Hand Movement Speed Research Articles

Object Weight and Hand Dominance Impact Kinematics in a Functional Reach-to-Drink Task in School-Aged Children.

This study evaluates the effects of object weight and hand dominance on the end-point kinematics of the hand-to-mouth (withdrawal) movement in a functional reach-to-drink task for typically developing school-aged children. Using 3D motion capture, speed (average velocity and peak velocity), straightness (ratio), and smoothness (number of velocity peaks and log dimensionless jerk) of hand movements were calculated for the withdrawal motion with three different bottle weights (empty, half-filled, and full). Average velocity (550.4 ± 142.0 versus 512.1 ± 145.6 mm/s) and peak velocity (916.3 ± 234 versus 842.7 ± 198.4 mm/s) were significantly higher with the empty versus half-filled bottle and with the non-dominant (average: 543.5 ± 145.2 mm/s; peak: 896.5 ± 207 mm/s) versus dominant (average: 525.2 ± 40.7 mm/s; peak: 864.2 ± 209.2 mm/s) hand. There were no differences in straightness or smoothness. These findings indicate that increasing weight in reach-to-drink task puts greater constraints on the task. The slower movements with the dominant hand might denote better precision control than the non-dominant hand. The quantitative motion capture results show average values for the kinematic variables for a functional reach-to-drink task in a typically developing population of school-aged children with changing weights of the bottles that are relevant to a real-life scenario. These results could inform the design of individualized therapeutic interventions to improve functional upper-extremity use in children with neurodevelopmental motor disorders.

Virtual reality assessment of reaching accuracy in patients with recent cerebellar stroke

BackgroundDysmetria, the inability to accurately estimate distance in motor tasks, is a characteristic clinical feature of cerebellar injury. Even though subjective dysmetria can be quickly detected during the neurological examination with the finger-to-nose test, objective quantification of reaching accuracy for clinical assessment is still lacking. Emerging VR technology allows for the delivery of rich multisensory environmental stimuli with a high degree of control. Furthermore, recent improvements in the hand-tracking feature offer an opportunity to closely examine the speed, accuracy, and consistency of fine hand movements and proprioceptive function. This study aims to investigate the application of virtual reality (VR) with hand tracking in the rapid quantification of reaching accuracy at the bedside for patients with cerebellar stroke (CS).Methods and resultsThirty individuals (10 CS patients and 20 age-matched neurologically healthy controls) performed a simple task that allowed us to measure reaching accuracy using a VR headset (Oculus Quest 2). During this task, the participant was asked to reach for a target placed along a horizontal sixty-degree arc. Once the fingertip passed through the arc, the target immediately extinguished. 50% of the trials displayed a visible, real-time rendering of the hand as the participant reached for the target (visible hand condition), while the remaining 50% only showed the target being extinguished (invisible hand condition). The invisible hand condition isolates proprioception-guided movements by removing the visibility of the participant’s hand. Reaching error was calculated as the difference in degrees between the location of the target, and where the fingertip contacted the arc. Both CS patients and age-matched controls displayed higher average reaching error and took longer to perform a reaching motion in the invisible hand condition than in the visible hand condition. Reaching error was higher in CS than in controls in the invisible hand condition but not in the visible hand condition. Average time taken to perform each trial was higher in CS than in controls in the invisible hand conditions but not in the visible hand condition.ConclusionsReaching accuracy assessed by VR offers a non-invasive and rapid approach to quantifying fine motor functions in clinical settings. Furthermore, this technology enhances our understanding of proprioceptive function in patients with visuomotor disabilities by allowing the isolation of proprioception from vision. Future studies with larger cohorts and longitudinal designs will examine the quantitative changes in reaching accuracy after stroke and explore the long-term benefits of VR in functional recovery.

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.

Digital Marker for Early Screening of Mild Cognitive Impairment Through Hand and Eye Movement Analysis in Virtual Reality Using Machine Learning: First Validation Study.

