A Deep Survey on Human Activity Recognition Using Mobile and Wearable Sensors

Abstract

Activity-based wellness management is thought to be a powerful application for mobile health. It is possible to provide context-aware wellness services and track human activity thanks to accessing for multiple devices as well as gadgets that we use every day. Generally in smart gadgets like phones, watches, rings etc., the embedded sensors having a wealth data that can be incorporated to person task tracking identification. In a real-world setting, all researchers shown effective boosting algorithms can extract information in person task identification. Identifying basic person tasks such as talk, walk, sit along sleep. Our findings demonstrate that boosting classifiers perform better than conventional machine learning classifiers. Moreover, the feature engineering for differentiating an activity detection capability for smart phones and smart watches. For the purpose of improving the classification of fundamental human activities, upcoming mechanisms give the guidelines for identification for various sensors and wearable devices.