Description:
FIELD OF THE INVENTION

[001] Today’s emergency scenarios demand more than just conventional connectivity—they require seamless coordination of communication and patient data, even in the absence of infrastructure. In the evolving landscape of healthcare emergencies, systems must be resilient, responsive, and network-agnostic to serve vulnerable populations in critical moments. This invention concerns emergency communication systems that integrate long-range wireless technologies with centralized digital health record repositories. More specifically, the invention pertains to emergency response and healthcare monitoring solutions leveraging these wireless networks and secure health data systems.

[002] In particular, the invention relates to a system and method utilizing LoRa embedded device or LoRaWAN (Long Range Wide Area Network) communication protocols and DigiLocker/ABHA-linked health data retrieval for providing real-time, infrastructure-independent emergency alerts, patient information dissemination, and coordinated multi-platform notifications.

[003] The invention enables emergency medical response through automated patient data access, incident detection, and secure multi-channel notification delivery across smartphones, feature phones, smart wearables, hospital systems, public displays, and emergency services.

[004] The system is particularly suited for use in disaster-prone regions, rural healthcare delivery, pandemic outbreak management, elderly or disabled patient monitoring, and areas with unreliable cellular and broadband infrastructure.

[005] The invention is designed with future developments in mind. It leaves room for integration with emerging technologies like AI-driven emergency response systems, both public and private 5G and 6G networks, and secure communication protocols resistant to quantum threats. It also considers the use of blockchain for managing medical records, as well as compatibility with smart city systems that need to talk to each other. This forward-looking approach ensures that the solution remains adaptable, scalable, and relevant—not just within India’s Ayushman Bharat Digital Mission (ABDM), but also across global health and disaster response frameworks.

[006] The invention integrates a zero-trust security architecture wherein each participating device is continuously authenticated in real time, with encryption keys generated per session and validated through embedded hardware, ensuring that sensitive health data remains protected from unauthorized access, spoofing, or breach within the emergency mesh network.

BACKGROUND OF THE INVENTION

[007] Emergency communication and healthcare response systems have traditionally relied on centralized infrastructure—whether that’s mobile networks, internet services, or satellite links. The irony is, those very systems tend to fail when they’re needed the most. Natural disasters or major disruptions often damage network towers or overload bandwidth, rendering conventional channels unreliable or completely unavailable.

[008] In public emergencies—ranging from floods, earthquakes, and cyclones to pandemics or sudden medical incidents like heart attacks or accidental falls—real-time communication becomes a lifeline. But just when it’s most critical, traditional cellular or broadband services often collapse under the pressure. The result is a troubling communications gap, one that can delay help, hinder coordination, and put lives at further risk.

[009] Traditional public alert mechanisms, like the Wireless Emergency Alerts (WEA) used in the United States or India’s SMS-based disaster notification services, are entirely dependent on functioning mobile networks. The moment these networks fail—due to infrastructure damage, congestion, or blackout—the alerts also fail to reach those in need. Moreover, these systems operate in one direction only: they push messages outward but cannot collect responses, reroute messages through alternate paths, or enable devices in the field to talk to each other.

[0010] On an individual level, many emergency wearables—like fall detectors, SOS wristbands, and smartwatches with health sensors—rely heavily on Bluetooth or internet connectivity through a paired smartphone. This setup works well under normal conditions, but in a real emergency, it's fragile. If the phone is out of range or mobile data is unavailable, these devices can’t send help requests across any real distance, rendering them nearly useless when it matters most.

[0011] Even when alerts do go out, they often lack critical context. There’s rarely any automatic access to the patient’s medical background—no history, no allergy records, no real-time vitals. First responders are left guessing, especially when the person in need is unconscious or unable to speak. This delay in information can slow down triage decisions and delay potentially life-saving treatment.

[0012] In India, the Ayushman Bharat Digital Mission (ABDM) has introduced the ABHA — a 14-digit unique Health ID linking citizens’ personal health records via DigiLocker, a secure government-regulated digital document repository. Although the infrastructure for digital health data exists, its real-time, automated retrieval in emergency situations has not been effectively integrated with IoT-based emergency response systems.

[0013] Current practice requires manual ABHA code scanning by healthcare providers or explicit patient consent for record access. There is no system that triggers automatic record retrieval on the detection of an emergency incident by a wearable or IoT device, nor any provision for real-time propagation of this critical data to hospitals, first responders, and family members simultaneously across multiple device types and network layers.

