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

被引：0

作者：

Tripathy, Jyotsnarani ^{[1
]}

Balasubramani, M. ^{[2
]}

Rajan, V. Aravinda ^{[3
]}

S, Vimalathithan ^{[4
]}

Aeron, Anurag ^{[5
]}

Arora, Meena ^{[6
]}

机构：

[1] Department of CSE-AIML & IoT, VNR Vignana Jyothi Institute of Engineering & Technology, Telengana, Hyderabad

[2] Department of Computer Science and Engineering, VSB Engineering College, Tamil Naud, Karur

[3] Department of Computer Science and Engineering in Kalasalingam Academy of Research and Education, Krishnankovil, Tamil Nadu, Srivilliputtur

[4] Department of CSE, Mohamed Sathak AJ College of Engineering Chennai, Tamil Nadu

[5] Department of Computer Science and Engineering, Meerut Institute of Engineering & Technology, Meerut

[6] Department of IT, JSS Academy of Technical Education, U.P, Noida

来源：

Measurement: Sensors | 2024年 / 33卷

关键词：

Adaptive strategies; Health and wellness applications; Individualized responses; Machine learning; Personalized feedback; Real-time interventions; Reinforcement learning; Wearable sensors;

D O I：

10.1016/j.measen.2024.101151

中图分类号：

学科分类号：

摘要：

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. © 2024 The Authors

引用

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