With the global rise in Alzheimer disease (AD), early screening for mild cognitive impairment (MCI), which is a preclinical stage of AD, is of paramount importance. Although biomarkers such as cerebrospinal fluid amyloid level and magnetic resonance imaging have been studied, they have limitations, such as high cost and invasiveness. Digital markers to assess cognitive impairment by analyzing behavioral data collected from digital devices in daily life can be a new alternative. In this context, we developed a "virtual kiosk test" for early screening of MCI by analyzing behavioral data collected when using a kiosk in a virtual environment. We aimed to investigate key behavioral features collected from a virtual kiosk test that could distinguish patients with MCI from healthy controls with high statistical significance. Also, we focused on developing a machine learning model capable of early screening of MCI based on these behavioral features. A total of 51 participants comprising 20 healthy controls and 31 patients with MCI were recruited by 2 neurologists from a university hospital. The participants performed a virtual kiosk test-developed by our group-where we recorded various behavioral data such as hand and eye movements. Based on these time series data, we computed the following 4 behavioral features: hand movement speed, proportion of fixation duration, time to completion, and the number of errors. To compare these behavioral features between healthy controls and patients with MCI, independent-samples 2-tailed t tests were used. Additionally, we used these behavioral features to train and validate a machine learning model for early screening of patients with MCI from healthy controls. In the virtual kiosk test, all 4 behavioral features showed statistically significant differences between patients with MCI and healthy controls. Compared with healthy controls, patients with MCI had slower hand movement speed (t49=3.45; P=.004), lower proportion of fixation duration (t49=2.69; P=.04), longer time to completion (t49=-3.44; P=.004), and a greater number of errors (t49=-3.77; P=.001). All 4 features were then used to train a support vector machine to distinguish between healthy controls and patients with MCI. Our machine learning model achieved 93.3% accuracy, 100% sensitivity, 83.3% specificity, 90% precision, and 94.7% F1-score. Our research preliminarily suggests that analyzing hand and eye movements in the virtual kiosk test holds potential as a digital marker for early screening of MCI. In contrast to conventional biomarkers, this digital marker in virtual reality is advantageous as it can collect ecologically valid data at an affordable cost and in a short period (5-15 minutes), making it a suitable means for early screening of MCI. We call for further studies to confirm the reliability and validity of this approach.

Poster Session: Reaching Accuracy Assessment in Cerebellar Stroke using Virtual Reality.

To investigate the application of virtual reality (VR) in the rapid quantification of reaching accuracy at the bedside for patients with cerebellar stroke (CS). Dysmetria, the inability to measure distance in muscular tasks correctly, is a common clinical feature of cerebellar injury. Objective quantification of reaching accuracy for clinical assessment is lacking, and the emerging VR technology with hand-tracking offers a promising opportunity to examine the speed, accuracy, and consistency of hand movements and proprioceptive function. 29 individuals (10 CS patients and 19 age-matched not-disabled controls) performed a task measuring reaching accuracy on the VR headset (Oculus Quest 2). 50% of the trials displayed a visible rendering of the hand (visible hand condition), and 50% did not (invisible hand condition). Reaching error was higher in CS compared to age-matched controls in both visible and invisible hand conditions. Reaching error was higher in the invisible hand condition compared to the visible hand condition in healthy controls, right CS, left CS but not in bilateral CS patients. The average time per trial was higher in patients than in controls. VR technology promises to be a non-invasive and rapid approach to quantifying fine motor functions in clinical settings. Further studies are needed to examine quantitative changes in reaching accuracy during post-stroke progression.

Sensor-based Safety Alarm System for Injury Prevention in Chaff cutter machine

Agricultural activities pose significant hazards, leading to high rates of fatal and non-fatal injuries, particularly with the increased use of farm mechanization. One common machine used in farming is the chaff cutter or chaff cutter, which can cause permanent upper limb (hand) injuries during operation. To address this critical issue, a microcontroller-based alarming system was developed to provide advance warning when a worker's hand enters the injury-prone zone of the chaff cutter machine. The system incorporated a passive infrared (PIR) sensor that detected infrared radiation emitted by the human body. Extensive testing was conducted under various conditions, including different distances between the sensor and the human hand, hand orientations, room temperature, and fodder temperature, using subjects of different age groups. The Response Surface Methodology (RSM) was employed to derive regression equations for these parameters. The optimal distance for sensor placement was found to be 125mm ahead of the identified danger zone on the feeding chute. The sensor system showed good response for pronated and supinated hand orientations, followed by the holding position, under different temperature conditions. A temperature range of 24 to 27°C was identified as ideal for optimal sensor performance. Additionally, the speed of hand movement or fodder pushing and the worker's response to the audible frequency were found to effectively alert them. The developed alarming system can significantly enhance safety measures in chaff cutter machines, mitigating the risk of upper limb injuries and improving the overall well-being, productivity, and economic status of farmworkers.

