{'fraction_non_alphanumeric': 0.017189079878665317, 'fraction_numerical': 0.003033367037411527, 'mean_word_length': 4.892857142857143, '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 neural network-based sensor for elder falling detection

Falling down is going to be a crucial problem to an elder today. In many countries, unintentional injury was being one of the leading causes of death in persons over age 65 years. As the society now, there are more and more solitary elders of life alone and because of the isolation, it is necessary to design an intelligent and sensitive falling detector for the elderly people. In this paper, we present an intelligent and portable fall detection device based on artificial neural network technology. This fall detector consists of two main components: accelerometer and microprocessor. The tri-axis accelerometer is used to continuously measure the variation of elder’s 3 ways acceleration. The microprocessor reads the signals from the accelerometer and performs the fall activity recognition through a back-propagation neural network model. This device is integrated in a small box which can be holding on the belt for elder.