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—We deployed 72 sensors of 10 modalities in 15 wireless and wired networked sensor systems in the environment, in objects, and on the body to create a sensor-rich environment for the machine recognition of human activities. We acquired data from 12 subjects performing morning activities, yielding over 25 hours of sensor data. We report the number of(More)
A common assumption in activity recognition is that the system remains unchanged between its design and its posterior operation. However, many factors affect the data distribution between two different experimental sessions. One of these factors is the potential change in the sensor location (e.g. due to replacement or slippage) affecting the classification(More)
A common assumption in activity recognition is that the system remain unchanged between its design and its posterior operation. However, many factors can affect the data distribution between two different experimental sessions including sensor displacement (e.g. due to replacement or slippage), and lead to changes in the classification performance. We(More)
The non-invasive Brain-Computer Interface (BCI) developed in our lab targets asynchronous operation of devices by monitoring electroencephalographic (EEG) activity and identifying oscillatory patterns that the user can voluntary modulate through the execution of motor imagery (MI) tasks. Successful self-paced interaction under this framework requires the(More)
An increasing need for healthcare provision and assistive technologies (AT) calls for the development of machine learning techniques able to cope with the variability inherent to real-world deployments. In the particular case of activity recognition applications sensor networks may be prone to changes at different levels ranging from sensor data variability(More)
Detection of anomalies is a broad field of study, which is applied in different areas such as data monitoring , navigation, and pattern recognition. In this paper we propose two measures to detect anomalous behaviors in an ensemble of classifiers by monitoring their decisions; one based on Mahalanobis distance and another based on information theory. These(More)
OPPORTUNITY is project under the EU FET-Open funding 1 in which we develop mobile systems to recognize human activity in dynamically varying sensor setups [1,2]. The system autonomously discovers available sensors around the user and self-configures to recognize desired activities. It reconfigures itself as the environment changes, and encompasses(More)
BACKGROUND Attention deficit hyperactivity disorder and conduct disorder are amongrelatively prevalent disorders during childhood and adolescence.Considering the negative impact of the parents' drug dependency andbipolar disorder, the present study aimed to determine the prevalence ofADHD and conduct disorder in children of drug-dependent and bipolar(More)
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