Task-Related Differences in End-Point Kinematics in School-Age Children with Typical Development.

Understanding whether and how children with typical development adapt their reaches for different functional tasks could inform a more targeted design of rehabilitation interventions to improve upper extremity function in children with motor disabilities. This prospective study compares timing and coordination of a reach-to-drink, reach-to-eat, and a bilateral reaching task in typically developing school-aged children. Average speed, straightness, and smoothness of hand movements were measured in a convenience sample of 71 children, mean age 8.77 ± 0.48 years. Linear mixed models for repeated measures compared the variables by task, phases of the reach, task x phase interactions, and dominant versus non-dominant hands. There were significant main effects for task and phase, significant task x phase interactions (p < 0.05), and a significant difference between the dominant and non-dominant hand for straightness. Hand movements were fastest and smoothest for the reach-to-eat task, and least straight for the bilateral reaching task. Hand movements were also straighter in the object transport phases than the prehension and withdrawal phases. These results indicate that children with typical development change their timing and coordination of reach based on the task they are performing. These results can inform the design of rehabilitation interventions targeting arm and hand function.

A Finger Motion Monitoring Glove for Hand Rehabilitation Training and Assessment Based on Gesture Recognition

Hand rehabilitation training and assessment require the frequent participation of professional doctors, which is time-consuming, laborious, and sometime nonquantitative. To liberate both doctors and patients, a finger motion monitoring glove is expected to give a helping hand by monitoring the speed and state of hand movement in real-time. However, the existing finger motion monitoring gloves suffer from low sensitivity, narrow detection range and lack of fabrication consistency. Herein, a highly sensitive finger motion monitoring glove with an intrinsic surface microstructure is fabricated based on the stable printing method, and hand rehabilitation training and assessment are realized with the help of machine learning method. The printed strain sensors achieve a high strain sensitivity (GF reaches 100.69 at 30–50% strain), wide response range (0.1–50%), fast response/recovery speed (71.43/178.49 ms), satisfied durability (function properly after 5000 cycles), and low batch-to-batch variation (within 0.10). These advantages enable the printed sensors to monitor the finger movements quickly and comprehensively, thus making it practicable for hand rehabilitation training and assessment. This work provides a simple and stable method to obtain highly sensitive finger motion monitoring glove, which is expected to make hand rehabilitation training and assessment more convenient and reliable.

Reaching Accuracy Assessment in Cerebellar Stroke using Virtual Reality (S20.010)

<h3>Objective:</h3> To investigate the application of virtual reality (VR) in the rapid quantification of reaching accuracy at the bedside for patients with cerebellar stroke (CS). <h3>Background:</h3> Dysmetria, the inability to measure distance in muscular tasks correctly, is a characteristic clinical feature of cerebellar injury. Even though dysmetria can be quickly detected during the neurological examination with the finger-to-nose test, objective quantification of reaching accuracy for clinical assessment is still lacking. Emerging VR technology, together with recent improvements in the hand-tracking feature, offers an opportunity to closely examine the speed, accuracy, and consistency of fine hand movements and proprioceptive function. <h3>Design/Methods:</h3> 29 individuals (10 CS patients and 19 age-matched not-disabled controls) performed a task measuring reaching accuracy on the VR headset (Oculus Quest 2). 50% of the trials displayed a visible rendering of the hand as the participant reached for the target (visible hand condition), while the remaining 50% only showed the target being extinguished (invisible hand condition). Reaching error was calculated as the difference in degrees between where the fingertip passed the arch and where the target was positioned. <h3>Results:</h3> Reaching error was higher in CS than in age-matched controls in both visible hand and invisible hand conditions. Reaching error was higher in the invisible hand condition than in the visible hand condition in both healthy controls and CS patients. Average time taken to perform each trial was higher in CS patients than in controls in both visible and invisible hand conditions. <h3>Conclusions:</h3> Reaching accuracy assessed by VR promises to be a non-invasive and rapid approach to quantifying fine motor functions in clinical settings. In addition, this device has the potential to be a useful supplemental technology in monitoring fine motor and proprioceptive functions during physical rehabilitation. Further studies are needed to examine quantitative changes in reaching accuracy during post-stroke progression. <b>Disclosure:</b> Mr. Du has nothing to disclose. Mr. Benavides has received personal compensation for serving as an employee of Stryker. Emily Isenstein has received research support from Autism Science Foundation. Prof. Tadin has received personal compensation in the range of $50,000-$99,999 for serving as a Consultant for NeuroTrainer. Prof. Tadin has received personal compensation in the range of $10,000-$49,999 for serving as an Expert Witness for Finkelstein and partners. The institution of Prof. Tadin has received research support from NIH. The institution of Prof. Tadin has received research support from The Brain &amp; Behavior Research Foundation. Prof. Tadin has received intellectual property interests from a discovery or technology relating to health care. Dr. Busza has received research support from NIH/NINDS. Dr. Busza has received personal compensation in the range of $500-$4,999 for serving as a Grant reviewer with NIH.