[0014] Prior attempts at integrated emergency systems, such as proprietary medical alert devices or emergency call centers, have been hindered by limited network coverage, lack of interoperability between medical records databases and personal alert devices, absence of long-range peer-to-peer relaying capability, or absence of structured health data retrieval frameworks linked to personal IDs.

[0015] Existing healthcare wearables typically notify only a predefined set of contacts via short-range or cloud-linked alerts without providing relevant health information, resulting in incomplete, non-contextual emergency notifications.

[0016] In a disaster zone scenario where power grids and cellular infrastructure collapse, conventional mobile-based emergency alerts cease to function, leaving vulnerable populations — including the elderly, chronically ill, and rural patients — without an effective communication channel to reach medical services or inform caretakers of their status and health risks.

[0017] The core technical challenge lies in establishing a secure, infrastructure-independent emergency communication system that functions reliably in low or no-connectivity zones using long-range, battery-efficient wireless protocols, while also enabling automated retrieval and seamless dissemination of authenticated medical records from platforms like DigiLocker or ABHA into multi-platform emergency response workflows.

[0018] While various systems exist for emergency alerting and health data access, they fail to combine resilient communication, real-time health record retrieval, and infrastructure-independent propagation across heterogeneous networks in a single, interoperable multi-network emergency healthcare communication framework incorporating LoRaWAN-based IoT mesh networking with real-time ABHA-linked patient data retrieval, secure data propagation, and decentralized alert dissemination through diverse output channels such as hospitals, first responders, feature phones, public displays, and situational mapping for emergency teams.

[0019] Accordingly, there exists a long-felt and unmet technical need in the field of public safety and healthcare informatics for an integrated system capable of bridging the communication and information gap during emergencies by combining resilient, low-power long-range wireless networks with secure, policy-compliant digital health data retrieval and multi-channel alert propagation, adaptable for both India’s ABDM framework and international emergency health response models.

SUMMARY OF THE INVENTION

[0020] To address these long-standing gaps, this invention brings together emerging technologies and national healthcare frameworks into a unified, infrastructure-resilient solution. This invention introduces a multi-network, patient-integrated emergency healthcare response system and method that overcomes the aforementioned limitations by integrating long-range wireless communication technologies, specifically LoRaWAN, with India’s national digital health infrastructure, notably the Ayushman Bharat Health Account (ABHA) and DigiLocker health record repository.

[0021] The system includes a plurality of LoRaWAN-enabled devices such as smartwatches, health monitors, smartphones, and IoT nodes, each equipped with embedded sensors such as accelerometers, gyroscopes, pulse oximeters, heart rate monitors, and explicit panic buttons. These devices continuously monitor for emergency indicators such as fall events, abnormal vital signs, or manual distress signals, utilizing ultra-low power algorithms for threshold-based event detection.

[0022] Once an emergency is detected—be it a fall, irregular vitals, or a panic trigger—the device’s embedded LoRaWAN module is activated and securely transmits an encrypted, prioritized alert packet to the nearest LoRaWAN gateway. The gateway forwards the alert to a network server, which authenticates and routes the data payload to a secure cloud-hosted application server.

[0023] The application server, upon receiving the alert and identifying the affected individual via a unique user identifier (such as an ABHA number), automatically establishes a secure, OAuth 2.0-authenticated connection with the DigiLocker API and queries for the user’s digital health records.

[0024] Relevant patient data — including recent prescriptions, chronic conditions, known allergies, lab reports, and pre-existing medical history — is retrieved in structured formats (e.g., HL7 FHIR or JSON) via encrypted channels and integrated into a real-time emergency dossier.

[0025] The system then coordinates the simultaneous, multi-channel delivery of emergency alerts and the compiled patient dossier to various recipients, including the patient’s registered emergency contacts via SMS, voice call, and push notification; nearby hospitals through secure HL7/FHIR APIs; first responders using mobile or dispatch applications; and public safety infrastructure such as digital signboards, loudspeakers, and IoT-enabled displays via LoRaWAN or internet-based networks.

[0026] The system further enables coordination between responders by clustering multiple incident reports within a geographic area, providing situational awareness to disaster management authorities through a real-time emergency response dashboard.

[0027] A key feature of the invention is its infrastructure-independent operational resilience. By leveraging LoRaWAN’s long-range, battery-efficient, infrastructure-agnostic mesh networking, emergency alerts can be propagated even in the absence of cellular or broadband connectivity, ensuring life-critical communication remains functional during disasters, power outages, or remote area deployments.

