Optimal configurations of an electromagnetic tracking system for 3D ultrasound imaging of pediatric hips - A phantom study.

Tracking the position and orientation of a two-dimensional (2D) ultrasound scanner to reconstruct a 3D volume is common, and its accuracy is important. In this study, a specific miniaturized electromagnetic (EM) tracking system was selected and integrated with a 2D ultrasound scanner, which was aimed to capture hip displacement in children with cerebral palsy. The objective of this study was to determine the optimum configuration, including the distance between the EM source and sensor, to provide maximum accuracy. The scanning volume was aimed to be 320 mm × 320 mm × 76 mm. The accuracy of the EM tracking was evaluated by comparing its tracking with those from a motion capture camera system. A static experiment showed that a warm-up time of 20 min was needed. The EM system provided the highest precision of 0.07 mm and 0.01° when the distance between the EM source and sensor was 0.65 m. Within the testing volume, the maximum position and rotational errors were 2.31 mm and 1.48°, respectively. The maximum error of measuring hip displacement on the 3D hip phantom study was 4 %. Based on the test results, the tested EM system was suitable for 3D ultrasound imaging of pediatric hips to assess hip displacement when optimal configuration was used.

Kinematic-kinetic compliant acetabular cup positioning based on preoperative motion tracking and musculoskeletal modeling for total hip arthroplasty.

The invention of the surgical robot enabled accurate component implantation during total hip arthroplasty (THA). However, a preoperative surgical planning methodology is still lacking to determine the acetabular cup alignment considering the patient-specific hip functions during daily activities such as walking. To simultaneously avoid implant edgeloading and impingement, this study established a kinematic-kinetic compliant (KKC) acetabular cup positioning method based on preoperative gait kinematics measurement and musculoskeletal modeling. Computed tomography images around the hip joint and their biomechanical data during gait, including motion tracking and foot-ground reaction forces, were collected. Using the reconstructed pelvic and femur geometries, the patient-specific hip muscle insertions were located in the lower limb musculoskeletal model via point cloud registration. The designed cup orientation has to be within the patient-specific safe zone to prevent implant impingement, and the optimized value selected based on the time-dependent hip joint reaction force to minimize the risk of edgeloading. As a validation of the proposed musculoskeletal model, the predicted lower limb muscle activations for seven patients were correlated with their surface electromyographic measurements, and the computed hip contact force was also in quantitative agreement with data from the literature. However, the designed cup orientations were not always within the well-known Lewinnek safe zone, highlighting the importance of KKC surgical planning based on patient-specific biomechanical evaluations.

Respiratory motion tracking of the thoracoabdominal surface based on defect-aware point cloud registration.

The performance of conventional lung puncture surgery is a complex undertaking due to the surgeon's reliance on visual assessment of respiratory conditions and the manual execution of the technique while the patient maintains breath-holding. However, the failure to correctly perform a puncture technique can lead to negative outcomes, such as the development of sores and pneumothorax. In this work, we proposed a novel approach for monitoring respiratory motion by utilizing defect-aware point cloud registration and descriptor computation. Through a thorough examination of the attributes of the inputs, we suggest the incorporation of a defect detection branch into the registration network. Additionally, we developed two modules with the aim of augmenting the quality of the extracted features. A coarse-to-fine respiratory phase recognition approach based on descriptor computation is devised for the respiratory motion tracking. The efficacy of the suggested registration method is demonstrated through experimental findings conducted on both publicly accessible datasets and thoracoabdominal point cloud datasets. We obtained state-of-the-art registration results on ModelNet40 datasets, with 1.584∘ on rotation mean absolute error and 0.016 mm on translation mean absolute error, respectively. The experimental findings conducted on a thoracoabdominal point cloud dataset indicate that our method exhibits efficacy and efficiency, achieving a frame matching rate of 2 frames per second and a phase recognition accuracy of 96.3%. This allows identifying matching frames from template point clouds that display different parts of a patient's thoracoabdominal surface while breathing regularly to distinguish breathing stages and track breathing.

Estimation of human body 3D pose for parent-infant interaction settings using azure Kinect and OpenPose.

