Motion Capture Technology Research Articles

Retracted: Motion Capture Technology of Digital Animation Art Based on Machine Learning

Retracted: Motion Capture Technology of Digital Animation Art Based on Machine Learning

Australia and New Zealand Clinical Motion Analysis Group (ANZ-CMAG) clinical practice recommendations

Clinical motion analysis involves quantitative measurement of gait patterns to identify gait anomalies that currently or have the potential to impact function, activities of daily living and participation. Clinical motion analysis services are equipped with motion capture technology and comprise specialised staff who deliver 3-dimensional motion analysis services to children and adults who present with varying levels of gait impairment. Data is then used to inform intervention recommendations to clinicians with a view to maintaining independent, functional and pain free walking (or appropriate mobility).The ANZ-CMAG (established in 2013) identified a need to establish recommendations to assist in standardising practice guidelines for both current and new clinical motion analysis services within the region. The group serves to promote collaboration between services in quality assurance processes, clinical practices, data sets and research activities. The clinical practice recommendations described in this paper cover: i) requirements for a motion analysis service (including staffing, facilities and equipment), ii) patient assessments (requirements, clinical information and data gathered, reporting and interpretation of patient data), iii) quality assurance processes (including motion capture system / biomechanical models & limitations, marker placement, data storage / record keeping, creation of normative dataset); iv) helpful resources.Better outcomes for children and adults with gait deviations is dependent upon accurate measurement and evaluation of walking and requires input from multidisciplinary clinical teams with specialist knowledge and skills. The ANZ-CMAG hopes these clinical practice recommendations are beneficial to motion analysis services with an aim to improve clinical practices, patient outcomes, and support research collaboration.

Motion Capture Technologies for Ergonomics: A Systematic Literature Review.

Muscular skeletal disorder is a difficult challenge faced by the working population. Motion capture (MoCap) is used for recording the movement of people for clinical, ergonomic and rehabilitation solutions. However, knowledge barriers about these MoCap systems have made them difficult to use for many people. Despite this, no state-of-the-art literature review on MoCap systems for human clinical, rehabilitation and ergonomic analysis has been conducted. A medical diagnosis using AI applies machine learning algorithms and motion capture technologies to analyze patient data, enhancing diagnostic accuracy, enabling early disease detection and facilitating personalized treatment plans. It revolutionizes healthcare by harnessing the power of data-driven insights for improved patient outcomes and efficient clinical decision-making. The current review aimed to investigate: (i) the most used MoCap systems for clinical use, ergonomics and rehabilitation, (ii) their application and (iii) the target population. We used preferred reporting items for systematic reviews and meta-analysis guidelines for the review. Google Scholar, PubMed, Scopus and Web of Science were used to search for relevant published articles. The articles obtained were scrutinized by reading the abstracts and titles to determine their inclusion eligibility. Accordingly, articles with insufficient or irrelevant information were excluded from the screening. The search included studies published between 2013 and 2023 (including additional criteria). A total of 40 articles were eligible for review. The selected articles were further categorized in terms of the types of MoCap used, their application and the domain of the experiments. This review will serve as a guide for researchers and organizational management.

Analysing the contributions of lower limb muscles to eccentric cycling using musculoskeletal modeling and simulation.

Eccentric (ECC) cycling, compared to traditional concentric cycling, has been shown to improve muscle strength and neuromuscular control at a lower metabolic cost. Despite the popularity of this exercise in the sports and rehabilitation contexts, there is a gap in our knowledge of which muscles are behaving eccentrically during ECC cycling. To this end, we used a musculoskeletal model and computer simulations to calculate joint kinematics and muscle lengths during ECC cycling. Movements were recorded using 3D motion capture technology while cycling eccentrically on a custom-built semi-recumbent ergometer. The software Opensim was used to calculate joint kinematics and muscle lengths from recorded movements. We found that among the primary knee extensors, it was predominantly the Vastii muscles that acted eccentrically in the ECC cycling phase, with other lower limb muscles showing mixed eccentric/concentric activation. Additionally, the muscle force-length and force-velocity factors in the ECC phase suggest that changes to the participant's pose and pedaling speed may elicit larger active muscle forces. Our work provides an interesting application of musculoskeletal modeling to ECC cycling, and an alternative way to help understand in-vivo muscle mechanics during this activity.

