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Distinguishing pathological gait is challenging in neurology because of the difficulty of capturing total body movement and its analysis. We aimed to obtain a convenient recording with an iPhone and establish an algorithm based on deep learning. From May 2021 to November 2022 at Yamagata University Hospital, Shiga University, and Takahata Town, patients with idiopathic normal pressure hydrocephalus (n = 48), Parkinson’s disease (n = 21), and other neuromuscular diseases (n = 45) comprised the pathological gait group (n = 114), and the control group consisted of 160 healthy volunteers. iPhone application TDPT-GT captured the subjects walking in a circular path of about 1 meter in diameter, a markerless motion capture system, with an iPhone camera, which generated the three-axis 30 frames per second (fps) relative coordinates of 27 body points. A light gradient boosting machine (Light GBM) with stratified k-fold cross-validation (k = 5) was applied for gait collection for about 1 min per person. The median ability model tested 200 frames of each person’s data for its distinction capability, which resulted in the area under a curve of 0.719. The pathological gait captured by the iPhone could be distinguished by artificial intelligence.

In this paper, we use motion tracking technology to document the birth of a brand new language: Nicaraguan Sign Language. Languages are dynamic entities that undergo change and growth through use, transmission, and learning, but the earliest stages of this process are generally difficult to observe as most languages have been used and passed down for many generations. Here, we observe a rare case of language emergence: the earliest stages of the new sign language in Nicaragua. By comparing the signing of the oldest and youngest signers of Nicaraguan Sign Language, we can track how the language itself is changing. Using motion tracking technology, we document a decrease in the size of articulatory space of Nicaraguan Sign Language signers over time. The reduction in articulatory space in Nicaraguan Sign appears to be the joint product of several decades of use and repeated transmission of this new language.

The gait cycle of humans may be influenced by a range of variables, including neurological, orthopedic, and pathological conditions. Thus, gait analysis has a broad variety of applications, including the diagnosis of neurological disorders, the study of disease development, the assessment of the efficacy of a treatment, postural correction, and the evaluation and enhancement of sport performances. While the introduction of new technologies has resulted in substantial advancements, these systems continue to struggle to achieve a right balance between cost, analytical accuracy, speed, and convenience. The target is to provide low-cost support to those with motor impairments in order to improve their quality of life. The article provides a novel automated approach for motion characterization that makes use of artificial intelligence to perform real-time analysis, complete automation, and non-invasive, markerless analysis. This automated procedure enables rapid diagnosis and prevents human mistakes. The gait metrics obtained by the two motion tracking systems were compared to show the effectiveness of the proposed methodology.

The evolution of technologies for the capture of human movement has been motivated by a number of potential applications across a wide variety of fields. However, capturing human motion in 3D is difficult in an outdoor environment when it is performed without controlled surroundings. In this paper, a stereo camera rig with an ultra-wide baseline distance and conventional cameras with fish-eye lenses is proposed. Its cameras provide a wide field of view (FOV) which increases the coverage area and also enables the baseline distance to be increased to cover the common area required for both cameras' views to perform as a stereo camera. We propose a passive marker-based approach to track the motion of the object. In this method, an adaptive thresholding method is applied to extract each small pink polyester marker from the video frames. As the cameras have fish-eye lenses, it is difficult to estimate the depth information using a pinhole camera model. We use a unique method to restore the 3D positions by developing a relationship between the pixel dimensions and distances in an image and real world coordinates. In this paper, occlusion detection is considered because, in the marker-based capturing of articulated human kinematics, the occlusion of a marker is one of the major challenges. The detection algorithm differentiates among types of occlusions and predicts any missing marker position where necessary. As this design is intended to be mounted on a moving carrier, such as a drone or car, a method for compensating the camera's ego-motion is proposed. The proposed 3D positioning and tracking system is tested in different situations to validate its applicability as a stereo camera rig as well as its performance for motion capture. The performance of the proposed system is compared with that of a standard motion capture system called Vicon and is shown to have the same order of accuracy while incurring less cost.

Visual inspection and maintenance ofindustrialpipes using robots represent an emerging application in Oil & Gas and refinery facilities. In this domain, we present a pose tracking system based on a single camera sensor to localize an unmanned aerial vehicle operating over a pipe-rack to carry out inspection activities. We propose a unified framework based on the Lie group SE(3) which allows the simultaneous estimation of the pose of the UAV along with some parameters of the pipe-rack model. Numerical simulations have been performed to demonstrate the effectiveness of the proposed approach.

