Full-body Motion Research Articles

Miniaturized wireless, skin-integrated sensor networks for quantifying full-body movement behaviors and vital signs in infants

Early identification of atypical infant movement behaviors consistent with underlying neuromotor pathologies can expedite timely enrollment in therapeutic interventions that exploit inherent neuroplasticity to promote recovery. Traditional neuromotor assessments rely on qualitative evaluations performed by specially trained personnel, mostly available in tertiary medical centers or specialized facilities. Such approaches are high in cost, require geographic proximity to advanced healthcare resources, and yield mostly qualitative insight. This paper introduces a simple, low-cost alternative in the form of a technology customized for quantitatively capturing continuous, full-body kinematics of infants during free living conditions at home or in clinical settings while simultaneously recording essential vital signs data. The system consists of a wireless network of small, flexible inertial sensors placed at strategic locations across the body and operated in a wide-bandwidth and time-synchronized fashion. The data serve as the basis for reconstructing three-dimensional motions in avatar form without the need for video recordings and associated privacy concerns, for remote visual assessments by experts. These quantitative measurements can also be presented in graphical format and analyzed with machine-learning techniques, with potential to automate and systematize traditional motor assessments. Clinical implementations with infants at low and at elevated risks for atypical neuromotor development illustrates application of this system in quantitative and semiquantitative assessments of patterns of gross motor skills, along with body temperature, heart rate, and respiratory rate, from long-term and follow-up measurements over a 3-mo period following birth. The engineering aspects are compatible for scaled deployment, with the potential to improve health outcomes for children worldwide via early, pragmatic detection methods.

Effects of Computerized Emotional Training on Children with High Functioning Autism

An evaluation study of a serious game and a system for the automatic emotion recognition designed for helping autistic children to learn to recognize and express emotions by means of their full-body movement is presented. Three-dimensional motion data of full-body movements are obtained from RGB-D sensors and used to recognize emotions by means of linear SVMs. Ten children diagnosed with High Functioning Autism or Asperger Syndrome were involved in the evaluation phase, consisting of repeated sessions to play a specifically designed serious game. Results from the evaluation study show an increase of tasks accuracy from the beginning to the end of training sessions in the trained group. In particular, while the increase of recognition accuracy was concentrated in the first sessions of the game, the increase for expression accuracy is more gradual throughout all sessions. Moreover, the training seems to produce a transfer effect on facial expression recognition.

A markerless platform for ambulatory systems neuroscience.

Motor systems neuroscience seeks to understand how the brain controls movement. To minimize confounding variables, large-animal studies typically constrain body movement from areas not under observation, ensuring consistent, repeatable behaviors. Such studies have fueled decades of research, but they may be artificially limiting the richness of neural data observed, preventing generalization to more natural movements and settings. Neuroscience studies of unconstrained movement would capture a greater range of behavior and a more complete view of neuronal activity, but instrumenting an experimental rig suitable for large animals presents substantial engineering challenges. Here, we present a markerless, full-body motion tracking and synchronized wireless neural electrophysiology platform for large, ambulatory animals. Composed of four depth (RGB-D) cameras that provide a 360° view of a 4.5-square-meters enclosed area, this system is designed to record a diverse range of neuroethologically relevant behaviors. This platform also allows for the simultaneous acquisition of hundreds of wireless neural recording channels in multiple brain regions. As behavioral and neuronal data are generated at rates below 200 megabytes per second, a single desktop can facilitate hours of continuous recording. This setup is designed for systems neuroscience and neuroengineering research, where synchronized kinematic behavior and neural data are the foundation for investigation. By enabling the study of previously unexplored movement tasks, this system can generate insights into the functioning of the mammalian motor system and provide a platform to develop brain-machine interfaces for unconstrained applications.

