Ambient assisted living (AAL) environments are currently a key focus of interest as an option to assist and monitor disabled and elderly people. These systems can improve their quality of life and personal autonomy by detecting events such as entering potentially dangerous areas, potential fall events, or extended stays in the same place. Nonetheless, there are areas that remain outside the scope of AAL systems due to the placement of cameras. There also exist sources of danger in the scope of the camera that the AAL system cannot detect. These sources of danger are relatively small in size, occluded, or nonstatic. To solve this problem, we propose the inclusion of a robot which maps such uncovered areas looking for new potentially dangerous areas that go unnoticed by the AAL. The robot then sends this information to the AAL system in order to improve its performance. Experimentation in real-life scenarios successfully validates our approach.
This study proposes an intervention for stroke patients in which electrical stimulation of muscles in the affected arm is supplied when movement intention is detected from the electroencephalographic signal. The detection relies on the combined analysis of two movement related cortical patterns: the event-related desynchronization and the bereitschaftspotential. Results with two healthy subjects and three chronic stroke patients show that reliable EEG-based estimations of the movement onsets can be generated (on average, 66.9 ± 26.4 % of the movements are detected with 0.42 ± 0.17 false activations per minute) which in turn give rise to electrical stimuli providing sensory feedback tightly associated to the movement planning (average detection latency of the onsets of the movements was 54.4 ± 287.9 ms).
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