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Misurazione e Modellazione Multisensore del Movimento Umano
No full textL'analisi del movimento umano rappresenta un argomento poliedrico con diverse applicazioni in molteplici campi.
Medicina, riabilitazione, biomeccanica, ma anche robotica, logistica e, ultimamente, l'ambiente industriale, sono accomunati dalla necessità di quantificare come una persona si sta muovendo.
Attualmente lo standard di riferimento nell'analisi del movimento umano consiste nell'uso di sistemi optoelettronici estremamente accurati, in grado di tracciare la posizione di una serie di marcatori retroriflettenti con precisione submillimetrica.
Sebbene questo approccio si presti ad applicazioni in ambito medico, l'avvento dell'Industria 5.0, incentrata sul benessere dell'operatore, ha introdotto una nuova serie di requisiti.
Infatti, per garantire una collaborazione sicura ed efficace tra operatori umani e dispositivi robotici, questi ultimi devono essere costantemente informati delle persone all'interno del proprio spazio di lavoro.
Di conseguenza, il movimento umano deve essere misurato in ambienti non vincolati, in tempo reale e senza influire sulla libertà di movimento della persona.
Il prossimo progresso fondamentale nell'analisi del movimento umano consisterà nello sviluppo di sistemi in grado di fornire misurazioni estremamente accurate, in tempo reale e senza ostacolare in alcun modo la libertà di movimento.
Questo lavoro presenta le attività di ricerca che ho condotto in questa direzione durante il mio dottorato.
Lo scopo della mia ricerca ha riguardato lo sviluppo di nuovi algoritmi per massimizzare l'accuratezza della stima del movimento, supportare dati eterogenei da diversi sistemi di motion capture e minimizzare il numero di sensori richiesti sul corpo.
Un obiettivo così complesso ha richiesto la definizione di tre macro livelli in cui ho suddiviso il mio lavoro: il livello di Sensing, il livello di Tracking e il livello di Modeling.
Il primo livello ha lo scopo di integrare, senza soluzione di continuità, diverse tipologie di sensori con gli algoritmi sviluppati.
L'obiettivo principale di questo livello è fornire un collegamento tra le grandezze fisiche misurate e gli algoritmi sviluppati, indipendentemente dalla tipologia di sensore utilizzato.
Questo è di fondamentale importanza per consentire la stima del movimento in ambienti non vincolati, permettendo al sistema di adattarsi allo specifico utente e scenario.
Il secondo livello si concentra sulla massimizzazione dell'accuratezza della stima della posa attraverso l'utilizzo di una rete distribuita di sensori.
All'interno di questo livello ho sviluppato strumenti all'avanguardia per permettere tracciamento, fusione, ottimizzazione e filtraggio in tempo reale di più persone.
L'aumento di precisione ottenuto dal sistema proposto è stato valutato su di un dataset recentemente acquisito che prevediamo di rilasciare nel prossimo futuro.
Infine, l'ultimo livello consente la fusione in tempo reale dei dati ottenuti da sensori eterogenei, associando tutte le grandezze misurate ad un unico modello muscoloscheletrico dell'essere umano.
All'interno di questo livello, i protocolli considerati standard in ambiente biomeccanico sono stati adattati con successo per consentirne l'utilizzo in contesti diversi.
L'ampio lavoro all'interno dei tre livelli sopracitati ha portato allo sviluppo di un framework open-source efficiente, flessibile e modulare per la misurazione e modellazione multisensore in tempo reale del movimento umano.
Per concludere, il lavoro sviluppato nell'ambito della mia ricerca di dottorato ha le potenzialità di fare avanzare lo stato dell'arte nell'ambito della stima non invasiva del movimento umano in ambienti non vincolati attraverso un approccio multidisciplinare.
Il sistema proposto, infatti, consente lo sviluppo di una varietà di applicazioni emergenti in cui la conoscenza precisa della posa umana in ambienti complessi, con un numero minimo di sensori sul corpo, e in tempo reale, è un requisito fondamentale.The analysis of human motion is a multifaceted topic with crucial applications in several fields.
Medicine, rehabilitation, biomechanics, but also robotics, logistics, and, lately, an increasing number of industrial scenarios share the need to quantify how a person is moving.
The current gold standard for the assessment of human motion makes use of highly accurate optoelectronic systems capable of tracking the three-dimensional positions of a set of retroreflective markers with submillimeter precision.
Although this approach is suitable for medical applications, the advent of Industry 5.0, which focuses on the well-being of the human operator, brought a new set of requirements for the assessment of human motion.
In fact, to ensure a safe and productive collaboration between human operators and robotic devices, the latter must be constantly aware of the people within their workspace.
Thus, the human motion needs to be measured in unconstrained environments, in real-time, and without impacting the person's dexterity.
The next critical advancement in human motion analysis will consist of developing accurate non-invasive systems.
Ideally, a complete MoCap system should be able to provide highly accurate measurements, without requiring complex hardware setups nor hindering the freedom of movement in any way, and in real-time.
This work presents the research activities that I conducted during my Ph.D. in this direction.
The main objective of my research was to provide novel tools and algorithms to maximize the motion estimation accuracy, support heterogeneous quantities measured/estimated by different sensing systems, and minimize the number of sensors required on the body.
Such a complex objective required the definition of three macro levels in which I divided my work: the Sensing level, the Tracking level, and the Modeling level.