During radiotherapy, the organs and tumour move as a result of the dynamic nature of the body; this is known as intrafraction motion. Intrafraction motion can result in tumour underdose and healthy tissue overdose, thereby reducing the effectiveness of the treatment while increasing toxicity to the patients. There is a growing appreciation of intrafraction target motion management by the radiation oncology community. Real-time image-guided radiation therapy (IGRT) can track the target and account for the motion, improving the radiation dose to the tumour and reducing the dose to healthy tissue. Recently, artificial intelligence (AI)-based approaches have been applied to motion management and have shown great potential. In this review, four main categories of motion management using AI are summarised: marker-based tracking, markerless tracking, full anatomy monitoring and motion prediction. Marker-based and markerless tracking approaches focus on tracking the individual target throughout the treatment. Full anatomy algorithms monitor for intrafraction changes in the full anatomy within the field of view. Motion prediction algorithms can be used to account for the latencies due to the time for the system to localise, process and act.

Direct monocular simultaneous localization and mapping (SLAM) methods, for which the image intensity is used for tracking and mapping instead of sparse feature points, have gained in popularity in recent years. However, feature-based methods usually have more accurate camera localization results than most direct methods, though direct methods can work better in a textureless environment. To tackle the localization issue, we develop a novel real-time large-scale direct SLAM model, namely, GCP-SLAM, by integrating the learning-based confidence estimation into the depth fusion and motion tracking optimization. In GCP-SLAM, a random regression forest is trained off-line with pre-defined confidence measures for learning confidence and detecting the ground control points (GCPs). Then, the confidence value along with the selected GCPs is utilized for depth refinement and camera localization. Our proposed method is shown experimentally more reliable in tracking and relocalization than the previous state-of-the-art direct method when compared with feature-based and RGBD SLAMs.

Developing an emergency procedure for cable-driven parallel robots is not a trivial process, since it is not possible to halt the end-effector by quickly braking the actuators as in rigid-link manipulators. For this reason, the cable robot recovery strategy is an important topic of research, and the literature provides several approaches. However, the computational efficiency of the recovery algorithm is fundamental for real-time applications. Thus, this paper presents a recovery strategy adopted in an experimental setup consisting of a three degrees-of-freedom (3-DOF) suspended cable robot controlled by an industrial PC. The presentation of the used control system lists the industrial-grade components installed, further highlighting the industrial implication of the work. Lastly, the experimental validation of the recovery strategy proves the effectiveness of the work.

International audience; A smartphone is a low-cost pocket wireless multichannel multiphysical data acquisition system: the use of such a device for noise and vibration analysis is a challenging task. To what extent is it possible to carry out relevant analysis from it? The Survishno conference, held in Lyon in July 2019, proposed a contest to participants based on this subject. Two challenges were proposed, wherein each a mute video showing an object moving/excited at different frequencies was provided. Due to the frequencies set and the video sampling characteristics, special effects occurred and are visible on both videos. From the first video, participants were asked to estimate the Instantaneous Angular Speed (IAS) of a rotating fan. From the second video, they were asked to perform the modal analysis of a cantilever beam. This paper gathers the interesting ideas proposed by the contestants and proposes a global method to solve these two problems. One major point of the paper might be the advantageous use of the rolling shutter effect, a well-known artefact of smartphone videos, to perform advanced mechanical analyses: the consideration of the unavoidable slight phase shift between the acquisition of each pixel opens up the possibility to perform a dynamic analysis at frequencies that are much higher than the video frame rate.

Medical informatics in planning the surgeries is rapidly changing lately with new systems and approaches and one of the latest is contactless approach to visualization systems used during the surgeries. This approach is based on contactless together with adopting algorithms from augmented and virtual reality, enabling surgeons in the operating room more freedom in touchless human-computer interaction in analyzing and visualization of complex medical data. We are proposing the novel contactless plug-in interface for the DICOM viewer platform using a camera controller that tracks hand/finger motions, with no hand contact, touching, or voice navigation. Our proposal flow is oriented to solve several issues we faced during the proof-of-the-concept of previous contactless surgery approach while using the Leap Motion as a tracking camera, both with software and hardware for spatial interaction. In this paper, we will focus on increasing the user experience in 3D-virtual rendered space and proposing the solution that could be used as a benchmark for 3D virtual navigation which integrates high-resolution stereo depth camera with medical imaging systems in order to obtain contactless ‘in the air’ real control of medical imaging systems with surgeon’s hands.

