Full-body Motion Research Articles

Aesthetic evaluation of body movements shaped by embodied and arts experience: Insights from behaviour and fNIRS

Aesthetic appreciation of full-body movements is likely shaped by our cumulative bodily experiences, yet most of the extant literature in this domain has focused on expertise and familiarity. We ran two experiments exploring individual differences in embodied experience and experience with the arts: In Study 1, we explored how participants’ (n = 41) abilities to learn a choreography shaped their aesthetic perceptions while viewing learned vs. unknown movements, using functional near-infrared spectroscopy (fNIRS) to measure cortical activation over the Action Observation Network (i.e., inferior frontal gyrus [IFG], inferior parietal lobule, middle temporal gyrus [MTG]). Study 1 demonstrated that embodied experience enhanced ratings of enjoyment, familiarity, and reproducibility of movements, and that individual differences in participants’ performance of the learned choreography were not associated with aesthetic ratings, but rather cortical activation in IFG and right MTG while viewing learned choreography. In Study 2, we combined the behavioural data from Study 1 with data from additional participants (total n = 141) to examine the relationship between arts experience and aesthetic perceptions of movements robustly. Study 2 revealed that previous arts and sports experience correlated with aesthetic judgements of familiarity and reproducibility of movements. Our findings highlight the relevance of examining individual experiences to fill theoretical gaps in our understanding of action aesthetics.

PSVIII-10 Genetic parameter estimates of postmortem muscle activity in purebred male turkeys (Meleagris gallopovo)

Abstract The poultry processing sector has a critical role in fulfilling the global demand for high-quality protein sources and turkey meat is a significant contributor to that market. During poultry slaughter, wing flapping and body movement may occur, primarily driven by the large breast muscles (Pectoralis major and minor). This postmortem muscular activity may have an impact on meat quality attributes such as drip loss, shear force, and breast meat color, and this can influence consumer satisfaction and the marketability of poultry products. The objective of this study was to use an animal model to estimate genetic parameters for postmortem muscle activity in turkeys during slaughter at a commercial poultry processing plant. A dataset including observations from male turkeys (n = 4,899) was collected between June 2018 and November 2019, with a pedigree comprising 25,081 individuals from six purebred genetic lines. An animal model including the fixed effects genetic line, age, hatch week-year, and body weight (BW) was implemented into ASReml 4.2. Postmortem muscle activity was scored by a single observer using a 1 to 4 categorical scale, where score 1 represents none to minimal muscle quivering, score 2 represents mild wing flapping, score 3 represents moderate spasmodic body movement and curled body posture, and score 4 represents severe wing flapping and full body movement. Heritability for postmortem wing flapping activity was low and estimated to be 0.06 ± 0.022. Further research is needed to evaluate the best model for this trait and estimate genetic correlations with other economically important traits. This study represents a first step toward improving meat quality through the investigation of postmortem muscle activity.

Sense of embodiment with synchronized avatar during walking in mixed reality.

Gait guidance systems that synchronize the gait rhythm with an avatar in a mixed reality (MR) environment are attracting attention owing to their rehabilitation applications. More effective gait guidance can be achieved by changing body sensations for the sense of embodiment (SoE), which refers to the feeling of owning, controlling, and being inside a body in MR. This study investigated full-body synchronous motion between a human and a virtual avatar to enhance the SoE in walking with actual position changes in the real world. The full-body motion and gait rhythm were measured using body-worn inertial measurement units and a visual avatar was provided through a transparent head-mounted display. The results showed that the SoE of the participants was enhanced under higher synchronization conditions. In addition, questionnaire results showed that the SoE in the synchronous condition was significantly higher than that in the asynchronous condition, and the SoE in the self-avatar condition was significantly higher than that in the other-avatar condition. This indicates that a higher synchronization level with the appearance of an avatar leads to a stronger SoE in the human perception mechanism, which is important for potential application in medical or other fields.

