{'fraction_non_alphanumeric': 0.01506740681998414, 'fraction_numerical': 0.0, 'mean_word_length': 5.373737373737374, '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}}
innovative framework for the recognition of human activity in smart healthcare

Health services are one of the most difficult aspects of the massive influx of people into urban centers. With global digitization, especially in healthcare, the ability to collect, receive and present data has become a top priority. Currently, many applications in the healthcare business use Machine Learning to give individualized therapies, which will be more dynamic and efficient once personal health and predictive analytics are combined.  Furthermore, machine learning-based tools aid in the treatment of patients starting at the ground level, with clinical practice diagnosis and suggestions. Massive amounts of data may be collected from smart devices in our digital age. Human activity recognition is a classification problem in which data is used to identify events performed by humans. This data is categorized and analyzed to discover patterns in the data that may be used to create future predictions. In theory, activity detection can provide significant societal benefits. Activity recognition and pattern discovery are two aspects of human activity comprehension. The first is concerned with detecting human activities accurately using a specified activity model.