Posture Matching Research Articles

Bayesian inference in arm posture perception.

To configure our limbs in space the brain must compute their position based on sensory information provided by mechanoreceptors in the skin, muscles, and joints. Because this information is corrupted by noise, the brain is thought to process it probabilistically, and integrate it with prior belief about arm posture, following Bayes' rule. Here, we combined computational modeling with behavioral experimentation to test this hypothesis. The model conceives the perception of arm posture as the combination of a probabilistic kinematic chain composed by the shoulder, elbow, and wrist angles, compromised with additive Gaussian noise, with a Gaussian prior about these joint angles. We tested whether the model explains errors in a VR-based posture matching task better than a model that assumes a uniform prior. Human participants were required to align their unseen right arm to a target posture, presented as a visual configuration of the arm in the horizontal plane. Results show idiosyncratic biases in how participants matched their unseen arm to the target posture. We used maximum likelihood estimation to fit the Bayesian model to these observations and estimate key parameters including the prior means and its variance-covariance structure. The Bayesian model including a Gaussian prior explained the response biases and variance much better than a model with a uniform prior. The prior varied across participants, consistent with the idiosyncrasies in arm posture perception, and in alignment with previous behavioral research. Our work clarifies the biases in arm posture perception within a new perspective on the nature of proprioceptive computations.

The effect of posture on virtual walking experience using foot vibrations

Virtual walking systems for stationary observers have been developed using multimodal stimulation such as vision, touch, and sound to overcome physical limitation. In previous studies, participants were typically positioned in either a standing or a seated position. It would be beneficial if bedridden users could have enough virtual walking experience. Thus, we aimed to investigate the effects of participants’ posture and foot vibrations on the experience of virtual walking. They were either sitting, standing, or lying during observing a virtual scene of a walking avatar in the first-person perspective, while vibrations either synchronized or asynchronized (randomized) to the avatar’s walking were applied to their feet. We found that the synchronized foot vibrations improved virtual walking experiences compared to asynchronous vibrations. The standing position consistently offered an improved virtual walking experience compared to sitting and lying positions with either the synchronous or asynchronous foot vibrations, while the difference between the siting and lying postures was small and not significant. Furthermore, subjective scores for posture matching between real and virtual postures, illusory body ownership, and sense of agency were significantly higher with the synchronous than the asynchronous vibration. These findings suggest that experiencing virtual walking with foot vibrations in a lying position is less effective than a standing position, but not much different from a sitting position.

A study on the guiding role of educational management informatization in integrated physical education teaching and sports training

Abstract Employing the OpenPose model, this research advances the prediction and analysis of human skeletal points in videos, paving the way for refined action differentiation and posture analysis. By isolating unique posture features and developing a DTW-based algorithm for posture matching, we identify deviations in movements for corrective action in sports training. This methodology promotes the digitization of physical education, enabling personalized coaching and a detailed understanding of student performance. Our findings reveal a marked improvement in the experimental group’s movement evaluation scores, surpassing the control group by 82.86 points, with a P value of 0.000, and demonstrating significant skill test advancements (t=3.129, P<0.01). These results validate the approach’s potential to revolutionize physical education management and sports training with data-driven insights.

Practical Analysis of Digital Technology in an Electronic Education Platform in PE Training

To improve the practical effect of digital technology (DT) in electronic education platform (ECP) in sports training, this study proposes using OpenPose as the main method of posture matching in sports teaching. MobileNet was introduced to replace the backbone network in OpenPose to improve the efficiency of the model. At the same time, self-attention mechanism (SAM) is introduced to improve the accuracy of the model. In the performance verification results, the proposed algorithm has a mean mean absolute error (MAE) of 0.793 and a root mean square error (RMSE) of 0.628. The accuracy of the proposed algorithm reached a maximum of 0.893 in the test set. The accuracy of the proposed algorithm exceeds 90% in the training set. In the practice of physical education (PE) and training, the absolute error between the actual value and the predicted value does not exceed 1.5 cm, while the relative error does not exceed 1.2%. The minimum absolute error and relative error are 0.01 cm and 0.2%, respectively. The proposed algorithm has high prediction effect and performance, and can be applied to PE training.