How the human central nervous system (CNS) copes with the several degrees of freedom (DoF) of the muscle-skeletal system for the generation of complex movements has not been fully understood yet. Many studies in literature have stated that likely the CNS does not independently control DoF but combines few building blocks that consider the synergistic actuation of each DoF. Such building blocks are called synergies. Synergies have been defined both at muscle level, i.e. muscle synergies, and kinematic level, i.e. kinematic synergies. Kinematic synergies consider the synergistic movement of several human articulations during the performance of a complex task, e.g. a reaching-grasping task. The principal component analyses (PCA) is the most used approach in literature for the kinematic synergy extraction. However, the PCA only considers linear correlations among DoFs which can be considered as the most-simple model of inter-joint coupling. In this work, we have extracted synergies from kinematics data (five upper limb angles) acquired during 12 different reaching movements with a tracking system based on the HTC Vive Trackers. After the extraction of the upper-limb joint angles with the OpenSim software, the kinematic synergies have been extracted using nonlinear under-complete autoencoders. Different models of nonlinear autoencoders were investigated and evaluated with R2 index and normalized reconstruction error. The results showed that 4 synergies were enough for describing the 0.973 ± 0.005 (R2 index of log sigmoid model) and 0.979 ± 0.004 (R2 index of tan sigmoid model) of the movement variance for the entire experiment with respectively a Normalized Reconstruction Error (ERMS) of 0.03 ± 0.005 and 0.034 ± 0.004. Comparing the non-linear autoencoders (AE) with the standard linear PCA it emerged that the AE performance are comparable with the PCA results. However, more experiments are needed to perform a deep comparison on a dataset including more joint angles.

This study presents a robust, low-cost hand motion recognition approach designed to run on low-end computer systems. Our method detects and tracks hand region using real-time images obtained from a low-resolution camera (i.e. webcam) and is not depended on any training or calibration and is not required any special camera apparatus or selectors. The proposed system involves several image processing techniques such as background subtraction, face detection, skin colour detection and template matching. The experimental results show promising performance under various conditions. The method has a wide range of applications where more natural ways of interaction required, such as virtual reality applications, assistive technologies and simulation.

This article describes a dataset collected in a set of experiments that involves human participants and a robot. The set of experiments was conducted in the computing science robotics lab in Simon Fraser University, Burnaby, BC, Canada, and its aim is to gather data containing common gestures, movements, and other behaviours that may indicate humans' navigational intent relevant for autonomous robot navigation. The experiment simulates a shopping scenario where human participants come in to pick up items from his/her shopping list and interact with a Pepper robot that is programmed to help the human participant. We collected visual data and motion capture data from 108 human participants. The visual data contains live recordings of the experiments and the motion capture data contains the position and orientation of the human participants in world coordinates. This dataset could be valuable for researchers in the robotics, machine learning and computer vision community., 5 pages, paper submitted to Data In Brief Journal

This paper contributes towards the development of motion tracking algorithms for time-critical applications, proposing an infrastructure for dynamically solving the inverse kinematics of highly articulate systems such as humans. The method presented is model-based, it makes use of velocity correction and differential kinematics integration in order to compute the system configuration. The convergence of the model towards the measurements is proved using Lyapunov analysis. An experimental scenario, where the motion of a human subject is tracked in static and dynamic configurations, is used to validate the inverse kinematics method performance on human and humanoid models. Moreover, the method is tested on a human-humanoid retargeting scenario, verifying the usability of the computed solution in real-time robotics applications. Our approach is evaluated both in terms of accuracy and computational load, and compared to iterative optimization algorithms.

The rapid technological advancements of Industry 4.0 have opened up new vectors for novel industrial processes that require advanced sensing solutions for their realization. Motion capture (MoCap) sensors, such as visual cameras and inertial measurement units (IMUs), are frequently adopted in industrial settings to support solutions in robotics, additive manufacturing, teleworking and human safety. This review synthesizes and evaluates studies investigating the use of MoCap technologies in industry-related research. A search was performed in the Embase, Scopus, Web of Science and Google Scholar. Only studies in English, from 2015 onwards, on primary and secondary industrial applications were considered. The quality of the articles was appraised with the AXIS tool. Studies were categorized based on type of used sensors, beneficiary industry sector, and type of application. Study characteristics, key methods and findings were also summarized. In total, 1682 records were identified, and 59 were included in this review. Twenty-one and 38 studies were assessed as being prone to medium and low risks of bias, respectively. Camera-based sensors and IMUs were used in 40% and 70% of the studies, respectively. Construction (30.5%), robotics (15.3%) and automotive (10.2%) were the most researched industry sectors, whilst health and safety (64.4%) and the improvement of industrial processes or products (17%) were the most targeted applications. Inertial sensors were the first choice for industrial MoCap applications. Camera-based MoCap systems performed better in robotic applications, but camera obstructions caused by workers and machinery was the most challenging issue. Advancements in machine learning algorithms have been shown to increase the capabilities of MoCap systems in applications such as activity and fatigue detection as well as tool condition monitoring and object recognition.