Kinect-based objective evaluation of bradykinesia in patients with Parkinson's disease.

To quantify bradykinesia in Parkinson's disease (PD) with a Kinect depth camera-based motion analysis system and to compare PD and healthy control (HC) subjects. Fifty PD patients and twenty-five HCs were recruited. The Movement Disorder Society-Sponsored Revision of the Unified Parkinson's Disease Rating Scale part III (MDS-UPDRS III) was used to evaluate the motor symptoms of PD. Kinematic features of five bradykinesia-related motor tasks were collected using Kinect depth camera. Then, kinematic features were correlated with the clinical scales and compared between groups. Significant correlations were found between kinematic features and clinical scales (P < 0.05). Compared with HCs, PD patients exhibited a significant decrease in the frequency of finger tapping (P < 0.001), hand movement (P < 0.001), hand pronation-supination movements (P = 0.005), and leg agility (P = 0.003). Meanwhile, PD patients had a significant decrease in the speed of hand movements (P = 0.003) and toe tapping (P < 0.001) compared with HCs. Several kinematic features exhibited potential diagnostic value in distinguishing PD from HCs with area under the curve (AUC) ranging from 0.684-0.894 (P < 0.05). Furthermore, the combination of motor tasks exhibited the best diagnostic value with the highest AUC of 0.955 (95% CI = 0.913-0.997, P < 0.001). The Kinect-based motion analysis system can be applied to evaluate bradykinesia in PD. Kinematic features can be used to differentiate PD patients from HCs and combining kinematic features from different motor tasks can significantly improve the diagnostic value.

Interpretable functional specialization emerges in deep convolutional networks trained on brain signals

Objective. Functional specialization is fundamental to neural information processing. Here, we study whether and how functional specialization emerges in artificial deep convolutional neural networks (CNNs) during a brain–computer interfacing (BCI) task. Approach. We trained CNNs to predict hand movement speed from intracranial electroencephalography (iEEG) and delineated how units across the different CNN hidden layers learned to represent the iEEG signal. Main results. We show that distinct, functionally interpretable neural populations emerged as a result of the training process. While some units became sensitive to either iEEG amplitude or phase, others showed bimodal behavior with significant sensitivity to both features. Pruning of highly sensitive units resulted in a steep drop of decoding accuracy not observed for pruning of less sensitive units, highlighting the functional relevance of the amplitude- and phase-specialized populations. Significance. We anticipate that emergent functional specialization as uncovered here will become a key concept in research towards interpretable deep learning for neuroscience and BCI applications.

High proprioceptive acuity in slow and fast hand movements.

We can accurately reach to touch our index fingertip to various points on the body without vision. Awareness of location/motion of the index fingertip and other body parts through proprioception is required for such movements. Proprioception involves processing sensory information, but it is also debated whether internal model estimates of body state from motor commands improve proprioception. We tested the hypothesis that proprioceptive errors increase with increases in speed of hand movement and whether an internal model contributes to more accurate proprioception, especially in higher speed movements. Ten subjects made voluntary reaching movements with their dominant arm to touch its index-tip to the index-tip of the non-dominant arm that was moved passively or actively at three speeds (slow, comfortable, fast) in various directions. Four conditions required the experimenter to passively move the subject's target arm at slow, comfortable and fast speeds and in different directions. A fifth condition required the subject to actively move both arms to perform the task. Subjects performed these tasks with high accuracy during slow and comfortable speed movements of the target arm. Errors averaged 3.7mm larger when the target was moved faster and were equivalent to errors for slower movements (p < 0.014). Errors in the active and passive target movement conditions were also equivalent (p < 0.001). These findings show that proprioception is accurate across many different speeds of passive and active target motion and that there was no evidence than an internal model contributes to improved accuracy of proprioception during active movements.