Neural animation layering for synthesizing martial arts movements

Interactively synthesizing novel combinations and variations of character movements from different motion skills is a key problem in computer animation. In this paper, we propose a deep learning framework to produce a large variety of martial arts movements in a controllable manner from raw motion capture data. Our method imitates animation layering using neural networks with the aim to overcome typical challenges when mixing, blending and editing movements from unaligned motion sources. The framework can synthesize novel movements from given reference motions and simple user controls, and generate unseen sequences of locomotion, punching, kicking, avoiding and combinations thereof, but also reconstruct signature motions of different fighters, as well as close-character interactions such as clinching and carrying by learning the spatial joint relationships. To achieve this goal, we adopt a modular framework which is composed of the motion generator and a set of different control modules. The motion generator functions as a motion manifold that projects novel mixed/edited trajectories to natural full-body motions, and synthesizes realistic transitions between different motions. The control modules are task dependent and can be developed and trained separately by engineers to include novel motion tasks, which greatly reduces network iteration time when working with large-scale datasets. Our modular framework provides a transparent control interface for animators that allows modifying or combining movements after network training, and enables iterative adding of control modules for different motion tasks and behaviors. Our system can be used for offline and online motion generation alike, and is relevant for real-time applications such as computer games.

Robust Foot Motion Recognition Using Stride Detection and Weak Supervision-Based Fast Labeling

Foot motion recognition in daily life faces two challenges imposed by traditional machine learning frameworks: how to robustly recognize various foot motions from continuous movements in uncontrolled environments, and how to accurately extract ground truths. To address these challenges, in this paper, we propose a stride detection method to robustly identify each stride (over 99% accuracy). We then investigate two weak supervision-based fast labeling frameworks to automatically label the stride segmentations. Finally, we use these two frameworks to identify foot motions from continuous movements integrated on a route map. The route map can be replaced by a virtual-reality video game to play in daily life so that the user's long-term foot functionality can be profiled and evaluated. We test our proposed approaches using a smart insole with twenty-two subjects whose movement data are collected through the route map setting while video camera recordings serve as the ground truth. The route map integrates seven foot motions in one complete play, which includes three continuous motions resulted from continuous full-body movements (named as continuous motions) and four intermittent foot motions that are produced repetitively (named as repetitive motions). Compared to the best traditional machine learning methods, our proposed approach improves the leave-one-subject-out cross-validation accuracy of all subjects by 6.12% for the three continuous motions, 2.71% for the four repetitive motions and 4.90% for the total of seven foot motions. In addition, our proposed method saves 25% to 50% time in data labeling.

Inertial Measurement Unit Sensors in Assistive Technologies for Visually Impaired People, a Review.

A diverse array of assistive technologies have been developed to help Visually Impaired People (VIP) face many basic daily autonomy challenges. Inertial measurement unit sensors, on the other hand, have been used for navigation, guidance, and localization but especially for full body motion tracking due to their low cost and miniaturization, which have allowed the estimation of kinematic parameters and biomechanical analysis for different field of applications. The aim of this work was to present a comprehensive approach of assistive technologies for VIP that include inertial sensors as input, producing results on the comprehension of technical characteristics of the inertial sensors, the methodologies applied, and their specific role in each developed system. The results show that there are just a few inertial sensor-based systems. However, these sensors provide essential information when combined with optical sensors and radio signals for navigation and special application fields. The discussion includes new avenues of research, missing elements, and usability analysis, since a limitation evidenced in the selected articles is the lack of user-centered designs. Finally, regarding application fields, it has been highlighted that a gap exists in the literature regarding aids for rehabilitation and biomechanical analysis of VIP. Most of the findings are focused on navigation and obstacle detection, and this should be considered for future applications.

CoolMoves

Current Virtual Reality (VR) systems are bereft of stylization and embellishment of the user's motion - concepts that have been well explored in animations for games and movies. We present CooIMoves, a system for expressive and accentuated full-body motion synthesis of a user's virtual avatar in real-time, from the limited input cues afforded by current consumer-grade VR systems, specifically headset and hand positions. We make use of existing motion capture databases as a template motion repository to draw from. We match similar spatio-temporal motions present in the database and then interpolate between them using a weighted distance metric. Joint prediction probability is then used to temporally smooth the synthesized motion, using human motion dynamics as a prior. This allows our system to work well even with very sparse motion databases (e.g., with only 3-5 motions per action). We validate our system with four experiments: a technical evaluation of our quantitative pose reconstruction and three additional user studies to evaluate the motion quality, embodiment and agency.

