Motion Tracking Data Research Articles

Learning Motion Primitives for the Quantification and Diagnosis of Mobility Deficits.

The severity of mobility deficits is one of the most critical parameters in the diagnosis and rehabilitation of Parkinson's disease (PD). The current approach for severity evaluation is clinical scaling that relies on a clinician's subjective observations and experience, and the observation in laboratories or clinics may not suffice to reflect the severity of motion deficits as compared to daily living activities. The paper presents an approach to modeling and quantifying the severity of mobility deficits from motion data by using nonintrusive wearable physio-biological sensors. The approach provides a user-specific metric that measures mobility deficits in terms of the quantities of motion primitives that are learned from motion tracking data. The proposed method achieved 99.84% prediction accuracy on laboratory data and 93.95% prediction accuracy on clinical data. This approach presents the potential to supplant traditional observation-based clinical scaling, providing an avenue for real-time feedback to fortify positive progression throughout the course of rehabilitation.

BORS/BJR TRAVELLING FELLOWSHIP ABSTRACT: MOTION CAPTURE OF NEONATAL INFANT KICKING MOVEMENTS CAN PROVIDE AN EARLY PREDICTION OF CEREBRAL PALSY

AbstractObjectivesNeonatal motor development transitions from initially spontaneous to later increasingly complex voluntary movements. A delay in transitioning may indicate cerebral palsy (CP). The general movement optimality score (GMOS) evaluates infant movement variety and is used to diagnose CP, but depends on specialized physiotherapists, is time-consuming, and is subject to inter-observer differences. We hypothesised that an objective means of quantifying movements in young infants using motion tracking data may provide a more consistent early diagnosis of CP and reduce the burden on healthcare systems. This study assessed lower limb kinematic and muscle force variances during neonatal infant kicking movements, and determined that movement variances were associated with GMOS scores, and therefore CP.MethodsElectromagnetic motion tracking data (Polhemus) was collected from neonatal infants performing kicking movements (min 50° knee extension-flexion, <2 seconds) in the supine position over 7 minutes. Tracking data from lower limb anatomical landmarks (midfoot inferior, lateral malleolus, lateral knee epicondyle, ASIS, sacrum) were applied to subject-scaled musculoskeletal models (Gait2354_simbody, OpenSim). Inverse kinematics and static optimisation were applied to estimate lower limb kinematics (knee flexion, hip flexion, hip adduction) and muscle forces (quadriceps femoris, biceps femoris) for isolated kicks. Functional principal component analysis (fPCA) was carried out to reduce kicking kinematic and muscle force waveforms to PC scores capturing ‘modes’ of variance. GMOS scores (lower scores = reduced variety of movement) were collected in parallel with motion capture by a trained operator and specialised physiotherapist. Pearson's correlations were performed to assess if the standard deviation (SD) of kinematic and muscle force waveform PC scores, representing the intra-subject variance of movement or muscle activation, were associated with the GMOS scores.ResultsThe study compared GMOS scores, kinematics, and muscle force variances from a total of 26 infants with a mean corrected gestational age of 39.7 (±3.34) weeks and GMOS scores between 21 and 40. There was a significant association between the SD of the PC scores for knee flexion and the GMOS scores (PC1: R = 0.59, p = 0.002; PC2: R = 0.49, p = 0.011; PC3: R = 0.56, p = 0.003). The three PCs captured variances of the overall flexion magnitude (66% variance explained), early-to-late kick knee extension (20%), and continual to biphasic kicking (6%). For hip flexion, only the SD of PC1 correlated with GMOS scores (PC1: R = 0.52, p = 0.0068), which captured the variance of the overall flexion magnitude (81%). For the biceps femoris, the SD of PC1 and PC3 associated with GMOS scores (PC1: R = 0.50, p = 0.002; PC3: R = 0.45, p = 0.03), which captured the variance of the overall bicep force magnitude (79%) and early-to-late kick bicep activation (8%).ConclusionsInfants with reduced motor development as scored in the GMOS displayed reduced variances of knee and hip flexion and biceps femoris activation across kicking cycles. These findings suggest that combining objectively measured movement variances with existing classification methods could facilitate the development of more consistent and accurate diagnostic tools for early detection of CP.Declaration of Interest(b) declare that there is no conflict of interest that could be perceived as prejudicing the impartiality of the research reported:I declare that there is no conflict of interest that could be perceived as prejudicing the impartiality of the research project.