Research on Speed and Acceleration of Hand Movements as Command Signals for Anthropomorphic Manipulators as a Master-Slave System

Due to threats to human safety, remotely controlled manipulators are more and more often used to carry out rescue tasks in hazardous zones. To ensure high efficiency and productivity of their work, intuitive control systems are necessary, e.g., master-slave and drive systems that maximize the speed of working movements by copying the movements of the operator’s hands and are adapted to human perception and capabilities. Proper design of manipulator drive and control systems, therefore, requires knowledge of the acceleration and velocity of hand movements as signals controlling manipulators. This paper presents the results of tests of speed and acceleration in the implementation of the hand when making precise movements and moving objects over short distances (0.4–0.5 m) and during relatively long-distance reaching movements (0.73–0.93 m). Research has shown that, at short distances, the hand movements do not reach the maximum speed, while at longer distances, there is a period of constant maximal speed. In addition, studies have shown that the maximum speed of manipulation movements (longitudinal, lateral, and vertical) does not depend on the direction of movement. Moreover, precise movements were performed at a much slower velocity than reaching movements.

Model to Relationship the Speed of Hand Movements with the SEMG Signals from the Forearm

Various previous works have sought to achieve hand prostheses with natural movements. It has been employed tools to recognize patterns of surface electromyographic signals associated with each move. Although many successful studies classify some types of hand movements with high performance, the results show that the speed and strength must be analyzed so that the resulting moves are like those of a natural hand. This study evaluates 23 healthy subjects at two different speeds and six types of movements (pronation, supination, ulnar deviation, radial deviation, flexion, and extension -276 records of SEMG and Velocity). The objective was to obtain a model (transfer function) that would allow the relation of the velocity profiles with the tone of the forearm SEMG signals. The results show models with an average RMSE of 18.55% for slow movements using low-order systems (2). The parameters of the models between subjects are very different, with high coefficients of variation and standard deviations, which implies that the fitting must be for each subject.

Remote Evaluation of Parkinson's Disease Using a Conventional Webcam and Artificial Intelligence.

Objective: This study aimed to prove the concept of a new optical video-based system to measure Parkinson's disease (PD) remotely using an accessible standard webcam.Methods: We consecutively enrolled a cohort of 42 patients with PD and healthy subjects (HSs). The participants were recorded performing MDS-UPDRS III bradykinesia upper limb tasks with a computer webcam. The video frames were processed using the artificial intelligence algorithms tracking the movements of the hands. The video extracted features were correlated with clinical rating using the Movement Disorder Society revision of the Unified Parkinson's Disease Rating Scale and inertial measurement units (IMUs). The developed classifiers were validated on an independent dataset.Results: We found significant differences in the motor performance of the patients with PD and HSs in all the bradykinesia upper limb motor tasks. The best performing classifiers were unilateral finger tapping and hand movement speed. The model correlated both with the IMUs for quantitative assessment of motor function and the clinical scales, hence demonstrating concurrent validity with the existing methods.Conclusions: We present here the proof-of-concept of a novel webcam-based technology to remotely detect the parkinsonian features using artificial intelligence. This method has preliminarily achieved a very high diagnostic accuracy and could be easily expanded to other disease manifestations to support PD management.

Measuring age differences in executive control using rapid motor decisions in a robotic object hit and avoid task.

Age-related declines in executive control are commonly assessed with neuropsychological tests that also rely on sensory and motor processes that are not typically measured in those tasks. It is therefore difficult to isolate the cognitive contributions from sensorimotor contributions to performance impairments. Rapid motor decision-making tasks may also be sensitive to age differences in executive control but allow for the measurement of sensorimotor contributors to task performance. Recently developed object hit (OH) and object hit and avoid (OHA) tasks using a robotic manipulandum are sensitive to motor and cognitive aspects of performance in stroke and Parkinson's disease. However, the impact of healthy aging, and the specific cognitive mechanisms involved in these tasks has not been assessed. We administered the OH and OHA tasks to 77 younger and 59 healthy older adults to evaluate the relative age differences in the perceptual-motor/sensory, movement coordination, and cognitive measures of performance. The Trail Making Test (TMT) Parts A and B were administered to assess the extent to which the cognitive contributors to OHA task performance are associated with executive functioning. After controlling for hand movement speed, age differences were largest for cognitive measures, with smaller differences in perceptual-motor speed and sensory measures, and little differences in bimanual and spatial coordination measures of performance. The cognitive measures were associated with executive functioning measures from the TMT task. These findings provide evidence that rapid motor decision-making tasks are sensitive to age differences in executive control and can isolate the cognitive from the sensorimotor contributions to task performance. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Hand movement speed in advertising elicits gender stereotypes and consumer responses