[0028] The invention incorporates advanced capabilities such as AI-driven incident prioritization based on biometric data patterns, blockchain-backed emergency logging for tamper-proof audits, tunable multi-band LoRa antennas for region-specific ISM compliance, seamless integration with 5G/6G networks, and dynamic switching across LoRa, cellular, satellite, and Wi-Fi links based on real-time network health assessment.

[0029] The system is further designed to adhere to Indian data privacy regulations, ABHA/ABDM policy frameworks, and international standards for medical device communication and interoperability, ensuring safe, secure, and scalable deployment within India and other jurisdictions.

[0030] Together, these innovations form a powerful, real-time safety net for patients, responders, and health systems. By integrating hardware sensors, long-range wireless communication, secure digital health record retrieval, and multi-platform emergency notification, the invention provides a comprehensive, technically superior, future-proof, and policy-compliant emergency healthcare response ecosystem.

BRIEF DESCRIPTION OF DRAWINGS

[0031] To help visualize the structure and operation of the proposed system, the following figures are provided as illustrative examples. These drawings offer a clearer understanding of the invention’s key components and workflow based on preferred embodiments. They are intended solely to aid explanation and should not be interpreted as limiting the scope of the invention outlined in the accompanying claims.

[0032] Figure 1 is a flowchart illustrating the emergency detection and alert workflow. It depicts the sequence where a LoRa-enabled wearable device detects an emergency event (such as a fall, abnormal heart rate, or manual SOS trigger), transmits a LoRaWAN message to the nearest gateway, initiates automatic retrieval of the user’s health records from DigiLocker using the ABHA identity, and propagates encrypted multi-channel notifications to medical facilities, personal contacts, and public alert systems.
Figure 1 is also submitted as ‘Figure of Abstract’.

[0033] Figure 2 is a system architecture block diagram depicting the main components of the invention. It illustrates LoRaWAN end devices (including smartphones, smartwatches, IoT sensors), a LoRaWAN gateway interfacing with the Internet, a cloud-based application server managing alert processing, secure API-based integration with DigiLocker and ABHA health records, and various notification delivery channels such as mobile phones, hospital emergency room systems, public displays, and emergency management dashboards.

[0034] Figure 3 is a timeline diagram showing the chronological sequence of operations from the moment a sensor anomaly is detected by the wearable device to the generation and delivery of emergency notifications and patient data to the intended recipients, concluding with the acknowledgment of receipt and optional escalation mechanisms.

[0035] Figure 4 is a schematic diagram illustrating the internal hardware architecture of a typical LoRaWAN-enabled wearable device used in the system. It details the interconnection of motion sensors (accelerometers, gyroscopes), biometric sensors (heart-rate, SpO₂, temperature), LoRaWAN transceiver module, microcontroller unit (MCU), GPS receiver (optional), memory, and battery management circuitry, emphasizing the integration of hardware components necessary to trigger, encrypt, and transmit emergency messages.

[0036] Figure 5 is a situational map view generated by the emergency response dashboard application. It displays clustered emergency events on a geographic grid, color-coded by severity or event type (e.g., cardiac alert, fall, trauma). Each node displays real-time status, patient identity (ABHA code), current vital data, and proximity to responding teams, enabling efficient dispatch allocation during mass casualty incidents or disaster scenarios.

DETAILED DESCRIPTION OF THE INVENTION

[0037] The present invention pertains to an integrated system for emergency healthcare response, incorporating long-range wireless communication modules, secure access to digital health records, and multi-channel mechanisms for issuing emergency notifications across various platforms. The invention combines hardware, communication protocols, application servers, secure APIs, and policy-driven data privacy safeguards into a unified operational architecture.

[0038] The system is structured across three core functional tiers: (i) LoRaWAN-enabled end devices such as wearables, smartphones, and IoT-based health monitors; (ii) LoRaWAN network infrastructure comprising gateways and network servers; and (iii) cloud-hosted application servers integrated with national digital health platforms like DigiLocker and ABHA for secure data access and coordination.

[0039] These components work in concert to detect emergency events, transmit secured alerts over infrastructure-independent networks, retrieve the affected individual’s authenticated health records, and propagate emergency notifications across multiple communication channels.

[0040] The system employs a diverse range of LoRaWAN-capable end devices, including smartwatches with built-in accelerometers, gyroscopes, heart-rate and SpO₂ sensors, and emergency panic buttons; IoT sensor nodes featuring environmental, motion, or biometric detectors; and smartphones or tablets equipped with either embedded or externally retrofitted LoRaWAN modules.