Retracted: Deep-Learning-Based Motion Capture Technology in Film and Television Animation Production

Retracted: Deep-Learning-Based Motion Capture Technology in Film and Television Animation Production

Desktop vs. Headset: A Comparative Study of User Experience and Engagement for Flexibility Exercise in Virtual Reality

This study aimed to investigate the effectiveness of Virtual Reality (VR) technology for flexibility exercise and compare the physical outcomes, user experience, and engagement of VR desktops and VR headsets. The VR exercise application was designed using motion capture technology and exported to different VR devices. Each of the devices was used by 30 participants to perform a flexibility exercise in VR. Physical outcomes were measured using the sit-and-reach test, and user experience and engagement were evaluated using questionnaires and group discussions. The results showed that VR desktop participants had higher sit-and-reach scores. However, VR headset participants reported a more immersive experience (reality judgment) and motivation (value and usefulness). They also had higher engagement (focused attention and reward) levels than VR desktop participants. There were no significant differences between the two approaches in terms of enjoyment, effort, pressure, choice, correspondence, absorption, perceived usability, and aesthetic appeal. The study highlights the importance of considering physical outcomes, user experience, and engagement by comparing two different VR approaches for flexibility exercise. Further research is needed to explore the limitations and potential benefits of VR technology for physical activity. Doi: 10.28991/ESJ-2023-07-04-03 Full Text: PDF

Application of motion capture technology based on dance big data in dance retrieval

Abstract Under the background of globalization, the speed of information dissemination is accelerated, the spatial distance is gradually narrowed, the types and quantities of information are becoming more and more abundant, and the data presents diverse characteristics, so big data thinking emerges as the times require. “Dance” is a very classic art form. With the development of science and technology, people begin to think about how to show dance in a richer form. In this paper, through optical motion capture, firstly, optical motion capture is used as the representation of motion content, which effectively reflects the locality, rhythm, and subdivision of dance movement, and provides the basis for the realization of national dance motion data retrieval. Then, the dance posture analysis method based on 21 key nodes and 7 feature-matching planes of the feature plane vector is used. Finally, the feedback correlation mechanism is used to capture the query interest of users, Improve the retrieval performance, and under the verification of a large number of experimental data, it is verified that this method has good retrieval performance and can realize dance movement data retrieval.

Are hip biomechanics during running associated with symptom severity or cam morphology size in male football players with FAI syndrome?

BackgroundFemoroacetabular impingement (FAI) syndrome is considered a motion-related condition. Little is known about the influence of symptom severity and cam morphology on hip biomechanics for individuals with FAI syndrome. Research questionAre hip biomechanics during running associated with symptom severity or cam morphology size in male football players with FAI syndrome? MethodsForty-nine male, sub-elite football (soccer or Australian football) players (mean age= 26 years) with FAI syndrome completed the International Hip Outcome Tool-33 (iHOT-33) and Copenhagen Hip and Groin Outcome Score (HAGOS) and underwent radiographic evaluation. Biomechanical data were collected during overground running (3–3.5 m∙s−1) using three-dimensional motion capture technology and an embedded force plate. Various discrete hip angles and impulses of joint moments were analysed during the stance phase. Linear regression models investigated associations between running biomechanics data (dependent variables) and iHOT-33 and HAGOS scores and cam morphology size (independent variables). ResultsHip joint angles during running were not associated with symptom severity in football players with FAI syndrome. A positive association was found between the impulse of the hip external rotation moment and HAGOS-Sport scores, such that a smaller impulse magnitude occurred with a lower HAGOS-Sport score (0.026 *10−2 [95%CI <0.001 *10−2 to 0.051 *10−2], P = 0.048). Larger cam morphology was associated with a greater peak hip adduction angle at midstance (0.073 [95%CI 0.002–0.145], P = 0.045). SignificanceHip biomechanics during running did not display strong associations with symptom severity or cam morphology size in male football players with FAI syndrome who were still participating in training and match play. Future studies might consider investigating associations during tasks that utilise end range hip joint motion or require greater muscle forces.