A high-efficiency sharp-interface immersed boundary method based on multi-linear interpolation

The sharp-interface immersed boundary method (IBM) reconstructs the flow locally to achieve the precise identification of solid boundaries and their consequential influences on the fluid dynamics. However, the computational accuracy and efficiency are notably impacted by the diverse interaction between the fluid's Euler grid and the solid's Lagrange mesh. Taking the two-dimensional (2D) case as an example, we analyze the characteristics of various grid cutting classes, with a particular focus on how they reduce the solution speed and computational precision. To address these challenges, we propose a multi-linear interpolation method that enriches flow field information by expanding the interpolation template. Computational results of 2D flow past a stationary cylinder and airfoil, and the takeoff of airfoil takeoff demonstrate that the multi-linear interpolation method increases computational efficiency by up to 20%, while maintaining accuracy. Furthermore, we extend this method to three-dimensional (3D) calculations, enhancing efficiency by about 5% and improving accuracy. Finally, by simulating the full-body motion of a penguin swimming, we showcase the robustness of the method in simulating complex geometric moving boundary problems.

The Effectiveness of Warm-Up Using an Assistive Device in Wheelchair Basketball: A Feasibility Study of Able-Bodied Participants.

Introduction A warm-up is often performed to prevent injury and prepare for optimal performance. Nonetheless, research on its impact on performance, particularly in para-sports, remains limited. We hypothesized that the use of an assistive device during warm-up would enable wheelchair basketball players to perform full-body movements efficiently and effectively, contributing to enhanced wheelchair mobility. Therefore, this feasibility study aimed to assess the safety of warm-up exercise with an assistive device and the changes in wheelchair mobility performance before and after warm-up in able-bodied participants. Methods Thirteen able-bodied participants (nine males and four females; mean age: 34.3 ± 6.11 years) were analyzed. Warm-up consisted of a five-minute stand-up exercise using the lumbar-type Hybrid Assistive Limb®. Before and after warm-up, a 3-3-6 m sprint was performed as a wheelchair mobility performance test. The 3-3-6 m sprint is a test in which the athlete repeatedly accelerates, decelerates, and stops while driving at maximum effort for a total of 12 m (0-3 m, 3-6 m, and 6-12 m). The time required for the 3-3-6 m sprint and maximum instantaneous speed, acceleration time, deceleration time, hip joint angle, and average muscle activity of the lower limb and trunk muscles during the acceleration/deceleration phase of each section were compared before and after warm-up exercise. Results Warm-up with an assistive device was safe in healthy participants. The time required for the 3-3-6 m sprint was significantly reduced after the warm-up compared to that before the warm-up (p=0.005). Although not significant, there was a trend toward shorter deceleration times after the warm-up for participants herein. Conclusions In able-bodied participants, warm-up with an assistive device is safe; it may improve wheelchair mobility performance. Further research is required to determine its impact on para-athletes with disabilities.

Exploring the Psychological Effects and Physical Exertion of Using Different Movement Interactions in Casual Exergames That Promote Active Microbreaks: Quasi-Experimental Study.

Prolonged sedentary behavior, such as sitting or reclining, has consistently been identified as a stand-alone risk factor for heightened cardiometabolic risk and overall mortality. Conversely, interrupting sedentary periods by incorporating short, active microbreaks has been shown to mitigate the negative effects of sedentary behavior. Casual exergames, which mix elements of casual gaming with physical activity, are one prospective intervention to reduce sedentary behavior because they require physical exertion. Casual exergames have shown promise in fostering emotional and physical advantages when played in specific circumstances. However, little research exists on how different types of movement interactions impact the psychological effects as well as the physical exertion of playing casual exergames. The primary aim of this work was to explore the psychological effects and physical exertion of playing casual exergames lasting 2 minutes. More precisely, the investigation focused on comparing upper body and full body movement interactions. In addition, the work examined variations in body positions, considering both standing and seated positions during upper body movement interactions. Two casual exergames were developed and investigated through 2 quasi-experimental studies. In study 1, we investigated how players' perceptions of control, exertion, and immersion were affected by using upper body as opposed to full body exergame controllers when playing casual exergames. In study 2, we investigated differences in positive affect, performance, enjoyment, and exertion when playing casual exergames with upper body movement interactions in seated and standing positions. Study 1 showed that perceived control was significantly higher for upper body movement interactions than for full body movement interactions (P=.04), but there were no significant differences regarding perceived exertion (P=.15) or immersion (P=.66). Study 2 showed that positive affect increased significantly for both standing (P=.003) and seated (P=.001) gameplay. The participants in the standing gameplay group showed slightly higher actual exertion; however, there were no differences between the groups in terms of positive affect, perceived exertion, enjoyment, or performance. Casual exergames controlled by upper body movement interactions in seated gameplay can produce similar psychological effects and physical exertion as upper body movement interactions in standing gameplay and full body movement interactions. Therefore, upper body and seated casual exergames should not be overlooked as a suitable microbreak activity.