The recognition of human activities is usually considered to be a simple procedure. Problems occur in complex scenes involving high speeds. Activity prediction using Artificial Intelligence (AI) by numerical analysis has attracted the attention of several researchers. Human activities are an important challenge in various fields. There are many great applications in this area, including smart homes, assistive robotics, human&ndash, computer interactions, and improvements in protection in several areas such as security, transport, education, and medicine through the control of falling or aiding in medication consumption for elderly people. The advanced enhancement and success of deep learning techniques in various computer vision applications encourage the use of these methods in video processing. The human presentation is an important challenge in the analysis of human behavior through activity. A person in a video sequence can be described by their motion, skeleton, and/or spatial characteristics. In this paper, we present a novel approach to human activity recognition from videos using the Recurrent Neural Network (RNN) for activity classification and the Convolutional Neural Network (CNN) with a new structure of the human skeleton to carry out feature presentation. The aims of this work are to improve the human presentation through the collection of different features and the exploitation of the new RNN structure for activities. The performance of the proposed approach is evaluated by the RGB-D sensor dataset CAD-60. The experimental results show the performance of the proposed approach through the average error rate obtained (4.5%).

Pedestrian Dead Reckoning (PDR) uses inertial measurement units (IMUs) and combines velocity and orientation estimates to determine a position. The estimation of the velocity is still challenging, as the integration of noisy acceleration and angular speed signals over a long period of time causes large drifts. Classic approaches to estimate the velocity optimize for specific applications, sensor positions, and types of movement and require extensive parameter tuning. Our novel hybrid filter combines a convolutional neural network (CNN) and a bidirectional recurrent neural network (BLSTM) (that extract spatial features from the sensor signals and track their temporal relationships) with a linear Kalman filter (LKF) that improves the velocity estimates. Our experiments show the robustness against different movement states and changes in orientation, even in highly dynamic situations. We compare the new architecture with conventional, machine, and deep learning methods and show that from a single non-calibrated IMU, our novel architecture outperforms the state-of-the-art in terms of velocity (≤0.16 m/s) and traveled distance (≤3 m/km). It also generalizes well to different and varying movement speeds and provides accurate and precise velocity estimates.

The pressing demand for workload along with social media interaction leads to diminished alertness during work hours. Researchers attempted to measure alertness level from various cues like EEG, EOG, video-based eye movement analysis, etc. Among these, video-based eyelid and iris motion tracking gained much attention in recent years. However, most of these implementations are tested on video data of subjects without spectacles. These videos do not pose a challenge for eye detection and tracking. In this work, the authors have designed an experiment to yield a video database of 58 human subjects wearing spectacles and are at different levels of alertness. Along with spectacles, they introduced variation in session, recording frame rate (fps), illumination, and time of the experiment. They carried out an analysis to detect the reliableness of facial and ocular features like yawning and eye-blinks in the context of alertness level detection capability. Also, they observe the influence of spectacles on ocular feature detection performance under spectacles and propose a simple preprocessing step to alleviate the specular reflection problem. Extensive experiments on real-world images demonstrate that the authors’ approach achieves desirable reflection suppression results within minimum execution time compared to the state-of-the-art.

Purpose Cardiac motion tracking enables quantitative evaluation of myocardial strain, which is clinically interesting in cardiovascular disease research. However, motion tracking is difficult to perform manually. In this paper, we aim to develop and compare two fully automated motion tracking methods for the steady state free precession (SSFP) cine magnetic resonance imaging (MRI), and explore their use in real clinical scenario with different patient groups. Methods We proposed two automated cardiac motion tracking method: (1) a traditional registration-based method, named Full Cardiac Cycle Registration, which simultaneously tracks all cine frames within a full cardiac cycle by joint registration of all frames; and (2) a modern convolutional neural network (CNN)-based method, named Groupwise MotionNet, which enhances the temporal coherence by fusing motion along a continuous time scale. Both methods were evaluated on the healthy volunteer data from the MICCAI 2011 STACOM Challenge, as well as on patient data including hypertrophic cardiomyopathy (HCM) and myocardial infarction (MI). Results The Full Cardiac Cycle Registration method achieved an average end-point-error 2.89 ± 1.57 mm for cardiac motion tracking, with computation time of around 9 minutes per short-axis cine MRI (size 128×128, 30 cardiac phases). In comparison, the Groupwise MotionNet achieved an average end-point-error of 0.94±1.59 mm, taking less than 1 second for a full cardiac phases. Further experiments showed that registration method had stable performance, independent of patient cohort and MRI machine, while the CNN-based method relied on the training data to deliver consistently accurate results. Conclusion Both registration-based and CNN-based method can track the cardiac motion from SSFP cine MRI in a fully automated manner, while taking temporal coherence into account. The registration method is generic, robust, but relatively slow; the CNN-based method trained with heterogeneous data was able to achieve high tracking accuracy with real-time performance.