Smooth Trajectory Planning for a Cable Driven Parallel Waist Rehabilitation Robot Based on Rehabilitation Evaluation Factors

Rehabilitation robots can help physiatrists to assist patients in improving their movement ability. Due to the interaction between rehabilitation robots and patients, the robots need to complete rehabilitation training on a safe basis. This paper presents an approach for smooth trajectory planning for a cable-driven parallel waist rehabilitation robot (CDPWRR) based on the rehabilitation evaluation factors. First, motion capture technology is used to collect the motion data of several volunteers in waist twisting. Considering the impact of motion variability, the feature points at the center of the human pelvis are obtained after eliminating unreasonable data through rationality judgments. Then, point-to-point waist training trajectory planning based on quintic polynomial and cycloid functions, and multipoint waist training trajectory planning based on quintic B-spline functions are carried out. The corresponding planned curves and kinematics characteristics using three methods are compared and analyzed. Subsequently, the rehabilitation evaluation factors are introduced to conduct smooth trajectory planning for waist training, and the waist trajectory with better compliance is obtained based on the safety and feasibility of waist motion. Finally, the physical prototype of the CDPWRR is built, and the feasibility and effectiveness of the proposed smooth trajectory planning method are proved by numerical analysis and experiments.

Evaluation of masticatory performance by motion capture analysis of jaw movement.

The assessment of masticatory performance (MP) is conducted in hospitals, but is difficult to perform in nursing facilities that lack specialists in dysphagia. To select the appropriate food textures in nursing practice, a simple method of evaluating the MP should be developed. The purpose of this study was to investigate motion parameters that influence MP by motion capture analysis of maxillofacial movement on chewing gummy jelly in healthy adults. The subjects were 50 healthy adults. The state of chewing gummy jelly was photographed using a high-speed camera. Simultaneously, we evaluated the amount of glucose extracted (AGE) obtained with gummy jelly as a reference value for MP. The subjects were divided into two groups: normal and low masticatory groups (NG and LG, respectively) based on the AGE. The cycle of mastication was classified into 3 phases: closing phase (CP), transition phase (TP), and opening phase (OP) through motion capture analysis of the video photographed. Parameters of jaw movement, and their associations with the AGE were examined. The transition phase rate (TR), and opening phase rate (OR) were correlated with the AGE. Furthermore, the TR in the NG was significantly higher than in the LG, whereas the OR was significantly lower than in the LG. The age, TR, and Opening velocity were significant independent variables. Motion capture technology facilitated the analysis of jaw movement. The results suggested that MP can be evaluated by analyzing the TP and OP rates.

Use of Motion Capture Technology to Study Extrinsic Laryngeal Muscle Tension and Hyperfunction.

Patients with primary muscle tension dysphonia (pMTD) commonly report paralaryngeal pain and discomfort, and extrinsic laryngeal muscle (ELM) tension and hyperfunction are commonly implicated. However, quantitative physiological metrics to study ELM movement patterns for the characterization of pMTD diagnosis and monitoring of treatment progress are lacking. The objectives of this study were to validate motion capture (MoCap) technology to study ELM kinematics, determine whether MoCap could distinguish ELM tension and hyperfunction between individuals with and without pMTD, and investigate relationships between common clinical voice metrics and ELM kinematics. Thirty subjects (15 with pMTD and 15 controls) were recruited for the study. Sixteen markers were placed on different anatomical landmarks on the chin and anterior neck. Movements across these regions were tracked during four voice and speech tasks using two three-dimensional cameras. Movement displacement and variability were determined based on 16 key-points and 53 edges. Intraclass correlation coefficients demonstrated high intra- and inter-rater reliability (p's < 0.001). Other than greater movement displacements around the thyrohyoid space during longer phrasing (reading passage, 30-s diadochokinetics) and more movement variability in patients with pMTD, kinematic patterns between groups were similar across the 53 edges for the four voice and speech tasks. There were also no significant correlations between ELM kinematics and standard voice metrics. Results demonstrate the feasibility and reliability of MoCap for the study of ELM kinematics. 3 Laryngoscope, 133:3472-3481, 2023.

