Wearable magnetic induction-based approach toward 3D motion tracking.
Ontology highlight
ABSTRACT:
SUBMITTER: Golestani N
PROVIDER: S-EPMC8460632 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:Development of a human-interactive display enabling the simultaneous sensing, visualisation, and memorisation of a magnetic field remains a challenge. Here we report a skin-patchable magneto-interactive electroluminescent display, which is capable of sensing, visualising, and storing magnetic field information, thereby enabling 3D motion tracking. A magnetic field-dependent conductive gate is employed in an alternating current electroluminescent display, which is used to produce non-volatile and rewritable magnetic field-dependent display. By constructing mechanically flexible arrays of magneto-interactive displays, a spin-patchable and pixelated platform is realised. The magnetic field varying along the z-axis enables the 3D motion tracking (monitoring and memorisation) on 2D pixelated display. This 3D motion tracking display is successfully used as a non-destructive surgery-path guiding, wherein a pathway for a surgical robotic arm with a magnetic probe is visualised and recorded on a display patched on the abdominal skin of a rat, thereby helping the robotic arm to find an optimal pathway.
| S-EPMC7695719 | biostudies-literature
Project description:Spatiotemporal tracking of tracer particles or objects of interest can reveal localized behaviors in biological and physical systems. However, existing tracking algorithms are most effective for relatively low numbers of particles that undergo displacements smaller than their typical interparticle separation distance. Here, we demonstrate a single particle tracking algorithm to reconstruct large complex motion fields with large particle numbers, orders of magnitude larger than previously tractably resolvable, thus opening the door for attaining very high Nyquist spatial frequency motion recovery in the images. Our key innovations are feature vectors that encode nearest neighbor positions, a rigorous outlier removal scheme, and an iterative deformation warping scheme. We test this technique for its accuracy and computational efficacy using synthetically and experimentally generated 3D particle images, including non-affine deformation fields in soft materials, complex fluid flows, and cell-generated deformations. We augment this algorithm with additional particle information (e.g., color, size, or shape) to further enhance tracking accuracy for high gradient and large displacement fields. These applications demonstrate that this versatile technique can rapidly track unprecedented numbers of particles to resolve large and complex motion fields in 2D and 3D images, particularly when spatial correlations exist.
| S-EPMC5882970 | biostudies-literature
Project description:The study of eye movements and the measurement of the resulting biopotential, referred to as electrooculography (EOG), may find increasing use in applications within the domain of activity recognition, context awareness, mobile human-computer and human-machine interaction (HCI/HMI), and personal medical devices; provided that, seamless sensing of eye activity and processing thereof is achieved by a truly wearable, low-cost, and accessible technology. The present study demonstrates an alternative to the bulky and expensive camera-based eye tracking systems and reports the development of a graphene textile-based personal assistive device for the first time. This self-contained wearable prototype comprises a headband with soft graphene textile electrodes that overcome the limitations of conventional "wet" electrodes, along with miniaturized, portable readout electronics with real-time signal processing capability that can stream data to a remote device over Bluetooth. The potential of graphene textiles in wearable eye tracking and eye-operated remote object interaction is demonstrated by controlling a mouse cursor on screen for typing with a virtual keyboard and enabling navigation of a four-wheeled robot in a maze, all utilizing five different eye motions initiated with a single channel EOG acquisition. Typing speeds of up to six characters per minute without prediction algorithms and guidance of the robot in a maze with four 180° turns were successfully achieved with perfect pattern detection accuracies of 100% and 98%, respectively.
| S-EPMC7884882 | biostudies-literature
Project description:Recognizing human physical activities using wireless sensor networks has attracted significant research interest due to its broad range of applications, such as healthcare, rehabilitation, athletics, and senior monitoring. There are critical challenges inherent in designing a sensor-based activity recognition system operating in and around a lossy medium such as the human body to gain a trade-off among power consumption, cost, computational complexity, and accuracy. We introduce an innovative wireless system based on magnetic induction for human activity recognition to tackle these challenges and constraints. The magnetic induction system is integrated with machine learning techniques to detect a wide range of human motions. This approach is successfully evaluated using synthesized datasets, laboratory measurements, and deep recurrent neural networks.
| S-EPMC7096402 | biostudies-literature
Project description:The development of stretchable smart electronics has attracted great attentions due to their potential applications in human motions energy collection systems and self-powered biomechanical tracking technologies. Here, we present a newly stretchable all-rubber-based thread-shaped triboelectric nanogenerator (TENG) composed of the silver-coated glass microspheres/silicone rubber as the stretchable conductive thread (SCT) and the silicone rubber-coated SCT (SSCT) as the other triboelectric thread. The stretchable all-rubber-based thread-shaped TENG (SATT) generates an open-circuit voltage of 3.82 V and short-circuit current of 65.8 nA under the 100% strain and can respond to different finger motion states. Furthermore, the self-powered smart textile (SPST) woven by the SCT and SSCT units has two kinds of working mechanisms about stretch-release and contact-separation modes. The stretching-releasing interaction between knitting units can generate an open-circuit voltage of 8.1 V and short-circuit current of 0.42 ?A, and the contacting-separating mode occurs between cotton and two types material outside the SPST producing peak voltage of 150 V and peak current of 2.45 ?A. To prove the promising applications, the SPST device is capable to provide electrical energy to commercial electronics and effectively scavenge full-range biomechanical energy from human joint motions. Therefore, this work provides a new approach in the applications of stretchable wearable electronics for power generation and self-powered tracking.