Offline Evaluation Matters: Investigation of the Influence of Offline Performance on Real-time Operation of Electromyography-based Neural-Machine Interfaces.

There has been a debate on the most appropriate way to evaluate electromyography (EMG)-based neural-machine interfaces (NMIs). Accordingly, this study examined whether a relationship between offline kinematic predictive accuracy (R2) and user real-time task performance while using the interface could be identified. A virtual posture-matching task was developed to evaluate motion capture-based control and myoelectric control with artificial neural networks (ANNs) trained to low (R2 ≈ 0.4), moderate (R2 ≈ 0.6), and high (R2 ≈ 0.8) offline performance levels. Twelve able-bodied subjects trained with each offline performance level decoder before evaluating final real-time posture matching performance. Moderate to strong relationships were detected between offline performance and all real-time task performance metrics: task completion percentage (r=0.66, p<0.001), normalized task completion time (r = -0.51, p = 0.001), path efficiency (r = 0.74, p < 0.001), and target overshoots (r = -0.79, p < 0.001). Significant improvements in each real-time task evaluation metric were also observed between the different offline performance levels. Additionally, subjects rated myoelectric controllers with higher offline performance more favorably. The results of this study support the use and validity of offline analyses for optimization of NMIs in myoelectric control research and development.

Action imitation via trajectory-based or posture-based planning.

Imitation is a significant daily activity involved in social interaction and motor learning. Imitation has been theorized to be performed in at least two ways. In posture-based imitation, individuals reproduce how the body should look and feel, and are sensitive to the relative positioning of body parts. In trajectory imitation, individuals mimic the spatiotemporal motion path of the end effector. There are clear anecdotal situations in which one might benefit from imitating postures (when learning ballet) or trajectories (when learning to reach around objects). However, whether these are in fact distinct methods of imitation, and if so, whether they may be applied interchangeably to perform the same task, remain unknown. If these are indeed separate mechanisms that rely on different computational and neural resources, a cost should be incurred when switching from using one mechanism to the other within the context of a single task. Therefore, observing a processing cost would both provide evidence that these are indeed two distinct mechanisms, and that they may be used interchangeably when trying to imitate the same stimulus. To test this, twenty-five healthy young adults performed a sequential multitasking imitation task. Participants were first instructed to pay attention to the limb postures or the hand path of a video-recorded model, then performed a neutral, congruent, or incongruent intervening motor task. Finally, participants imitated the modeled movement. We examined both spatial and temporal imitation accuracy as well as individual spatial consistency. When the primary task involved imitating trajectories, analysis of individual consistency suggested a processing cost: movements following the posture-matching intervening task were less consistent with baseline (neutral) performance, suggesting performance may be disrupted by the incongruence. This effect was not observed when imitating limb postures. In summary, we present initial evidence for a difference between posture matching and trajectory imitation as a result of instructions and intervening tasks that is consistent with the existence of two computationally distinct imitation mechanisms.

Neutral Body Posture in Spaceflight

The neutral body posture (NBP) has been characteristically observed from crewmembers in a 0 g space flight. This study quantified the NBP from International Space Station crewmembers using a new technique for structured camera calibration and multiple-camera 3D triangulation. Template manikins derived from 3D body scans of the individual subjects also were used to improve accuracy. A skeletal link system was embedded in the manikin and articulated to reproduce the posture matching with the calibrated video images. The estimated body shapes and postures were compared against the NASA Technical Standards. The NBP, as more reliably estimated from this study, can help to improve spaceflight hardware and spacesuit design.