Contains fulltext : 212986.pdf (Publisher’s version ) (Open Access) There is increasing evidence that hand gestures and speech synchronize their activity on multiple dimensions and timescales. For example, gesture's kinematic peaks (e.g., maximum speed) are coupled with prosodic markers in speech. Such coupling operates on very short timescales at the level of syllables (200 ms), and therefore requires high-resolution measurement of gesture kinematics and speech acoustics. High-resolution speech analysis is common for gesture studies, given that field's classic ties with (psycho)linguistics. However, the field has lagged behind in the objective study of gesture kinematics (e.g., as compared to research on instrumental action). Often kinematic peaks in gesture are measured by eye, where a "moment of maximum effort" is determined by several raters. In the present article, we provide a tutorial on more efficient methods to quantify the temporal properties of gesture kinematics, in which we focus on common challenges and possible solutions that come with the complexities of studying multimodal language. We further introduce and compare, using an actual gesture dataset (392 gesture events), the performance of two video-based motion-tracking methods (deep learning vs. pixel change) against a high-performance wired motion-tracking system (Polhemus Liberty). We show that the videography methods perform well in the temporal estimation of kinematic peaks, and thus provide a cheap alternative to expensive motion-tracking systems. We hope that the present article incites gesture researchers to embark on the widespread objective study of gesture kinematics and their relation to speech. 18 p.

In this chapter, we explore the use of motion tracking methodology in developmental research. With motion tracking, also called motion capture, human movements can be precisely recorded and analyzed. Motion tracking provides developmental researchers with objective measurements of motor and (socio-)cognitive development. It can further be used to create carefully-controlled stimuli videos and can offer means of measuring development outside of the lab. We discuss three types of motion tracking that lend themselves to developmental applications. First, marker-based systems track optical or electromagnetic markers or sensors placed on the body and offer high accuracy measurements. Second, markerless methods entail image processing of videos to track the movement of bodies without participants being hindered by physical markers. Third, inertial motion tracking measures three-dimensional movements and can be used in a variety of settings. The chapter concludes by examining three example topics from developmental literature in which motion tracking applications have contributed to our understanding of human development.

Tracking moving objects is a relevant topic in a wide range of research activities. To meet this goal, sensor fusion is a frequently used approach that is able to exploit the synergies from different sensors, achieving high precision and accuracy. In this article, an application of this technique is presented for an actual industrial scenario, namely, the shield metal arc welding (SMAW) process. A wearable real-time tracking system, employing a camera and an inertial unit on a commercial helmet, is described and assessed to estimate the speed of a light-emitting target, namely, the welding pool, despite the noises introduced by sparks, smoke, and high-density illumination. The vision-based module was used to follow the weld pool and to calibrate automatically the system, while the inertial one was used to compensate for the influence of the yaw-angular displacements. Results from tests performed on four batches of electrodes showed how the system is able to catch the phenomenon with errors less than 25%. Compared with other systems, our prototype is able to acquire data from moving objects in real time through sensors positioned onto a moving reference system.

Motion tracking techniques have been extensively studied in recent years. However, capturing movements of the upper limbs is a challenging task. This document presents the estimation of arm orientation and elbow and wrist position using wearable flexible sensors (WFSs). A study was developed to obtain the highest range of motion (ROM) of the shoulder with as few sensors as possible, and a method for estimating arm length and a calibration procedure was proposed. Performance was verified by comparing measurement of the shoulder joint angles obtained from commercial two-axis soft angular displacement sensors (sADS) from Bend Labs and from the ground truth system (GTS) OptiTrack. The global root-mean-square error (RMSE) for the shoulder angle is 2.93 degrees and 37.5 mm for the position estimation of the wrist in cyclical movements; this measure of RMSE was improved to 13.6 mm by implementing a gesture classifier., This work has been partially supported by the project “LUXBIT: Lightweight Upper limbs eXosuit for BImanual Task Enhancement” under RTI2018-094346-B-I00 grant, funded by the Spanish “Ministerio de Ciencia, Innovación y Universidades”.

Motion tracking software for assessing laparoscopic surgical proficiency has been proven to be effective in differentiating between expert and novice performances. However, with several indices that can be generated from the software, there is no set threshold that can be used to benchmark performances. The aim of this study was to identify the best possible algorithm that can be used to benchmark expert, intermediate and novice performances for objective evaluation of psychomotor skills. 12 video recordings of various surgeons were collected in a blinded fashion. Data from our previous study of 6 experts and 23 novices was also included in the analysis to determine thresholds for performance. Video recording were analyzed both by the Kinovea 0.8.15 software and a blinded expert observer using the CAT form. Multiple algorithms were tested to accurately identify expert and novice performances. ½ L + $$ \raisebox{1ex}{$1$}\!\left/ \!\raisebox{-1ex}{$3$}\right. $$13 A + $$ \raisebox{1ex}{$1$}\!\left/ \!\raisebox{-1ex}{$6$}\right. $$16 J scoring of path length, average movement and jerk index respectively resulted in identifying 23/24 performances. Comparing the algorithm to CAT assessment yielded in a linear regression coefficient R2 of 0.844. The value of motion tracking software in providing objective clinical evaluation and retrospective analysis is evident. Given the prospective use of this tool the algorithm developed in this study proves to be effective in benchmarking performances for psychomotor skills evaluation.

