Machine Learning Model for Human Activity Analysis

Human Activity Recognition is an active subject of research and scientific progress in which several models have been presented for identifying and categorizing activities using Machine Learning utilizing various methodologies. The purpose of human activity recognition is to look at activities in video or still photos. Human activity recognition systems are motivated by this fact, and their goal is to appropriately classify input data into its underlying activity category. Human activities are classified as (a) gestures, (b) atomic actions, (c) human-to-object or human-to-human interactions, (d) collective actions, (e) behaviors, and (f) events, depending on their complexity. Today, health informatics is a critical field for improving healthcare efficiency by streamlining the collecting, storage, and retrieval of critical patient health data. In this paper, an intelligent smart healthcare system is provided that uses machine learning approaches to deliver ubiquitous human activity recognition (HAR) in an automated manner. The goal is to model and recognize activities of everyday living in a precise and efficient manner. Furthermore, for HAR purposes, we focus on a dataset collecting body motion and vital sign recordings from volunteers of various profiles while performing various physical activities. This research has demonstrated that identifying human activity from sensor data is extremely difficult, even with the availability of a number of machine learning approaches. When it comes to machine learning techniques, there is no one-size-fits-all approach.