Brain modulation after exergaming training in advanced forms of Parkinson’s disease: a randomized controlled study

BackgroundPhysical activity combined with virtual reality and exergaming has emerged as a new technique to improve engagement and provide clinical benefit for gait and balance disorders in people with Parkinson’s disease (PD).ObjectiveTo investigate the effects of a training protocol using a home-based exergaming system on brain volume and resting-state functional connectivity (rs-FC) in persons with PD.MethodsA single blind randomized controlled trial was conducted in people with PD with gait and/or balance disorders. The experimental (active) group performed 18 training sessions at home by playing a custom-designed exergame with full body movements, standing in front of a RGB-D Kinect® motion sensor, while the control group played using the computer keyboard. Both groups received the same training program. Clinical scales, gait recordings, and brain MRI were performed before and after training. We assessed the effects of both training on both the grey matter volumes (GVM) and rs-FC, within and between groups.ResultsTwenty-three patients were enrolled and randomly assigned to either the active (n = 11) or control (n = 12) training groups. Comparing pre- to post-training, the active group showed significant improvements in gait and balance disorders, with decreased rs-FC between the sensorimotor, attentional and basal ganglia networks, but with an increase between the cerebellar and basal ganglia networks. In contrast, the control group showed no significant changes, and rs-FC significantly decreased in the mesolimbic and visuospatial cerebellar and basal ganglia networks. Post-training, the rs-FC was greater in the active relative to the control group between the basal ganglia, motor cortical and cerebellar areas, and bilaterally between the insula and the inferior temporal lobe. Conversely, rs FC was lower in the active relative to the control group between the pedunculopontine nucleus and cerebellar areas, between the temporal inferior lobes and the right thalamus, between the left putamen and dorsolateral prefrontal cortex, and within the default mode network.ConclusionsFull-body movement training using a customized exergame induced brain rs-FC changes within the sensorimotor, attentional and cerebellar networks in people with PD. Further research is needed to comprehensively understand the neurophysiological effects of such training approaches.Trial registration ClinicalTrials.gov NCT03560089.

Multimodal Technologies for Remote Assessment of Neurological and Mental Health.

Automated remote assessment and monitoring of patients' neurological and mental health is increasingly becoming an essential component of the digital clinic and telehealth ecosystem, especially after the COVID-19 pandemic. This review article reviews various modalities of health information that are useful for developing such remote clinical assessments in the real world at scale. We first present an overview of the various modalities of health information-speech acoustics, natural language, conversational dynamics, orofacial or full body movement, eye gaze, respiration, cardiopulmonary, and neural-which can each be extracted from various signal sources-audio, video, text, or sensors. We further motivate their clinical utility with examples of how information from each modality can help us characterize how different disorders affect different aspects of patients' spoken communication. We then elucidate the advantages of combining one or more of these modalities toward a more holistic, informative, and robust assessment. We find that combining multiple modalities of health information allows for improved scientific interpretability, improved performance on downstream health applications such as early detection and progress monitoring, improved technological robustness, and improved user experience. We illustrate how these principles can be leveraged for remote clinical assessment at scale using a real-world case study of the Modality assessment platform. This review article motivates the combination of human-centric information from multiple modalities to measure various aspects of patients' health, arguing that remote clinical assessment that integrates this complementary information can be more effective and lead to better clinical outcomes than using any one data stream in isolation.

