Motion Tracking Research Articles

Recent Advances in Self-Powered Electronic Skin Based on Triboelectric Nanogenerators

Human skin, the body’s largest organ, plays a crucial role in perceiving mechanical stimulation and facilitating interaction with the external environment. Leveraging the unique attributes of human skin, electronic skin technology aimed at replicating and surpassing the capabilities of natural skin holds significant promise across various domains, including medical care, motion tracking, and intelligent robotics. In recent research, triboelectric nanogenerators have emerged as a compelling solution for addressing the energy challenge in electronic skins. Triboelectric nanogenerators harness the combination of the triboelectric effect and electrostatic induction to efficiently convert mechanical energy into electrical power, serving as self-powered sensors for electronic skins, which possess the advantages of self-powered operation, cost-effectiveness, and compatibility with a wide range of materials. This review provides an introduction to the working principles and the four operational modes of triboelectric nanogenerators, highlighting the functional features of electronic skins, such as stretchability, self-healing, and degradability. The primary focus is on the current applications of self-powered electronic skins based on triboelectric nanogenerators in medical care, motion tracking, and machine tactile recognition. This review concludes by discussing the anticipated challenges in the future development of self-powered electronic skins based on triboelectric nanogenerators. This review holds practical significance for advancing the practical use of self-powered electronic skins based on triboelectric nanogenerators and offers valuable guidance for individuals interested in pursuing scientific and healthy endeavors.

Effects of Screen Color Mode and Color Temperature on Visual Fatigue under Different Ambient Illuminations

Viewing electronic screens for a long time can cause visual fatigue, even induce eye problems such as refractive errors, dry eyes, etc. The relationship between screen display and visual fatigue has received much attention. This article aims to study the optimal display color temperature and color mode under two common ambient illuminations—normal office lighting (450 lux) and dark environment (3 lux). In a 2 × 2×3 experimental design, 36 participants evaluated the effects of two display color modes (light mode and dark mode) and three screen color temperatures (2800 K, 4500 K, and 6500 K) on visual fatigue under two ambient illuminations. We used eye movement tracking technology to collect blink rate and pupil diameters as objective indicators and used the Richter scale to collect subjective visual fatigue and preference score as subjective indicators. Results showed that at 450 lux, lower visual fatigue was found in the dark mode, 4500 K and 6500 K. At 3 lux, the visual fatigue was lower in the dark mode and 4500 K color temperature. Whether at daytime or night, the 2800 K color temperature resulted in the highest visual fatigue. Our findings complement the research on screen display and visual fatigue. They have practical implications for reducing visual fatigue caused by prolonged viewing of electronic screens.

Retracted: Motion Control and Tracking Control of UAV Based on Adaptive Sensor

Retracted: Motion Control and Tracking Control of UAV Based on Adaptive Sensor

Retracted: The Accurate Repair of Image Contour of Human Motion Tracking Based on Improved Snake Model

Retracted: The Accurate Repair of Image Contour of Human Motion Tracking Based on Improved Snake Model

Persistent avoidance of virtual food in anorexia nervosa-restrictive type: Results from motion tracking in a virtual stopping task.

Food avoidance is central to patients with anorexia nervosa-restrictive type (AN-R). Competing accounts in experimental psychopathology research suggest that food avoidance may result from automatic, habitual responses or from elevated inhibitory control abilities. This study investigated behavioral trajectories of food avoidance in a novel virtual reality stopping task. Sixty patients with AN-R and 29 healthy controls with normal weight were investigated using a novel, kinematic task in virtual reality. We recorded spatial displacement in stop- and go-trials to virtual food and control objects. Inhibitory control abilities were operationalized by the VR task in stopping performance (i.e., interrupted movement in stop-trials), whereas we also measured habitual avoidance of virtual food across both go- and stop-trials (i.e., delayed movement relative to nonfood objects). In patients with AN-R, hand displacements were shorter to food versus nonfood across stop- and go-trials, reflected in a Stimulus × Group interaction. Healthy controls showed no differences. Importantly, the food-specific effect in AN-R was identical across stop- and go-trials, indicating habitual food avoidance. Moreover, stop error rates (i.e., stop-trials with response) were lower in patients with AN-R. The findings suggest food-specific habitual avoidance and heightened generalized inhibitory control in AN-R. The continuously delayed displacements during active hand movements across stop- and go-trials indicated the persistence of patients' avoidance of food. Experimental research investigates the mechanisms underlying mental disorders such as anorexia nervosa. In this study, we measured interrupted hand movements in response to food pictures or neutral pictures (shoes) in patients with anorexia nervosa and healthy controls. A virtual reality scenario was used. Findings indicated that patients were slower at approaching food, interrupted or not. Key mechanisms of food avoidance can be translated into habit-based treatment options in future research.