| S-EPMC6650513 | biostudies-literature
Project description:Soft electromechanical sensors have led to a new paradigm of electronic devices for novel motion-based wearable applications in our daily lives. However, the vast amount of random and unidentified signals generated by complex body motions has hindered the precise recognition and practical application of this technology. Recent advancements in artificial-intelligence technology have enabled significant strides in extracting features from massive and intricate data sets, thereby presenting a breakthrough in utilizing wearable sensors for practical applications. Beyond traditional machine-learning techniques for classifying simple gestures, advanced machine-learning algorithms have been developed to handle more complex and nuanced motion-based tasks with restricted training data sets. Machine-learning techniques have improved the ability to perceive, and thus machine-learned wearable soft sensors have enabled accurate and rapid human-gesture recognition, providing real-time feedback to users. This forms a crucial component of future wearable electronics, contributing to a robust human-machine interface. In this review, we provide a comprehensive summary covering materials, structures and machine-learning algorithms for hand-gesture recognition and possible practical applications through machine-learned wearable electromechanical sensors.
| S-EPMC10776364 | biostudies-literature
Project description:ObjectiveLow-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.MethodsTwenty-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.ResultsTime 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.ConclusionExtracted 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 impactThe 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.
| S-EPMC11329219 | biostudies-literature
Project description:Force feedback is often beneficial for robotic teleoperation, as it enhances the user's remote perception. Over the years, many kinesthetic haptic displays (KHDs) have been proposed for this purpose, which have different types of interaction and feedback, depending on their kinematics and their interface with the operator, including, for example, grounded and wearable devices acting either at the joint or operational space (OS) level. Most KHDs in the literature are for the upper limb, with a majority acting at the shoulder/elbow level, and others focusing on hand movements. A minority exists which addresses wrist motions. In this paper, we present the Wearable Delta (W), a proof-of-concept wearable wrist interface with hybrid parallel-serial kinematics acting in the OS, able to render a desired force directly to the hand involving just the forearm-hand subsystem. It has six degrees of freedom (DoFs), three of which are actuated, and is designed to reduce the obstruction of the range of the user's wrist. Integrated with positions/inertial sensors at the elbow and upper arm, the W allows the remote control of a full articulated robotic arm. The paper covers the whole designing process, from the concept to the validation, as well as a multisubject experimental campaign that investigates its usability. Finally, it presents a section that, starting from the experimental results, aims to discuss and summarize the W advantages and limitations and look for possible future improvements and research directions.
| S-EPMC10936305 | biostudies-literature
Project description:Object tracking is an important research direction in computer vision and is widely used in video surveillance, security monitoring, video analysis and other fields. Conventional tracking algorithms perform poorly in specific scenes, such as a target with fast motion and occlusion. The candidate samples may lose the true target due to its fast motion. Moreover, the appearance of the target may change with movement. In this paper, we propose an object tracking algorithm based on motion consistency. In the state transition model, candidate samples are obtained by the target state, which is predicted according to the temporal correlation. In the appearance model, we define the position factor to represent the different importance of candidate samples in different positions using the double Gaussian probability model. The candidate sample with highest likelihood is selected as the tracking result by combining the holistic and local responses with the position factor. Moreover, an adaptive template updating scheme is proposed to adapt to the target's appearance changes, especially those caused by fast motion. The experimental results on a 2013 benchmark dataset demonstrate that the proposed algorithm performs better in scenes with fast motion and partial or full occlusion compared to the state-of-the-art algorithms.
| S-EPMC5854992 | biostudies-literature
Project description:Cardiovascular diseases can be diagnosed by assessing abnormal flow behavior in the heart. We introduce, for the first time, a magnetic resonance imaging-based diagnostic that produces sectional flow maps of cardiac chambers, and presents cardiac analysis based on the flow information. Using steady-state free precession magnetic resonance images of blood, we demonstrate intensity contrast between asynchronous and synchronous proton spins. Turbulent blood flow in cardiac chambers contains asynchronous blood proton spins whose concentration affects the signal intensities that are registered onto the magnetic resonance images. Application of intensity flow tracking based on their non-uniform signal concentrations provides a flow field map of the blood motion. We verify this theory in a patient with an atrial septal defect whose chamber blood flow vortices vary in speed of rotation before and after septal occlusion. Based on the measurement of cardiac flow vorticity in our implementation, we establish a relationship between atrial vorticity and septal defect. The developed system has the potential to be used as a prognostic and investigative tool for assessment of cardiac abnormalities, and can be exploited in parallel to examining myocardial defects using steady-state free precession magnetic resonance images of the heart.
| S-EPMC2682657 | biostudies-literature