Closed-loop cortical control of virtual reach and posture using Cartesian and joint velocity commands

Objective. Brain–computer interfaces (BCIs) are a promising technology for the restoration of function to people with paralysis, especially for controlling coordinated reaching. Typical BCI studies decode Cartesian endpoint velocities as commands, but human arm movements might be better controlled in a joint-based coordinate frame, which may match underlying movement encoding in the motor cortex. A better understanding of BCI controlled reaching by people with paralysis may lead to performance improvements in brain-controlled assistive devices. Approach. Two intracortical BCI participants in the BrainGate2 pilot clinical trial performed a visual 3D endpoint virtual reality reaching task using two decoders: Cartesian and joint velocity. Task performance metrics (i.e. success rate and path efficiency) and single feature and population tuning were compared across the two decoder conditions. The participants also demonstrated the first BCI control of a fourth dimension of reaching, the arm’s swivel angle, in a 4D posture matching task. Main results. Both users achieved significantly higher success rates using Cartesian velocity control, and joint controlled trajectories were more variable and significantly more curved. Neural tuning analyses showed that most single feature activity was best described by a Cartesian kinematic encoding model, and population analyses revealed only slight differences in aggregate activity between the decoder conditions. Simulations of a BCI user reproduced trajectory features seen during closed-loop joint control when assuming only Cartesian-tuned features passed through a joint decoder. With minimal training, both participants controlled the virtual arm’s swivel angle to complete a 4D posture matching task, and achieved significantly higher success using a Cartesian + swivel velocity decoder compared to a joint velocity decoder. Significance. These results suggest that Cartesian velocity command interfaces may provide better BCI control of arm movements than other kinematic variables, even in 4D posture tasks with swivel angle targets.

A taxonomic overview and pilot study for evaluation of Augmented Reality based posture matching technique using Technology Acceptance Model

Motor skill training, posture matching and Augmented Reality (AR) based learning technology are considered to be the cornerstones of dance learning paradigm. In this paper, an AR based posture matching technique is presented based upon skeletal mapping and movement matching where each posture is modelled by a sequence of pivotal movements and continuous data frames. An extension of the previously published work by the authors, this paper aims to provide a taxonomic overview of dance learning/ training technologies with respect to existing learning theories. Furthermore, the proposed system is evaluated using the Technology Acceptance Model (TAM) and a pilot study is carried out to assess the acceptability of the system. The results of the pilot study are also utilized for identifying flaws and to calculate the sample size for further evaluation using a larger group of subjects. This research also aims at providing a taxonomical knowledge and categorization of the proposed Augmented Reality Dance Training System (ARDTS) in the state-of-art hierarchical structure of learning theories.

Observer performance in estimating upper arm elevation angles under ideal viewing conditions when assisted by posture matching software

Selecting a suitable body posture measurement method requires performance indices of candidate tools. Such data are lacking for observational assessments made at a high degree of resolution. The aim of this study was to determine the performance (bias and between- and within-observer variance) of novice observers estimating upper arm elevation postures assisted by posture matching software to the nearest degree from still images taken under ideal conditions. Estimates were minimally biased from true angles: the mean error across observers was less than 2°. Variance between observers was minimal. Considerable variance within observers, however, underlined the risk of relying on single observations. Observers were more proficient at estimating 0° and 90° postures, and less proficient at 60°. Thus, under ideal visual conditions observers, on average, proved proficient at high resolution posture estimates; further investigation is required to determine how non-optimal image conditions, as would be expected from occupational data, impact proficiency.

Human Left Ventral Premotor Cortex Mediates Matching of Hand Posture to Object Use

Visuomotor transformations for grasping have been associated with a fronto-parietal network in the monkey brain. The human homologue of the parietal monkey region (AIP) has been identified as the anterior part of the intraparietal sulcus (aIPS), whereas the putative human equivalent of the monkey frontal region (F5) is located in the ventral part of the premotor cortex (vPMC). Results from animal studies suggest that monkey F5 is involved in the selection of appropriate hand postures relative to the constraints of the task. In humans, the functional roles of aIPS and vPMC appear to be more complex and the relative contribution of each region to grasp selection remains uncertain. The present study aimed to identify modulation in brain areas sensitive to the difficulty level of tool object - hand posture matching. Seventeen healthy right handed participants underwent fMRI while observing pictures of familiar tool objects followed by pictures of hand postures. The task was to decide whether the hand posture matched the functional use of the previously shown object. Conditions were manipulated for level of difficulty. Compared to a picture matching control task, the tool object – hand posture matching conditions conjointly showed increased modulation in several left hemispheric regions of the superior and inferior parietal lobules (including aIPS), the middle occipital gyrus, and the inferior temporal gyrus. Comparison of hard versus easy conditions selectively modulated the left inferior frontal gyrus with peak activity located in its opercular part (Brodmann area (BA) 44). We suggest that in the human brain, vPMC/BA44 is involved in the matching of hand posture configurations in accordance with visual and functional demands.