Real-Time Spinal Cord Motion Tracking for MR-guided Spine SBRT (A Pilot Study)

The aim of this study was to assess the feasibility of real-time motion tracking of the spinal cord using 2D-cine MRI on the MR-Linac system. Spinal SBRT delivers high-precision doses to targets near the spinal cord but with the dosimetric cord tolerance sometimes defined using a spinal cord PRV and the treatment alignment visualizing a surrogate measure (spinal canal) with cone beam CT guidance. MR-guided spine SBRT enables clear visualization of the true spinal cord and real-time monitoring of the cord during treatment, which may allow for increased cord dose constraints and offer new treatment options for patients with epidural disease. A Motion Monitoring Research Package (MMRP) that has the similar tracking functionality as the future motion management system for a 1.5 T MR-Linac system was used to track real-time target motion on 2D orthogonal T2-weighted bFFE cine MRI using a reference 3D T2 MRI scan to locate tracking object initially. The spinal cord was contoured from the T2 MRI scan as the tracking object, and the cine images were obtained in sagittal and coronal planes at the center of the contoured spinal cord with a temporal resolution of 1.2 seconds. The motion tracking data was collected for 20 min and retrospectively analyzed using standard deviation of motion was collected from two volunteers immobilized with the Klarity SBRT system on the MR-Linac couch during MRI scans of their cervical, thoracic, and lumbar spines. In the thoracic spinal cord, the was 0.07mm (left-right excursion range: ±0.15mm), 0.15mm (anterior-posterior: ±0.26mm), and 0.27mm (superior-inferior: ±0.53mm). In the lumbar spinal cord, the standard deviation was 0.06mm (±0.12mm), 0.1mm (±0.14mm), and 0.1mm (±0.27mm). In the cervical spine, since the tracking accuracy in Sup/Inf direction was compromised by tilted spine orientation in the orthogonal 2D image, a single entire vertebra including the spinal cord was tracked with a standard deviation of 0.19mm (±0.31mm), 0.27mm (±0.62mm), and 0.25mm (±0.4mm). This study demonstrated the possibility of real-time tracking of the spinal cord using 2D-cine MRI on an MR-Linac system. Future work will aim to increase sample size and evaluate the reproducibility of the tracking results over time. The excursion of the thoracic/lumbar spinal cord was found to be less than 0.5mm, while the anterior/posterior or superior-inferior excursion of the cervical spine was greater than 1.0mm during the 20-minute scan.

Correction of motion tracking errors for PET head rigid motion correction

Objective. In positron emission tomography (PET) rigid motion correction, erroneous tracking information translates into reduced quality in motion corrected reconstructions. We aim to improve the accuracy of the motion tracking data, to improve the quality of motion corrected reconstructions. Approach. We developed a method for correction of marker/skin displacement over the skull, for tracking methods which require multiple markers attached on the subject head. Additionally, we correct for small magnitude (∼1–2 mm) residual translation tracking errors that can still be present after other corrections. We performed [18F]FDG scans in awake mice (n = 8) and rats (n = 8), and dynamic [18F]SynVesT-1 scans in awake mice (n = 8). Head tracking was performed with the point source tracking method, attaching 3–4 radioactive fiducial markers on the animals’ heads. List-mode even-by-event motion correction reconstruction was performed using tracking data obtained from the point source tracking method (MC), tracking data corrected for marker displacement (MC-DC), and tracking data with additional correction for residual translation tracking errors (MC-DCT). Image contrast, and the image enhancement metric (IEM, with MC as reference) were calculated in these 3 reconstructions. Main results. In mice [18F]FDG scans, the contrast increased on average 3% from MC to MC-DC (IEM: 1.01), and 5% from MC to MC-DCT (IEM: 1.02). For mice [18F]SynVesT-1 scans the contrast increased 6% from MC to MC-DC (IEM: 1.03), and 7% from MC to MC-DCT (IEM: 1.05). In rat [18F]FDG scans contrast increased 5% (IEM: 1.04), and 9% (IEM: 1.05), respectively. Significance. The methods presented here serve to correct motion tracking errors in PET brain scans, which translates into improved image quality in motion corrected reconstructions.

