Wearable Biosensors and AI for Real-Time Health Monitoring

Abstract

Wearable biosensors and artificial intelligence (AI) are radically improving healthcare by facilitating continuous, non-intrusive, real-time monitoring of physiological and biochemical signals. Wearable biosensors are small, personal devices that can be worn on the body and gather metrics including heart rate, body temperature, blood glucose, and respiratory rate. Wearable biosensors lay the foundation for proactive, personalized healthcare. Chronic disease rates are on the rise, populations are aging across the globe, and there is need for smart systems that support remote care. Smart monitoring systems that utilize biosensors are being adopted for these reasons, especially AI-based monitoring.

AI allows for data generated by biosensors to be processed, and used reliably. Machine learning and deep learning models are particularly useful in processing biosensor data. Example machine learning algorithms include convolutional neural networks (CNNs) and recurrent neural networks (RNNs). Deep learning methods are able to remove background noise, feature extraction, data interpretation, and anomaly detection, along with retrospective analysis to predict health events including arrhythmia, stress episodes, hypoglycemia. AI and ML algorithms can use biosensor data for on-time feedback and decision support to clinicians with high amounts of specificity and sensitivity and low connectivity latency. Using Internet of Medical Things (IoMT) based infrastructure, wearable biosensors can securely transmit data privately to discrete clinical or edge cloud providers, to facilitate either clinician oversight of patients in real-time as they do their monitoring, or clinicians being forewarned to support early intervention when monitoring for acute patient events.

While these systems can be considered game-changing advancements which include many advantages, there still exists hurdles to adopting systems to improve healthcare.

There must be work done to address issues with data privacy, algorithmic biases, device interoperability, and sensor accuracy. Compliance with some privacy laws, such as HIPAA and GDPR is needed and the use of ethical and transparent AI models is necessary. Technical issues that must be addressed more generally include motion-induced signal noise, battery lifespan, and hardware-software integration.

In conclusion, wearable biosensors empowered by AI may represent a new frontier in personalized, real-time health monitoring that enables early recognition of health issues, remote supervision, and chronic disease monitoring while alleviating the need for traditional healthcare capacity. To realize the full potential of this functionality, subsequent research should strive to improve data security, model transparency, and system scalability, and to keep patients as the focus of innovation.