Automatic pose estimation has become a valuable tool for the study of human behavior, including dyadic interactions. It allows researchers to analyze the nuanced dynamics of interactions more effectively, and facilitates the integration of behavioral data with other modalities (EEG, etc.). However, many technical difficulties remain. Particularly, for parent-infant interactions, automatic pose estimation for infants is unpredictable; the immature proportions and smaller bodies of children may cause misdetections. OpenPose is one tool that has shown high performance in pose tracking from video, even in infants. However, OpenPose is limited to 2D (i.e., coordinates relative to the image space). This may be undesirable in a multitude of paradigms (e.g., naturalistic settings). We developed a method for expanding the functionality of OpenPose to 3D, tailored to parent-infant interaction paradigms. This method merges the estimations from OpenPose with the depth information from a depth camera to obtain a 3D pose that works even for young infants.•Video recordings of interactions of parents and infants are taken using a dual color-depth camera.•2D-positions of parents and their infants are estimated from the color video.•Using the depth camera, we transform the 2D estimations into real-world 3D positions, allowing movement analysis in full-3D space.

Artificial Amacrine Retinal Circuits.

Event-based imaging represents a new paradigm in visual information processing that addresses the speed and energy efficiency shortcomings inherently present in the current complementary metal oxide semiconductor-based machine vision. Realizing such imaging systems has previously been sought using very large-scale integration technologies that have complex circuitries consisting of many photodiodes, differential amplifiers, capacitors, and resistors. Here, we demonstrate that event-driven sensing can be achieved using a simple one-resistor, one-capacitor (1R1C) circuit, where the capacitor is modified with colloidal quantum dots (CQDs) to have a photoresponse. This sensory circuit emulates the motion-tracking function of the biological retina, in which the amacrine cells in the bipolar-to-ganglion synaptic pathway produce a transient spiking signal only in response to changes in light intensity but remain inactive under constant illumination. When extended to a 2D imaging array, the individual sensors work independently and output signals only when a change in the light intensity is detected; hence, the concept of the frame in image processing is thereby removed. In this work, we present the fabrication and characterization of a CQD photocapacitor-based 1R1C circuit that has a spectral response at 1550 nm in the short-wave infrared (SWIR). We report on the key performance parameters including peak responsivity, noise, and optical noise equivalent power and discuss the operating mechanism that is responsible for spiking responses in these artificial retinal circuits. The present work sets the foundation for expanding the bioinspired vision sensor capability toward midwave infrared (MWIR) and long-wave infrared (LWIR) spectral regions that are invisible to human eyes and mainstream semiconductor technologies.

Concurrent Validity of Motion Parameters Measured With an RGB-D Camera-Based Markerless 3D Motion Tracking Method in Children and Young Adults.

OBJECTIVE: Low-cost, portable RGB-D cameras with integrated motion tracking functionality enable easy-to-use 3D motion analysis without requiring expensive facilities and specialized personnel. However, the accuracy of existing systems is insufficient for most clinical applications, particularly when applied to children. In previous work, we developed an RGB-D camera-based motion tracking method and showed that it accurately captures body joint positions of children and young adults in 3D. In this study, the validity and accuracy of clinically relevant motion parameters that were computed from kinematics of our motion tracking method are evaluated in children and young adults. METHODS: Twenty-three typically developing children and healthy young adults (5-29 years, 110-189 cm) performed five movement tasks while being recorded simultaneously with a marker-based Vicon system and an Azure Kinect RGB-D camera. Motion parameters were computed from the extracted kinematics of both methods: time series measurements, i.e., measurements over time, peak measurements, i.e., measurements at a single time instant, and movement smoothness. The agreement of these parameter values was evaluated using Pearson's correlation coefficients r for time series data, and mean absolute error (MAE) and Bland-Altman plots with limits of agreement for peak measurements and smoothness. RESULTS: Time series measurements showed strong to excellent correlations (r-values between 0.8 and 1.0), MAE for angles ranged from 1.5 to 5 degrees and for smoothness parameters (SPARC) from 0.02-0.09, while MAE for distance-related parameters ranged from 9 to 15 mm. CONCLUSION: Extracted motion parameters are valid and accurate for various movement tasks in children and young adults, demonstrating the suitability of our tracking method for clinical motion analysis. CLINICAL IMPACT: The low-cost portable hardware in combination with our tracking method enables motion analysis outside of specialized facilities while providing measurements that are close to those of the clinical gold-standard.

Synthetic 3D full-body skeletal motion from 2D paths using RNN with LSTM cells and linear networks.

