Enhancing Human Activity Recognition with Advanced Machine Learning Techniques

One of the most popular computer research methodologies is human activity recognition, which finds application in a wide range of fields including education, gaming and amusement, healthcare, security and visual surveillance, patient rehabilitation, and human-computer interaction. Human activity identification has gained significant attention in the last ten years due to the growing usage of electronic devices to monitor human activity, including smartphones, smart watches, fitness trackers, video cameras, and ad hoc replaceable devices. A machine learning technique uses data from three axial linear acceleration and angular velocity, together with an integrated accelerometer and gyroscope sensor, to classify actual human activity. This study looks at the important role of machines in developing human activity recognition applications based on physiological and environmental sensors as well as passive sensors. And 5 machine learning algorithms such as lr (Logistic Regression), lr_l2 (Logistic Regression CV), SVM (Default Hyperparameters), SVM (Hyperparameters with Kernel), rf (Random Forest) are discussed whose accuracy is 0.9832, 0.986401, 0.9748, 0.9884, 0.9816, respectively. SVM (Hyperparameters with Kernel) has the best accuracy, which is 0.9884.