Ergonomic risks in fish processing workers in Atlantic Canada

Background: The aquaculture industry is growing in Canada and is particularly strong in Atlantic Canada. Workers in the fish processing industry are required to complete a variety of tasks in a typical day and there is concern for musculoskeletal disorder. Objective: The purpose of this study was to examine the daily operations of fish processing workers to determine any musculoskeletal concerns. Methods: The ergonomic assessment consisted of several plant visits to observe the processing line and the requirements of the workers. Video recordings were made of each stage of the assembly lines. The video data was analyzed to determine high-risk jobs and to identify areas of concern. Cumulative loading was assessed using posture matching software and the video data. A Job Strain Index (JSI), Rapid Upper Limb Assessment (RULA) and the revised NIOSH lifting equation were used to identify high-risk tasks. Results: The data showed that six tasks were considered high risk; sorting fish, removal of fish bones, trimming of fish, pallet loading/conveyor operation, fish processing and cleaning of the trim machine. In addition, four categories of occupational health and safety (OHS) hazard concerns were identified (physical, chemical, biological, and psychosocial). Each category was then broken into their causative agents and potential health effects on the worker. Conclusions: Several areas for improvement were identified at this seafood processing plant. Six jobs were identified as high risk and in need of intervention. Changes in pace of work, workstation height, and new equipment would also help reduce the number of musculoskeletal injuries. The issue of job rotation should also be examined to determine its impact on musculoskeletal health. Implementation of strategies to reduce musculoskeletal disorders will help to improve the health of these workers.

Development of Human Posture Simulation Method for Assessing Posture Angles and Spinal Loads.

Video-based posture analysis employing a biomechanical model is gaining a growing popularity for ergonomic assessments. A human posture simulation method of estimating multiple body postural angles and spinal loads from a video record was developed to expedite ergonomic assessments. The method was evaluated by a repeated measures study design with three trunk flexion levels, two lift asymmetry levels, three viewing angles and three trial repetitions as experimental factors. The study comprised two phases evaluating the accuracy of simulating self and other people's lifting posture via a proxy of a computer-generated humanoid. The mean values of the accuracy of simulating self and humanoid postures were 12° and 15°, respectively. The repeatability of the method for the same lifting condition was excellent (~2°). The least simulation error was associated with side viewing angle. The estimated back compressive force and moment, calculated by a three dimensional biomechanical model, exhibited a range of 5% underestimation. The posture simulation method enables researchers to simultaneously quantify body posture angles and spinal loading variables with accuracy and precision comparable to on-screen posture matching methods.

User-in-the-loop continuous and proportional control of a virtual prosthesis in a posture matching task.

As the development of dexterous prosthetic hand and wrist units continues, there is a need for command interfaces that will enable a user to operate these multi-joint devices in a natural, coordinated manner. In previous work, we have demonstrated that it is possible to simultaneously decode hand and wrist kinematics from myoelectric signals recorded from the forearm in an offline manner. The goal of this study was to quantify the performance of this command interface during real-time control of a kinematic prosthesis. One subject with intact limbs controlled a virtual prosthesis and attempted to match a series of target postures using the proposed control scheme as well as using the movements of the intact limb. Initial results indicate that subjects can complete these target matching tasks in the virtual environment. Future work will evaluate the controllability of the proposed strategy relative to traditional control schemes.