Dynamic stability during stair negotiation after total knee arthroplasty

BackgroundThe assessment of dynamic stability is crucial for the prevention of falls in the elderly and people with functional impairments. Evidence that total knee arthroplasty improves balance in patients with severe osteoarthritis is scarce and no information exists about how the surgery affects dynamic stability during stair negotiation. MethodsThis study aims to investigate if patients before and one year after surgery are less stable compared to asymptomatic controls. Seventeen control and twenty-seven patient participants with end-stage knee osteoarthritis that were scheduled to undergo unilateral total knee arthroplasty were recruited in this study. Participants' assessment was carried out by means of marker-based optical full-body motion capture with force platforms. The extrapolated Centre of mass and the margin of stability metrics were used to examine dynamic stability during stair ascent and descent. FindingsPatient participants, during both pre-operative and post-operative assessments, were equally balanced to the asymptomatic controls during stair gait (p > .188). Additionally, the patients' overall stability did not improve significantly one year after arthroplasty surgery (p > .252). InterpretationEven if pain from arthritis and fear of falling is decreased following surgery, our results indicate that stability in stair walking in not affected by osteoarthritis and total knee arthroplasty.Clinical trial registration number: NCT02422251.

Gaze behavior during pedestrian interactions in a community environment: a real-world perspective.

Locomotor adaptations, as required for community walking, rely heavily on the sense of vision. Little is known, however, about gaze behavior during pedestrian interactions while ambulating in the community. Our objective was to characterize gaze behavior while walking in a community environment and interacting with pedestrians of different locations and directions. Twelve healthy young individuals were assessed as they walked in a shopping mall from a pre-set location to a goal located 20m ahead. Eye movements were recorded with a binocular eye-tracker and temporal distance factors were assessed using wearable sensors from a full-body motion capture system. Participants exhibited more numerous and longer gaze episodes on pedestrians (GEP) that were walking in the same direction as themselves vs. those that were in the opposite direction. The relative durations of GEPs, however, showed no significant differences between pedestrians walking in the same vs. opposite direction. Longer durations of GEPs were also observed for centrally located pedestrians compared to those located on either side, but this was the case only for pedestrians that were walking in the same direction as participants. In addition, pedestrians in the centre, and even more so those on the right, were fixated at farther distances compared to those on the left. Results indicate that healthy young individuals modulate their gaze behavior as a function of the location and direction of pedestrians when ambulating in a community environment. The observed modulation is interpreted as being caused by an interplay between collision risk, pedestrian visibility, presence of leaders and social conventions (right-sided circulation). Present results also establish baseline measures for the quantification of defective visuomotor strategies in individuals with mobility disorders.

Analysis of EEG-EMG Coherence in Low Frequency Bands.

In this, study, an attempt is made to analyze the corticomuscular coupling of the brain and muscular system in the low-frequency components during ramp descent (RD) and stair descent (SD) locomotion. For this purpose, magnitude squared coherence (MSC) is computed from the simultaneous EEG and EMG signals recorded during the ramp and stair descent tasks. The MSC is extracted from the low- frequency bands such as delta (0.1-3 Hz) and theta bands (4-7 Hz). The study utilizes a publicly available database consisting of simultaneous recorded EEG, lower limb EMG and full body motion information from ten healthy subjects. The results show that there exists corticomuscular coupling between motor cortex (C1, C2 and Cz contacts) and tibialis anterior muscle activities during RD and SD. In addition, the MSC differs for both the tasks and frequency bands. In delta band frequencies, the MSC is found to be higher in C2 regions. In the case of theta, the MSC is higher in C1 during RD and in Cz during SD. Therefore, the MSC associated with the low frequency components could be used to detect walking intentions.