Reconstructive interpolation for pulse wave estimation to improve local PWV measurement of carotid artery.

Ultrasonic transit time (TT)-based local pulse wave velocity (PWV) measurement is defined as the distance between two beam positions on a segment of common carotid artery (CCA) divided by the TT in the pulse wave propagation. However, the arterial wall motions (AWMs) estimated from ultrasonic radio frequency (RF) signals with a limited number of frames using the motion tracking are typically discrete. In this work, we develop a method involving motion tracking combined with reconstructive interpolation (MTRI) to reduce the quantification errors in the estimated PWs, and thereby improve the accuracy of the TT-based local PWV measurement for CCA. For each beam position, normalized cross-correlation functions (NCCFs) between the reference (the first frame) and comparison (the remaining frames) RF signals are calculated. Thereafter, the reconstructive interpolation is performed in the neighborhood of the NCCFs' peak to identify the interpolation-deduced peak locations, which are more exact than the original ones. According to which, the improved AWMs are obtained to calculate their TT along a segment of the CCA. Finally, the local PWV is measured by applying a linear regression fit to the time-distance result. In ultrasound simulations based on the pulse wave propagation models of young, middle-aged, and elderly groups, the MTRI method with different numbers of interpolated samples was used to estimate AWMs and local PWVs. Normalized root mean squared errors (NRMSEs) between the estimated and preset values of the AWMs and local PWVs were calculated and compared with ones without interpolation. The means of the NRMSEs for the AWMs and local PWVs based on the MTRI method with one interpolated sample decrease from 1.14% to 0.60% and 7.48% to 4.61%, respectively. Moreover, Bland-Altman analysis and coefficient of variation were used to validate the performance of the MTRI method based on the measured local PWVs of 30 healthy subjects. In conclusion, the reconstructive interpolation for the pulse wave estimation improves the accuracy and repeatability of the carotid local PWV measurement.

On the soft tissue ultrasound elastography using FEM based inversion approach.

Elastography is a medical imaging modality that enables visualization of tissue stiffness. It involves quasi-static or harmonic mechanical stimulation of the tissue to generate a displacement field which is used as input in an inversion algorithm to reconstruct tissue elastic modulus. This paper considers quasi-static stimulation and presents a novel inversion technique for elastic modulus reconstruction. The technique follows an inverse finite element framework. Reconstructed elastic modulus maps produced in this technique do not depend on the initial guess, while it is computationally less involved than iterative reconstruction approaches. The method was first evaluated using simulated data (in-silico) where modulus reconstruction's sensitivity to displacement noise and elastic modulus was assessed. To demonstrate the method's performance, displacement fields of two tissue mimicking phantoms determined using three different motion tracking techniques were used as input to the developed elastography method to reconstruct the distribution of relative elastic modulus of the inclusion to background tissue. In the next stage, the relative elastic modulus of three clinical cases pertaining to liver cancer patient were determined. The obtained results demonstrate reasonably high elastic modulus reconstruction accuracy in comparison with similar direct methods. Also it is associated with reduced computational cost in comparison with iterative techniques, which suffer from convergence and uniqueness issues, following the same formulation concept. Moreover, in comparison with other methods which need initial guess, the presented method does not require initial guess while it is easy to understand and implement.