Development of full-body rhythmic synchronization in middle childhood

Rhythmic entrainment is a fundamental aspect of musical behavior, but the skills required to accurately synchronize movement to the beat seem to develop over many years. Motion capture studies of corporeal synchronization have shown immature abilities to lock in to the beat in children before age 5, and reliable synchronization ability in adults without musical training; yet there is a lack of data on full-body synchronization skills between early childhood and adulthood. To document typical rhythmic synchronization during middle childhood, we used a wireless motion capture device to measure period- and phase-locking of full body movement to rhythm and metronome stimuli in 6 to 11 year-old children in comparison with adult data. Results show a gradual improvement with age; however children’s performance did not reach adult levels by age 12, suggesting that these skills continue to develop during adolescence. Our results suggest that in the absence of specific music training, full-body rhythmic entrainment skills improve gradually during middle childhood, and provide metrics for examining the continued maturation of these skills during adolescence.

Predicting the bodily self in space and time

To understand how the human brain distinguishes itself from external stimulation, it was examined if motor predictions enable healthy adult volunteers to infer self-location and to distinguish their body from the environment (and other agents). By uniquely combining a VR-setup with full-body motion capture, a full-body illusion paradigm (FBI) was developed with different levels of motion control: (A) a standard, passive FBI in which they had no motion control; (B) an active FBI in which they made simple, voluntary movements; and (C) an immersive game in which they real-time controlled a human-sized avatar in third person. Systematic comparisons between measures revealed a causal relationship between (i) motion control (prospective agency), (ii) self-other identification, and (iii) the ability to locate oneself. Healthy adults could recognise their movements in a third-person avatar and psychologically align with it (action observation); but did not lose a sense of place (self-location), time (temporal binding), nor who they are (self/other). Instead, motor predictions enabled them to localise their body and to distinguish self from other. In the future, embodied games could target and strengthen the brain’s control networks in psychosis and neurodegeneration; real-time motion simulations could help advance neurorehabilitation techniques by fine-tuning and personalising therapeutic settings.

EMOKINE: A software package and computational framework for scaling up the creation of highly controlled emotional full-body movement datasets

EMOKINE is a software package and dataset creation suite for emotional full-body movement research in experimental psychology, affective neuroscience, and computer vision. A computational framework, comprehensive instructions, a pilot dataset, observer ratings, and kinematic feature extraction code are provided to facilitate future dataset creations at scale. In addition, the EMOKINE framework outlines how complex sequences of movements may advance emotion research. Traditionally, often emotional-‘action’-based stimuli are used in such research, like hand-waving or walking motions. Here instead, a pilot dataset is provided with short dance choreographies, repeated several times by a dancer who expressed different emotional intentions at each repetition: anger, contentment, fear, joy, neutrality, and sadness. The dataset was simultaneously filmed professionally, and recorded using XSENS® motion capture technology (17 sensors, 240 frames/second). Thirty-two statistics from 12 kinematic features were extracted offline, for the first time in one single dataset: speed, acceleration, angular speed, angular acceleration, limb contraction, distance to center of mass, quantity of motion, dimensionless jerk (integral), head angle (with regards to vertical axis and to back), and space (convex hull 2D and 3D). Average, median absolute deviation (MAD), and maximum value were computed as applicable. The EMOKINE software is appliable to other motion-capture systems and is openly available on the Zenodo Repository. Releases on GitHub include: (i) the code to extract the 32 statistics, (ii) a rigging plugin for Python for MVNX file-conversion to Blender format (MVNX=output file XSENS® system), and (iii) a Python-script-powered custom software to assist with blurring faces; latter two under GPLv3 licenses.