Experimental and Modeling Analyses of Human Motion Across the Static Magnetic Field of an MRI Scanner.

It is established that human movements in the vicinity of a permanent static magnetic field, such as those in magnetic resonance imaging (MRI) scanners induce electric fields in the human body; this raises potential severe risks of health to radiographers and cleaners exposed routinely to these fields in MRI rooms. The relevant directives and parameters, however, are based on theoretical models, and accurate studies on the simulation of the effects based on human movement data obtained in real conditions are still lacking. Two radiographers and one cleaner, familiar with MRI room activities and these directives, were gait analyzed during the execution of routine job motor tasks at different velocities. Full body motion was recorded in a gait laboratory arranged to reproduce the workspace of a room with an MRI full-body scanner. Body segments were tracked with clusters of at least three markers, from which position and velocity of the centroids were calculated. These were used as input in an established computer physical model able to map the stray field in an MRI room. The spatial peak values of the calculated electric field induced by motion of the head and of the entire body during these tasks, for both the health and sensory effects, were found smaller than the thresholds recommended by the European directives, for both 1.5 T and 3.0 T MRI. These tasks therefore seem to guarantee the safety of MRI room operators according to current professional good practice for exposure risks. Physical modeling and experimental measures of human motion can also support occupational medicine.

Motion based message carrier for disabled people

We come across many disabled people in NGO’s and hospitals. These people find difficult to convey message as they cannot make full body movement like normal people. Looking at such condition we tried to make a system which can be used to convey messages on behalf of such people. This system give different messages by simple movement of hand of the person. The device is placed on person’s hand so the he can tilt it in particular direction. By tilting the device in different direction in a specified range of angle different messages are displayed. Here an accelerometer is used to measure the tilt. The data from the accelerometer is then passed to the microcontroller. The microcontroller takes the information, process the data and shows the particular message depending on the tilt by the person. The microcontroller send data to a LCD screen and desired message is displayed. On the receiver side along with LCD screen a buzzer is also present and in gets on as soon as it receives signal from the accelerometer. On person’s hand the device consists of an RF transmitter which is used to transfer the data signal in encoded form. On the receiving end the RF receiver gets the data from the transmitter and then decodes it and which is then passed to the microcontroller for reading the data, processing the data and responding to it.

Biomechanical joint loading mechanism of Tai Chi gait in individuals with knee osteoarthritis: A pilot simulation study

ObjectivesTai Chi (TC) has shown beneficial effects on joint function in knee osteoarthritis (OA). Biomechanical mechanisms of knee joint contact load (JCL) and muscle activations during TC are less understood. The purpose of this biomechanical simulation study was to examine JCL of TC gait, the most common used TC from and its causal interactions with muscle activations in knee OA. DesignSix knee OA and five healthy participants were recruited. Their full body motion of TC gait was collected. The JCL and muscle forces were quantified using a musculoskeletal simulation approach based on collected kinematics and kinetics. The JCL and muscle activations were compared between knee OA and healthy control group. In addition, the muscle contributions to the JCL were determined and compared between the two groups. ResultsKnee OA subjects had lower peak anterior-posterior shear forces and higher lateral shear forces than healthy control subjects during TC gait. Knee OA subjects also showed higher activations of knee flexor muscles than control subjects. Both knee extensor and flexors of the knee OA group were contributing to JCL and in the control group mainly the knee extensors. ConclusionsOur simulation results showed the JCL, muscle forces profiles, and muscle contributions to the JCL during TC gait in knee OA. The findings of this study provided a direct scientific link between JCL and muscle forces during TC gait in knee OA. This would allow us to develop more effective TC interventions for knee OA in the future.

Emotion Transfer for 3D Hand and Full Body Motion Using StarGAN

In this paper, we propose a new data-driven framework for 3D hand and full-body motion emotion transfer. Specifically, we formulate the motion synthesis task as an image-to-image translation problem. By presenting a motion sequence as an image representation, the emotion can be transferred by our framework using StarGAN. To evaluate our proposed method’s effectiveness, we first conducted a user study to validate the perceived emotion from the captured and synthesized hand motions. We further evaluate the synthesized hand and full body motions qualitatively and quantitatively. Experimental results show that our synthesized motions are comparable to the captured motions and those created by an existing method in terms of naturalness and visual quality.