Kinect-ing the Dots: Using Motion-Capture Technology to Distinguish Sign Language Linguistic From Gestural Expressions.

Just as vocalization proceeds in a continuous stream in speech, so too do movements of the hands, face, and body in sign languages. Here, we use motion-capture technology to distinguish lexical signs in sign language from other common types of expression in the signing stream. One type of expression is constructed action, the enactment of (aspects of) referents and events by (parts of) the body. Another is classifier constructions, the manual representation of analogue and gradient motions and locations simultaneously with specified referent morphemes. The term signing is commonly used for all of these, but we show that not all visual signals in sign languages are of the same type. In this study of Israeli Sign Language, we use motion capture to show that the motion of lexical signs differs significantly along several kinematic parameters from that of the two other modes of expression: constructed action and the classifier forms. In so doing, we show how motion-capture technology can help to define the universal linguistic category "word," and to distinguish it from the expressive gestural elements that are commonly found across sign languages.

An Exploration of Prediction Accuracy of Developmental Degrees of Music Expression in Early Childhood Based on A Simultaneous Analysis of Both Eye Movement and Full-Body Movement

This article explores validity to introduce eye movement of children in association with body movement as feature quantities of machine learning, in particular, to assess children’s maturity level of musical expression. There is a growing awareness of applying machine learning technique to yield practical benefit on predicting developmental degrees of music education by observing body movements with motion capture technology. From professional teaching perspective, it is believed only experienced teachers can evaluate children’s music achievement because coordinated functioning of body movements need carefully be observed in connection with various music factor such as rhythm, beat strength, tones, etc. If new teachers can take advantage of having objective results of evaluation of children’s expression level by machines, it will enhance educational efficiency to certain levels which experts could attain. The author aims to improve classification accuracy of machine learning by focusing on eye movements data simultaneously recorded with body movements. In this study, children (n=43) at two child facilities participated in the data capture of both eye movement and body movement during musical expression. Feature quantities were extracted from the results of a three-way of ANOVA, and applied to machine learning to improve the classification accuracy of developmental degree in musical expression. Specifically, when using several classifiers such as NN (Neural Network model), the classification accuracy of developmental degree of musical development was more precisely in the case of both body movement data and eye movement data included in feature quantities than in the case of only body movement data as feature quantities.

Construction and practice of training system for dance professionals based on OBE teaching model combined with Kinect motion capture technology

Abstract With the increasing prosperity of the cultural market, the dance culture industry is also developing rapidly. In the face of opportunities and challenges brought by dance industrialization, dance professionals trained by the traditional and single-skill training system of art colleges and universities expose the contradiction between supply and demand. In this paper, the SAA-training model is taken as the calculation tool, and the OBE and Kinect motion capture technology are combined to explore the construction and practice of the training system for dance professionals. The research shows that the training system of dance professionals can finally obtain three types of dance professionals. The first type is compound dance talents, accounting for about 33%. The second category consists of dancers with rich market experience, accounting for up to 46 percent. The third category is dance talent with innovative consciousness, limited by the existing training mode, its proportion is only 21 percent.

Variability of Spatiotemporal Gait Kinematics During Treadmill Walking: Is There a Hawthorne Effect?

Spatiotemporal gait kinematics and their variability are commonly assessed in clinical and laboratory settings to quantify fall risk. Although the Hawthorne effect, or modifications in participant behavior due to knowledge of being observed, has the potential to impact such assessments, it has received minimal attention in the study of gait-particularly gait variability. The purpose of this study was to quantify the Hawthorne effect on variability and central tendency measures of fall-related spatiotemporal gait parameters. Seventeen healthy young adults walked on a treadmill at a self-selected velocity for 2minutes under covert evaluation (ie,without awareness of being evaluated) and 2minutes under overt evaluation. The movement was recorded using motion capture technology, from which we calculated mean value and step-to-step variability (usingstandard deviation and mean absolute deviation) of step length, step width, percent double support, percent stance phase, and stride time. Although central tendencies were unaffected by evaluation type, four-of-five measures of variability were significantly lower during overt evaluation for at least one-of-two metrics. Our results suggest a Hawthorne effect on locomotor control. Researchers should be aware of this phenomenon when designing research studies and interpreting gait assessments.