Gait analysis has proven to be a key process in the functional assessment of people involving many fields, such as diagnosis of diseases or rehabilitation, and has increased in relevance lately. Gait analysis often requires gathering data, although this can be very expensive and time consuming. One of the main solutions applied in fields when data acquisition is a problem is augmentation of datasets with artificial data. There are two main approaches for doing that: simulation and synthetic data generation. In this article, we propose a parametrizable generative system of synthetic walking simplified human skeletons. For achieving that, a data gathering experiment with up to 26 individuals was conducted. The system consists of two artificial neural networks: a recurrent neural network for the generation of the movement and a multilayer perceptron for determining the size of the segments of the skeletons. The system has been evaluated through four processes: (i) an observational appraisal by researchers in gait analysis, (ii) a visual representation of the distribution of the generated data, (iii) a numerical analysis using the normalized cross-correlation coefficient, and (iv) an angular evaluation to check the kinematic validity of the data. The evaluation concluded that the system is able to generate realistic and accurate gait data. These results reveal a promising path for this research field, which can be further improved through increasing the variety of movements and the user sample.

Evaluating cervical spine mobility and Fitt's law compliance: The DidRen laser test adapted for virtual reality with age and sex effects.

Cervical spine mobility assessment is crucial in rehabilitation to monitor patient progress. This study introduces the DidRen VR test, a virtual reality (VR) adaptation of the conventional DidRen laser test, aimed at evaluating cervical spine mobility. We conducted a cross-sectional study involving fifty healthy participants that underwent the DidRen VR test. The satisfaction of Fitts' law within this VR adaptation was examined and we analyzed the effects of age and sex on the sensorimotor performance metrics. Our findings confirm that Fitts' law is satisfied, demonstrating a predictable relationship between movement time and the index of difficulty, which suggest that the DidRen VR test can simulate real-world conditions. A clear influence of age and sex on performance was observed, highlighting significant differences in movement efficiency and accuracy across demographics, which may necessitate personalized assessment strategies in clinical rehabilitation practices. The DidRen VR test presents an effective tool for assessing cervical spine mobility, validated by Fitts' law. It offers a viable alternative to real-world method, providing precise control over test conditions and enhanced engagement for participants. Since age and sex significantly affect sensorimotor performance, personalized assessments are essential. Further research is recommended to explore the applicability of the DidRen VR test in clinical settings and among patients with neck pain.

Automation in canine science: enhancing human capabilities and overcoming adoption barriers.

The emerging field of canine science has been slow in adopting automated approaches for data analysis. However, with the dramatic increase in the volume and complexity of the collected behavioral data, this is now beginning to change. This paper aims to systematize the field of automation in canine science. We provide an examination of current automation processes and pipelines by providing a literature review of state-of-the-art studies applying automation in this field. In addition, via an empirical study with researchers in animal behavior, we explore their perceptions and attitudes toward automated approaches for better understanding barriers for a wider adoption of automation. The insights derived from this research could facilitate more effective and widespread utilization of automation within canine science, addressing current challenges and enhancing the analysis of increasingly complex and voluminous behavioral data. This could potentially revolutionize the field, allowing for more objective and quantifiable assessments of dog behavior, which would ultimately contribute to our understanding of dog-human interactions and canine welfare.

Head tracking using an optical soft tactile sensing surface.

This research proposes a sensor for tracking the motion of a human head via optical tactile sensing. It implements the use of a fibrescope a non-metal alternative to a webcam. Previous works have included robotics grippers to mimic the sensory features of human skin, that used monochrome cameras and depth cameras. Tactile sensing has shown advantages in feedback-based interactions between robots and their environment. The methodology in this paper is utilised to track motion of objects in physical contact with these sensors to replace external camera based motion capture systems. Our immediate application is related to detection of human head motion during radiotherapy procedures. The motion was analysed in two degrees of freedom, respective to the tactile sensor (translational in z-axis, and rotational around y-axis), to produce repeatable and accurate results. The movements were stimulated by a robot arm, which also provided ground truth values from its end-effector. The fibrescope was implemented to ensure the device's compatibility with electromagnetic waves. The cameras and the ground truth values were time synchronised using robotics operating systems tools. Image processing methods were compared between grayscale and binary image sequences, followed by motion tracking estimation using deterministic approaches. These included Lukas-Kanade Optical Flow and Simple Blob Detection, by OpenCV. The results showed that the grayscale image processing along with the Lukas-Kanade algorithm for motion tracking can produce better tracking abilities, although further exploration to improve the accuracy is still required.

Accuracy of a new photometric jaw tracking system in the frontal plane at different recording distances: An in-vitro study.

