Hand Location Research Articles

Accuracy of Postures Predicted Using a Digital Human Model During Four Manual Exertion Tasks, and Implications for Ergonomic Assessments

OCCUPATIONAL APPLICATIONSVirtual ergonomic analyses can be used to guide significant engineering decisions and, therefore, must provide an accurate representation of real-world scenarios. To increase realism and reduce simulation effort, the Jack™ software includes the ability to predict postures that individuals would adopt when performing diverse tasks. In four simulation manual exertion tasks, we compared joint angles and the outputs of ergonomic analyses that were based on the postures produced by humans and those predicted the Jack™ software. Not all postures were well predicted by the software. This work shows the sensitivity of ergonomic tools to posture accuracy and highlights the importance of professional judgement when simulating human behaviors to ensure good decisions when evaluating work-task designs. Additionally, this work may help to refine the prediction algorithms within tools like Jack™.TECHNICAL ABSTRACT Background: To help control work-related injuries, numerous companies have elected to use computer-aided digital human simulation tools to identify and resolve ergonomic issues early in the design of the products and processes. To reduce the burden of manually posturing a digital, the Jack™ Human Simulation Software has incorporated posture prediction capabilities. Purpose: The purpose of this study was to compare the results obtained from Jack™ (version 8.0.1), in terms of joint angles and ergonomic analyses, using postures obtained from lab-based manual tasks to those predicted by the software. Methods: Twenty-seven participants completed four hand-exertion tasks. Surface marker data were transferred into the Jack™ software, which were used to reconstruct whole-body postures. Using the same foot restriction zone, hand location(s) and force efforts, the whole-body postures were also predicted using the software. Joint angles and outputs of ergonomic analyses (lumbar spine forces and joint strength capabilities) generated using the two methods were compared. Results: Significant differences between “real” and predicted postures were found for some joint angles, lumbar spine forces, and joint strength capabilities. Specifically, in the four tasks at least 5 of the 10 joints examined had significantly different angles between the real and predicted postures, with the largest difference being 40.4°. Among 15 correlations assessed, only one strong positive relationship was found between the real and predicted postures. Conclusions: An important difference in posturing strategy was noted between the real and predicted results. Our work illustrates the importance of accurate manikin postures when ergonomically evaluating work-related tasks within a digital environment, and emphasizes the need for professional judgement when simulating human behaviors even if the software has the capability to predict such behaviors. While posture prediction capabilities in Jack™ are a good way to rapidly, and repeatably create a posture, much more work is needed to understand complex movements and motion sequences, to improve existing prediction algorithms and more accurately represent human behaviors within a digital environment.

Lower body bracing behaviours during externally supported tasks with extended reaches

In many jobs, objects in the task environment can restrict a worker's posture, by constraining how close their body is to the object being acted on. Although this provides an obstacle for the worker, these objects can be used to externally support their body by means of lower body bracing. The purpose of this study was to determine when participants would brace, and to quantify the amount of force used for bracing. At four task hand locations, participants performed six exertions, with all combinations of two forces and three directions, and participants chose whether they braced or not. Participants were twice as likely to brace when the task had a far reach. Average brace forces were 117 N for upwards and pulling exertions, and 67 N for downward exertions. These data can be used to guide the prediction of external forces during work simulation and proactive ergonomics assessments.

Examining upper extremity muscle demand during selected push-up variants

Push-ups are regularly adapted for specific muscular demands. The push-up plus (PUP) has been used to emphasize serratus anterior activation. Alterations in body posture have been suggested for targeted activation of muscles surrounding the shoulder and rotator cuff, but little data exists to inform these changes. The purpose of this research was to examine upper extremity muscle activity changes when varying aspects of the PUP. Healthy male participants (n = 20) performed PUP trials using combinations of hand contact area (knuckles/palms), humeral rotation (neutral/60° internal/60° external) and hand location (shoulder height/±30% arm length superior/inferior) at a fixed cadence. Electromyographic (EMG) activation from 14 muscles of the right upper extremity during each trial were examined, as well as ratios of activation for 7 muscle pairs. Palm contact and a superior hand location increased serratus activation, but absolute EMG changes between these effects were marginal (standard error = 3.8). Each independent variable altered mean EMG for most muscles as a main effect, with the largest changes in infraspinatus, upper and lower trapezius, and anterior deltoid. Altering the standard PUP does not seem to enhance serratus activation, but diverse responses in other muscles have training implications. These changes in muscle strategies can tailor the use of modified PUP exercises to target individual muscles or groups.