Evaluating Gesture Generation in a Large-scale Open Challenge: The GENEA Challenge 2022

This article reports on the second GENEA Challenge to benchmark data-driven automatic co-speech gesture generation. Participating teams used the same speech and motion dataset to build gesture-generation systems. Motion generated by all these systems was rendered to video using a standardised visualisation pipeline and evaluated in several large, crowdsourced user studies. Unlike when comparing different research articles, differences in results are here only due to differences between methods, enabling direct comparison between systems. The dataset was based on 18 hours of full-body motion capture, including fingers, of different persons engaging in a dyadic conversation. Ten teams participated in the challenge across two tiers: full-body and upper-body gesticulation. For each tier, we evaluated both the human-likeness of the gesture motion and its appropriateness for the specific speech signal. Our evaluations decouple human-likeness from gesture appropriateness, which has been a difficult problem in the field. The evaluation results show some synthetic gesture conditions being rated as significantly more human-like than 3D human motion capture. To the best of our knowledge, this has not been demonstrated before. On the other hand, all synthetic motion is found to be vastly less appropriate for the speech than the original motion-capture recordings. We also find that conventional objective metrics do not correlate well with subjective human-likeness ratings in this large evaluation. The one exception is the Fréchet gesture distance (FGD), which achieves a Kendall’s tau rank correlation of around -0.5. Based on the challenge results we formulate numerous recommendations for system building and evaluation.

Quantifying the traverse: a comprehensive kinematic analysis of climbing technique

Biomechanical analysis plays a pivotal role in enhancing performance and preventing injuries in rock climbing. Despite a recent increase in popularity, there remains a lack of research quantifying full-body movements, resulting in uncertainty regarding biomechanical demands and variability. It should be noted that there is a scarcity of data in climbing research to quantify optimal techniques, particularly for traverses. This study aims to address this gap by examining the demands of a traverse in a controlled setting using Vicon marker-based three-dimensional (3D) motion capture. Fourteen experienced climbers (32 ± 13 years, 70 ± 9 kg) with varying skill levels (intermediate to advanced) and climbing backgrounds (sport climbing, trad climbing and bouldering) completed a two-minute standardised traverse on a custom-built Lattice circuit (2440 × 2440 × 1220 mm). A twelve-camera (200 Hz, Vantage) motion analysis system (Vicon, Motion Systems Ltd. Oxford, United Kingdom) acquired kinematic data. Retroreflective markers were attached to anatomical landmarks of the upper and lower body. The trials were preceded by a 10-minute self-selected warmup. This data will be analysed to assess the degree of inter-participant variability, observing if there is the possibility of quantifying an optimal traversing technique. Intra-participant variability will be assessed, to measure the effect of fatigue on the climbing movement. Previous literature has alluded to the possibility of fatigue increasing the use of the legs when climbing as compensation. Comparing the beginning of the traverse to the end, data should further substantiate this claim. This research will offer insights into the kinematic demands of rock climbing, laying the groundwork for further studies on a larger scale to assess the possibility of optimising technique, performance, and injury strategies. This study contributes to bridging the vast gap between biomechanical research and practical applications in the climbing community, facilitating a more evidence-based approach to performance.

Self-powered porous polymer sensors with high sensitivity for machine learning-assisted motion and rehabilitation monitoring

Muscle contraction and relaxation inherently contains valuable information crucial for monitoring physical states, rehabilitation, and injury prevention. However, the majority of flexible wearable devices struggle to offer customizable sensors with high sensitivity, durability, and stability, resulting in suboptimal biofeedback performance. Herein, the machine learning-assisted smart motion and rehabilitation monitoring system (SMRMS) is demonstrated for full-body motion recognition and rehabilitation assessment using a designed porous triboelectric nanogenerator (TENG) array. A theoretical model of porous materials is built to demonstrate the effect mechanism of porosity on TENG output. Through pore design on the tribolayer, theoretical simulation and experimental are performed to determine the key features of porous TENG sensors with high sensitivity (1.76 kPa−1), fast response time (50 ms) and high durability (over 100,000 cycles). A ring-shaped TENG (RS-TENG) is fabricated based on the porous TENG sensor, enabling real-time recording of leg/arm force without additional attachment. By integrating the RS-TENG array with a multichannel signal acquisition system, an SMRMS is designed to capture motion information during human subjects’ exercise and rehabilitation training. These data are then fused for machine learning analytics, resulting in significantly improved accuracy in motion pattern recognition (98.75 %) and rehabilitation monitoring (100 %). The simple, precise and durable porous sensors could help mitigate the risk of excessive exercise-induced muscle injuries, expanding self-powered wearable functionalities and adaptabilities.