OBJECTIVES: To evaluate the accuracy of a new photometric jaw tracking system (JTS) in recording linear vertical movements in the frontal plane at different distances. METHODS: A mandibular plaster cast of a patient was placed on a simulation machine capable of linear movements along two spatial axes. Cyclops JTS (Itaka) was adapted to the plaster cast, while the head frame was attached to the simulation machine. The latter performed five linear movements from 20 to 40 mm in the y-axis; each movement was repeated five times at five different recording distance (380 to 420 mm). The recorded movements were measured and compared with those obtained with a laser Doppler vibrometer (LDV) for accuracy analysis. Data were statistically processed (α = 0.05). RESULTS: No statistically significant differences were found between Cyclops and LDV measurements on the y- and z-axes (p = 0.5). Changes in linear vertical motion and distance positions did not affect the accuracy, which remained relatively constant with similar trends and values less than 1 % for each parameter variation. The best condition observed was linear vertical movement of 30 mm at 420 mm (0.010 ± 0.023 mm). CONCLUSIONS: Cyclops has proven to be an accurate JTS in recording linear vertical movements in the frontal plane at different recording distances. For optimal recordings, the scanner should be placed as close as possible to the markers; excessive vertical movements decreased the accuracy. However, this study has limitations and requires in-vivo confirmations. CLINICAL SIGNIFICANCE: The tested JTS proved accurate in recording linear vertical movements in the frontal plane. However, given the limitations of the study, further investigation under real conditions is needed to support prosthetic and gnathological rehabilitations.

Ultrasound-based bone tracking using cross-correlation enables dynamic measurements of knee kinematics during clinical assessments.

Purpose: Measuring joint kinematics in the clinic is important for diagnosing injuries, tracking healing and guiding treatments; however, current methods are limited by accuracy and/or feasibility of widespread clinical adoption. Therefore, the purpose of this study was to develop and validate an ultrasound (US)-based method for measuring knee kinematics during clinical assessments. Methods: We mimicked four clinical laxity assessments (i.e., anterior, posterior, varus, valgus) on five human cadaver knees using our robotic testing system. We simultaneously collected B-mode cine loops with an US transducer. We computed the errors in kinematics between those measured using our bone-tracking algorithm, which cross-correlated regions of interest across frames of the cine loops, and those measured using optical motion capture with bone pins. Additionally, we conducted studies to determine the effects of loading rate and transducer placement on kinematics measured using our US-based bone tracking. Results: Pooling the trials at experimental speeds and those downsampled to replicate clinical laxity assessments, the maximum root-mean-square errors of knee kinematics using our bone-tracking algorithm were 2.2 mm and 1.3° for the anterior-posterior and varus-valgus laxity assessments, respectively. Repeated laxity assessments proved to have good-to-excellent repeatability (intraclass correlation coefficients [ICCs] of 0.81-0.99), but ICCs from repositioning the transducer varied more widely, ranging from poor-to-good reproducibility (0.19-0.89). Conclusion: Our results demonstrate that US is capable of tracking knee kinematics during dynamic movement. Because US is a safe and commonly used imaging modality, when paired with our bone-tracking algorithm, US has the potential to assess dynamic knee kinematics across a wide variety of applications in the clinic. Level of Evidence: Not applicable.

Nonlinear adaptive pose motion control of a servicer spacecraft in approximation with an accelerated tumbling target.

Removing a limited number of large debris can significantly reduce space debris risks. These bodies are generally exposed to extreme environmental disturbance torques or consecutive accidents due to their large wet area, which causes them to experience accelerated high-rate tumbling motion. The existing literature has adequately explored the approximation operations with non-cooperative targets exhibiting 3-axis tumbling motion. However, the research gap lies in the lack of attention given to addressing this approximation for targets undergoing accelerated motion. Agile, accurate, and large-angle maneuvers are three common necessities for safely capturing such targets. Changes in the moment of inertia brought on by fuel slushing cannot be disregarded during such a maneuver. To deal with nonlinearities, adverse coupling effects, actuator saturation constraints, time-varying moment of inertia, and external disturbances that worsen during accelerated agile large-angle maneuvers, a novel adaptive control approach is developed in this paper. The controller's main advantage is its adjustable desired acceleration, which maintains its performance even when dealing with accelerated motion. The control law is directly synthesized from the nonlinear relative equations of motion, without any linearization or simplification of the system dynamics, making it robust to a variety of orbital elements and target behaviors. Adaptation laws are extracted from the Lyapunov stability theorem in a way that guarantees asymptotic stability. Moreover, control actuator roles (delay, saturation, and allocation) are accounted for in modeling and simulation. Finally, a comprehensive numerical simulation based on three different realistic and strict scenarios is carried out to demonstrate the effectiveness and performance of the proposed control approach. The controller's robustness against time-varying dynamic parameters (sharp and sudden change, smooth and slow change, and periodic change) is extensively demonstrated through simulation.

