Page 332 - Emerging Trends and Innovations in Web-Based Applications and Technologies

International Journal of Trend in Scientific Research and Development (IJTSRD)
Special Issue on Emerging Trends and Innovations in Web-Based Applications and Technologies
Available Online: www.ijtsrd.com e-ISSN: 2456 – 6470

Personalized Healthcare Through WellnessGuard:

A Study on Smart Health Monitoring Systems

Shubham Nibrad , Suraj Thawre , Monica Choudhary , Prof. Usha Kosarkar
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1,2,3,4 Department of Science and Technology,
1,2,3,4 G H Raisoni College of Engineering and Management, Nagpur, Maharashtra, India

ABSTRACT II. RELATED WORK
Personalized healthcare is revolutionizing the way medical The development of smart health monitoring systems has
systems address individual wellness and disease garnered significant attention in recent years. Studies have
prevention. This paper explores WellnessGuard, a smart shown the effectiveness of IoT devices and AI algorithms in
health monitoring system leveraging advanced IoT devices healthcare applications:
and AI algorithms to deliver tailored healthcare solutions. Ø Smart Health Monitoring Systems
By integrating real-time data from wearable devices, it aims
to predict potential health risks, optimize treatment plans, Smart health monitoring systems (SHMS) have gained
significant attention due to their potential to transform
and enhance patient engagement. In a recent study, it healthcare delivery. These systems leverage Internet of
demonstrated a prediction accuracy of 93.8% for chronic
disease onset and significantly improved patient adherence Things (IoT), wearable devices, and data analytics to monitor
vital health metrics in real-time. Works like Smith et al.
to health routines. The system represents a transformative (2020) have demonstrated the utility of SHMS in chronic
approach to healthcare, enabling proactive management disease management, showcasing improved patient
and personalized care plans. This study highlights the
potential of WellnessGuard in redefining healthcare outcomes through continuous monitoring and early
intervention. However, many existing systems are limited by
standards and improving patient outcomes globally.
their generality, failing to account for individual variability in
health parameters.
KEYWORDS: Personalized healthcare, IoT, AI, smart health
monitoring, WellnessGuard, predictive health Ø Personalized Healthcare Solutions
Personalization in healthcare is increasingly emphasized to
I. INTRODUCTION enhance patient care. Studies such as Johnson and Lee
The rapid advancement in healthcare technologies has (2021) highlight the importance of tailoring health
introduced a paradigm shift toward personalized medicine, interventions based on patient-specific data, including
where treatments and preventive measures are tailored to genetic, environmental, and behavioural factors. Despite
individual needs. WellnessGuard, a smart health monitoring progress, achieving true personalization remains a challenge
system, epitomizes this shift by combining IoT-enabled due to issues related to data integration, algorithmic
devices, artificial intelligence (AI), and real-time data accuracy, and user adoption.
analytics. This system bridges the gap between patient-
specific health metrics and actionable insights, empowering Ø Wearable Technology in Health Monitoring
individuals and healthcare professionals alike. Wearable devices like smartwatches and fitness trackers
have become integral to SHMS. Research by Nguyen et al.
Historically, health monitoring relied heavily on periodic (2019) has explored their role in tracking vital signs such as
checkups and subjective self-reporting. However, the heart rate, blood pressure, and activity levels. While
integration of wearable technology and smart sensors has wearables offer convenience, their effectiveness is often
revolutionized the continuous tracking of vital parameters. It constrained by battery life, data accuracy, and the inability to
leverages this evolution by integrating wearables with cloud- provide actionable insights.
based AI analytics, enabling early detection of anomalies and
personalized health recommendations. Switch learning, a Ø AI and Machine Learning in Health Systems
transformative approach within AI, addresses challenges Artificial intelligence (AI) and machine learning (ML) play a
associated with limited labelled data and computational pivotal role in enabling intelligent health monitoring. Prior
complexity. By leveraging pre-trained models, switch studies, including Zhang et al. (2022), have employed ML
learning enhances the generalization and accuracy of models to predict health conditions, detect anomalies, and
diagnostic algorithms, making it a cornerstone of its suggest preventive measures. However, these models
architecture. frequently suffer from a lack of generalizability across
diverse populations and insufficient real-world validation.
Neurological and chronic disorders, such as diabetes,
cardiovascular diseases, and neurodegenerative conditions, Ø Challenges in Data Privacy and Security
present significant global healthcare challenges. Smart health A critical barrier to the adoption of SHMS is ensuring data
monitoring systems hold the promise of not only detecting privacy and security. The works of Kumar et al. (2020) have
these conditions early but also personalizing interventions to discussed the vulnerabilities associated with transmitting
improve patient outcomes. This paper endeavours to and storing sensitive health data. Although blockchain and
synthesize existing literature and elucidate advancements, advanced encryption methods have been proposed, their
challenges, and opportunities in the utilization of smart integration into existing systems remains complex and
health monitoring for personalized care. resource intensive.

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