A Hybrid Tracking System of Full-Body Motion Inside Crowds.

For our understanding of the dynamics inside crowds, reliable empirical data are needed, which could enable increases in safety and comfort for pedestrians and the design of models reflecting the real dynamics. A well-calibrated camera system can extract absolute head position with high accuracy. The inclusion of inertial sensors or even self-contained full-body motion capturing systems allows the relative tracking of invisible people or body parts or capturing the locomotion of the whole body even in dense crowds. The newly introduced hybrid system maps the trajectory of the top of the head coming from a full-body motion tracking system to the head trajectory of a camera system in global space. The fused data enable the analysis of possible correlations of all observables. In this paper we present an experiment of people passing though a bottleneck and show by example the influences of bottleneck width and motivation on the overall movement, velocity, stepping locomotion and rotation of the pelvis. The hybrid tracking system opens up new possibilities for analyzing pedestrian dynamics inside crowds, such as the space requirement while passing through a bottleneck. The system allows linking any body motion to characteristics describing the situation of a person inside a crowd, such as the density or movements of other participants nearby.

Striatal Direct Pathway Targets Npas1+ Pallidal Neurons.

The classic basal ganglia circuit model asserts a complete segregation of the two striatal output pathways. Empirical data argue that, in addition to indirect-pathway striatal projection neurons (iSPNs), direct-pathway striatal projection neurons (dSPNs) innervate the external globus pallidus (GPe). However, the functions of the latter were not known. In this study, we interrogated the organization principles of striatopallidal projections and their roles in full-body movement in mice (both males and females). In contrast to the canonical motor-promoting response of dSPNs in the dorsomedial striatum (DMSdSPNs), optogenetic stimulation of dSPNs in the dorsolateral striatum (DLSdSPNs) suppressed locomotion. Circuit analyses revealed that dSPNs selectively target Npas1+ neurons in the GPe. In a chronic 6-hydroxydopamine lesion model of Parkinson's disease, the dSPN-Npas1+ projection was dramatically strengthened. As DLSdSPN-Npas1+ projection suppresses movement, the enhancement of this projection represents a circuit mechanism for the hypokinetic symptoms of Parkinson's disease that has not been previously considered. In sum, our results suggest that dSPN input to the GPe is a critical circuit component that is involved in the regulation of movement in both healthy and parkinsonian states.SIGNIFICANCE STATEMENT In the classic basal ganglia model, the striatum is described as a divergent structure: it controls motor and adaptive functions through two segregated, opposing output streams. However, the experimental results that show the projection from direct-pathway neurons to the external pallidum have been largely ignored. Here, we showed that this striatopallidal subpathway targets a select subset of neurons in the external pallidum and is motor-suppressing. We found that this subpathway undergoes changes in a Parkinson's disease model. In particular, our results suggest that the increase in strength of this subpathway contributes to the slowness or reduced movements observed in Parkinson's disease.

Multi-segment phase coupling to oscillatory visual drive

BackgroundIt has been shown that humans adapt their postural sway to oscillatory, visually simulated self-motion. However, little is still known about the way individual body segments contribute to this adjustment of body sway and how this contribution varies with different environmental conditions. Research questionHow do the centre of pressure (COP) and individual body segments phase-lock to a sinusoidal visual drive depending on the frequency of stimulation? MethodsIn this study, we introduce phase coupling as a method for assessing full body motion in response to visual stimuli presented in virtual reality (VR). 12 participants (mean age: 31 ± 9 years) stood inside a virtual tunnel which oscillated sinusoidally in the anterior-posterior direction at a frequency of 0.2 Hz, 0.8 Hz or 1.2 Hz. Primary outcome measures were the trajectories of their COP as well as of 25 body segments obtained by a motion tracking system. ResultsSubjects significantly coupled the phase of their COP and body segments to the visual drive. Our analysis yielded significant phase coupling of the COP to the stimulus for all tested frequencies. The phase coupling of body segments revealed a shift in postural response as a function of frequency. At the low frequency of 0.2 Hz, we found strong and significant phase coupling homogeneously distributed across the body. At the higher frequencies of 0.8 Hz and 1.2 Hz, however, overall phase coupling became weaker and was centred around the lower torso and hip segments. SignificanceInformation on how the visual percept of self-motion affects balance control is crucial for understanding visuomotor processing in health and disease. Our setup and methods constitute a reliable tool for assessing perturbed balance control, which can be utilized in future clinical trials.