DeepOF: a Python package for supervised and unsupervised pattern recognition in mice motion tracking data

DeepOF: a Python package for supervised and unsupervised pattern recognition in mice motion tracking data

ADVANCES IN FIXATION STRENGTH OF REORIENTATING RECTANGULAR TRIPLE PELVIC INNOMINATE OSTEOTOMY

Reorientating pelvic osteotomies are performed to improve femoral head coverage and secondary degenerative arthritis. A rectangular triple pelvic innominate osteotomy (3PIO) is performed in symptomatic cases. However, deciding optimal screw fixation type to avoid complications is questionable. Therefore, this study aimed to investigate the biomechanical behavior of two different acetabular screw configurations used for rectangular 3PIO osteosynthesis. It was hypothesized that bi-directional screw fixation would be biomechanically superior to mono-axial screw fixation technique.A rectangular 3PIO was performed in twelve right-side artificial Hemi-pelvises. Group 1 (G1) had two axial and one transversal screw in a bi-directional orientation. Group 2 (G2) had three screws in the axial direction through the iliac crest. Acetabular fragment was reoriented to 10.5° inclination in coronal plane, and 10.0° increased anteversion along axial plane. Specimens were biomechanically tested until failure under progressively increasing cyclic loading at 2Hz, starting at 50N peak compression, increasing 0.05N/cycle. Stiffness was calculated from machine data. Acetabular anteversion, inclination and medialization were evaluated from motion tracking data from 250-2500 at 250 cycle increments. Failure cycles and load were evaluated for 5° change in anteversion.Stiffness was higher in G1 (56.46±19.45N/mm) versus G2 (39.02±10.93N/mm) but not significantly, p=0.31. Acetabular fragment anteversion, inclination and medialization increased significantly each group (p≤0.02) and remained non-significantly different between the groups (p≥0.69). Cycles to failure and failure load were not significantly different between G1 (4406±882, 270.30±44.10N) and G2 (5059±682, 302.95±34.10N), p=0.78.From a biomechanical perspective, the present study demonstrates that a bi-directional screw orientation does not necessarily advantageous versus mono-axial alignment when the latter has all three screws evenly distributed over the osteotomy geometry. Moreover, the 3PIO fixation is susceptible to changes in anteversion, inclination and medialization of the acetabular fragment until the bone is healed. Therefore, cautious rehabilitation with partial weight-bearing is recommended.

Motion Tracking of Daily Living and Physical Activities in Health Care: Systematic Review From Designers' Perspective.

Motion tracking technologies serve as crucial links between physical activities and health care insights, facilitating data acquisition essential for analyzing and intervening in physical activity. Yet, systematic methodologies for evaluating motion tracking data, especially concerning user activity recognition in health care applications, remain underreported. This study aims to systematically review motion tracking in daily living and physical activities, emphasizing the critical interaction among devices, users, and environments from a design perspective, and to analyze the process involved in health care application research. It intends to delineate the design and application intricacies in health care contexts, focusing on enhancing motion tracking data's accuracy and applicability for health monitoring and intervention strategies. Using a systematic review, this research scrutinized motion tracking data and their application in health care and wellness, examining studies from Scopus, Web of Science, EBSCO, and PubMed databases. The review used actor network theory and data-enabled design to understand the complex interplay between humans, devices, and environments within these applications. Out of 1501 initially identified studies, 54 (3.66%) were included for in-depth analysis. These articles predominantly used accelerometer and gyroscope sensors (n=43, 80%) to monitor and analyze motion, demonstrating a strong preference for these technologies in capturing both dynamic and static activities. While incorporating portable devices (n=11, 20%) and multisensor configurations (n=16, 30%), the application of sensors across the body (n=15, 28%) and within physical spaces (n=17, 31%) highlights the diverse applications of motion tracking technologies in health care research. This diversity reflects the application's alignment with activity types ranging from daily movements to specialized scenarios. The results also reveal a diverse participant pool, including the general public, athletes, and specialized groups, with a focus on healthy individuals (n=31, 57%) and athletes (n=14, 26%). Despite this extensive application range, the focus primarily on laboratory-based studies (n=39, 72%) aimed at professional uses, such as precise activity identification and joint functionality assessment, emphasizes a significant challenge in translating findings from controlled environments to the dynamic conditions of everyday physical activities. This study's comprehensive investigation of motion tracking technology in health care research reveals a significant gap between the methods used for data collection and their practical application in real-world scenarios. It proposes an innovative approach that includes designers in the research process, emphasizing the importance of incorporating data-enabled design framework. This ensures that motion data collection is aligned with the dynamic and varied nature of daily living and physical activities. Such integration is crucial for developing health applications that are accessible, intuitive, and tailored to meet diverse user needs. By leveraging a multidisciplinary approach that combines design, engineering, and health sciences, the research opens new pathways for enhancing the usability and effectiveness of health technologies.