A low-cost underactuated compliant gripper with multifunctional jaws for precision manipulation

Precision manipulation tasks often require the use of compliant grippers to handle small and delicate objects accurately. This paper proposes a novel low-cost underactuated compliant gripper with integrated position and force sensors for precision manipulation. The gripper has a millimeter-scale grasping range and can adapt to objects of varying sizes and shapes. This is made possible by a flexure motion transmission mechanism that can undergo significant deformation, an underactuated seesaw flexure mechanism, and a pair of multifunctional jaws. Analytical models based on the pseudo-rigid-body approach are developed to analyze the kinematic, static, and dynamic characteristics of the gripper design. Finite element simulations are conducted to investigate the static and dynamic performance of the gripper. The gripper prototype is fabricated using 3D printing technology for a series of experimental tests. The results of these experiments demonstrate that the developed gripper has excellent motion tracking capabilities and high motion and force resolutions, enabling precise and accurate manipulation tasks. Furthermore, the gripper is capable of successfully grasping objects of different sizes and shapes through parallel and enveloping grasps.

National health insurance contribution to family planning program funding in Indonesia: A fund flow analysis.

Launched in 2014, Indonesia's national health insurance system (JKN) aimed to provide universal health coverage, including contraceptive services, to its population. We aim to evaluate the contribution of JKN to the overall spending for the family planning program in Indonesia. Data from the Indonesian Demographic Health Survey, Survey on Financial Flows for Family Planning, Indonesia Motion Tracker Matrix, World Population Prospect, and Indonesian ministries' budget accountability reports were entered into the CastCost Contraceptive Projection Tool to define budgetary allocation and spending for the family planning program at the national level in 2019. Indonesia's family planning program in 2019 was financed mostly by the national budget (64.0%) and out-of-pocket payments (34.6%). There were three main ministries responsible for family planning financing: the National Population and Family Planning Board (BKKBN) (35.8%), the Ministry of Finance (26.2%), and the Ministry of Health (2.0%). Overall, JKN contributed less than 0.4% of the funding for family planning services in Indonesia in 2019. The majority of family planning spending was by public facilities (57.3%) as opposed to private facilities (28.6%). JKN's contribution to funding Indonesia's family planning programs in 2019 was low and highlights a huge opportunity to expand these contributions. A coordinated effort should be conducted to identify possible opportunities to realign BKKBN and JKN roles in the family planning programs and lift barriers to accessing family planning services in public and private facilities. This includes a concerted effort to improve integration of private family planning providers into the JKN program.

Autonomous Obstacle Avoidance and Trajectory Planning for Mobile Robot Based on Dual-Loop Trajectory Tracking Control and Improved Artificial Potential Field Method

In order to better meet the practical application needs of mobile robots, this study innovatively designs an autonomous obstacle avoidance and trajectory planning control strategy with low computational complexity, high cost-effectiveness, and the ability to quickly plan a collision-free smooth trajectory curve. This article constructs the kinematic model of the mobile robot, designs a dual-loop trajectory tracking control strategy for position control law and attitude control law algorithms, and improves the traditional artificial potential field method to achieve a good obstacle avoidance strategy for mobile robots. Based on the dual-loop trajectory tracking control and the improved artificial potential field method, the autonomous obstacle avoidance and trajectory planning scheme of the mobile robot is designed, and closed-loop stability verification and analysis are conducted on the overall control system. And through the detailed simulation and experiments, the advantages of the proposed method in trajectory tracking accuracy and motion stability compared to the existing methods are verified, showing good effectiveness and feasibility and laying a good foundation for the application of mobile robots in practical complex scenes.

Wearable Sensors Assess the Effects of Human-Robot Collaboration in Simulated Pollination.

Pollination for indoor agriculture is hampered by environmental conditions, requiring farmers to pollinate manually. This increases the musculoskeletal illness risk of workers. A potential solution involves Human-Robot Collaboration (HRC) using wearable sensor-based human motion tracking. However, the physical and biomechanical aspects of human interaction with an advanced and intelligent collaborative robot (cobot) during pollination remain unknown. This study explores the impact of HRC on upper body joint angles during pollination tasks and plant height. HRC generally resulted in a significant reduction in joint angles with flexion decreasing by an average of 32.6 degrees (p ≤ 0.001) for both shoulders and 30.5 degrees (p ≤ 0.001) for the elbows. In addition, shoulder rotation decreased by an average of 19.1 (p ≤ 0.001) degrees. However, HRC increased the left elbow supination by 28.3 degrees (p ≤ 0.001). The positive effects of HRC were reversed when the robot was unreliable (i.e., missed its target), but this effect was not applicable for the left elbow. The effect of plant height was limited with higher plant height increasing right shoulder rotation but decreasing right elbow pronation. These findings aim to shed light on both the benefits and challenges of HRC in agriculture, providing valuable insights before deploying cobots in indoor agricultural settings.

