Towards a Smart Drone Cinematographer for Filming Human Motion

Affordable consumer drones have made capturing aerial footage more convenient and accessible. However, shooting cinematic motion videos using a drone is challenging because it requires users to analyze dynamic scenarios while operating the controller. In this thesis, our task is to develop an autonomous drone cinematography system to capture cinematic videos of human motion. We understand the system's filming performance to be influenced by three key components: 1) video quality metric, which measures the aesthetic quality -- the angle, the distance, the image composition -- of the captured video, 2) visual feature, which encapsulates the visual elements that influence the filming style, and 3) camera planning, which is a decision-making model that predicts the next best movement. By analyzing these three components, we designed two autonomous drone cinematography systems using both heuristic-based methods and learning-based methods.For the first system, we designed an Autonomous CinemaTography system -- "ACT" by proposing a viewpoint quality metric focusing on the visibility of the 3D human skeleton of the subject. We expanded the application of human motion analysis and simplified manual control by assisting viewpoint selection using a through-the-lens method. For the second system, we designed an imitation-based system that learns the artistic intention of the cameramen through watching professional aerial videos. We designed a camera planner that analyzes the video contents and previous camera motion to predict future camera motion. Furthermore, we propose a planning framework, which can imitate a filming style by ``seeing" only one single demonstration video of such style. We named it ``one-shot imitation filming." To the best of our knowledge, this is the first work that extends imitation learning to autonomous filming. Experimental results in both simulation and field test exhibit significant improvements over existing techniques and our approach managed to help inexperienced pilots capture cinematic videos

Sensors and Technologies in Spain: State-of-the-Art

The aim of this special issue was to provide a comprehensive view on the state-of-the-art sensor technology in Spain. Different problems cause the appearance and development of new sensor technologies and vice versa, the emergence of new sensors facilitates the solution of existing real problems. [...

Physical Analysis of Handshaking Between Humans: Mutual Synchronisation and Social Context

International audienceOne very popular form of interpersonal interaction used in various situations is the handshake (HS), which is an act that is both physical and social. This article aims to demonstrate that the paradigm of synchrony that refers to the psychology of individuals' temporal movement coordination could also be considered in handshaking. For this purpose, the physical features of the human HS are investigated in two different social situations: greeting and consolation. The duration and frequency of the HS and the force of the grip have been measured and compared using a prototype of a wearable system equipped with several sensors. The results show that an HS can be decomposed into four phases, and after a short physical contact, a synchrony emerges between the two persons who are shaking hands. A statistical analysis conducted on 31 persons showed that, in the two different contexts, there is a significant difference in the duration of HS, but the frequency of motion and time needed to synchronize were not impacted by the context of an interaction

Wearable Movement Sensors for Rehabilitation: From Technology to Clinical Practice

This Special Issue shows a range of potential opportunities for the application of wearable movement sensors in motor rehabilitation. However, the papers surely do not cover the whole field of physical behavior monitoring in motor rehabilitation. Most studies in this Special Issue focused on the technical validation of wearable sensors and the development of algorithms. Clinical validation studies, studies applying wearable sensors for the monitoring of physical behavior in daily life conditions, and papers about the implementation of wearable sensors in motor rehabilitation are under-represented in this Special Issue. Studies investigating the usability and feasibility of wearable movement sensors in clinical populations were lacking. We encourage researchers to investigate the usability, acceptance, feasibility, reliability, and clinical validity of wearable sensors in clinical populations to facilitate the application of wearable movement sensors in motor rehabilitation

Une méthode de mesure du mouvement humain pour la programmation par démonstration