Do we still need to screen our patients?—Orthopaedic scoring based on motion tracking

PurposeOrthopaedic scores are essential for the clinical assessment of movement disorders but require an experienced clinician for the manual scoring. Wearable systems are taking root in the medical field and offer a possibility for the convenient collection of motion tracking data. The purpose of this work is to demonstrate the feasibility of automated orthopaedic scorings based on motion tracking data using the Harris Hip Score and the Knee Society Score as examples.MethodsSeventy-eight patients received a clinical examination and an instrumental gait analysis after hip or knee arthroplasty. Seven hundred forty-four gait features were extracted from each patient’s representative gait cycle. For each score, a hierarchical multiple regression analysis was conducted with a subsequent tenfold cross-validation. A data split of 70%/30% was applied for training/testing.ResultsBoth scores can be reproduced with excellent coefficients of determination R2 for training, testing and cross-validation by applying regression models based on four to six features from instrumental gait analysis as well as the patient-reported parameter ‘pain’ as an offset factor.ConclusionComputing established orthopaedic scores based on motion tracking data yields an automated evaluation of a joint function at the hip and knee which is suitable for direct clinical interpretation. In combination with novel technologies for wearable data collection, these computations can support healthcare staff with objective and telemedical applicable scorings for a large number of patients without the need for trained clinicians.

The CABB dataset: A multimodal corpus of communicative interactions for behavioural and neural analyses

We present a dataset of behavioural and fMRI observations acquired in the context of humans involved in multimodal referential communication. The dataset contains audio/video and motion-tracking recordings of face-to-face, task-based communicative interactions in Dutch, as well as behavioural and neural correlates of participants’ representations of dialogue referents. Seventy-one pairs of unacquainted participants performed two interleaved interactional tasks in which they described and located 16 novel geometrical objects (i.e., Fribbles) yielding spontaneous interactions of about one hour. We share high-quality video (from three cameras), audio (from head-mounted microphones), and motion-tracking (Kinect) data, as well as speech transcripts of the interactions. Before and after engaging in the face-to-face communicative interactions, participants’ individual representations of the 16 Fribbles were estimated. Behaviourally, participants provided a written description (one to three words) for each Fribble and positioned them along 29 independent conceptual dimensions (e.g., rounded, human, audible). Neurally, fMRI signal evoked by each Fribble was measured during a one-back working-memory task. To enable functional hyperalignment across participants, the dataset also includes fMRI measurements obtained during visual presentation of eight animated movies (35 min total). We present analyses for the various types of data demonstrating their quality and consistency with earlier research. Besides high-resolution multimodal interactional data, this dataset includes different correlates of communicative referents, obtained before and after face-to-face dialogue, allowing for novel investigations into the relation between communicative behaviours and the representational space shared by communicators. This unique combination of data can be used for research in neuroscience, psychology, linguistics, and beyond.

SkiMon: A Wireless Body Area Network for Monitoring Ski Flex and Motion during Skiing Sports.

Monitoring and gathering data on sporting activities holds significant promise for athletes, equipment developers, and physical fitness clinicians. Wireless Body Area Networks are being used in sporting environments as a means of gathering data, providing feedback, and helping to gain understanding of athletic activities. Applying WBANs to skiing situations, which have higher vibration, velocities, and damp environments than many other sports, can open up opportunities to understand the dynamics of skiing equipment behaviors, skiing routes on mountains, and how individuals react when skiing. To support these outcomes, a prototype WBAN-style off the shelf component system called SkiMon was proposed, implemented, and tested. The SkiMon system uses inexpensive ESP8266, Raspberry Pi, and sensor devices to gather high quality motion and location tracking data on skiers in real-world skiing conditions. By using IEEE 802.11b/g/n wireless networks, SkiMon is able to sample data at a minimum of 50 Hz, which is enough to model most ski vibration behaviors. These data results are shown to reflect ground truth 3D maps and the acceleration data comports with earlier works on ski vibration testing. Overall, a WBAN-based commodity components solution shows promise as a high quality sensor platform for tracking and modeling skiing activities.