The estimation of the Instantaneous Angular Speed (IAS) has in recent years attracted a growing interest in the diagnostics of rotating machines. Measurement of the IAS can be used as a source of information of the machine condition per se, or for performing angular resampling through Computed Order Tracking, a practice which is essential to highlight the machine spectral signature in case of non-stationary operational conditions. In these regards, the SURVISHNO 2019 international conference held at INSA Lyon on 8&ndash, 10 July 2019 proposed a challenge about the estimation of the instantaneous non-stationary speed of a fan from a video taken by a smartphone, a pocket, low-cost device which can nowadays be found in everyone's pocket. This work originated by the author to produce an offline motion-tracking of the fan (actually, of the head of its locking-screw) and obtaining then a reliable estimate of the IAS. The here proposed algorithm is an update of the established Template Matching (TM) technique (i.e., in the Signal Processing community, a two-dimensional matched filter), which is here integrated into a Genetic Algorithm (GA) search. Using a template reconstructed from a simplified parametric mathematical model of the features of interest (i.e., the known geometry of the edges of the screw head), the GA can be used to adapt the template to match the search image, leading to a hybridization of template-based and feature-based approaches which allows to overcome the well-known issues of the traditional TM related to scaling and rotations of the search image with respect to the template. Furthermore, it is able to resolve the position of the center of the screw head at a resolution that goes beyond the limit of the pixel grid. By repeating the analysis frame after frame and focusing on the angular position of the screw head over time, the proposed algorithm can be used as an effective offline video-tachometer able to estimate the IAS from the video, avoiding the need for expensive high-resolution encoders or tachometers.

The specific objective of this thesis is to offer an excursion through the metric potentialities of different data available in historical archives, by considering the essential role of photogrammetry. The aim is to explore how metric information about buildings which no longer exist or transformed over time could be extracted from old photographs and videos of different quality, for their 3D virtual reconstruction analysing the material stored in historical archives to support researchers and experts in historical research of Cultural Heritage.In order to process these data and to obtain metrically certified results, a modification of the algorithms of the standard photogrammetric pipeline was necessary. This purpose was achieved with the use of open-source Structure-from-Motion algorithms and the creation of a specific benchmark to compare the results.Besides the processing of historical photograph, photogrammetry is combined with Artificial Intelligence to improve ways to search for architectural heritage in video material and to reduce the effort of manually examining them by the operator in the archive in terms of efficiency and time.

The ability to realize human‐motion imitation using robots is closely related to developments in the field of artificial intelligence. However, it is not easy to imitate human motions entirely owing to the physical differences between the human body and robots. In this paper, we propose a work chain‐based inverse kinematics to enable a robot to imitate the human motion of upper limbs in real time. Two work chains are built on each arm to ensure that there is motion similarity, such as the end effector trajectory and the joint‐angle configuration. In addition, a two‐phase filter is used to remove the interference and noise, together with a self‐collision avoidance scheme to maintain the stability of the robot during the imitation. Experimental results verify the effectiveness of our solution on the humanoid robot Nao‐H25 in terms of accuracy and real‐time performance.

Animal-attached sensors are increasingly used to provide insights on behaviour and physiology. However, such tags usually lack information on the structure of the surrounding environment from the perspective of a study animal and thus may be unable to identify potentially important drivers of behaviour. Recent advances in robotics and computer vision have led to the availability of integrated depth-sensing and motion-tracking mobile devices. These enable the construction of detailed 3D models of an environment within which motion can be tracked without reliance on GPS. The potential of such techniques has yet to be explored in the field of animal biotelemetry. This report trials an animal-attached structured light depth-sensing and visual–inertial odometry motion-tracking device in an outdoor environment (coniferous forest) using the domestic dog (Canis familiaris) as a compliant test species. A 3D model of the forest environment surrounding the subject animal was successfully constructed using point clouds. The forest floor was labelled using a progressive morphological filter. Trees trunks were modelled as cylinders and identified by random sample consensus. The predicted and actual presence of trees matched closely, with an object-level accuracy of 93.3%. Individual points were labelled as belonging to tree trunks with a precision, recall, and F $$_{\beta }$$ score of 1.00, 0.88, and 0.93, respectively. In addition, ground-truth tree trunk radius measurements were not significantly different from random sample consensus model coefficient-derived values. A first-person view of the 3D model was created, illustrating the coupling of both animal movement and environment reconstruction. Using data collected from an animal-borne device, the present study demonstrates how terrain and objects (in this case, tree trunks) surrounding a subject can be identified by model segmentation. The device pose (position and orientation) also enabled recreation of the animal’s movement path within the 3D model. Although some challenges such as device form factor, validation in a wider range of environments, and direct sunlight interference remain before routine field deployment can take place, animal-borne depth sensing and visual–inertial odometry have great potential as visual biologging techniques to provide new insights on how terrestrial animals interact with their environments.