Programming by demonstration (PbD) is an intuitive approach to impart a task to a robot from one or several demonstrations by the human teacher. The acquisition of the demonstrations involves the solution of the correspondence problem when the teacher and the learner differ in sensing and actuation. Kinesthetic guidance is widely used to perform demonstrations. With such a method, the robot is manipulated by the teacher and the demonstrations are recorded by the robot's encoders. In this way, the correspondence problem is trivial but the teacher dexterity is afflicted which may impact the PbD process. Methods that are more practical for the teacher usually require the identification of some mappings to solve the correspondence problem. The demonstration acquisition method is based on a compromise between the difficulty of identifying these mappings, the level of accuracy of the recorded elements and the user-friendliness and convenience for the teacher. This thesis proposes an inertial human motion tracking method based on inertial measurement units (IMUs) for PbD for pick-and-place tasks. Compared to kinesthetic guidance, IMUs are convenient and easy to use but can present a limited accuracy. Their potential for PbD applications is investigated. To estimate the trajectory of the teacher's hand, 3 IMUs are placed on her/his arm segments (arm, forearm and hand) to estimate their orientations. A specific method is proposed to partially compensate the well-known drift of the sensor orientation estimation around the gravity direction by exploiting the particular configuration of the demonstration. This method, called heading reset, is based on the assumption that the sensor passes through its original heading with stationary phases several times during the demonstration. The heading reset is implemented in an integration and vector observation algorithm. Several experiments illustrate the advantages of this heading reset. A comprehensive inertial human hand motion tracking (IHMT) method for PbD is then developed. It includes an initialization procedure to estimate the orientation of each sensor with respect to the human arm segment and the initial orientation of the sensor with respect to the teacher attached frame. The procedure involves a rotation and a static position of the extended arm. The measurement system is thus robust with respect to the positioning of the sensors on the segments. A procedure for estimating the position of the human teacher relative to the robot and a calibration procedure for the parameters of the method are also proposed. At the end, the error of the human hand trajectory is measured experimentally and is found in an interval between $28.5$ mm and $61.8$ mm. The mappings to solve the correspondence problem are identified. Unfortunately, the observed level of accuracy of this IHMT method is not sufficient for a PbD process. In order to reach the necessary level of accuracy, a method is proposed to correct the hand trajectory obtained by IHMT using vision data. A vision system presents a certain complementarity with inertial sensors. For the sake of simplicity and robustness, the vision system only tracks the objects but not the teacher. The correction is based on so-called Positions Of Interest (POIs) and involves 3 steps: the identification of the POIs in the inertial and vision data, the pairing of the hand POIs to objects POIs that correspond to the same action in the task, and finally, the correction of the hand trajectory based on the pairs of POIs. The complete method for demonstration acquisition is experimentally evaluated in a full PbD process. This experiment reveals the advantages of the proposed method over kinesthesy in the context of this work.La programmation par démonstration est une approche intuitive permettant de transmettre une tâche à un robot à partir d'une ou plusieurs démonstrations faites par un enseignant humain. L'acquisition des démonstrations nécessite cependant la résolution d'un problème de correspondance quand les systèmes sensitifs et moteurs de l'enseignant et de l'apprenant diffèrent. De nombreux travaux utilisent des démonstrations faites par kinesthésie, i.e., l'enseignant manipule directement le robot pour lui faire faire la tâche. Ce dernier enregistre ses mouvements grâce à ses propres encodeurs. De cette façon, le problème de correspondance est trivial. Lors de telles démonstrations, la dextérité de l'enseignant peut être altérée et impacter tout le processus de programmation par démonstration. Les méthodes d'acquisition de démonstration moins invalidantes pour l'enseignant nécessitent souvent des procédures spécifiques pour résoudre le problème de correspondance. Ainsi l'acquisition des démonstrations se base sur un compromis entre complexité de ces procédures, le niveau de précision des éléments enregistrés et la commodité pour l'enseignant. Cette thèse propose ainsi une méthode de mesure du mouvement humain par capteurs inertiels pour la programmation par démonstration de tâches de ``pick-and-place''. Les capteurs inertiels sont en effet pratiques et faciles à utiliser, mais sont d'une précision limitée. Nous étudions leur potentiel pour la programmation par démonstration. Pour estimer la trajectoire de la main de l'enseignant, des capteurs inertiels sont placés sur son bras, son avant-bras et sa main afin d'estimer leurs orientations. Une méthode est proposée afin de compenser partiellement la dérive de l'estimation de l'orientation des capteurs autour de la direction de la gravité. Cette méthode, appelée ``heading reset'', est basée sur l'hypothèse que le capteur passe plusieurs fois par son azimut initial avec des phases stationnaires lors d'une démonstration. Cette méthode est implémentée dans un algorithme d'intégration et d'observation de vecteur. Des expériences illustrent les avantages du ``heading reset''. Cette thèse développe ensuite une méthode complète de mesure des mouvements de la main humaine par capteurs inertiels (IHMT). Elle comprend une première procédure d'initialisation pour estimer l'orientation des capteurs par rapport aux segments du bras humain ainsi que l'orientation initiale des capteurs par rapport au repère de référence de l'humain. Cette procédure, consistant en une rotation et une position statique du bras tendu, est robuste au positionnement des capteurs. Une seconde procédure est proposée pour estimer la position de l'humain par rapport au robot et pour calibrer les paramètres de la méthode. Finalement, l'erreur moyenne sur la trajectoire de la main humaine est mesurée expérimentalement entre 28.5 mm et 61.8 mm, ce qui n'est cependant pas suffisant pour la programmation par démonstration. Afin d'atteindre le niveau de précision nécessaire, une nouvelle méthode est développée afin de corriger la trajectoire de la main par IHMT à partir de données issues d'un système de vision, complémentaire des capteurs inertiels. Pour maintenir une certaine simplicité et robustesse, le système de vision ne suit que les objets et pas l'enseignant. La méthode de correction, basée sur des ``Positions Of Interest (POIs)'', est constituée de 3 étapes: l'identification des POIs dans les données issues des capteurs inertiels et du système de vision, puis l'association de POIs liées à la main et de POIs liées aux objets correspondant à la même action, et enfin, la correction de la trajectoire de la main à partir des paires de POIs. Finalement, la méthode IHMT corrigée est expérimentalement évaluée dans un processus complet de programmation par démonstration. Cette expérience montre l'avantage de la méthode proposée sur la kinesthésie dans le contexte de ce travail