Attenuation of access to internal states in high obsessive-compulsive individuals might increase susceptibility to false feedback: Evidence from a visuo-motor hand-reaching task

Background and objectivesThe Seeking Proxies for Internal States (SPIS) model of obsessive-compulsive disorder (OCD) posits that obsessive-compulsive (OC) individuals have attenuated access to their internal states. Hence, they seek and rely on proxies, or discernible substitutes for these internal states. In previous studies, participants with high OC tendencies and OCD patients, compared to controls, showed increased reliance on external proxies and were more influenced by false feedback when judging their internal states. This study is the first to examine the effects of false feedback on performance of hand movements in participants with high and low OC tendencies. MethodThirty-four participants with high OC tendencies and 34 participants with low OC tendencies were asked to perform accurate hand reaches without visual feedback in two separate sessions of a computerized hand-reaching task: once after valid feedback training of their hand location and once with false-rotated feedback. We assessed the accuracy and directional adaptation of participants’ reaches. ResultsAs predicted, high OC participants evidenced a larger decrease in their hand positioning accuracy after training with false feedback compared to low OC participants. LimitationsThe generalization of our findings to OCD requires replication with a clinical sample. ConclusionsThese results suggest that in addition to self-perceptions, motor performance of OC individuals is prone to be overly influenced by false feedback, possibly due to attenuated access to proprioceptive cues. These findings may be particularly relevant to understanding the distorted sense of agency in OCD.

Design of a Haptic-Gripper Virtual Reality System (Hg) for Analyzing Fine Motor Behaviors in Children with Autism

Fine motor skills, including grasping, manipulating, and reaching for objects, are a documented weakness for many children with Autism Spectrum Disorders (ASD). However, relatively less research has attempted to address these motor deficits, especially by taking advantage of advanced technology. To explore potential mechanisms for expanding accessibility to fine motor intervention for people with ASD, we present the design and implementation of a feasibility study of a novel Haptic-Gripper Virtual Reality System (Hg). Hg is capable of providing analysis and practice opportunities of fine motor skills in an adaptive and low-cost virtual environment with real-time auditory, visual, and haptic feedback. The Haptic Gripper in Hg can detect a user's grip force and hand location and provide haptic feedback to guide hand movement and grip control while completing several simple and engaging virtual fine motor tasks. We conducted a feasibility study with six children with ASD and six typically developing (TD) children and found that participants were interested in using the Haptic Gripper and could quickly get used to the system. Although the results are preliminary and limited, we observed medium to strong correlations between the proposed fine motor skill metrics and the scores achieved with a standardized fine motor skill test and improvements of participants in accuracy and steadiness of movement and force control. This study provides important guidance for future investigations of the Hg's potential for assessing and improving fine motor manipulation skills.

A Novel Finger Illusion Reveals Reduced Weighting of Bimanual Hand Cortical Representations in People With Complex Regional Pain Syndrome

Complex regional pain syndrome (CRPS) is associated with deficits in sensorimotor control. Herein we have used a novel finger illusion to investigate whether CRPS is associated with reduced weighting of bimanual hand representations. The illusion normally induces a compelling feeling that the hands are close together when in fact they are 12 cm apart. People with CRPS and age, gender, and dominant hand-matched controls tested the illusion in the midline then on either side of the midline. The illusion had 2 variants; the passive pincer-grip position, without contact (no grasp condition) and with contact (grasp condition) of the artificial finger. The primary outcome was the perceived vertical distance between the index fingers. Twenty people with CRPS and 20 controls participated (mean age 44.4 ± 11.7 years). During the no grasp condition, participants with CRPS perceived the vertical distance significantly closer to the actual 12 cm (mean 8.0 cm, 95% confidence interval 6.5-9.5 cm), than controls did (mean 6.4 cm, 95% confidence interval 5.5-7.2 cm]). That is, the illusion was weaker in people with CRPS than in controls during no grasp. There was no such difference during grasp; that is, both groups showed the predicted illusion response. There was no effect of hand placement relative to midline or relative to the opposite hand. We conclude that people with unilateral CRPS have lower weighting of bimanual hand representation than controls have, independent of hand location. However, adding additional cutaneous input returns those with CRPS to the expected performance. We suggest the results have clear clinical and research implications. PerspectiveAn abnormal weighting of bilateral hand representation may reflect a vulnerability for chronic CRPS, an adaptation to the disease, and/or a potential therapeutic target. That addition of cutaneous input immediately normalizes the problem points to the possible role of bimanual tasks in prevention or rehabilitation.