The respiration pattern represents the volume of air in the lungs as afunction of time during human respiration process. Abnormal changes in thispattern can be signs of several diseases or conditions. There exit severalrespiration pattern detection methods. Among them, an easy technique relies onsensing the movements of thoracic and (or) abdominal regions. In this study, adevice based on thoracic motion tracking with complementary filtering has beendeveloped to detect the respiration pattern. The device is equipped with a motionsensor placed in a flexible belt housing a three-axis accelerometer and a three-axisgyroscope and a UART-to-USB converter providing computer connectivity. Thedevice is operated by a microcontroller that controls the operation of the motionsensor, applies complementary filtering to the motion data acquired and transfersthe results to a personal computer. The device is powered from the computer it isconnected to. Experiments with using the device during continues inhaling andexhaling, deep inhaling followed by breath-hold and deep exhaling followed bybreath-hold respiration activities in standing, lying and seated postures show thatthoracic motion tracking with complementary filtering may provide quite wellrespiration pattern detections. Solunum motifi, insan solunum işlemi sırasında zamanın bir fonksiyonuolarak akciğerlerdeki hava hacmini temsil eder. Bu desendeki anormaldeğişiklikler birtakım hastalıkların veya durumların belirtileri olabilir. Solunummotifinin tespitinde birçok yöntem bulunmaktadır. Bunlar arasında kolay birteknik göğüs ve (veya) karın bölgelerinin hareketlerinin algılanmasınadayanmaktadır. Bu çalışmada, tamamlayıcı filtreleme ile göğüs hareket izlemeyedayalı bir solunum deseni tespit cihazı geliştirilmiş bulunmaktadır. Cihaz esnek birkemer içine yerleştirilmiş üç eksenli bir ivmeölçer ve üç eksenli bir jiroskopbarındıran bir hareket algılayıcısı ve bilgisayar bağlantısı sağlayan bir UART-USBdönüştürücüsü ile donatılmıştır. Cihaz hareket algılayıcısının çalışmasını kontroleden, elde edilen hareket verilerine tamamlayıcı filtreleme uygulayan ve sonuçlarıkişisel bir bilgisayara aktaran bir mikrodenetleyici tarafından işletilmektedir.Cihaz bağlı olduğu bilgisayardan beslenmektedir. Ayakta durma, yatma ve oturmapozisyonlarında; sürekli nefes alma ve verme, derin nefes almayı takiben nefestutma ve derin nefes vermeyi takiben nefes tutma solunum aktiviteleri süresincecihaz kullanarak gerçekleştirilen deneyler tamamlayıcı filtreleme ile göğüshareketi izlemenin oldukça iyi solunum deseni tespitlerine imkân tanıyabildiğinigöstermektedir.

This paper presents a wearable motion tracking system with recording and playback features. This system has been designed for gait analysis and interlimb coordination studies. It can be implemented to help reduce fall risk and to retrain gait in a rehabilitation setting. Our system consists of ten custom wearable straps, a receiver, and a central computer. Comparing with similar existing solutions, the proposed system is affordable and convenient, which can be used in both indoor and outdoor settings. In the experiment, the system calculates five gait parameters and has the potential to identify deviant gait patterns. The system can track upper body parameters such as arm swing, which has potential in the study of pathological gaits and the coordination of the limbs.

This work explores and evaluates the designs of finger-worn radio-frequency identification (RFID) motion tracking for activity recognition on tagged objects. We propose an index-finger-worn device that consists of a short-range (∼2cm) RFID reader and a pair of two inertial measurement units (IMUs), which are mounted at the locations where an artificial nail and a ring are worn. The short-range RFID reader recognizes the tagged object on finger touch, and then the IMU data are used for activity recognition. Data collected from the user of this device allows for a post-hoc analysis, which informs the activity recognition performance in various RFID+IMU and IMU-only configurations on the same task. The results of a ten-participant user study show that when the objects have similar physical form factors, the hybrid RFID motion tracking significantly outperforms the IMU-only tracking, especially in a larger-number set of objects. In our test, three IMU configurations (i.e., NailOnly, RingOnly, and Nail+Ring) achieved comparable action recognition performances, i.e., ≥90% accuracy, with 500 ms recognition time, though the NailOnly RFID+IMU configuration provided the highest wearability. The practical challenges toward a real-world deployment of a finger-worn RFID motion tracking system are also discussed.