Full-body kinematics and head stabilisation strategies during walking in patients with chronic unilateral and bilateral vestibulopathy

Chronic imbalance is a frequent and limiting symptom of patients with chronic unilateral and bilateral vestibulopathy. A full-body kinematic analysis of the movement of patients with vestibulopathy would provide a better understanding of the impact of the pathology on dynamic tasks such as walking. Therefore, this study aimed to investigate the global body movement during walking, its variability (assessed with the GaitSD), and the strategies to stabilise the head (assessed with the head Anchoring Index). The full-body motion capture data of 10 patients with bilateral vestibulopathy (BV), 10 patients with unilateral vestibulopathy (UV), and 10 healthy subjects (HS) walking at several speeds (slow, comfortable, and fast) were analysed in this prospective cohort study. We observed only a few significant differences between groups in parts of the gait cycle (shoulder abduction–adduction, pelvis rotation, and hip flexion–extension) during the analysis of kinematic curves. Only BV patients had significantly higher gait variability (GaitSD) for all three walking speeds. Head stabilisation strategies depended on the plan of motion and walking speed condition, but BV and UV patients tended to stabilise their head in relation to the trunk and HS tended to stabilise their head in space. These results suggest that GaitSD could be a relevant biomarker of chronic instability in BV and that the head Anchoring Index tends to confirm clinical observations of abnormal head-trunk dynamics in patients with vestibulopathy while walking.

DTP: learning to estimate full-body pose in real-time from sparse VR sensor measurements

For virtual reality (VR) applications, estimating full-body pose in real-time is becoming increasingly popular. Previous works have reconstructed full-body motion in real time from an HTC VIVE headset and five VIVE Tracker measurements by solving the inverse kinematics (IK) problem. However, an IK solver may yield unnatural poses and shaky motion. This paper introduces Deep Tracker poser (DTP): a method for real-time full-body pose estimation in VR. This task is difficult due to the ambiguous mapping from the sparse measurements to full-body pose. The data obtained from VR sensors is calibrated, normalized and fed into the deep neural networks (DNN). To learn from sufficient data, we propose synthesizing a VR sensor dataset called AMASS-VR from the AMASS, a collection of various motion capture datasets. Furthermore, feet tracking loss is a common problem of VIVE Tracker. To improve the accuracy and robustness of DTP to the occlusion noise, we simulate the occlusion noise by Gaussian random noise. Then we synthesize an occlusion dataset AMASS-OCC and fine-tune DTP on that. We evaluate DTP by comparing with other popular methods in terms of the accuracy and computational cost. The results indicate that DTP outperforms others in terms of the positional error (1.04 cm) and rotational error (4.22 °). The quantitative and qualitative results show that DTP reconstructs accurate and natural full-body pose even under serious feet occlusion, which indicates the superiority of the DTP in modelling the mapping from sparse joint data to the full-body pose.

A pose estimation for motion tracking of infants cerebral palsy

AbstractThe General Movements Analysis (GMA) has demonstrated noteworthy promise in the early detection of infantile Cerebral Palsy (CP). However, it is subjective and requires highly trained clinicians, making it costly and time-consuming. Automation of GMA could potentially enhance accessibility and further our comprehension of infants’ full-body movements. This paper investigates the feasibility of using 2D and 3D pose estimation strategies to observe and scrutinize the infant’s comprehensive body movement attributes to improve our perspective to consider joint movement and positions over time as an alternative to GMA for early CP prediction. The study includes comprehensive movement analysis from video recordings for accurate and efficient analysis of infant movement by computing various metrics such as angle orientations at different predicted joint locations, postural information, postural variability, movement velocity, movement variability, and left–right movement coordination. Along with antigravity movements are assessed and tracked as indicators of CP. We employed a variety Machine Learning (ML) algorithms for CP classification based on a series of robust features that have been developed to enhance the interpretability of the model. The proposed approach is assessed through experimentation using the MINI-RGBD and RVI-38 datasets with a classification accuracy of 92% and 97.37% respectively. These results substantiate the efficacy of employing pose estimation techniques for the precocious prediction of infantile CP, highlighting the importance of monitoring changes in joint angles over time for accurate diagnosis and treatment planning.

