A Modular Approach for Synchronized Wireless Multimodal Multisensor Data Acquisition in Highly Dynamic Social Settings

Existing data acquisition literature for human behavior research provides wired solutions, mainly for controlled laboratory setups. In uncontrolled free-standing conversation settings, where participants are free to walk around, these solutions are unsuitable. While wireless solutions are employed in the broadcasting industry, they can be prohibitively expensive. In this work, we propose a modular and cost-effective wireless approach for synchronized multisensor data acquisition of social human behavior. Our core idea involves a cost-accuracy trade-off by using Network Time Protocol (NTP) as a source reference for all sensors. While commonly used as a reference in ubiquitous computing, NTP is widely considered to be insufficiently accurate as a reference for video applications, where Precision Time Protocol (PTP) or Global Positioning System (GPS) based references are preferred. We argue and show, however, that the latency introduced by using NTP as a source reference is adequate for human behavior research, and the subsequent cost and modularity benefits are a desirable trade-off for applications in this domain. We also describe one instantiation of the approach deployed in a real-world experiment to demonstrate the practicality of our setup in-the-wild.Comment: 9 pages, 8 figures, Proceedings of the 28th ACM International Conference on Multimedia (MM '20), October 12--16, 2020, Seattle, WA, USA. First two authors contributed equall

A Simple Method for Synchronising Multiple IMUs Using the Magnetometer

This paper presents a novel method to synchronise multiple IMU (inertial measurement units) devices using their onboard magnetometers. The method described uses an external electromagnetic pulse to create a known event measured by the magnetometer of multiple IMUs and in turn used to synchronise these devices. The method is applied to 4 IMU devices decreasing their de-synchronisation from 270ms when using only the RTC (real time clock) to 40ms over a 1 hour recording. It is proposed that this can be further improved to approximately 3ms by increasing the magnetometer’s sample frequency from 25Hz to 300Hz

Synchronization and Analysis of Multimodal Medical Data

The United States suffers from a significant disparity in the availability of the medical resources and expertise among different regions of the country. Patients in rural areas may not have the opportunity to consult with a physician until their disease progresses to later stages, resulting in a considerable decrease in quality of life. Advances in telemedicine systems that can provide remote communication, medical data acquisition, and medical data analysis promise a significant improvement to early access to medical care and diagnoses for disadvantaged individuals. In this thesis, we make several contributions on topics that contribute to the improvement of telemedicine systems. First, we propose several synchronization approaches for the acquisition of multimodal medical data. Second, we explore several machine learning techniques that analyze cardiovascular data and provide feedback about the patient\u27s health to the physician. We found that the Random Forest algorithm was the most accurate in predicting heart disease in a patient

Method of synchronization and data processing from differents inertial sensors kits sources for the human gait analysis

The article talks about results of data synchronization measurements sourced from two recording gait systems for human gait analyses. Two systems are Xsens sensor kits: MT Awinda, Xbus Kit. The article cover file format used to save data and synchronization method for sensor measurement from above mentioned kits. On the basis of the studies carried out, sensor measurement from different places on human body are unify to a common frame of reference. The discussed method provides also progressive data processing for angles range from -180° to 180° conversion to the absolute angle value from initial sensor settings

Human action recognition and mobility assessment in smart environments with RGB-D sensors

Questa attività di ricerca è focalizzata sullo sviluppo di algoritmi e soluzioni per ambienti intelligenti sfruttando sensori RGB e di profondità. In particolare, gli argomenti affrontati fanno riferimento alla valutazione della mobilità di un soggetto e al riconoscimento di azioni umane. Riguardo il primo tema, l'obiettivo è quello di implementare algoritmi per l'estrazione di parametri oggettivi che possano supportare la valutazione di test di mobilità svolta da personale sanitario. Il primo algoritmo proposto riguarda l'estrazione di sei joints sul piano sagittale utilizzando i dati di profondità forniti dal sensore Kinect. La precisione in termini di stima degli angoli di busto e ginocchio nella fase di sit-to-stand viene valutata considerando come riferimento un sistema stereofotogrammetrico basato su marker. Un secondo algoritmo viene proposto per facilitare la realizzazione del test in ambiente domestico e per consentire l'estrazione di un maggior numero di parametri dall'esecuzione del test Timed Up and Go. I dati di Kinect vengono combinati con quelli di un accelerometro attraverso un algoritmo di sincronizzazione, costituendo un setup che può essere utilizzato anche per altre applicazioni che possono beneficiare dell'utilizzo congiunto di dati RGB, profondità ed inerziali. Vengono quindi proposti algoritmi di rilevazione della caduta che sfruttano la stessa configurazione del Timed Up and Go test. Per quanto riguarda il secondo argomento affrontato, l'obiettivo è quello di effettuare la classificazione di azioni che possono essere compiute dalla persona all'interno di un ambiente domestico. Vengono quindi proposti due algoritmi di riconoscimento attività i quali utilizzano i joints dello scheletro di Kinect e sfruttano un SVM multiclasse per il riconoscimento di azioni appartenenti a dataset pubblicamente disponibili, raggiungendo risultati confrontabili con lo stato dell'arte rispetto ai dataset CAD-60, KARD, MSR Action3D.This research activity is focused on the development of algorithms and solutions for smart environments exploiting RGB and depth sensors. In particular, the addressed topics refer to mobility assessment of a subject and to human action recognition. Regarding the first topic, the goal is to implement algorithms for the extraction of objective parameters that can support the assessment of mobility tests performed by healthcare staff. The first proposed algorithm regards the extraction of six joints on the sagittal plane using depth data provided by Kinect sensor. The accuracy in terms of estimation of torso and knee angles in the sit-to-stand phase is evaluated considering a marker-based stereometric system as a reference. A second algorithm is proposed to simplify the test implementation in home environment and to allow the extraction of a greater number of parameters from the execution of the Timed Up and Go test. Kinect data are combined with those of an accelerometer through a synchronization algorithm constituting a setup that can be used also for other applications that benefit from the joint usage of RGB, depth and inertial data. Fall detection algorithms exploiting the same configuration of the Timed Up and Go test are therefore proposed. Regarding the second topic addressed, the goal is to perform the classification of human actions that can be carried out in home environment. Two algorithms for human action recognition are therefore proposed, which exploit skeleton joints of Kinect and a multi-class SVM for the recognition of actions belonging to publicly available datasets, achieving results comparable with the state of the art in the datasets CAD-60, KARD, MSR Action3D