Predicting Sagittal Plane Lifting Postures From Image Bounding Box Dimensions.

A method for automatically classifying lifting postures from simple features in video recordings was developed and tested. We explored if an "elastic" rectangular bounding box, drawn tightly around the subject, can be used for classifying standing, stooping, and squatting at the lift origin and destination. Current marker-less video tracking methods depend on a priori skeletal human models, which are prone to error from poor illumination, obstructions, and difficulty placing cameras in the field. Robust computer vision algorithms based on spatiotemporal features were previously applied for evaluating repetitive motion tasks, exertion frequency, and duty cycle. Mannequin poses were systematically generated using the Michigan 3DSSPP software for a wide range of hand locations and lifting postures. The stature-normalized height and width of a bounding box were measured in the sagittal plane and when rotated horizontally by 30°. After randomly ordering the data, a classification and regression tree algorithm was trained to classify the lifting postures. The resulting tree had four levels and four splits, misclassifying 0.36% training-set cases. The algorithm was tested using 30 video clips of industrial lifting tasks, misclassifying 3.33% test-set cases. The sensitivity and specificity, respectively, were 100.0% and 100.0% for squatting, 90.0% and 100.0% for stooping, and 100.0% and 95.0% for standing. The tree classification algorithm is capable of classifying lifting postures based only on dimensions of bounding boxes. It is anticipated that this practical algorithm can be implemented on handheld devices such as a smartphone, making it readily accessible to practitioners.

Maturity prediction of banana with different bunch cover materials using artificial neural networks

Banana production in the northern part of Thailand has grown rapidly in the past few years. Bunch cover is the common practice to prevent disease infection, damage of insects and thermal damage. Generally, the commercial bunch cover comprises several layers of the material, i.e., thin non-woven, paper, foam, and plastic sheet. Unfortunately, after harvesting, the excessive amount of the bunch cover materials cannot be reused and is always dumped as a landfill which might lead to environmental problems soon. In order to reduce waste, the recyclable non-woven based material (Tyvek) was proposed to be used as a new bunch cover material. The experiment was conducted at the banana orchard in Phaya Meng Rai District, Chiang Rai Province during December 2016-February 2017. Bananas were harvested at 15 weeks after inflorescence emergence. Image analysis was employed to analyze banana qualities, i.e., color and fruit roundness. The results showed that the temperature inside the bag played an important role for fruit maturity. The fruit weight, length, pulp/peel ratio and fruit roundness of the commercial bunch cover were significantly greater than those of Tyvek bunch cover (P>0.05). As for the lightness of banana, the Tyvek bunch cover provided a significantly darker peel color (P<0.05) due to a greater amount light penetration intensity. In terms of maturity prediction, the predicting models were developed using artificial neural network. Temperature, bunch cover type, light intensity and hand location were assigned to be the inputs. On the other hands, hand weight, color, fruit length and peel/pulp ratio and fruit roundness were used as the outputs. The different model architectures, number of nodes, and learning functions were tested. The results showed that the developed 'Wardnet' with 3-hidden-layer provided the best prediction results with highest R2 (0.97-0.98) and with lowest SE (0.01-0.04).

Roles of the right temporo-parietal and premotor cortices in self-location and body ownership.

