Relevance of ASR for the Automatic Generation of Keywords Suggestions for TV programs

Semantic access to multimedia content in audiovisual archives is to a large extent dependent on quantity and quality of the metadata, and particularly the content descriptions that are attached to the individual items. However, given the growing amount of materials that are being created on a daily basis and the digitization of existing analogue collections, the traditional manual annotation of collections puts heavy demands on resources, especially for large audiovisual archives. One way to address this challenge, is to introduce (semi) automatic annotation techniques for generating and/or enhancing metadata. The NWO funded CATCH-CHOICE project has investigated the extraction of keywords form textual resources related to the TV programs to be archived (context documents), in collaboration with the Dutch audiovisual archives, Sound and Vision. Besides the descriptions of the programs published by the broadcasters on their Websites, Automatic Speech Transcription (ASR) techniques from the CATCH-CHoral project, also provide textual resources that might be relevant for suggesting keywords. This paper investigates the suitability of ASR for generating such keywords, which we evaluate against manual annotations of the documents and against keywords automatically generated from context documents

Online Human Activity Recognition for Ergonomics Assessment

International audienceWe address the problem of recognizing the current activity performed by a human worker, providing an information useful for automatic ergonomic evaluation of workstations for industrial applications.Traditional ergonomic assessment methods rely on pen-and-paper worksheet, such as the Er-gonomic Assessment Worksheet (EAWS). Nowadays, there exists no tool to automatically estimate the ergonomics score from sensors (external cameras or wearable sensors). As the ergonomic evaluation depends of the activity that is being performed, the first step towards a fully automatic ergonomic assessment is to automatically identify the different activities within an industrial task. To address this problem, we propose a method based on wearable sensors and supervised learning based on Hidden Markov Model (HMM). The activity recognition module works in two steps. First, the parameters of the model are learned offline from observation based on both sensors, then in a second stage, the model can be used to recognize the activity offline and online. We apply our method to recognize the current activity of a worker during a series of tasks typical of the manufacturing industry. We recorded 6 participants performing a sequence of tasks using wearable sensors.Two systems were used: the MVN Link suit from Xsens and the e-glove from Emphasis Telematics (See Fig. 1). The first consists of 17 wireless inertial sensors embedded in a lycra suit, and is used to track the whole-body motion. The second is a glove that includes pressure sensors on fingertips, and finger flexion sensors. The motion capture data are combined with the one from the glove and fed to our activity recognition model. The tasks were designed to involve elements of EAWS such as load handling, screwing and manipulating objects while in different static postures. The data are labeled following the EAWS categories such as " standing bent forward " , " overhead work " or " kneeling ". In terms of performances, the model is able to recognize the activities related to EAWS with 91% of precision by using a small subset of features such as the vertical position of the center of mass, the velocity of the center of mass and the angle of the L5S1 joint

Online Human Activity Recognition for Ergonomics Assessment

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Using XSLT for interoperability: DOE and the travelling domain experiment

troncy2003cInternational audienceNo abstract available

Activity Recognition for Ergonomics Assessment of Industrial Tasks with Automatic Feature Selection

International audienceIn industry, ergonomic assessment is currently performed manually based on the identification of postures and actions by experts. We aim at proposing a system for automatic ergonomic assessment based on activity recognition. In this paper, we define a taxonomy of activities, composed of four levels, compatible with items evaluated in standard ergonomic worksheets. The proposed taxonomy is applied to learn activity recognition models based on Hidden Markov Models. We also identify dedicated sets of features to be used as input of the recognition models so as to maximize the recognition performance for each level of our taxonomy. We compare three feature selection methods to obtain these subsets. Data from 13 participants performing a series of tasks mimicking industrial tasks are collected to train and test the recognition module. Results show that the selected subsets allow us to successfully infer ergonomically relevant postures and actions

Online Human Activity Recognition for Ergonomics Assessment

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Ethical and Social Considerations for the Introduction of Human-Centered Technologies at Work

International audienceHuman-centered technologies such as collaborative robots, exoskeletons, and wearable sensors are rapidly spreading in industry and manufacturing because of their intrinsic potential at assisting workers and improving their working conditions. The deployment of these technologies, albeit inevitable, poses several ethical and societal issues. Guidelines for ethically aligned design of autonomous andintelligent systems do exist, however we argue that ethical recommendations must necessarily be complemented by ananalysis of the social impact of these technologies. In this paper, we report on our preliminary studies on the opinion of factoryworkers and of people outside this environment on human-centered technologies at work. In light of these studies, we discuss ethical and social considerations for deploying these technologies in a way that improves acceptance

Towards collaboration between professional caregivers and robots - A preliminary study

International audienceIn this paper, we address the question of which potential use of a robot in a health-care environment is imagined by people that are not experts in robotics, and how these people imagine to teach new movements to a robot. We report on the preliminary results of our investigation , in which we conducted 40 interviews with non-experts in robotics and a focus group with professional caregivers