User Identification Utilizing Minimal Eye-Gaze Features in Virtual Reality Applications

Emerging Virtual Reality (VR) displays with embedded eye trackers are currently becoming a commodity hardware (e.g., HTC Vive Pro Eye). Eye-tracking data can be utilized for several purposes, including gaze monitoring, privacy protection, and user authentication/identification. Identifying users is an integral part of many applications due to security and privacy concerns. In this paper, we explore methods and eye-tracking features that can be used to identify users. Prior VR researchers explored machine learning on motion-based data (such as body motion, head tracking, eye tracking, and hand tracking data) to identify users. Such systems usually require an explicit VR task and many features to train the machine learning model for user identification. We propose a system to identify users utilizing minimal eye-gaze-based features without designing any identification-specific tasks. We collected gaze data from an educational VR application and tested our system with two machine learning (ML) models, random forest (RF) and k-nearest-neighbors (kNN), and two deep learning (DL) models: convolutional neural networks (CNN) and long short-term memory (LSTM). Our results show that ML and DL models could identify users with over 98% accuracy with only six simple eye-gaze features. We discuss our results, their implications on security and privacy, and the limitations of our work.

Examination of a foot mounted IMU-based methodology for a running gait assessment.

Gait assessment is essential to understand injury prevention mechanisms during running, where high-impact forces can lead to a range of injuries in the lower extremities. Information regarding the running style to increase efficiency and/or selection of the correct running equipment, such as shoe type, can minimize the risk of injury, e.g., matching a runner's gait to a particular set of cushioning technologies found in modern shoes (neutral/support cushioning). Awareness of training or selection of the correct equipment requires an understanding of a runner's biomechanics, such as determining foot orientation when it strikes the ground. Previous work involved a low-cost approach with a foot-mounted inertial measurement unit (IMU) and an associated zero-crossing-based methodology to objectively understand a runner's biomechanics (in any setting) to learn about shoe selection. Here, an investigation of the previously presented ZC-based methodology is presented only to determine general validity for running gait assessment in a range of running abilities from novice (8 km/h) to experienced (16 km/h+). In comparison to Vicon 3D motion tracking data, the presented approach can extract pronation, foot strike location, and ground contact time with good [ICC(2,1) > 0.750] to excellent [ICC(2,1) > 0.900] agreement between 8–12 km/h runs. However, at higher speeds (14 km/h+), the ZC-based approach begins to deteriorate in performance, suggesting that other features and approaches may be more suitable for faster running and sprinting tasks.

P21. Home-based functional outcome measurements for spine patients: validation and feasibility study