Knee joint biomechanics of simplified 24 Tai Chi forms and association with pain in individuals with knee osteoarthritis: A pilot study

ObjectivesTai Chi (TC) is a multi-beneficial exercise for improving health and function in knee osteoarthritis (OA). Biomechanical insights of 24 ​TC forms at the knee joint are not well understood. We aimed to examine knee joint biomechanics of TC actions form by form and their interactions with pain in individuals with knee OA. MethodsTen knee OA participants were recruited. Their full body motion during performance of 24 ​TC forms was collected. The knee joint biomechanics were determined by using an inverse dynamic approach based on collected full body kinematics and kinetics. In addition, the knee joint pain level was scored during each TC form. The joint moments were compared between walking trials and each TC form. The relationship between knee joint biomechanics and pain scale was assessed. ResultsThe knee adduction moment for five TC forms was different from the walking trial. The knee extension moment for 21 ​TC forms differed from the walking trial. For TC trials, the knee extension moment, but not the adduction moment, was positively correlated with pain level. Similarly, the knee extension moment was moderately proportional to pain level during the walking trials, but not the adduction moment. ConclusionsOur pilot results explored the knee joint biomechanics profiles of individual TC forms and examined their associations with knee joint pain. The findings in this study could provide scientific basis to select the best TC forms for the purpose of reducing knee joint pain among individuals with knee OA.

Fall detecting clothes in realtime based seniors full body motion capture system using multiple inertial sensors

The physical ability of the elderly will decrease along with the aging process, thus increasing the potential for falls. An elderly person who has experienced a fall and is not detected for a long time will bring many possible consequences. Early detection of an elderly fall will help to minimize this possibility by reducing the time between the occurrence of an event and the arrival of medical assistance The purpose of this research is to build a Fall Detecting Clothes in Realtime Based Art Full Body Motion Capture System Using Multiple Inertial Sensors. This system builds by Waterfall methodology where every research step is done in sequence, start from analysis, design, coding, testing. Testing methodology is black box, which is a test every feature function. The Fall Detecting Clothes in Real Time Based Systems Full Body Motion Capture System Using Multiple Inertial Sensors has been tested using black testing box with the results of all feature functions working properly. The system has been tested with several conditions with the results of all controls and output data works properly as well.

Gait and Full Body Movement Dataset of Autistic Children Classified by Rough Set Classifier

Gait and body movement are a window to human brain which make these activities unique for each person. These activities are used to diagnose some disorders related to parts of brain which causes have not been known such as Autism Disorders (AD). The traditional diagnostic methods of AD are time-consuming and highly dependent on clinician’s judgment which is based on behaviour assessment. This approach leads to subjective interpretations that differ from doctor to another and affect by strengths and weaknesses of patient. Therefore this paper aims to diagnosis of AD based on gait and body movement analysis. At first, Kinect v2 uses to create a 3D dataset, which includes three dimensional joints positions, joints trajectories video, skeleton movement video captured by Kinect v2, and color videos captured by Samsung Note 9 camera. This paper also aims to classify children with autism from normal children by proposed system based on four stages: Augmentation of the database by using seven transformations to deal with small number of autism cases; Extracting features that we think play an important role in classification; Reducing data dimensions using Principal Component Analysis; using Rough Set to classify dataset. Results show that classification is 92% after augmentation.