Machine Learning-Enhanced Anomaly Detection in Healthcare Monitoring:A Survey

The intersection of healthcare and technology has ushered in a new frontier for anomaly detection, particularly within the domains of Wireless Sensor Networks (WSNs) and the Internet of Things (loT). This survey paper ventures into the sophisticated realm of machine learningenhanced anomaly detection, pivotal for the security and operational integrity of healthcare monitoring systems. We synthesize cutting-edge research that employs a blend of supervised and unsupervised learning techniques, spotlighting a hybrid approach that marries autoencoders with XGBoost algorithms for the nuanced detection of anomalies in physiological data streams. Our exploration extends to the environmental sphere, where we assess the efficacy of a novel Anomaly Detection Framework tailored for large-scale sensing systems, proving its mettle in pinpointing emission irregularities. The paper also traverses the sustainability landscape, evaluating the application of contextual anomaly detection in energy management, bolstered by Power smiths' collaboration. We present a diverse array of anomaly detection algorithms, each tailored to meet the specific challenges of their application domains. These range from variance-based algorithms for standardizing sensor data to BRBAR for navigating the uncertainties inherent in sensor outputs, and from sophisticated outlier detection in voluminous sensor data to the integration of Support Vector Machines (SVM) and Yet Another Segmentation Algorithm (YASA) for refined activity recognition. The survey culminates with a forward-looking discussion on the future trajectory of research in WSNs and loT. It underscores the imperative to overcome challenges such as resource constraints and to harmonize anomaly detection with preventative techniques. Our vision includes the adoption of data stream mining techniques, the tailoring of anomaly detection methods to niche industries, and the rigorous selection of benchmark data for comprehensive evaluations. This survey not only categorizes existing techniques, models, and architectures but also serves as a beacon for researchers and practitioners steering through the complex waters of anomaly detection in sensor networks. It lays the groundwork for open research inquiries, heralding an era of enhanced anomaly detection methodologies poised to evolve with the technological advancements of our time.