<h3>BACKGROUND CONTEXT</h3> Functional outcome measurements (FOMs) have been shown to be effective tools in diagnosing, planning treatments and tracking outcomes in several subspecialties of medicine. FOMs often produce large volumes of complex data that require sophisticated collection, storage and analytic techniques to describe functional measures of items like gait performance, postural stability, neuromuscular activity and movement strategies. These complexities pose particular challenges for the application of FOMs in the clinical setting where the primary focus is on patient care. Currently, utilization of FOMs is primarily driven by individual surgeons explicitly interested in the development and dissemination of FOMs metrics and related technologies. Unfortunately, there are capital and support costs associated with running and maintaining FOMs labs which pose additional hurdles that may inhibit otherwise interested surgeons. As interest and use of clinically derived functional evaluations tailored specifically for spine patients grow, there is a need for simple, objective measures to summarize the complexity of modern motion tracking data sets to simple, clinically meaningful and interpretable terms. <h3>PURPOSE</h3> To develop and validate low-cost FOMs wearable sensor that quantifies common activities in a home-based environment. <h3>STUDY DESIGN/SETTING</h3> Prospective, concurrent control cohort study. <h3>PATIENT SAMPLE</h3> Twelve lumbar degenerative surgical candidates and 12 healthy controls participated in this study. <h3>OUTCOME MEASURES</h3> Level of activity, spatiotemporal parameters, and balance effort. <h3>METHODS</h3> Subjects wore a small noninvasive sensor (30 × 44 × 8mm, weight: 12 grams) with an adhesive patch on T1 in addition to traditional gait lab sensors. Validation of the proposed sensor and common FOMs metrics generated by a gait lab were compared. Furthermore, each subject wore the wearable sensor on T1 for additional 24 hours. The sensor detected different types and levels of activities during the day (ie, standing, walking, sitting, etc.) and also captured trunk kinematics in the home-based environment. <h3>RESULTS</h3> The wearable sensor was able to reliably measure all trunk kinematics during standing, walking and transition from sitting (p>0.05) when compared to gold standard human motion capture in a gait lab. The sensor data collected during the additional 24-hours at home was passed through a machine learning activity classification algorithm. The predicted results indicate that patients with degenerative lumbar spinal pathologies presented with a lower level of activity (walking: 4.7%, standing: 11.6%, sitting: 25.3%) in comparison to controls (walking: 7.9%, standing: 21.7%, sitting: 17.1%). Balance effort and the CoE dimensions were found to be significantly larger in these patients (sagittal: 7.9°, coronal: 7.2°) compared to controls (sagittal: 5.8°, coronal: 3.2°; p<0.035). <h3>CONCLUSIONS</h3> The purpose of this study was to validate and prove the feasibility of home-based functional outcome measurements, which can provide relevant details in a digestible format that conveys the functional status of the patient and raises flags for areas of concern. Such insights may lead to changes in assessments of disability, treatment strategies or modifications of rehabilitation regimens. Several benefits are anticipated from a wearable-based quantitative tool to assist with preoperative planning for patient-specific alignment objectives such as assisting in choosing the right surgical procedure for the right patient, recognition of red flags, leading to avoidance of surgery where it is not going to help, recovery monitoring, early detection of perioperative complications, prognostic information, and prediction of treatment outcomes. <h3>FDA DEVICE/DRUG STATUS</h3> This abstract does not discuss or include any applicable devices or drugs.

Spatiotemporal Coupling of Hand and Eye Movements When Using a Myoelectric Prosthetic Hand.

Upper limb prosthesis users have disruptions in hand-eye coordination, with increased fixations towards the hand and less visual allocation for feedforward planning. The purpose of this study was to explore whether improved motor planning, as reflected by eye gaze behaviour, was associated with more efficient hand movement patterns. Able-bodied participants wore a simulated prosthesis while performing a functional object movement task. Motion and eye tracking data were collected to quantify the eye gaze and hand movement during object interaction. The results of this study demonstrated that the latency of the eye to precede the hand at pick-up was correlated with measures of hand function, including hand variability, movement units, and grasp time, but not reach time. During transport and release, longer latency to disengage gaze from the grasped object and look ahead towards the target was correlated to hand kinematics of hand variability, distance travelled, and transport time. In addition, the latency of the eye to disengage the drop-off location was correlated to release time. Together these may point to control issues with opening and closing the prosthetic hand. Overall, increased feedforward fixations towards the target and reduced feedback fixations towards the hand were related to improved measures of hand function. Hence, coordination between eye and hand movements when using a myoelectric prosthesis may prove to be a useful metric to assess motor planning.

Operation Stability Analysis of Basic Acupuncture Manipulation Based on Three-Dimensional Motion Tracking Data

Objective. To analyse the operational stability of basic acupuncture manipulation (AM) based on three-dimensional (3D) motion tracking. Method. Two quantitative indicators (spatial and temporal dispersions) and corresponding algorithms of operation stability were established based on the coordinate-time data derived from 3D motion tracking of basic AM. The differences in stability were compared between 20 acupuncture teachers and 20 acupuncture students. Results. The teachers and students had similar temporal stability, but the teachers were more stable in their spatial control, perhaps because of the teachers’ better fingertip force and more practice with feedback. Conclusion. The spatial and temporal dispersions can be used to evaluate operational stability in basic AM. Repetitive training and finger force enhancement with more accurate feedback and rhythmic auditory stimulation are recommended for improving operation stability in basic AM.