System Architecture for VR Yoga Therapy Platform with 6-DoF Whole-Body Avatar Tracking.

Chronic pain is a leading cause of morbidity among children and adolescents affecting 35% of the global population. Pediatric chronic pain management requires integrative health methods spanning physical and psychological subsystems through various mind-body interventions. Yoga therapy is one such method, known for its ability to improve the quality of life both physically and psychologically in chronic pain conditions. However, maintaining the clinical outcomes of personalized yoga therapy sessions at-home is challenging due to fear of movement, lack of motivation, and boredom. Virtual Reality (VR) has the potential to bridge the gap between the clinic and home by motivating engagement and mitigating pain-related anxiety or fear of movement. We developed a multi-modal algorithmic architecture for fusing real-time 3D human body pose estimation models with custom developed inverse kinematics models of physical movement to render biomechanically informed 6-DoF whole-body avatars capable of embodying an individual's real-time yoga poses within the VR environment. Experiments conducted among control participants demonstrated superior movement tracking accuracy over existing commercial off-the-shelf avatar tracking solutions, leading to successful embodiment and engagement. These findings demonstrate the feasibility of rendering virtual avatar movements that embody complex physical poses such as those encountered in yoga therapy. The impact of this work moves the field one step closer to an interactive system to facilitate at-home individual or group yoga therapy for children with chronic pain conditions.

Comparison of Shoulder Range of Motion Quantified with Mobile Phone Video-Based Skeletal Tracking and 3D Motion Capture-Preliminary Study.

The accuracy of human pose tracking using smartphone camera (2D-pose) to quantify shoulder range of motion (RoM) is not determined. Twenty healthy individuals were recruited and performed shoulder abduction, adduction, flexion, or extension, captured simultaneously using a smartphone-based human pose estimation algorithm (Apple's vision framework) and using a skin marker-based 3D motion capture system. Validity was assessed by comparing the 2D-pose outcomes against a well-established 3D motion capture protocol. In addition, the impact of iPhone positioning was investigated using three smartphones in multiple vertical and horizontal positions. The relationship and validity were analysed using linear mixed models and Bland-Altman analysis. We found that 2D-pose-based shoulder RoM was consistent with 3D motion capture (linear mixed model: R2 > 0.93) but was somewhat overestimated by the smartphone. Differences were dependent on shoulder movement type and RoM amplitude, with adduction the worst performer among all tested movements. All motion types were described using linear equations. Correction methods are provided to correct potential out-of-plane shoulder movements. Shoulder RoM estimated using a smartphone camera is consistent with 3D motion-capture-derived RoM; however, differences between the systems were observed and are likely explained by differences in thoracic frame definitions.

Smart Body Posture Recognition and Guiding System

In an era prioritizing health and wellness, the integration of the Smart Body Posture Recognition & Guiding System into self- service health kiosks signifies a paradigm shift in healthcare management. This groundbreaking research project leverages cutting-edge MediaPipe Body Tracking technology to revolutionize health monitoring capabilities. At its core, MediaPipe Body Tracking enables precise real-time identification and tracking of human body key points and movements. This breakthrough technology ensures unparalleled accuracy in health parameter measurements within the kiosk interface. A standout feature of this system is its embedded alert mechanism, surpassing conventional health monitoring by actively detecting and notifying individuals about poor body posture. Providing immediate corrective feedback, this feature significantly impacts wellness, fostering a proactive approach to posture and overall health management. This research delves into the intricacies of MediaPipe Body Tracking's algorithms and sensors, elucidating the technology's real-time feedback mechanism. The system's ability to address poor posture not only prompts immediate corrective action but also raises awareness about its long-term health implications. Beyond individual health monitoring, this innovation promises a transformative shift in healthcare paradigms, encouraging proactive health measures and empowering individuals to take charge of their well-being. The Smart Body Posture Recognition & Guiding System represents a significant advancement at the nexus of technology and healthcare, redefining how societies approach proactive health management. Keywords- MediaPipe, Posture Recognition, Heathcare