Collaborative robotics aims to make possible a close collaboration between human operators and robots in the industry scenario. To achieve this goal, the robot must be able to adapt its behavior to the movements of human co-worker. This work presents a control strategy that permits to drive a UR3 collaborative robot in order to perform a hand-over task with a human operator. The movements of the operator are acquired by a 3D vision system made of multiple Microsoft Kinect sensors, so to prevent possible occlusions related to the presence of the robot or dynamic obstacles in the field of view of the sensors. Thus, the human skeletons extracted from depth sensors are handled by an algorithm that generates an optimized skeleton. A path-planning algorithm uses the skeleton information to calculates the robot joints velocities to accomplish the task. Experimental tests have been conducted to verify the effectiveness of the hand-over strategy here proposed. The hardware setup prepared for the experimental phase of the work and the results obtained from the tests are presented and discussed.

Background Numerous self-gated cardiac imaging techniques have been reported in the literature. Most can track either cardiac or respiratory motion, and many incur some overhead to imaging data acquisition. We previously described a Cartesian cine imaging technique, pseudo-projection motion tracking with golden-step phase encoding, capable of tracking both cardiac and respiratory motion at no cost to imaging data acquisition. In this work, we describe improvements to the technique by dramatically reducing its vulnerability to eddy current and flow artifacts and demonstrating its effectiveness in expanded cardiovascular applications. Methods As with our previous golden-step technique, the Cartesian phase encodes over time were arranged based on the integer golden step, and readouts near ky = 0 (pseudo-projections) were used to derive motion. In this work, however, the readouts were divided into equal and consecutive temporal segments, within which the readouts were sorted according to ky. The sorting reduces the phase encode jump between consecutive readouts while maintaining the pseudo-randomness of ky to sample both cardiac and respiratory motion without comprising the ability to retrospectively set the temporal resolution of the original technique. On human volunteers, free-breathing, electrocardiographic (ECG)-free cine scans were acquired for all slices of the short axis stack and the 4-chamber view of the long axis. Retrospectively, cardiac motion and respiratory motion were automatically extracted from the pseudo-projections to guide cine reconstruction. The resultant image quality in terms of sharpness and cardiac functional metrics was compared against breath-hold ECG-gated reference cines. Results With sorting, motion tracking of both cardiac and respiratory motion was effective for all slices orientations imaged, and artifact occurrence due to eddy current and flow was efficiently eliminated. The image sharpness derived from the self-gated cines was found to be comparable to the reference cines (mean difference less than 0.05 mm− 1 for short-axis images and 0.075 mm− 1 for long-axis images), and the functional metrics (mean difference

In this paper, we propose a ROS-based system to reconstruct the motion of human upper limb based on data collected with two Myo armbands in a hybrid manner. The inertial sensors' information are fused to reconstruct shoulder and elbow kinematics. Electromyographic (EMG) signals are used to estimate wrist kinematics, to fully capture the motion of the 5-DoF (degree of freedom) user's arm. The system shows a good pose estimation accuracy compared to the XSens suit with an average RMSE of $6.61^{\circ}\pm 3.31^{\circ}$ and a $R^{2}$ of $0.90\pm 0.07$ .

Tracking accuracy for the CyberKnife's Synchrony system is commonly evaluated using a film‐based verification method. We have evaluated a verification system that uses a webcam and a printed calibrated grid to verify tracking accuracy over three different motion patterns. A box with an attached printed calibrated grid and four fiducial markers was attached to the motion phantom. A target marker was positioned at the grid's center. The box was set up using the other three markers. Target tracking accuracy was evaluated under three conditions: 1) stationary; 2) sinusoidal motion with different amplitudes of 5, 10, 15, and 20 mm for the same cycle of 4 s and different cycles of 2, 4, 6, and 8 s with the same amplitude of 15 mm; and 3) irregular breathing patterns in six human volunteers breathing normally. Infrared markers were placed on the volunteers’ abdomens, and their trajectories were used to simulate the target motion. All tests were performed with one‐dimensional motion in craniocaudal direction. The webcam captured the grid's motion and a laser beam was used to simulate the CyberKnife's beam. Tracking error was defined as the difference between the grid's center and the laser beam. With a stationary target, mean tracking error was measured at 0.4 mm. For sinusoidal motion, tracking error was less than 2 mm for any amplitude and breathing cycle. For the volunteers’ breathing patterns, the mean tracking error range was 0.78‐1.67 mm. Therefore, accurate lesion targeting requires individual quality assurance for each patient. PACS number(s): 87.55.D‐, 87.55.km, 87.55.Qr, 87.56.Fc