Expression Techniques for Human Body Motion Using a Game Engine

In this paper, we acquired Taekwondo motion data using motion capture technology and conducted realistic and abstract visualization studies of the motion data. The motion expression techniques was composed of four stages of character, mesh, skeleton and joint visualization according to the degree of realism to abstraction. The Unity game engine was used to create interactive content that can visualize Taekwondo movements in real time with various expression methods. According to the expression method, the audience's opinions on the four perspectives of understanding, creativity, aesthetics, and interaction were surveyed and analyzed.

The Automated Test of Embodied Cognition: Concept, Development, and Preliminary Findings.

(1) Background: The Automated Test of Embodied Cognition (ATEC) uses video administration of cognitively demanding physical tasks and motion capture technology to assess cognition in action. Embodied cognition is a radical departure from conventional approaches to cognitive assessment and is in keeping with contemporary neuroscience. (2) Methods: ATEC was administered to a convenience sample of 20 patients with substance use disorder and 25 age-matched community controls. Patients were administered concurrent cognitive assessments. (3) Results: Psychometric analysis revealed excellent internal consistency, test-retest reliability and small practice effects. Groups were significantly different on ATEC scores and ATEC scores significantly related to concurrent measures of cognition. (4) Conclusions: The preliminary results support the reliability and validity of ATEC for older adults.

Holographic ABBA: Examining Fan Responses to ABBA’s Virtual “Live” Concert

ABSTRACT In 2021, ABBA reunited, released a new album, Voyage, and announced holographic concerts featuring their digital avatars or “ABBAtars.” Using visual effects and motion capture technology, these performances create a realistic illusion without physical presence. This study analyzed reactions to these virtual musicians, highlighting concerns of ageism, undermining live music, and profit-driven motives. While the public was excited to “experience” ABBA again, there was speculation about the nature of a virtual concert.

Tele-Snap: a joint impedance estimation system using snap motor and openPose for remote rehabilitation diagnosis

We propose a remote joint impedance estimation system called Tele-snap for a rehabilitation diagnosis under the COVID-19 pandemic. Dynamic resistance of the human joint is essential physical information reflecting the motor function. The resistance is assessed based on the touching sensation of the doctor (physiotherapist), but the pandemic restricts such an in-person manner. Our proposing system aims to provide this physical information quantified by the joint impedance for a diagnosis in the telerehabilitation context. The proposed system employs a compact impulsive perturbation generator called the snap motor and a marker-less motion capture technology called the OpenPose. The subsystem installed in the patient's place is then simplified remarkably, which consists of the wearable snap motor and Raspberry Pi with a built-in camera module. The proposed system can collect the dataset for impedance estimation through the examiner's teleoperation of the snap motor and camera via a virtual private network, with no need for the operation by the patient. We verify the proposed system through an in-person experiment and then demonstrate the remote impedance estimation scheme.

From capture to texture: affective environments for theatre training in virtual reality (VR)

ABSTRACT The notion of ‘pre-expressivity' is central to the field of ‘theatre anthropology', explored by Eugenio Barba and his theatre group Odin Teatret. It refers to the elementary level of the energetic dynamics that render an actor's body scenically ‘alive' and 'present'. In the current study, we explore the potential of immersive technologies and bodily interaction interfaces for supporting the transmission of embodied knowledge. We begin by outlining a critical epistemological framework that calls for new accounts of knowledge distribution. We describe the process of mapping different training strands developed by Odin Teatret's actors, using motion capture technology. Further on, we approach the translation of such embodied practices into data and, consequently, into an immersive experience of archival navigation. In our study, we used audiovisual objects as environmental constraints, what we term as immersive archi-textures, and employ Laban efforts applied to motion capture data analysis as a model for qualifying movement patterns. We will base our writings on a particular experiment, which has captured Odin actress Roberta Carreri’s exercise Six States of Water within the virtual archive. In this exercise, the trainee is called to perform a specific score of actions with different qualities of energy, translated as different states of water.