ASD with ADHD vs. ASD and ADHD alone: a study of the QbTest performance and single-dose methylphenidate responding in children and adolescents

BackgroundThe continuous performance task (CPT) may help identify coexistent attention deficit hyperactivity disorder (ADHD) in autism spectrum disorder (ASD). The Quantified behavior Test (QbTest) combines a CPT and motion-tracking data to assess ADHD symptoms. This study aimed to evaluate the QbTest performance of children and adolescents with ASD plus ADHD, including estimating the effects of single-dose methylphenidate (MPH). To achieve these aims, (1) the QbTest performances were evaluated in ASD alone, ASD plus ADHD, and ADHD alone, and (2) the effects on the QbTest performance of single-dose MPH before and after intake were estimated across the groups. It was assumed that the ASD plus ADHD performance, including the MPH response, would preferably resemble the performance in ADHD alone, rather than ASD alone.MethodsRetrospective data were analyzed for 482 children and adolescents: 69 with ASD alone, 142 with ASD plus ADHD (ASD/ADHD), and 271 with ADHD alone. For 343 subjects, the QbTest was performed before and up to four hours after a single-dose MPH intake. A summary index of the CPT and motion-capture data was provided for QbTest cardinal parameters.ResultsOf 12 QbTest parameters assessed before given MPH, the ASD/ADHD group had scores in line with the ASD group regarding four parameters and the ADHD group regarding nine parameters. Significant differences between groups were seen with respect to QbInattention (p > 0.05); the lowest scores in ASD and the highest in ADHD. Those with ASD/ADHD and ADHD had similar QbActivity and QbImpulsivity scores, but significantly higher than those with ASD. After MPH intake, scores for QbActivity decreased similarly in ASD/ADHD and ADHD, as well as scores for QbImpulsivity. QbImpulsivity increased in ASD. QbInattention scores decreased similarly in all groups after MPH intake.ConclusionsChildren and adolescents with ASD plus ADHD exhibited more atypical QbTest performances than those with ASD alone, while most of their performances were similar to those observed in ADHD alone. In addition, a single dose of MPH mitigated attention deficits and decreased hyperactivity while improved impulsivity in these children. Prospective studies should further clarify the role of the QbTest in the diagnostic and therapeutic interventions in ASD with ADHD.

Quad-copter noise measurements under realistic flight conditions

This work experimentally studies the noise features of flying drones under representative working conditions, including hover, spin, climb, vertical flight, and cruise. Being different from outdoor experiments where many uncertain factors, such as gust wind and ground reflections, could affect test results, measurements in this study were conducted in a large anechoic chamber. The noise was measured with a ground-based rectangular microphone array covering an area of 5.6 m×6.4 m and a 4.6 m linear array on the sidewall to account for emission directivity. A binocular vision tracking system was used to trace drones' motion in real-time. Results show that a flying drone's noise feature is sensitive to its flight altitude, relative distance from observers, and motion status. Interestingly, under hovering conditions, the noise directivity is similar to that of an isolated propeller, and a low-noise region is found underneath the fuselage. While for cruising flight, the region with higher noise levels is observed behind the drone due to the fuselage's pitch angle. More importantly, the synchronized systems for noise and position measurements allow finding of an acoustic response to any change in motion status across flight tests. A coherence between real-time changes in the noise spectrogram and variations of the propeller rotating speed is found, thanks to the short-time Fourier analysis of both noise and motion-tracking data. The present finding extends the authors' previous work, enabling a deeper understanding of general drone noise features under different operating conditions.

Optimal Biomechanical Performance in Upper-Limb Gestures Depends on Velocity and Carried Load.