In the rubber hand illusion (RHI), the feeling that a fake hand belongs to oneself can be induced by the simultaneous, congruent touch of the fake visible hand and one's own hidden hand. This condition is also associated with a recalibration of the perceived location of the real hand. A cortical network, including premotor and temporo-parietal areas, has been proposed as the basis of the RHI. However, the causal contribution of these areas to the discrete illusory components remains unclear. We used transcranial direct current stimulation (tDCS) to assess the contribution of the right premotor cortex (rPMc) and the right temporo-parietal junction (rTPJ) to the RHI and explored the role of these areas in modulating the subjective experience of embodiment and the misperception of the hand position. We found that anodal tDCS of both rPMc and rTPJ increased the misjudgement of the real hand location towards the fake hand. Crucially, the difference in proprioceptive displacement evoked by the congruent and incongruent visuo-tactile stroking was minimised when tDCS was applied over the rPMc, while it was amplified when the rTPJ was targeted. The parietal effects of tDCS also extended to the self-report components of the RHI. These findings suggest that the tDCS of rTPJ modulates the RHI depending on the temporal congruency of the visuo-tactile stimulation, while the tDCS of rPMc induces a general recalibration of hand coordinates, regardless of the visuo-tactile congruency. The present results are discussed in the view of a multicomponent model of the RHI.

Feeling but not seeing the hand: Occluded hand position reduces the hand proximity effect in ERPs.

The hand proximity effect (nearby hands influence visual processing) reflects the integration of vision and proprioception for upcoming action; it is reduced when hand position is occluded. In an ERP study, we investigate whether hand proximity, without vision of the hand, accentuates the processing of stimuli requiring actions (targets) early (N1) and later (P3) in processing. In a go/no-go paradigm, participants viewed stimuli between two panels with hands placed near or far from stimuli. Occlusion of the hand eliminated near-hand target vs. non-target differentiation of the N1; amplification of near-hand target amplitudes emerged at the P3. Visual hand location appears necessary to draw visual attention to intended-action objects to integrate body and visual information early in processing. The integration of visual stimulus information and hand position from proprioception appears later in processing, indicating greater reliance on cognitive systems for discriminating the task-relevance of a stimulus.

Smartphone Carrying Location and Accuracy of Popular Pedometer Smartphone Apps While Jogging

Common pedometer smartphone apps suggest multiple acceptable sites where the phone may be carried to obtain accurate measurements, however, the phone location may have a significant effect on outcomes reported by the app. PURPOSE: To determine if smartphone location has a significant effect on the accuracy of popular smartphone pedometer applications at measuring steps while jogging on a treadmill. METHODS: Fifty-two participants (Mean±SD; 22.9±4.2yrs; BMI: 24.8±4.1kg/m2; 22 Male, 30 Female) consented to jog on a treadmill at 5 mph for 500 steps, while using a pedometer and 4 smartphones placed in commonly used locations (pocket, armband, waistband, hand). All smartphones were simultaneously running 5 applications throughout the trial: Moves, Google-Fit (G-Fit), Runtastic, Accupedo, and S-Health. Steps were verified using a hand tally counter. Zero, negative, and significant outlier values were replaced with the mean value for that app. A separate one-way repeated measures ANOVA was used for each app with the pedometer and tally counter. Significance was set at p<0.05. Pairwise comparisons with Bonferroni corrections were used for post-hoc analysis. Mean bias scores were calculated between the step count for each app and the tally counter. RESULTS: Repeated Measures ANOVA detected significant differences between phone locations and the step counter for only G-Fit (p=0.029) and Runtastic (p<0.01), while other apps were not different by location (p>0.05 for all). Using pairwise comparisons, G-Fit did not show significant differences with the tally counter for any of the phone locations (p>0.05) while pairwise comparisons for Runtastic indicated differences between the arm and hand locations compared to the tally counter (p=0.039, p=0.007, respectively). Lowest mean bias values for each app were as follows: Moves, arm (mean bias ± SD; 5.8 steps ± 103.3 steps); G-Fit, hand (13.7 ± 135.4); Runtastic, waist (8.6 ± 37.1); Accupedo, pocket (17.5 ± 46.9); S-Health, arm (-1.6 ± 26.9). CONCLUSION: The pocket, arm, waist, and hand all appear to produce relatively accurate step counts for pedometer smartphone apps while jogging on a treadmill. Although the phone location and app did not result in significant changes in step count, some locations and apps were better at providing more consistent and accurate results.