Pelaksanaan pembelajaran berasaskan kerja politeknik bersama industri

embelajaran Berasaskan Kerja (PBK) merupakan satu kaedah pembelajaran yang menggabungkan pembelajaran teori dan amali secara serentak dalam lapangan kerja sebenar, dengan tujuan untuk melahirkan graduan yang memiliki nilai kebolehkerjaan. Walaupun kaedah ini telah lama dilaksanakan di negara maju seperti Amerika Syarikat dan United Kingdom, tetapi di Malaysia ianya baru dilaksanakan pada tahun 2007 dan hanya melibatkan beberapa buah kolej komuniti pada peringkat awal. Walau bagaimanapun pada tahun 2010, pelaksanaan PBK telah dihentikan di kolej komuniti, dan dipindahkan di politeknik. Antara isu yang berlaku dalam pelaksanaan PBK politeknik semasa dalam industri ialah konsep pelaksanaan PBK, gaya pengajaran dan pembelajaran, kaedah penilaian, hubungan politeknik dengan industri, keseragaman konsep pelaksanaan PBK, isu dan cabaran dalam pelaksanaan PBK, dan perbezaan kaedah pelaksanaan PBK antara politeknik dengan kolej komuniti. Oleh itu, tujuan kajian ini dijalankan ialah untuk meneroka, memahami dan menjelaskan pelaksanaan PBK politeknik bersama industri. Kajian ini dijalankan menggunakan metodologi kajian kes kualitatif. Proses pengumpulan data di lapangan kajian dilaksanakan selama setahun menggunakan tek:nik temubual, pemerhatian dan analisis dokumen. Strategi persampelan variasi maksima, teknik persampelan snowball dan jenis persampelan bertujuan digunakan. Peserta kajian adalah daripada kalangan pengurusan dan pensyarah penyelaras PBK, penyelia industri dan pelajar yang terlibat dengan PBK. Dapatan kajian menunjukkan bahawa pelaksanaan PBK politeknik bersama industri berlaku banyak penambahbaikan dalam pelaksanaannya jika dibandingkan dengan pelaksanaan PBK di kolej komuniti sebelum ini, namun terdapat beberapa isu yang wujud, iaitu melibatkan kurikulum PBK yang tidak selari dengan dasar industri dan kelemahan penyelia industri dalam pengajaran dan pembelajaran

