Intelligent wearable health monitoring and emergency response system

Abstract

An intelligent health monitoring and emergency response system consisting of a wearable IoT device with at least one biosensor for measuring physiological parameters such as heart rate, blood pressure, oxygen saturation, body temperature, and glucose levels; a data acquisition unit that is communicatively connected to the wearable device and preprocesses real-time health data and transmits it to a cloud server via a wireless communication module; and a cloud health data processing unit consisting of a data storage module for real-time and historical data and a data analysis module for processing stored health data using machine learning algorithms to detect anomalies that indicate potential health risks.

Description

AREA OF INVENTION

[0001] The invention relates to portable health monitoring devices that integrate the acquisition of biosignals by means of multiple sensors, wireless telemetry for storage in the cloud and machine learning for anomaly detection in order to trigger emergency alerts and responses in real time. BACKGROUND OF THE INVENTION

[0002] Continuous monitoring of physiological parameters such as heart rate, blood pressure, oxygen saturation, temperature, and glucose enables the early detection of deterioration. However, conventional systems rely on threshold rules with high false alarm rates and fragmented device-cloud integrations, which delay response. Current prototypes and validations demonstrate that wearable IoT devices can stream multimodal signals to cloud platforms for analysis and alerting. Smartphone connectivity and GPS enable notifications to caregivers and location-based emergency call alerts in the event of anomalies. Frameworks optimized for wearable IoT devices and health data improve sensitivity and specificity by modeling temporal patterns.The collaboration between edge and cloud devices reduces latency and ensures reliable operation even with intermittent connectivity in clinical and home environments. A device-centric architecture that combines a biosensor-equipped wearable, a data acquisition unit for preprocessing and secure data transmission, and a cloud analytics service with longitudinal storage and machine learning-based recognition provides a robust foundation for proactive healthcare management and rapid emergency response. SUMMARY OF THE INVENTION

[0003] The invention relates to an intelligent health monitoring and emergency response system. This system comprises a wearable IoT device with at least one biosensor for measuring heart rate, blood pressure, oxygen saturation, body temperature, and blood glucose; a data acquisition unit connected to the wearable device for preprocessing real-time data and transmitting it wirelessly to a cloud server; and a cloud-based health data processing unit with a storage module for real-time and historical data and a data analysis module that executes machine learning algorithms to detect anomalies indicating health risks. Upon detection of an anomaly, the system generates emergency alerts to designated contacts and services, optionally including geolocation for notification.Dashboards and a mobile app provide users with current status, historical trends and medically relevant summaries for timely intervention. DETAILED DESCRIPTION

[0004] The wearable IoT device integrates biosensors, including optical pulse oximetry, cuffless blood pressure measurement, skin or core body temperature measurement, and continuous or intermittent glucose monitoring. The data is pre-processed internally to filter noise and tag readings for reliable subsequent analysis and storage. The data acquisition unit communicates with the wearable via BLE or similar low-power connections and performs local validation, normalization, and feature extraction to reduce bandwidth and increase robustness. The data is then transmitted to the cloud data storage via Wi-Fi or cellular with authenticated sessions and encrypted transmission. The cloud storage module stores both streaming and historical datasets for longitudinal analysis and personalized baselines.It supports time-series queries that define custom thresholds and reduce false alarms compared to static limits. The data analytics module uses machine learning for anomaly detection, optimized for wearable data streams. It employs temporal models and hybrid classifiers that have been proven to improve detection accuracy in healthcare while optimizing latency and resource utilization between the edge and cloud. The alerting logic applies severity assessments and persistence checks to minimize unwanted notifications. Notifications are then sent to users, caregivers, and emergency services via various channels (push, SMS, voice call). Optionally, GPS coordinates and current signal snippets can be added for rapid triage and response.An administrative dashboard and a mobile patient app display real-time vital signs, anomaly flags, and trends, allow for contact and consent configuration, and generate practice-oriented reports for clinicians that summarize episodes and risk histories from the historical data store. Security and reliability are ensured through monitoring of the IoT network and devices with intrusion detection. Anomaly detection extends to network behavior to protect data integrity and availability in critical healthcare areas. The architecture supports edge-to-cloud collaboration, enabling basic, low-latency anomaly checks to be performed on the data acquisition unit, while complex models and their retraining are executed in the cloud to adapt to user-specific patterns and new patient groups without impacting device performance.The system is adaptable to different sensor configurations and user profiles, enabling the modular addition or removal of sensor modalities and the dynamic recalibration of the models over time as health conditions change, thus providing personalized monitoring. For protection and swift legal enforcement in Germany, the product is presented as a device / system with functional modules, in accordance with utility model practice, which permits product claims and, following formal examination, allows for rapid registration.

Claims

[1] An intelligent health monitoring and emergency response system comprising a wearable IoT device with at least one biosensor for measuring physiological parameters such as heart rate, blood pressure, oxygen saturation, body temperature and glucose levels; a data acquisition unit communicatively connected to the wearable device and preprocessing real-time health data and transmitting it to a cloud server via a wireless communication module; and a cloud health data processing unit comprising a data storage module for real-time and historical data and a data analytics module for processing stored health data using machine learning algorithms to detect anomalies that indicate potential health risks. [2] System according to claim 1, wherein the data acquisition unit performs normalization and feature extraction and low-latency anomaly screening at the network edge to reduce bandwidth and latency of alerts prior to cloud analysis. [3] System according to claim 1 or 2, wherein, upon detection of an anomaly, the system generates emergency notifications including optional geolocation to specific caregivers and services via multi-channel messaging and displays real-time status and historical summaries via a mobile application and a dashboard. [4] System according to one of the preceding claims, wherein the data analysis module uses temporal anomaly detection models trained on portable datasets to personalize baselines and reduce false positive results, while continuously updating thresholds based on historical data.