{'fraction_non_alphanumeric': 0.013769363166953529, 'fraction_numerical': 0.0008605851979345956, 'mean_word_length': 5.286486486486487, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
A networked multisensor system for ambient assisted living application

The paper proposes new technologies to support independence and engagement in elderly people living alone at home in the framework of a new UE Integrated Project. Aim of the study is the development of a light technological infrastructure to be integrated in the homes of old people at reduced costs, allowing the detection of critical health situations. In particular a reliable fall detector is presented, with focus on the protection and assistance to the elderly in the home environment. The integrated fall detector prototype includes two different sensors: a 3D Time-of-Flight range camera and a wearable MEMS accelerometer. The devices are connected in a networked configuration with custom interface circuits to a central PC that collects and processes the information with a multi-threading approach. For each sensor, an optimized algorithm for fall-detection has been developed and benchmarked on a collected multimodal database. This work is expected to lead to a multi-sensory approach employing appropriate fusion techniques aiming to improve system efficiency and reliability.