Enhancing sleep pattern assessment with biocompatible smart materials

¹ Research scholar, Mechanical and Aerospace Engineering department, Institute of Infrastructure Technology Research and Management (IITRAM), Ahmedabad.
² Hilla University College, Babylon, Iraq.
³ Department of Civil Engineering, IES Institute of Technology and Management, IES University, Bhopal, Madhya Pradesh, India.
⁴ Department of ECE, GRIET, Bachupally, Hyderabad, Telangana, India.
⁵ Department of Mechanical Engineering, New Horizon College of Engineering, Bangalore, India.
⁶ Lovely Professional University, Phagwara, India.
⁷ Department of Electronics and Communication Engineering, MLR Institute of Technology, Hyderabad, Telangana, India - 500049

^* Corresponding Author: dhaval.makwana.17pm@iitram.ac.in

Abstract

Biomaterials with intelligence can respond to variations in physiological factors. Additionally, they react to external stimuli that influence many attributes of allopathic drugs (technological advances medicine). Smart biomaterials are employed in a variety of therapies to enhance the care of different illnesses. Bio-based smart materials can be molded into a variety of soft designs, such as textiles, hydrogel, membranes film, aerogels, nanofibers, and fabrics, which are advantageous for wearable sensors when compared to polymers generated from petroleum. In this paper, sleep patterns are examined closely in relation to mental health, with a particular focus on bio-signal processing in identifying sleep-related disorders. According to the study, sleep stage analysis is critical to improving therapeutic outcomes for individuals suffering from depression due to its physiological influence. Biologically compatible smart devices enhance advanced biological capture techniques such as electroencephalography (EEG), electrocardiogram (ECG), and electromyography (EMG). As a result, these features increase sensor reliability, accuracy and reliability, ensuring high signal fidelity. The use of biocompatible smart-material based devices with artificial intelligence provides a revolutionary approach to the diagnosis of complex interconnected disorders of mental illness, sleep disorders and schizophrenia, including neural changes and its recurrence to identify sleep phases and identify trauma-related disturbances, and sophisticated machine learning provides in-depth insights.