## 2 Literature Review
### Mental Health Management Through Remote Healthcare Monitoring Systems
#### 2.1.2 Advancements in Mental Health Management Through AI-Enabled RPM Systems  
While traditional methods of mental health management rely heavily on in-person consultations and periodic assessments, these approaches often lack the ability to provide continuous, real-time care. Effective mental health management is particularly critical for severe conditions such as depression, PTSD, and schizophrenia. AI-enabled RPM systems significantly enhance patient outcomes by providing continuous, non-invasive monitoring and early detection of potential health deteriorations. Innovations in wearable technology and AI facilitate real-time remote measurement and analysis of patient data, improving care responsiveness and reducing the burden on patients. An advanced framework introduced in [36] uses an AI-enabled RPM system to improve patient safety and therapeutic environments in mental health facilities, particularly for individuals experiencing acute behavioral disorders, such as aggression or severe PTSD. The system uses _radio frequency identification_ (RFID) technology with a built-in _near-field coherent sensor_ (NCS) to continuously monitor key health indicators, such as heart rate andrespiration, without direct contact. AI models analyze the collected time series data to predict future vital signs for up to three hours and classify physical actions into predefined categories. This framework allows healthcare providers to intervene at the right time, potentially preventing incidents of self-harm, violence, or clinical deterioration. The system aims to improve patient care while minimizing invasiveness and improving safety for patients and medical staff, particularly in cases involving aggressive or agitated individuals. In inpatient psychiatric settings, engagement is vital for creating a safe and therapeutic environment, leading to improved safety and treatment outcomes; however, balancing risk management with therapeutic approaches can be challenging, as a strict focus on safety may hinder personalized care. A positive environment enhances therapeutic relationships, which are essential to patient care and safety [37]. Wearable biometric devices combined with AI facilitate remote real-time analysis of patient data, enhancing care responsiveness while reducing treatment burden. Successful implementation requires addressing patient concerns regarding privacy and data misuse [38]. Multimodal smartphone sensors unobtrusively monitor behavioral markers associated with stress, depression, and loneliness, offering a scalable method for continuous psychiatric assessment outside clinical settings. Recent studies demonstrate the effectiveness of using smartphone and wearable sensors to monitor depression severity, showing significant correlations between sensor data and clinical assessments [39]. Internet of Things (IoT)-based healthcare systems equipped with wearable sensors enable real-time monitoring of health parameters, like heart rate and stress levels, essential for remotely managing psychiatric conditions [40]. The integration of 5G technology with AI in remote diagnostics reduces the burden on specialists by providing real-time diagnostic support, especially in rural areas, ensuring timely interventions and improved access to advanced medical care [41]. AI-powered medical devices collect precise clinical data remotely, aiding health professionals in evaluating conditions with high accuracy, reducing the need for in-person assessments, and ensuring consistent monitoring [42]. Continuous monitoring of vital parameters in Intensive Care Units, enhanced by AI, can detect complications early and reduce false-positive alarms, enhancing patient care efficiency and supporting early detection of clinical deterioration [43]. The integration of wearable devices, AI, and IoT technology enables continuous, real-time monitoring that significantly enhances patient outcomes and the quality of care.