Integrated health monitoring, alerting and surveillance system

Abstract

A health monitoring system consisting of a variety of sensors, a mobile communication module for uploading recorded health parameters to a cloud database, a processor communicatively connected to the cloud database for analyzing the parameters and identifying health anomalies, and an alarm device for issuing audio, visual, and haptic warnings upon anomaly detection.

Description

AREA OF INVENTION

[0001] The invention relates to health monitoring devices that integrate multi-sensor acquisition, mobile / GPRS telemetry, cloud databases and anomaly analysis to generate real-time alerts for proactive health management of the user. BACKGROUND OF THE INVENTION

[0002] Connected healthcare systems are increasingly relying on IoT sensors for the continuous monitoring of vital signs. However, fragmented architectures can suffer from latency, false alarms, and security vulnerabilities when connecting edge devices and performing large-scale cloud analytics. Cellular / GPRS modules with integrated GPS enable comprehensive connectivity for mobile or stationary applications. Development boards like the A9G offer compact GSM / GPRS solutions with positioning and UART / GPIO interfaces for rapid sensor integration and remote transmission of health data to cloud endpoints. Cloud-based anomaly detection frameworks demonstrate that distributing preprocessing and detection close to the sensors reduces latency and improves reliability. Simultaneously, cloud aggregation supports long-term analysis and centralized enforcement of alert and monitoring policies.A device-centric architecture that combines sensor inputs, secure uplink connections, cloud storage, anomaly detection, and multimodal alerts can provide users and caregivers with timely, actionable feedback while ensuring data integrity and scalability for health monitoring use cases. SUMMARY OF THE INVENTION

[0003] The invention relates to an integrated system comprising multiple health sensors, an A9G-class communication module connected to the sensors, a cloud database for storage and analysis, a processor that receives parameters from the cloud, and an alarm system that issues multimodal notifications in the event of anomalies. The communication module transmits the measured values ​​via GSM / GPRS, while the processor executes anomaly detection algorithms that identify deviations from expected ranges or learned reference values ​​and generate alerts that are delivered via audio, visual, and haptic feedback to enable a timely response. The system supports configurable sensor sets, secure transmission with authenticated delivery, and long-term tracking for trend visualization. This enables personalized monitoring in various application scenarios with robust and scalable operation. DETAILED DESCRIPTION

[0004] The device includes interchangeable health sensors such as heart rate, blood pressure, blood sugar, or motion sensors, which are connected via ADC, I 2The A9G development platform communicates via C or UART with a local microcontroller connected to a cellular / GPRS module. It offers quad-band GSM / GPRS and GPS / BDS positioning with UART, GPIO, and antenna interfaces suitable for remote telemetry and device management. The communication module packages sensor readings with timestamps and optional location metadata and transmits them over GPRS to a cloud database using secure protocols. This enables remote access for analytics and dashboards and supports SMS / voice fallback in case of data limitations. At the cloud level, storage services manage user profiles, device identities, and time-series health data. Anomaly analysis applies thresholds, statistical models, or trained detectors to detect subtle deviations. This aligns with IoT anomaly frameworks that strike a balance between sensitivity and minimizing false alarms.A processor component connected to the cloud service orchestrates data retrieval, executes classification or detection models, and prioritizes alerts based on severity and persistence. Cloud-edge collaboration patterns are used to minimize latency and ensure consistent data schemas even under abnormal conditions. The alert system delivers multimodal outputs, including audible signals, visual on-screen warnings, and haptic cues on a wearable or mobile device, to ensure user attention in various contexts.

[0005] Alerts can be mirrored on dashboards for caregivers, along with escalation policies. Device provisioning and security include authenticated SIM / device registration, encrypted transmission, signed firmware updates, and access controls to protect health data and maintain system integrity, even with large device fleets. The system supports continuous data collection and configurable sampling strategies to balance energy consumption and data quality. Local preprocessing reduces noise and normalizes signals before transmission, improving anomaly detection performance and reducing bandwidth. Historical data collection visualizes trends and histories of vital signs and derived measurements.This enables the early detection of deterioration and the setting of personalized thresholds that adapt to individual baseline values ​​over time. Maintenance tools support remote diagnostics, module system checks, and watchdog resets, thus improving availability in home and community settings. GPS / BDS telemetry can support context-aware alerts and caregiver location tracking in mobile applications. The modular architecture allows for the integration of additional sensors or analytics tools. This enables the inclusion of new parameters and predictive models, as well as—if required—the integration of clinical systems for advanced monitoring capabilities.

Claims

[1] A health monitoring system consisting of a variety of sensors, a mobile communication module for uploading recorded health parameters to a cloud database, a processor communicatively connected to the cloud database for analyzing the parameters and identifying health anomalies, and an alarm device for issuing audio, visual and haptic alerts upon anomaly detection. [2] System according to claim 1, wherein the mobile communication module comprises a GSM / GPRS and GPS / BDS development board of class A9G, which provides UART / GPIO interfaces for sensor coupling and secure telemetry to the cloud. [3] System according to claim 1, wherein the processor executes anomaly detection algorithms tailored to IoT health data, utilizing edge preprocessing and cloud aggregation to reduce latency and false alarms while maintaining longitudinal analysis. [4] System according to claim 1, wherein the cloud database manages user profiles and historical health records and provides dashboards for trend visualization and alarm management with authenticated device provisioning and encrypted transmission.