A comparison of low back kinetic estimates obtained through posture matching, rigid link modeling and an EMG-assisted model

This study examined errors introduced by a posture matching approach (3DMatch) relative to dynamic three-dimensional rigid link and EMG-assisted models. Eighty-eight lifting trials of various combinations of heights (floor, 0.67, 1.2 m), asymmetry (left, right and center) and mass (7.6 and 9.7 kg) were videotaped while spine postures, ground reaction forces, segment orientations and muscle activations were documented and used to estimate joint moments and forces (L5/S1). Posture matching over predicted peak and cumulative extension moment ( p < 0.0001 for all variables). There was no difference between peak compression estimates obtained with posture matching or EMG-assisted approaches ( p = 0.7987). Posture matching over predicted cumulative ( p < 0.0001) compressive loading due to a bias in standing, however, individualized bias correction eliminated the differences. Therefore, posture matching provides a method to analyze industrial lifting exposures that will predict kinetic values similar to those of more sophisticated models, provided necessary corrections are applied.

Reliability of Distal Upper Extremity Posture Matching Using Slow-Motion and Frame-by-Frame Video Methods

The aim of this study was to determine the effects of video playback speed on posture matching reliability of the distal upper extremity. Video is frequently used in ergonomic assessments, yet there remains a need to determine the effects of viewing speed on posture observations. Participants were 7 graduate students experienced with posture-based observational methods. Categorical posture scales were used to evaluate forearm pronation/supination, wrist flexion/extension, wrist radioulnar deviation,and hand activity from workplace video at three playback speeds (quarter, half, and real time). Wrist flexion/extension was also evaluated with a frame-by-frame video method. Posture counts increased with slower viewing speeds for the wrist and hand, but percentage durations in each posture category were similar for all methods. Posture matching interrater reliability scores increased with slow-motion video playback but remained low even for quarter-time video playback. The highest interrater scores were found in the frame-by-frame analysis of wrist flexion/extension for three posture categories (percentage agreement = 84.9% +/- 1.3%; kappa = 0.54 +/- 0.02). Although slower video playback speeds increased the number of posture counts for the wrist and hand scales, percentage durations were similar, and reliability scores increased only slightly with slow-motion video playback. Reviewing video using slow-motion or frame-by-frame methods improves distal upper extremity posture matching reliability. However, ergonomic assessment tools based on percentage duration may not be appreciably enhanced by slowing viewing speed. Thus, the increased viewing time with slower playback should be justified with respect to assessment needs.

Inter-rater reliability of output measures for a posture matching assessment approach: a pilot study with food service workers

Despite the ongoing health problem of repetitive strain injuries, there are few tools currently available for ergonomic applications evaluating cumulative loading that have well-documented evidence of reliability and validity. The purpose of this study was to determine the inter-rater reliability of a posture matching based analysis tool (3DMatch, University of Waterloo) for predicting cumulative and peak spinal loads. A total of 30 food service workers were each videotaped for a 1-h period while performing typical work activities and a single work task was randomly selected from each for analysis by two raters. Inter-rater reliability was determined using intraclass correlation coefficients (ICC) model 2,1 and standard errors of measurement for cumulative and peak spinal and shoulder loading variables across all subjects. Overall, 85.5% of variables had moderate to excellent inter-rater reliability, with ICCs ranging from 0.30–0.99 for all cumulative and peak loading variables. 3DMatch was found to be a reliable ergonomic tool when more than one rater is involved.

Use of proprioception in normal and clumsy children

This study investigates the relation between performance on simple tasks dependent on proprioception, and performance of complex perceptual-motor skills in clumsy children and age-matched control children. One hundred and forty-six right-handed children aged between 5 and 8 years were tested on non-visual aiming, non-visual posture matching, the Kinaesthetic Sensitivity Test (KST), and the Movement Assessment Battery for Children (ABC). Half of the children had scores below the 15th centile on the Movement ABC and were classed into the developmental coordination disorder (clumsiness) group. Scores on the proprioceptive tasks were used to predict performance on complex tasks of the subscales of the Movement ABC (manual dexterity, ball skills, and balance). Specific relations were found between the proprioceptive tasks and the subscales of the Movement ABC, but the KST did not predict differences in motor skills, and no relation was found between tasks carried out without vision. Simple non-visual movement tasks do predict performance in more complex skilled tasks but this is affected by many task features rather than simply the reliance on proprioception for information about movement.