Roboto su tiesiaeigio judėjimo/pozicionavimo įrenginiu automatinės sistemos efektyvumo tyrimas

The work presents an assessment of the efficiency and operational costs of modernized manufacturing process. The process consists of turning operations on two CNC lathe machines. Two options for modernization are proposed: installation of 2 robots automated system and installation of an automated system of one robot on a linear motion track for positioning. The purpose of the study is to prove the advantage of option with one robot on a linear motion track device. The process modelling is performed in Visual Components software. ABB 6-axis robot IRB 4600 and the linear motion track device ABB IRBT 4004 of the same manufacturer were chosen for the research. Keywords: robot, linear motion track, Visual Components software.

Abnormality Determination of Spermatozoa Motility Using Gaussian Mixture Model and Matching-based Algorithm

Sperm analysis is an initial step in the examination conducted to identify infertility cases in humans. One aspect of sperm analysis involves observing the movement of spermatozoa and determining whether it is normal or abnormal. Normal spermatozoa movement is characterized by progressive motion at an average speed of 20 µm/second, while abnormal movement includes slow or non-motile spermatozoa. Traditional methods can be employed to assess the normality or abnormality of sperm movement, but they have drawbacks such as time-consuming procedures and diverse results depending on the expertise of the examiner. On the other hand, utilizing Computer-Assisted Sperm Analysis (CASA) equipment provides consistent results, albeit at a relatively high cost. Therefore, this research proposes an alternative method for determining sperm movement abnormalities using the Gaussian Mixture Model (GMM) for background subtraction and a matching-based algorithm to track and analyze the formed trajectories, distinguishing between normal and abnormal sperm movement. Human spermatozoa in real-time are used, and their movements are recorded in video format using a bright field microscope. The testing results for determining sperm movement abnormalities based on the GMM method and matching-based algorithm were successful, particularly in videos recorded at 50 fps recording speed, 20 minutes of liquefaction time, and 40x microscope lens magnification. This condition exhibited the highest average accuracy, with a tracking accuracy of 77.3% and an average accuracy for determining sperm motility abnormalities of 87.7%. Therefore, the combined tracking of sperm movement based on the GMM method and matching-based algorithm can be utilized to identify abnormalities in the movement of human spermatozoa.

Tactile corpuscle-inspired piezoresistive sensors based on (3-aminopropyl) triethoxysilane-enhanced CNPs/carboxylated MWCNTs/cellulosic fiber composites for textile electronics

Recently, wearable electronic products and gadgets have developed quickly with the aim of catching up to or perhaps surpassing the ability of human skin to perceive information from the external world, such as pressure and strain. In this study, by first treating the cellulosic fiber (modal textile) substrate with (3-aminopropyl) triethoxysilane (APTES) and then covering it with conductive nanocomposites, a bionic corpuscle layer is produced. The sandwich structure of tactile corpuscle-inspired bionic (TCB) piezoresistive sensors created with the layer-by-layer (LBL) technology consists of a pressure-sensitive module (a bionic corpuscle), interdigital electrodes (a bionic sensory nerve), and a PU membrane (a bionic epidermis). The synergistic mechanism of hydrogen bond and coupling agent helps to improve the adhesive properties of conductive materials, and thus improve the pressure sensitive properties. The TCB sensor possesses favorable sensitivity (1.0005 kPa−1), a wide linear sensing range (1700 kPa), and a rapid response time (40 ms). The sensor is expected to be applied in a wide range of possible applications including human movement tracking, wearable detection system, and textile electronics.

Initial Experience With 6D Skull Tracking and Intrafractional Motion Monitoring in the United Arab Emirates' First CyberKnife® Radiosurgery Center.