Simple SummaryIn the last few years, the physical health and mental health of workers in an industrial context while interacting with collaborative robots have become of primary interest in the research field. The characterization of the mental and psychophysical health of workers and subjects can be investigated with biomechanical analysis. In this work, a biomechanical model was used to perform simulations of the biomechanics of reaching against gravity movements as a paradigmatic motion primitive underlying several functional tasks. Motion tracking data were acquired with motion capture, and, in a simulation environment, multiple movement speeds and carried loads were changed in order to analyze the effects on upper-limb kinematics and dynamics. An optimal range of velocities for human motion was found in which the expended energy was lower. The deviation from energy optimum for the upper limb was evaluated when testing nonoptimal movement conditions. These results can be useful in a human–robot collaboration scenario to tune setup parameters to preserve the physical and mental health of the workers, to assess biomechanics, and to define fatigue and effort indices.In the last few years, there has been increased interest in the preservation of physical and mental health of workers that cooperate with robots in industrial contexts, such as in the framework of the European H2020 Mindbot Project. Since biomechanical analysis contributes to the characterization of the subject interacting with a robotic setup and platform, we tested different speed and loading conditions in a simulated environment to determine upper-limb optimal performance. The simulations were performed starting from laboratory data of people executing upper-limb frontal reaching movements, by scaling the motion law and imposing various carried loads at the hand. The simulated velocity ranged from 20% to 200% of the original natural speed, with step increments of 10%, while the hand loads were 0, 0.5, 1, and 2 kg, simulating carried objects. A 3D inverse kinematic and dynamic model was used to compute upper-limb kinematics and dynamics, including shoulder flexion, shoulder abduction, and elbow flexion. An optimal range of velocities was found in which the expended energy was lower. Interestingly, the optimal speed corresponding to lower exerted torque and energy decreased when the load applied increased. Lastly, we introduced a preliminary movement inefficiency index to evaluate the deviation of the power and expended energy for the shoulder flexion degree of freedom when not coinciding with the minimum energy condition. These results can be useful in human–robot collaboration to design minimum-fatigue collaborative tasks, tune setup parameters and robot behavior, and support physical and mental health for workers.

3-D Ultrasonographic Quantification of Hand and Calf Muscle Volume: Statistical Shape Modeling Approach.

Accurate acquisition and segmentation of muscles are essential in 3-D freehand ultrasonography (US) to estimate in vivo muscle volume, but the source of segmentation inaccuracy in shape variation has never been the focus. This study was aimed at investigating reliability of 3-D US in the acquisition and segmentation for muscle volume of two muscles of different sizes and in identifying a primary source of measurement difference. The lateral gastrocnemius and flexor pollicis brevis of 12 healthy adults were assessed using freehand 3-D US scans. The motion-tracking data of the probe were synchronized with the B-mode ultrasound scan to reconstruct 3-D muscle volume. Statistical shape modeling was used to provide a spatial segmentation volume difference that further explains the variation around segmentation repeatability. The absolute difference of the flexor pollicis brevis was 3.5 percentage points greater than that for the lateral gastrocnemius. The highest measurement differences were observed when for inter-acquirer analysis. Statistical shape modeling revealed that the primary segmentation volume differences were at the muscle ends and edges, where the muscle interfaces with the surrounding muscles. Three-dimensional US is a reliable tool in the clinical setting, but care must be taken to ensure that acquisition and segmentation are consistent, particularly in a small muscle that interfaces with tendons and other soft tissues.

Feasibility of a virtual reality-based exercise intervention and low-cost motion tracking method for estimation of motor proficiency in youth with autism spectrum disorder

BackgroundMotor impairment is widely acknowledged as a core feature in children with autism spectrum disorder (ASD), which can affect adaptive behavior and increase severity of symptoms. Low-cost motion capture and virtual reality (VR) game technologies hold a great deal of promise for providing personalized approaches to motor intervention in ASD. The present study explored the feasibility, acceptability and potential efficacy of a custom-designed VR game-based intervention (GaitWayXR™) for improving gross motor skills in youth with ASD.MethodsTen children and adolescents (10–17 years) completed six, 20-min VR-based motor training sessions over 2 weeks while whole-body movement was tracked with a low-cost motion capture system. We developed a methodology for using motion tracking data to quantify whole-body movement in terms of efficiency, synchrony and symmetry. We then studied the relationships of the above quantities with standardized measures of motor skill and cognitive flexibility.ResultsOur results supported our presumption that the VR intervention is safe, with no adverse events and very few minor to moderate side-effects, while a large proportion of parents said they would use the VR game at home, the most prohibitive reasons for adopting the system for home therapy were cost and space. Although there was little evidence of any benefits of the GaitWayXR™ intervention in improving gross motor skills, we showed several positive correlations between the standardized measures of gross motor skills in ASD and our measures of efficiency, symmetry and synchrony from low-cost motion capture.ConclusionsThese findings, though preliminary and limited by small sample size, suggest that low-cost motion capture of children with ASD is feasible with movement exercises in a VR-based game environment. Based on these preliminary findings, we recommend conducting larger-scale studies with methods for improving adherence to VR gaming interventions over longer periods.