Analysis and enhancement of interpersonal coordination using inertial measurement unit solutions

Die heutigen mobilen Kommunikationstechnologien haben den Umfang der verbalen und textbasierten Kommunikation mit anderen Menschen, sozialen Robotern und künstlicher Intelligenz erhöht. Auf der anderen Seite reduzieren diese Technologien die nonverbale und die direkte persönliche Kommunikation, was zu einer gesellschaftlichen Thematik geworden ist, weil die Verringerung der direkten persönlichen Interaktionen eine angemessene Wahrnehmung sozialer und umgebungsbedingter Reizmuster erschweren und die Entwicklung allgemeiner sozialer Fähigkeiten bremsen könnte. Wissenschaftler haben aktuell die Bedeutung nonverbaler zwischenmenschlicher Aktivitäten als soziale Fähigkeiten untersucht, indem sie menschliche Verhaltensmuster in Zusammenhang mit den jeweilgen neurophysiologischen Aktivierungsmustern analzsiert haben. Solche Querschnittsansätze werden auch im Forschungsprojekt der Europäischen Union "Socializing sensori-motor contingencies" (socSMCs) verfolgt, das darauf abzielt, die Leistungsfähigkeit sozialer Roboter zu verbessern und Autismus-Spektrumsstörungen (ASD) adäquat zu behandeln. In diesem Zusammenhang ist die Modellierung und das Benchmarking des Sozialverhaltens gesunder Menschen eine Grundlage für theorieorientierte und experimentelle Studien zum weiterführenden Verständnis und zur Unterstützung interpersoneller Koordination. In diesem Zusammenhang wurden zwei verschiedene empirische Kategorien in Abhängigkeit von der Entfernung der Interagierenden zueinander vorgeschlagen: distale vs. proximale Interaktionssettings, da sich die Struktur der beteiligten kognitiven Systeme zwischen den Kategorien ändert und sich die Ebene der erwachsenden socSMCs verschiebt. Da diese Dissertation im Rahmen des socSMCs-Projekts entstanden ist, wurden Interaktionssettings für beide Kategorien (distal und proximal) entwickelt. Zudem wurden Ein-Sensor-Lösungen zur Reduzierung des Messaufwands (und auch der Kosten) entwickelt, um eine Messung ausgesuchter Verhaltensparameter bei einer Vielzahl von Menschen und sozialen Interaktionen zu ermöglichen. Zunächst wurden Algorithmen für eine kopfgetragene Trägheitsmesseinheit (H-IMU) zur Messung der menschlichen Kinematik als eine Ein-Sensor-Lösung entwickelt. Die Ergebnisse bestätigten, dass die H-IMU die eigenen Gangparameter unabhängig voneinander allein auf Basis der Kopfkinematik messen kann. Zweitens wurden—als ein distales socSMC-Setting—die interpersonellen Kopplungen mit einem Bezug auf drei interagierende Merkmale von „Übereinstimmung“ (engl.: rapport) behandelt: Positivität, gegenseitige Aufmerksamkeit und Koordination. Die H-IMUs überwachten bestimmte soziale Verhaltensereignisse, die sich auf die Kinematik der Kopforientierung und Oszillation während des Gehens und Sprechens stützen, so dass der Grad der Übereinstimmung geschätzt werden konnte. Schließlich belegten die Ergebnisse einer experimentellen Studie, die zu einer kollaborativen Aufgabe mit der entwickelten IMU-basierten Tablet-Anwendung durchgeführt wurde, unterschiedliche Wirkungen verschiedener audio-motorischer Feedbackformen für eine Unterstützung der interpersonellen Koordination in der Kategorie proximaler sensomotorischer Kontingenzen. Diese Dissertation hat einen intensiven interdisziplinären Charakter: Technologische Anforderungen in den Bereichen der Sensortechnologie und der Softwareentwicklung mussten in direktem Bezug auf vordefinierte verhaltenswissenschaftliche Fragestellungen entwickelt und angewendet bzw. gelöst werden—und dies in zwei unterschiedlichen Domänen (distal, proximal). Der gegebene Bezugsrahmen wurde als eine große Herausforderung bei der Entwicklung der beschriebenen Methoden und Settings wahrgenommen. Die vorgeschlagenen IMU-basierten Lösungen könnten dank der weit verbreiteten IMU-basierten mobilen Geräte zukünftig in verschiedene Anwendungen perspektiv reich integriert werden.Today’s mobile communication technologies have increased verbal and text-based communication with other humans, social robots and intelligent virtual assistants. On the other hand, the technologies reduce face-to-face