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Research on fall and movement detection with wearable devices has witnessed promising growth. However, there are few publicly available datasets, all recorded with smartphones, which are insufficient for testing new proposals due to their absence of objective population, lack of performed activities, and limited information. Here, we present a dataset of(More)
The characterization of daily activities using accelerometers is a currently active research field, with special interest on fall detection of elderly people and sports performance. Most works that account walking and running are peak-acceleration based, however, false positives due to artefact acceleration peaks affect the estimation. Also, the proposed(More)
Elderly fall detection based on accelerometers is an active research area. Nowadays authors are addressing specific problems such as failure rates and energy consumption, but in most cases their strategies do not conciliate these objectives. In this paper we propose a double threshold based methodology with two novel detection features, a product between(More)
The traditional reconstruction of the sources of brain activity from MEG/EEG requires to previously acquire a structural image estimated from a magnetic resonance imaging scan. Then, the neural activity is mapped into this anatomical model by solving an inverse problem. However, obtaining structural images is expensive and time consuming. A widely used(More)
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