Short-term visual deprivation boosts the flexibility of body representation

Short-term visual deprivation by blindfolding influences tactile acuity and orientation in space and, on a neural level, leads to enhanced excitability of visual and motor cortices. However, to the best of our knowledge, the possible effects of short-term visual deprivation on body representation have not been examined. In the present study, we tested two groups of 30 healthy participants with the somatic rubber hand illusion, a well-established paradigm to probe the dynamic plasticity of body representation. Before the start of the procedure, the experimental group was blindfolded for 120 minutes, while the control group wore transparent goggles for the same amount of time. We found that although there was no difference in the subjective feeling of ownership of the rubber hand during the illusion, the blindfolded group showed a significantly larger recalibration of hand position sense towards the location of the rubber hand than the control group. This finding suggests that short-term visual deprivation boosts plasticity of body representation in terms of multisensory spatial recalibration of hand position sense.

Fast hand and finger detection algorithm for interaction on smart display

This paper presents a fast and accurate hand detection and tracking algorithm for depth images using an object contour analysis for which pairs of horizontal edges of an object are used. The computational complexity for generating the pairs of edges and estimating the object shape is low. A shape analysis is performed in the raster scan order and an object which can be a finger is selected as the finger candidate during the line scan. The initial hand is detected by searching for an object with a shape that corresponds to the predefined shape of the initial hand in the entire depth range. Thus, there is no assumption of the hand location throughout the depth, and the cluttered background has little effect on the detection. This paper also proposes a low-complexity hand tracking method which achieves 97.11% in the detection rate and 10.39 ms in the average processing time for a frame, respectively, when a single hand is detected. For five hands, the average processing time is 21.15 ms. These results show that the proposed method can be applied to the real-time application of vision-based HCI especially for smart display.

The effect of task characteristics on the choice to lean, upper body postures and joint loading during simulated automotive manufacturing tasks with one-handed, sub-maximal exertions

The purpose of this study was to investigate the preferred leaning postures, while giving participants the choice to lean when completing tasks with constrained reaches. Twenty female participants completed a variety of different exertions with and without a leaning surface available. The frequency of choice to lean changed with task hand location. The long reaching task hand locations resulted in the most frequent choice to lean, and this decreased trunk and task arm shoulder loading, while allowing the participant to get closer to the task. These findings will be of use to industry in validating future leaning posture prediction software to help guide leaning posture estimates during proactive risk assessments.

Normalization Models of Contextualized Touch

Divisive normalization is a canonical computation that explains contextual modulation of visual perception and neural responses in the visual system. Conceivably, normalization also underlies contextual modulation of bimanual touch, a perceptual process that likely requires combining what is felt on the hands with where the hands are located in space. Here, we evaluated participants’ ability to perform tactile discrimination tasks on one hand while we manipulated the tactile cues experienced on the other hand and the relative positions of the hands. We found that touch experienced on one hand systematically modulates how touch is perceived on the other hand. Notably, bimanual interaction patterns and their sensitivity to hand locations differed depending on whether participants performed a frequency or intensity discrimination task. In rigorous model competition, we found that these feature-dependent bimanual modulation patterns were well explained by distinct cue combination models that each comprised divisive normalization. Our findings indicate that, while feature-specific rules govern bimanual touch, normalization underlies contextual modulation between the hands.

Flexible Visuomotor Associations in Touchscreen Control.

