DANTE: A Video Based Annotation Tool for Smart Environments

Cruciani, Federico; Donnelly, Mark; Nugent, Chris; Parente, Guido; Paggetti, Cristiano; Burns, William C. G.

doi:10.1007/978-3-642-23583-2_13

Cited by 12 publications

(8 citation statements)

References 8 publications

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“…The video analysis toolbox allows the labeling of the children's behaviors in the recorded session and is based on the Dante annotation tool (Cruciani et al, 2011 ) customized for the labeling of behaviors of children with ASD.…”

Section: Methodsmentioning

confidence: 99%

An Integrated Approach for the Monitoring of Brain and Autonomic Response of Children with Autism Spectrum Disorders during Treatment by Wearable Technologies

et al. 2016

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Autism Spectrum Disorders (ASD) are associated with physiological abnormalities, which are likely to contribute to the core symptoms of the condition. Wearable technologies can provide data in a semi-naturalistic setting, overcoming the limitations given by the constrained situations in which physiological signals are usually acquired. In this study an integrated system based on wearable technologies for the acquisition and analysis of neurophysiological and autonomic parameters during treatment is proposed and an application on five children with ASD is presented. Signals were acquired during a therapeutic session based on an imitation protocol in ASD children. Data were analyzed with the aim of extracting quantitative EEG (QEEG) features from EEG signals as well as heart rate and heart rate variability (HRV) from ECG. The system allowed evidencing changes in neurophysiological and autonomic response from the state of disengagement to the state of engagement of the children, evidencing a cognitive involvement in the children in the tasks proposed. The high grade of acceptability of the monitoring platform is promising for further development and implementation of the tool. In particular if the results of this feasibility study would be confirmed in a larger sample of subjects, the system proposed could be adopted in more naturalistic paradigms that allow real world stimuli to be incorporated into EEG/psychophysiological studies for the monitoring of the effect of the treatment and for the implementation of more individualized therapeutic programs.

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Section: Methodsmentioning

confidence: 99%

An Integrated Approach for the Monitoring of Brain and Autonomic Response of Children with Autism Spectrum Disorders during Treatment by Wearable Technologies

et al. 2016

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“…The annotation for the performed activities was undertaken by the DANTE system, which is a video camera-based automatic annotation system [Cruciani et al 2011]. Activity Dataset Collection After instrumenting sensors in the target smart space, we collected activity datasets automatically using a sensor network, sensor boards and a computer.…”

Section: Methodsmentioning

confidence: 99%

Analyzing Activity Recognition Uncertainties in Smart Home Environments

Kim

Helal

Nugent

et al. 2015

ACM Trans. Intell. Syst. Technol.

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In spite of the importance of activity recognition (AR) for intelligent human-computer interaction in emerging smart space applications, state-of-the-art AR technology is not ready or adequate for real-world deployments due to its insufficient accuracy. The accuracy limitation is directly attributed to uncertainties stemming from multiple sources in the AR system. Hence, one of the major goals of AR research is to improve system accuracy by minimizing or managing the uncertainties encountered throughout the AR process. As we cannot manage uncertainties well without measuring them, we must first quantify their impact. Nevertheless, such a quantification process is very challenging given that uncertainties come from diverse and heterogeneous sources. In this article, we propose an approach, which can account for multiple uncertainty sources and assess their impact on AR systems. We introduce several metrics to quantify the various uncertainties and their impact. We then conduct a quantitative impact analysis of uncertainties utilizing data collected from actual smart spaces that we have instrumented. The analysis is intended to serve as groundwork for developing "diagnostic" accuracy measures of AR systems capable of pinpointing the sources of accuracy loss. This is to be contrasted with the currently used accuracy measures.

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“…Many solutions have been developed to support and facilitate the process of data annotation [ 5 , 8 , 9 ]. Similar systems can significantly reduce the time required by the data labeling process.…”

Section: Introductionmentioning

confidence: 99%

“…To deal with the problem, many approaches have been proposed. An example is the development of annotation tools that facilitate manual annotation attempting (at least partially) to automate the process [ 8 , 28 ]. These tools can drastically reduce the effort required to annotate large amounts of data; however, they can only be used in offline conditions.…”

Section: Introductionmentioning

confidence: 99%

Automatic Annotation for Human Activity Recognition in Free Living Using a Smartphone

Cruciani

Cleland

Nugent

et al. 2018

Sensors

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Data annotation is a time-consuming process posing major limitations to the development of Human Activity Recognition (HAR) systems. The availability of a large amount of labeled data is required for supervised Machine Learning (ML) approaches, especially in the case of online and personalized approaches requiring user specific datasets to be labeled. The availability of such datasets has the potential to help address common problems of smartphone-based HAR, such as inter-person variability. In this work, we present (i) an automatic labeling method facilitating the collection of labeled datasets in free-living conditions using the smartphone, and (ii) we investigate the robustness of common supervised classification approaches under instances of noisy data. We evaluated the results with a dataset consisting of 38 days of manually labeled data collected in free living. The comparison between the manually and the automatically labeled ground truth demonstrated that it was possible to obtain labels automatically with an 80–85% average precision rate. Results obtained also show how a supervised approach trained using automatically generated labels achieved an 84% f-score (using Neural Networks and Random Forests); however, results also demonstrated how the presence of label noise could lower the f-score up to 64–74% depending on the classification approach (Nearest Centroid and Multi-Class Support Vector Machine).

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DANTE: A Video Based Annotation Tool for Smart Environments

Cited by 12 publications

References 8 publications

An Integrated Approach for the Monitoring of Brain and Autonomic Response of Children with Autism Spectrum Disorders during Treatment by Wearable Technologies

An Integrated Approach for the Monitoring of Brain and Autonomic Response of Children with Autism Spectrum Disorders during Treatment by Wearable Technologies

Analyzing Activity Recognition Uncertainties in Smart Home Environments

Automatic Annotation for Human Activity Recognition in Free Living Using a Smartphone

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