Reinforcement learning for optimizing real-time interventions and personalized feedback using wearable sensors

Abstract

Many countries have seen a steady increase in lifetime duration through the past many years as a result of notable advancements in ecological and individual cleanliness, general health, including healthcare. Hence, it is anticipated that rising life duration and declining birth levels will result in a sizable aging population in the foreseeable future, placing a heavy strain on such nations' societal structures. For the benefit of senior medical and wellness, it is crucial to create affordable, user-friendly technologies. Older people can remain in their cozy homes rather than costly medical centers due to mobile health surveillance, which depends upon portable, non-intrusive devices, and actuators, as well as contemporary interaction and data innovations. It is an operational and economical approach. This work investigates how electronic devices can be used to optimize in-the-moment actions and deliver customized feedback using reinforced learning. By utilizing the information obtained from such sensors, the suggested structure dynamically modifies solutions according to each individual's reaction, increasing the efficacy of customized input. The reinforced method of learning optimizes the course of action for a variety of circumstances by interactively improving its tactics. By introducing an innovative strategy to improve immediate responses and provide customized input, this study advances the nascent field of wireless technologies as well as artificial learning and eventually raises the effectiveness of customized healthcare and well-being apps.