The use of multimodal sensors to describe activities of daily living in a noninvasive way is a promising research field in continuous development. In this work, we propose the use of ambient audio sensors to recognise events which are generated from the activities of daily living carried out by the inhabitants of a home. An edge–fog computing approach is proposed to integrate the recognition of audio events with smart boards where the data are collected. To this end, we compiled a balanced dataset which was collected and labelled in controlled conditions. A spectral representation of sounds was computed using convolutional network inputs to recognise ambient sounds with encouraging results. Next, fuzzy processing of audio event streams was included in the IoT boards by means of temporal restrictions defined by protoforms to filter the raw audio event recognition, which are key in removing false positives in real-time event recognition.
Recognition of Activities of Daily Living from Egocentric Videos Using Hands Detected by a Deep Convolutional Network