communication. This social issue is critical because decreasing direct interactions may cause difficulty in reading social and environmental cues, thereby impeding the development of overall social skills. Recently, scientists have studied the importance of nonverbal interpersonal activities to social skills, by measuring human behavioral and neurophysiological patterns. These interdisciplinary approaches are in line with the European Union research project, “Socializing sensorimotor contingencies” (socSMCs), which aims to improve the capability of social robots and properly deal with autism spectrum disorder (ASD). Therefore, modelling and benchmarking healthy humans’ social behavior are fundamental to establish a foundation for research on emergence and enhancement of interpersonal coordination. In this research project, two different experimental settings were categorized depending on interactants’ distance: distal and proximal settings, where the structure of engaged cognitive systems changes, and the level of socSMCs differs. As a part of the project, this dissertation work referred to this spatial framework. Additionally, single-sensor solutions were developed to reduce costs and efforts in measuring human behaviors, recognizing the social behaviors, and enhancing interpersonal coordination. First of all, algorithms using a head worn inertial measurement unit (H-IMU) were developed to measure human kinematics, as a baseline for social behaviors. The results confirmed that the H-IMU can measure individual gait parameters by analyzing only head kinematics. Secondly, as a distal sensorimotor contingency, interpersonal relationship was considered with respect to a dynamic structure of three interacting components: positivity, mutual attentiveness, and coordination. The H-IMUs monitored the social behavioral events relying on kinematics of the head orientation and oscillation during walk and talk, which can contribute to estimate the level of rapport. Finally, in a new collaborative task with the proposed IMU-based tablet application, results verified effects of different auditory-motor feedbacks on the enhancement of interpersonal coordination in a proximal setting. This dissertation has an intensive interdisciplinary character: Technological development, in the areas of sensor and software engineering, was required to apply to or solve issues in direct relation to predefined behavioral scientific questions in two different settings (distal and proximal). The given frame served as a reference in the development of the methods and settings in this dissertation. The proposed IMU-based solutions are also promising for various future applications due to widespread wearable devices with IMUs.European Commission/HORIZON2020-FETPROACT-2014/641321/E

Markerless 3D human pose tracking through multiple cameras and AI: Enabling high accuracy, robustness, and real-time performance

Tracking 3D human motion in real-time is crucial for numerous applications across many fields. Traditional approaches involve attaching artificial fiducial objects or sensors to the body, limiting their usability and comfort-of-use and consequently narrowing their application fields. Recent advances in Artificial Intelligence (AI) have allowed for markerless solutions. However, most of these methods operate in 2D, while those providing 3D solutions compromise accuracy and real-time performance. To address this challenge and unlock the potential of visual pose estimation methods in real-world scenarios, we propose a markerless framework that combines multi-camera views and 2D AI-based pose estimation methods to track 3D human motion. Our approach integrates a Weighted Least Square (WLS) algorithm that computes 3D human motion from multiple 2D pose estimations provided by an AI-driven method. The method is integrated within the Open-VICO framework allowing simulation and real-world execution. Several experiments have been conducted, which have shown high accuracy and real-time performance, demonstrating the high level of readiness for real-world applications and the potential to revolutionize human motion capture.Comment: 19 pages, 7 figure