AbstractMerely observing the hand movement speed with an advertised product can affect consumer perception. Five studies show that hand movement speed when observed (e.g., watching or even reading the description of slow vs. fast hand interaction with a product) elicits distinct associations in the consumer's mind and affects their responses. We suggest that people implicitly associate speedy movements with a more masculine (than feminine) behavior and use hand movement speed as an input to form evaluations of a touched product. Additionally, we demonstrate that consumers elicit higher product preference when their associations from observed hand movement speed match their own social identity. Thus, female (than male) consumers would prefer an advertised product that is depicted with a gentle (instead of speedy) hand movement—as such observed movement makes, both, the product, and the action‐performer be perceived as more feminine. We find support for these effects across different product and advertising contexts. Our findings provide novel evidence on the effect of observed and described hand movements as a signal of gender identity and have significant implications for advertising.

Differences in the Level of Functional Fitness and Precise Hand Movements of People with and without Cognitive Disorders

ABSTRACT Objectives to assess and compare the gross and fine motor skills in people with identified cognitive impairment and in people from the control group. Method The research was conducted at the Center of Dementia-Related Diseases, involved participants with (n = 39) and without (n = 29) cognitive disorders. Fast, precise hand movements were measured via Vienna System Test. The up-and-go, chair-stand, 6-minute walk tests were used to assess functional fitness. The results for participants with and without cognitive disorders were compared. Results People from both groups do not differ significantly in terms of the level of condition-based functional fitness. Participants with cognitive disorders achieve worse results in hand coordination tests which are more complex and require both speed and accuracy of hand movements. Discussion The deterioration of precise hand movements with the correct functional efficiency may indicate degenerative changes in brain areas associated with complex thought processes, conceptual thinking, and may lead to dementia.

Automatic Water Dispenser Based on Hand Gesture Detection Using Arduino

Water dispensers are electronic devices that are widely available in households and offices. In general, water dispensers use faucets to drain water. During the pandemic, many people avoid touching equipment used by many people. Various ways have been done so that the water dispenser can be operated automatically without touching the faucet. Previous research on water dispensers was only applied to one type of water. This study aims to make an automatic water dispenser without touching the faucet used for two types of water, namely hot water or cold water. This research is based on hand gesture detection to choose hot water or cold water. The APDS-9960 gesture sensor detects hand movements to select hot or cold water, and then a servo motor is used to open the water faucet. After that, the position of the glass is validated by the ultrasonic sensor HC-SR04, and water will flow for 30 seconds into the glass. The entire input and output process is controlled using Arduino. The results show that this automatic water dispenser can detect hand gestures at a maximum distance of 15 cm with a hand movement speed of 2 to 3.7 seconds. This automatic water dispenser can detect three kinds of glass, namely ceramic, clear glass, and plastic, at a distance of 1 to 3 cm, and the volume of water flowing for 30 seconds is 240 ml.

Morphological Characteristics as a Predictive Factor of Biotic Motor Knowledge

On a sample of a total of 56 subjects, divided into two subsamples: 30 boys and 26 girls, measurements of 8 anthropometric measures and assessment of biotic motor knowledge were performed using the motor test “The Test of Gross Motor Development” (TGMD-2). The aim of the study was to determine gender differences in morphological characteristics between preschool children as well as the association of morphological characteristics with the motor test for the assessment of biotic motor skills for both sexes. The results of the research indicate the existence of statistically significant differences between boys and girls in morphological characteristics. These differences were manifested in the motor test, transverse dimensionality and subcutaneous fat in favor of boys, and in longitudinal dimensionality and volume and mass in favor of girls. It was further determined that the system of predictor variables in girls was significantly related to the variable for the assessment of biotic motor knowledge, while this cannot be stated for boys. This linearity of results indicates that the percentage of body fat in the body reduces the ability of girls to achieve better results in the motor test, which is largely under the control of the movement structuring system. It should also be noted that it would be good to use these types of motor tests with preschool children in which the child repeatedly confronts different situations on the one hand, and on the other hand improves the speed of alternative hand movements, explosive power, and a good part of the structuring mechanism. movements.