exercise program 5 times per week for 4 weeks. Pre- and post-exercise, the participants’ depression score (CES-10-D, PHQ9), fitness level (YMCA, grip strength) and movement characteristics (postural sway, movement ROM, movement speeds, etc.) for three Azure Kinect physical activity games based on different fitness factors (balance game, cardiovascular game, reaction game) were measured.ResultsMixed model ANOVA revealed significant differences between pre- and post-intervention depression scores on the PHQ9 (P = .001) and CES-10-D (P < .001) in both the exercise group and the control group, but not between groups. In terms of movement characteristics, there was an increase in body sway (P = .045) and vertical head movement (P = .02) in the cardiovascular game jogging condition for the exercise group. In the reaction game, the exercise group showed a significant reduction in the number of mistakes (P = .03). There were no other significant differences for the other variables.ConclusionThe results revealed no differences in the reduction in depression scores between the exercise group and the control group. However, this study showed that a mobile phone-based physical activity intervention affects in-game movement characteristics such as body sway and vertical head movement and therefore may show the potential of using activity-promoting mobile games for improving movement.

Postural Risks in Dental Practice: An Assessment of Musculoskeletal Health.

In recent years, Romania's stomatology private practice sector has seen substantial growth, with many dentists fully committing to building and expanding their own practices, often funded by their personal income. This study aimed to explore how various postures affect the muscle groups of dentists (380), particularly focusing on identifying positions that may jeopardize their musculoskeletal health. A group of dentists effectively participated in this study (10), adhering to their regular work routines while wearing wearable sensors on their backs to monitor posture and activity. The data gathered from these sensors were analyzed using the RULA (rapid upper-limb assessment) and REBA (rapid entire-body assessment) tools. The findings indicated that the head and shoulder movements during dental procedures involved considerable and repetitive angular shifts, which could strain the neck and back muscles and heighten the risk of musculoskeletal problems. Additionally, the standing postures adopted by the dentists were associated with an increased risk of postural issues and greater overall fatigue. Extended periods of trunk and head tilting were also identified as contributing factors to posture-related challenges.

Design of Mechatronics Based Virtual Telepresence for Robotic System Using Raspberry Python Interpreter

A telepresence robot is a remote-controlled, wheeled device with a display to enable video streaming which enable the participants to view remote locations, as if they were there. The project consists of a VR headset, with a smartphone in dual screen to experience virtual reality and 4 wheeled robotic vehicles. The movement of the Robot is controlled using a remote controller. The motion of the camera of the robot is controlled by the accelerometer and magnetometer data processed by Arduino and Raspberry Pi. Video streamed is received by the smartphone using the IP address specified by the Raspberry Pi. This robot with a camera is placed in a remote location to capture the environment in visual form using Raspberry Pi (R Pi).The captured visuals are displayed on the user’s virtual reality (VR) headset. An added feature allows the camera to move in the direction of the user’s head movements. This gives the user a real-time experience as if he is present where the robot is located. The robot can also be moved in any direction through an app installed in the user’s smartphone.