The first level aims to seamlessly integrate different sensor typologies with the developed algorithms.
The main goal of this level is to provide a bridge between the raw measured quantities and the developed tools, independently of the typology of sensors being used.
This is of paramount importance in the direction of enabling human MoCap in unconstrained environments, allowing the system to be adapted to the specific user and scenario.
The second level focuses on maximizing the pose estimation accuracy when using a distributed network of sensors.
Within this level, I developed state-of-the-art tools to enable robust, accurate, real-time multi-person tracking.
Four modules were developed to allow for real-time temporal tracking, merging, optimizing, and smoothing of multiple people's poses.
The accuracy increase achieved by exploiting the proposed workflow was assessed in a newly acquired dataset that we plan to release in the near future.
Finally, the latter level enables multimodal sensor fusion of heterogeneous data by referring all the measured quantities to a common underlying model of the human.
The information received from either the Sensing or Tracking levels is used to drive a musculoskeletal model of the human in real-time.
Within this level, protocols considered de-facto standards in the biomechanics field were successfully adapted to allow their use in different contexts.
The extensive work on sensing, tracking, and modeling converged on the development of an open-source efficient, flexible, modular framework for real-time multi-sensor measurement and modeling of human motion.
To conclude, the work developed within my Ph.D. research aimed to push forward, via a multidisciplinary approach, the state-of-the-art on accurate unintrusive assessment of human motion in unconstrained environments.
Indeed, the proposed system has the potential to enable the development of a variety of emerging applications, where precise knowledge of the human pose in complex environments, with a minimal number of on-body sensors, and in real-time, is a fundamental requirement
Towards Human Digital Twins to enhance workers' safety and production system resilience
Full text linkIndustry 5.0 complements Industry 4.0 aiming to create a sustainable, human-centered, and resilient industry. In this context, enabling technologies, such as artificial intelligence, internet of everything, and digital twins, can be used to monitor and enhance the workforce to improve the efficiency and resilience of the entire manufacturing system. By developing socio-technical digital twin architectures, companies will be able in the short future to monitor machines, products, and workers' real-time states as a whole ecosystem. In this study, the authors focus their attention on human digital twin solutions for manufacturing systems, enabling dynamic scheduling of jobs by minimizing the makespan and considering a set of workers’ parameters that are continuously monitored through an ergonomic digital platform. This paper proposes the architecture of a real-time monitoring system and how it can help detect awkward postural behavior or unbalanced workload among workers, according to their individual features. At the same time, the system interacts with the human digital twin system which proposes a rescheduling of the jobs whenever it is necessary. Finally, a discussion on the practical limitations of human digital twin implementations in industrial environments is provided
A Limb-based Approach for Body Pose Recognition Using a Predefined Set of Poses
Full text linkThis paper proposes a novel approach for the body pose recognition of multiple persons. Our system takes as input the 3D joint locations of each person's skeleton representation, as estimated by OpenPTrack, an open source project for RGB-D people tracking. The poses of the upper and lower limbs are computed separately by comparing them to the ones stored in a pre-recorded database. The two partial poses are then combined to obtain the full pose of each person on the scene. The system provides real-time outcomes, is markerless, and does not need any assumption on the orientation, initial position, or number of persons on the scene. It can be used as a base for more complex action recognition algorithms, for intelligent surveillance and security devices, or in human-computer interaction
A ROS driver for Xsens wireless inertial measurement unit systems
No full textThis paper presents an efficient open-source driver for interfacing Xsens inertial measurement systems (in particular the Xsens MTw Awinda wireless motion trackers) with the Robot Operating System (ROS).
The driver supports the simultaneous connection of up to 20 trackers, limit fixed by the Xsens software, to a master PC, and directly streams sensors data (linear accelerations, angular velocities, magnetic fields, orientations) up to 120 Hz to the ROS network through one or multiple configurable topics.
Moreover, a synchronization procedure is implemented to avoid possible partial frames where the readings from one (or multiple) trackers are missing.
The proposed messages are based on ROS standard ones and comply with the ROS developer guidelines.
This guarantees the compatibility of any ROS package requiring as input ROS messages with the proposed driver, thus effectively integrating Xsens inertial measurement systems with the ROS ecosystem.
This work aims to push forward the development of a large variety of human-robot interaction applications where accurate real-time knowledge of human motion is crucial
On the accuracy of IMUs for human motion tracking: a comparative evaluation
No full textInertial Measurement Units (IMUs) are becoming more and more popular in human motion tracking applications.
Among the other advantages, wearability, portability, limited costs, and accuracy are the main drivers of their increasing use.
These devices are nowadays well-established commercially available products, ranging from few to hundreds of Euros.
The main purpose of this study is to investigate the potentialities and the limits of IMUs belonging to different commercial segments in providing accurate orientation estimates within the operating conditions characterizing the human motion.
These are simulated by means of a direct drive servomotor, in order to ensure accuracy and repeatability of the whole assessment pipeline.
Both static and dynamic conditions are analyzed, the latter obtained by varying frequency and amplitude of a sinusoidal motion, thus evaluating the performances for a broad set of movements.
IMUs orientations are estimated through proprietary filters, when available, and then compared with the well-established Madgwick's filter, to effectively investigate the performances of the on-board sensors.
Results show that the low-cost IMUs are suited for applications requiring low bandwidth, while the comparison through Madgwick's filter did not highlight appreciable differences among the IMUs
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
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