Utility of visualization and quantification of surgical techniques using motion analysis software for thoracoscopic surgery.

In this era of endoscopic surgery, feedback from recorded surgical videos is useful and efficient; therefore, effective methods of obtaining this feedback are needed. We analyzed surgical videos using motion analysis software and verified the usefulness of visualizing and objectively evaluating surgical procedures. We measured the grasping and traction angles of the vascular sheath when using forceps and the trajectory of the forceps tip for the upper pulmonary vein during right upper lobectomy during video-assisted thoracoscopic surgery performed by three trainers and trainees. Compared with the trainers, the trainees exhibited insufficient traction of the vascular sheath, performed many slow and unnecessary manipulations, and sometimes performed sudden and fast movements. By visualizing the surgical procedures, the trainee will be better able to identify dangerous or futile movements. It may also make it easier to objectively recognize improvements in one's technique. Motion analysis software could allow for efficient surgical education and self-learning.

Virtual reality assessment of a high-calorie food bias: Replication and food-specificity in healthy participants.

BACKGROUND: Theoretical models and behavioural studies indicate faster approach behaviour for high-calorie food (approach bias) among healthy participants. A previous study with Virtual Reality (VR) and online motion-capture quantified this approach bias towards food and non-food cues in a controlled VR environment with hand movements. The aim of this study was to test the specificity of a manual approach bias for high-calorie food in grasp movements compared to low-calorie food and neutral objects of different complexity, namely, simple balls and geometrically more complex office tools. METHODS: In a VR setting, healthy participants (N = 27) repeatedly grasped or pushed high-calorie food, low-calorie food, balls and office tools in randomized order with 30 item repetitions. All objects were rated for valence and arousal. RESULTS: High-calorie food was less attractive and more arousing in subjective ratings than low-calorie food and neutral objects. Movement onset was faster for high-calorie food in push-trials, but overall push responses were comparable. In contrast, responses to high-calorie food relative to low-calorie food and to control objects were faster in grasp trials for later stages of interaction (grasp and collect). Non-parametric tests confirmed an approach bias for high-calorie food. CONCLUSION: A behavioural bias for food was specific to high-calorie food objects. The results confirm the presence of bottom-up advantages in motor-cognitive behaviour for high-calorie food in a non-clinical population. More systematic variations of object fidelity and in clinical populations are outstanding. The utility of VR in assessing approach behaviour is confirmed in this study by exploring manual interactions in a controlled environment.

MocapMe: DeepLabCut-Enhanced Neural Network for Enhanced Markerless Stability in Sit-to-Stand Motion Capture.

This study examined the efficacy of an optimized DeepLabCut (DLC) model in motion capture, with a particular focus on the sit-to-stand (STS) movement, which is crucial for assessing the functional capacity in elderly and postoperative patients. This research uniquely compared the performance of this optimized DLC model, which was trained using 'filtered' estimates from the widely used OpenPose (OP) model, thereby emphasizing computational effectiveness, motion-tracking precision, and enhanced stability in data capture. Utilizing a combination of smartphone-captured videos and specifically curated datasets, our methodological approach included data preparation, keypoint annotation, and extensive model training, with an emphasis on the flow of the optimized model. The findings demonstrate the superiority of the optimized DLC model in various aspects. It exhibited not only higher computational efficiency, with reduced processing times, but also greater precision and consistency in motion tracking thanks to the stability brought about by the meticulous selection of the OP data. This precision is vital for developing accurate biomechanical models for clinical interventions. Moreover, this study revealed that the optimized DLC maintained higher average confidence levels across datasets, indicating more reliable and accurate detection capabilities compared with standalone OP. The clinical relevance of these findings is profound. The optimized DLC model's efficiency and enhanced point estimation stability make it an invaluable tool in rehabilitation monitoring and patient assessments, potentially streamlining clinical workflows. This study suggests future research directions, including integrating the optimized DLC model with virtual reality environments for enhanced patient engagement and leveraging its improved data quality for predictive analytics in healthcare. Overall, the optimized DLC model emerged as a transformative tool for biomechanical analysis and physical rehabilitation, promising to enhance the quality of patient care and healthcare delivery efficiency.