Full-Body Motion Reconstruction with Sparse Sensing from Graph Perspective

Estimating 3D full-body pose from sparse sensor data is a pivotal technique employed for the reconstruction of realistic human motions in Augmented Reality and Virtual Reality. However, translating sparse sensor signals into comprehensive human motion remains a challenge since the sparsely distributed sensors in common VR systems fail to capture the motion of full human body. In this paper, we use well-designed Body Pose Graph (BPG) to represent the human body and translate the challenge into a prediction problem of graph missing nodes. Then, we propose a novel full-body motion reconstruction framework based on BPG. To establish BPG, nodes are initially endowed with features extracted from sparse sensor signals. Features from identifiable joint nodes across diverse sensors are amalgamated and processed from both temporal and spatial perspectives. Temporal dynamics are captured using the Temporal Pyramid Structure, while spatial relations in joint movements inform the spatial attributes. The resultant features serve as the foundational elements of the BPG nodes. To further refine the BPG, node features are updated through a graph neural network that incorporates edge reflecting varying joint relations. Our method's effectiveness is evidenced by the attained state-of-the-art performance, particularly in lower body motion, outperforming other baseline methods. Additionally, an ablation study validates the efficacy of each module in our proposed framework.

Biomechanical stresses on healthcare workers during manual patient bed-to-chair transfers

Healthcare workers are highly susceptible to musculoskeletal injury, particularly in their lower back and shoulders. Manual patient transfers are common and can generate physical stresses that contribute to these injuries. Few studies have used in vivo musculoskeletal modeling to estimate the effect of slide boards and patient cooperation, and none have used measured hand forces as an input to the models. This laboratory study evaluated manual, one-person bed to wheelchair transfers of a 64 kg simulated patient using an instrumented gait belt that measured hand forces. Thirteen healthcare workers performed transfers with and without a slide board and with up to three levels of vertical assistance (0, 18, and 36% of patient body weight). In vivo lower back forces and resultant shoulder moments were estimated with a thoracolumbar musculoskeletal model using directly measured hand forces and full-body motion capture. Results indicated that slide boards and vertical assistance reduce physical stresses. However, all transfer conditions had trials that exceeded an ergonomic guideline. To provide some guidance on when a transfer can safely be performed manually, a post hoc analysis was performed to estimate the patient mass that can be safely transferred manually under ideal circumstances with only a gait belt. These findings have the potential to guide and credibly educate healthcare workers on when manual transfers are appropriate and when lifts are required. Regardless, mechanical lifts are still recommended in most circumstances to protect caregivers from injury and the patient from falling.

Fast Human Motion reconstruction from sparse inertial measurement units considering the human shape

Inertial Measurement Unit-based methods have great potential in capturing motion in large-scale and complex environments with many people. Sparse Inertial Measurement Unit-based methods have more research value due to their simplicity and flexibility. However, improving the computational efficiency and reducing latency in such methods are challenging. In this paper, we propose Fast Inertial Poser, which is a full body motion estimation deep neural network based on 6 inertial measurement units considering body parameters. We design a network architecture based on recurrent neural networks according to the kinematics tree. This method introduces human body shape information by the causality of observations and eliminates the dependence on future frames. During the estimation of joint positions, the upper body and lower body are estimated using separate network modules independently. Then the joint rotation is obtained through a well-designed single-frame kinematics inverse solver. Experiments show that the method can greatly improve the inference speed and reduce the latency while ensuring the reconstruction accuracy compared with previous methods. Fast Inertial Poser runs at 65 fps with 15 ms latency on an embedded computer, demonstrating the efficiency of the model.