To move real objects, our hand needs to get in direct physical contact with the object. However, this is not necessarily the case when interacting with virtual objects, for example when displacing objects on tablets by swipe movements. Here, we performed two experiments to study the behavioral strategies of these movements, examining how visual information about the virtual object is mapped into a swipe that moves the object into a goal location. In the first experiment, we investigated how swiping behavior depends on whether objects were located within or outside the swiping workspace. Results show that participants do not start the swipe movement by placing their finger on the virtual object, as they do when reaching to real objects, but rather keep a systematic distance between the object location and the initial swipe location. This mismatch, which was experimentally imposed by placing the object outside the workspace, also occurred when the object was within the workspace. In the second experiment, we investigated which factors determine this mismatch by systematically manipulating the initial hand location, the location of the object and the location of the goal. Dimensionality reduction of the data showed that three factors are taken into account when participants choose the initial swipe location: the expected total movement distance, the distance between their finger on the screen and the object, and a preference not to cover the object. The weight given to each factor differed among individuals. These results delineate, for the first time, the flexibility of visuomotor associations in the virtual world.

A tool to predict physical workload and task times from workstation layout design data

This paper presents the development and proof of concept of a tool to predict worker and system performance using inputs of work element descriptions and hand locations from a seated, light assembly workstation layout. Tool inputs can be obtained in the design stage. Tool outputs include human factors (shoulder load, hand movement, reach zone acceptability) and system (element time and cycle time) information. Shoulder loads are predicted from two-dimensional shoulder models created from a digital human model. The tool is demonstrated on a previous observation-based assessment of a workstation redesign. Results reflected the findings of the observation assessment, but also provided more work cycle information as well as cumulative, work shift information. The tool enables prediction of workload and task performance times from design stage parameters without the need of an ergonomist. It can be used to predict critical components of the layout and plan workflow based on worker, workstation and task information. The tool is available for free downloaded at: www.researchgate.net/project/Workstation-Efficiency-Evaluator-WEE-Tool.

Multi-directional one-handed strength assessments using AnyBody Modeling Systems

Digital human modeling tools support proactive ergonomics in optimizing work tasks and workplace layouts. Empirical-statistical model based tools are often used to estimate the force exertion capability of the operators. This work is intended to serve as an initial probing into the usability of a musculoskeletal model based software, AnyBody Modeling Systems (AMS), in evaluating the force exertion capability at different points in the workspace and for various exertion directions. As a first step, it focuses on the modeling approach and the accuracy of one-handed isometric strength estimates of AMS. An existing literature database was used to compare the predicted strength at 8 hand locations and in 26 exertion directions, while simulating the empirical postures. The results show a correlation coefficient of 0.7 between the simulated and the experimental strength. AMS emphasizes the biomechanical advantages in strength due to the alignment of force exertion direction with the shoulder. Additionally, some discrepancies have been identified and discussed.

How Handheld Mobile Device Size and Hand Location May Affect Divided Attention

Previous research demonstrates that people increasingly utilize multiple displays along with mobile devices simultaneously, and that this split in attention has detrimental effects on goal-directed behavior. However, few studies have assessed the impact of the physical attributes of mobile devices–including dimensions, weight and screen size–on attention. Understanding how device dimensions and screen size affect attention is an important first step in creating safety guidelines for high risk industries that utilize displays, such as automotive and aeronautics engineering. The aim of this work is to determine to what extent the display dimensions and screen size of mobile devices influence attention. To explore this question, participants interacted with mobile devices of varying sizes while performing a change detection task on a stationary device located behind and above the mobile device. Results of this study suggest that those using a smaller mobile device achieved higher performance on the background change detection task than those using a larger device, while having similar performance in the mobile device task. This work demonstrates that when attention is divided, larger displays may be more attentionally demanding. We recommend that when users or designers are required to consider multitasking between a foreground and background task, in order to optimize background performance they should utilize smaller foreground displays.

Soft tissue sarcomas of the forearm, wrist and hand

Soft tissue sarcoma of the forearm, wrist and hand are rare. Their benign appearance leads often to primary inadequate treatment. Due to the complex anatomy of the hand and forearm, they are challenging to treat. The two goals are to obtain wide resection of the primary tumor while preserving function. Limb-sparing surgery is now the cornerstone for the treatment of most sarcomas of the forearm, hand and wrist. To achieve optimal oncological and functional outcomes, the surgical excision should be associated with early reconstructive procedures and a multidisciplinary meeting to define the treatment strategy including adjuvant medical treatments. This article outlines the current principles and presents the results of the treatment of soft tissue sarcomas with emphasis on to particularities related to their forearm, wrist and hand location.