A GM-JMNS-CPHD Filter for Different-Fields-of-View Stochastic Outlier Selection for Nonlinear Motion Tracking.

Most multi-target movements are nonlinear in the process of movement. The common multi-target tracking filtering methods directly act on the multi-target tracking system of nonlinear targets, and the fusion effect is worse under the influence of different perspectives. Aiming to determine the influence of different perspectives on the fusion accuracy of multi-sensor tracking in the process of target tracking, this paper studies the multi-target tracking fusion strategy of a nonlinear system with different perspectives. A GM-JMNS-CPHD fusion technique is introduced for random outlier selection in multi-target tracking, leveraging sensors with limited views. By employing boundary segmentation from distinct perspectives, the posterior intensity function undergoes decomposition into multiple sub-intensities through SOS clustering. The distribution of target numbers within the respective regions is then characterized by the multi-Bernoulli reconstruction cardinal distribution. Simulation outcomes demonstrate the robustness and efficacy of this approach. In comparison to other algorithms, this method exhibits enhanced robustness even amidst a decreased detection probability and heightened clutter rates.

Accuracy of robotic radiosurgery in renal cell carcinoma.

PURPOSE: Although emerging clinical evidence supports robotic radiosurgery as a highly effective treatment option for renal cell carcinoma (RCC) less than 4 cm in diameter, delivery uncertainties and associated target volume margins have not been studied in detail. We assess intrafraction tumor motion patterns and accuracy of robotic radiosurgery in renal tumors with real-time respiratory tracking to optimize treatment margins. METHODS: Delivery log files from 165 consecutive treatments of RCC were retrospectively analyzed. Five components were considered for planning target volume (PTV) margin estimation: (a) The model error from the correlation model between patient breath and tumor motion, (b) the prediction error from an algorithm predicting the patient breathing pattern, (c) the targeting error from the treatment robot, (d) the inherent total accuracy of the system for respiratory motion tracking, and (e) the margin required to cover potential target rotation, simulated with PTV rotations up to 10°. RESULTS: The median tumor motion was 10.5 mm, 2.4 mm and 4.4 mm in the superior-inferior, left-right, and anterior-posterior directions, respectively. The root of the sum of squares of all contributions to the system's inaccuracy results in a minimum PTV margin of 4.3 mm, 2.6 mm and 3.0 mm in the superior-inferior, left-right and anterior-posterior directions, respectively, assuming optimal fiducial position and neglecting target deformation. CONCLUSIONS: We have assessed kidney motion and derived PTV margins for the treatment of RCC with robotic radiosurgery, which helps to deliver renal treatments in a more consistent manner and potentially further improve outcomes.

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

BACKGROUND: 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. OBJECTIVE: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.

Simultaneous in-air and underwater 3D kinematic analysis of swimmers: Feasibility and reliability of action sport cameras.

This study explored the potential of reconstructing the 3D motion of a swimmer's hands with accuracy and consistency using action sport cameras (ASC) distributed in-air and underwater. To record at least two stroke cycles of an athlete performing a front crawl task, the cameras were properly calibrated to cover an acquisition volume of 3 m in X, 8 m in Y, and 3.5 m in Z axis, approximately. Camera calibration was attained by applying bundle adjustment in both environments. A testing wand, carrying two markers, was acquired to evaluate the three-dimensional (3D) reconstruction accuracy in-air, underwater, and over the water transition. The global 3D accuracy (mean absolute error) was less than 1.5 mm. The standard error of measurement and the coefficient of variation were smaller than 1 mm and 1%, respectively, revealing that the camera calibration procedure was highly repeatable. No significant correlation between the error magnitude (percentage error during the test and the retest sessions: 1.2 to 0.8%) and the transition from in-air to underwater was observed. The feasibility of the hand motion reconstruction was demonstrated by recording five swimmers during the front crawl stroke, in three different tasks performed at increasing efforts. Intra-class correlation confirmed the optimal agreement (ICC>0.90) among repeated stroke cycles of the same swimmer, irrespective of task effort. Skewness, close to 0, and kurtosis, close to 3.5, supported the hypothesis of negligible effects of the calibration and tracking errors on the motion and speed patterns. In conclusion, we may argue that ASCs, equipped with a robust bundle adjustment camera calibration technique, ensure reliable reconstruction of swimming motion in in-air and underwater large volumes.