{'fraction_non_alphanumeric': 0.01, 'fraction_numerical': 0.0, 'mean_word_length': 5.171806167400881, '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 study of fall detection monitoring system for elderly people through IOT and mobile based application devices in indoor environment

This research presents the structure and framework for identifying falls by remote observing of old individuals in indoor environments by taking advantages of the Internet of things as well as mobile-based applications. This smart framework identifies fall occurred to older individuals who are living alone or living in residential nursing homes. To monitor the fall it uses real-time monitoring by use of open source camera and wearable devices. The system is carried out by pose recognition and object detection method for identifying the object taken by an open-source camera.A systematic review was performed using the Primo Search tool for finding eBooks, articles, and journals from the CSU library database. To provide a high efficiency using this information all inclusion criteria were meet by choosing the journal article which was closely related to the topic. The proposed study of fall detection monitoring system for older people living in geriatric residents allows data for caregivers and clinicians to provide better control in monitoring the health status of older patients and allows closer communication with the patients’ family members and relatives. This study can be used as an approach for improving the cost of care in elderly population.