Introduction The introduction of the CyberKnife® system has marked a significant advancement in the field of radiosurgery, offering unparalleled precision in targeting and treating cranial and extracranial lesions. This paper details the first experience from the United Arab Emirates in implementing 6D skull tracking and intrafractional motion monitoring in CyberKnife® radiosurgery. The study aims to evaluate the system's efficacy in tracking and adjusting patient movement during treatment, enhancing treatment accuracy and patient safety. Methods and materials This retrospective study analyzed 732 images from six patients treated at the UAE's first CyberKnife® center. Patients were divided into two groups based on their treatment regimens: Patients 1 to 4 (P1 to P4) received multifractionated stereotactic radiotherapy, while Patients 5 and 6 (P5 and P6) underwent single-fraction stereotactic radiosurgery (SRS). The movements recorded included supero-inferior, lateral, antero-posterior, roll, pitch, and yaw. Statistical tools were employed to interpret the data, including heat maps, box-and-whisker plots, and correlation analysis. Results The study's results indicate varied patterns of intrafractional movement across the different axes and between the two treatment groups. Multifractionated therapy patients exhibited a specific range and frequency of movements compared to those undergoing single-fraction treatment. The most significant movements were observed in the supero-inferior and lateral axes. Discussion The findings suggest that the CyberKnife® system's real-time tracking and adaptive capabilities are crucial in managing patient movements, especially in prolonged treatment sessions. The differences in movement patterns between multifractionated and single-fraction treatments underscore the need for tailored approaches in intrafractional motion monitoring. Conclusion The initial experience of the UAE's first CyberKnife® center demonstrates the system's effectiveness in addressing intrafractional movements, enhancing the precision and safety of radiosurgery treatments. This study contributes valuable insights into optimizing treatment protocols and underscores the importance of continuous monitoring and adaptive strategies in advanced radiosurgery.

Vision-based tactile intelligence with soft robotic metamaterial

Robotic metamaterials represent an innovative approach to creating synthetic structures that combine desired material characteristics with embodied intelligence, blurring the boundaries between materials and machinery. Inspired by the functional qualities of biological skin, integrating tactile intelligence into these materials has gained significant interest for research and practical applications. This study introduces a Soft Robotic Metamaterial (SRM) design featuring omnidirectional adaptability and superior tactile sensing, combining vision-based motion tracking and machine learning. The study compares two sensory integration methods to a state-of-the-art motion tracking system and force/torque sensor baseline: an internal-vision design with high frame rates and an external-vision design offering cost-effectiveness. The results demonstrate the internal-vision SRM design achieving an impressive tactile accuracy of 98.96%, enabling soft and adaptive tactile interactions, especially beneficial for dexterous robotic grasping. The external-vision design offers similar performance at a reduced cost and can be adapted for portability, enhancing material science education and robotic learning. This research significantly advances tactile sensing using vision-based motion tracking in soft robotic metamaterials, and the open-source availability on GitHub fosters collaboration and further exploration of this innovative technology (https://github.com/bionicdl-sustech/SoftRoboticTongs).

Remote Photoplethysmography and Motion Tracking Convolutional Neural Network with Bidirectional Long Short-Term Memory: Non-Invasive Fatigue Detection Method Based on Multi-Modal Fusion.

Existing vision-based fatigue detection methods commonly utilize RGB cameras to extract facial and physiological features for monitoring driver fatigue. These features often include single indicators such as eyelid movement, yawning frequency, and heart rate. However, the accuracy of RGB cameras can be affected by factors like varying lighting conditions and motion. To address these challenges, we propose a non-invasive method for multi-modal fusion fatigue detection called RPPMT-CNN-BiLSTM. This method incorporates a feature extraction enhancement module based on the improved Pan-Tompkins algorithm and 1D-MTCNN. This enhances the accuracy of heart rate signal extraction and eyelid features. Furthermore, we use one-dimensional neural networks to construct two models based on heart rate and PERCLOS values, forming a fatigue detection model. To enhance the robustness and accuracy of fatigue detection, the trained model data results are input into the BiLSTM network. This generates a time-fitting relationship between the data extracted from the CNN, allowing for effective dynamic modeling and achieving multi-modal fusion fatigue detection. Numerous experiments validate the effectiveness of the proposed method, achieving an accuracy of 98.2% on the self-made MDAD (Multi-Modal Driver Alertness Dataset). This underscores the feasibility of the algorithm. In comparison with traditional methods, our approach demonstrates higher accuracy and positively contributes to maintaining traffic safety, thereby advancing the field of smart transportation.