[0041] Each device includes a microcontroller (e.g., ARM Cortex M0/M4 class MCU) for local sensor data acquisition and event detection algorithms. These devices operate in ultra-low-power mode, periodically sampling sensor data or remaining in passive standby, activating upon anomaly detection.

[0042] Upon detecting an emergency event—such as a fall, cardiac anomaly, or manual distress trigger—the device instantly generates an alert packet containing the user’s unique identifier (e.g., ABHA number), the type and severity of the event, a timestamp, optional GPS coordinates if available, and key device status indicators including battery level and signal strength.

[0043] The LoRaWAN radio module, operating in ISM bands (typically 865-867 MHz in India), transmits the alert packet using AES-128 encryption as per LoRaWAN security specifications.

[0044] The device firmware is designed to minimize power consumption, activating the radio only upon critical events and reverting to deep sleep post-transmission. To extend device uptime without compromising sensitivity, the system uses a duty-cycled operational mode. During normal conditions, biometric sensors function at low frequency to conserve power. However, if the system detects pre-emergency signals—like arrhythmia, unusual acceleration, or high stress indicators—it instantly switches to high-frequency sampling. This dynamic approach ensures that the device remains alert while optimizing battery life in standby phases.

[0045] LoRaWAN Network Infrastructure: LoRaWAN gateways are strategically deployed fixed or mobile units receiving RF packets from end devices and forwarding them via Internet (using TCP/IP) to a Network Server.

[0046] The Network Server performs: Decryption of the network-layer message using the device-specific network session key. To ensure efficient use of bandwidth, especially in narrowband LoRa environments, the system implements edge-level data compression. Medical telemetry such as ECG, SpO₂ trends, or motion graphs are compressed using lightweight, lossless algorithms before uplink. This reduces payload size and transmission time while maintaining the integrity of medical data.

[0047] Authentication of the end device. Forwarding of the encrypted application-layer payload to the cloud-hosted Application Server.

[0048] In more advanced configurations, the system supports AI-based self-healing mesh logic, where connected devices intelligently reconfigure their communication paths based on link degradation, battery life, or physical interference—ensuring message continuity even amid failures. This allows the system to maintain continuity of alerts even when some nodes or gateways become non-functional.

[0049] Gateways may be carrier-operated public infrastructure or privately managed by hospitals, municipal agencies, or emergency services.

[0050] Cloud Application Server: The Application Server represents the core intelligence of the system, responsible for:

[0051] The system processes incoming emergency messages by identifying the affected user, retrieving their health records from DigiLocker or ABHA, and composing multi-channel notifications for dispatch; notably, in one configuration, biometric data remains stored locally and is uploaded to cloud servers only with explicit patient consent, thereby upholding privacy-by-design principles.

[0052] Health Record Retrieval Process: Upon receiving an emergency message, the server identifies the user’s ABHA-linked health ID embedded in the message.

[0053] The server initiates a secure HTTPS/TLS connection to the DigiLocker API endpoint, using OAuth 2.0 client credentials authorized for emergency access as per ABDM guidelines.

[0054] A request is issued for critical medical records such as allergies, medications, chronic conditions, recent diagnostic reports, vaccination history, and blood group. In network-challenged environments, the system is capable of forming a multi-hop mesh using nearby LoRa-enabled devices. If a primary gateway is inaccessible due to terrain or infrastructure failure, emergency data packets are automatically rerouted through neighboring devices until they reach an active relay node. This distributed relay mechanism ensures message delivery continuity even in remote or disrupted regions.

[0055] DigiLocker, upon verifying access permissions, returns encrypted, structured health data in HL7 FHIR-compliant JSON format, or secure PDFs if unstructured.

[0056] Recognizing the sensitive nature of health information shared during emergencies, the system incorporates enhanced data protection mechanisms to uphold patient confidentiality. In one embodiment, medical records are encrypted using a public-private key infrastructure, where the private key is securely stored on the patient’s wearable device. This implementation follows a zero-trust security framework, ensuring that even if external servers or cloud infrastructure are compromised, unauthorized access to patient data is prevented. Decryption is only possible through authenticated access to the patient-held key, aligning the system with contemporary best practices in zero-trust architecture and regulatory privacy standards.

[0057] To strengthen defenses against physical tampering and hardware-level exploits, each wearable or portable device is equipped with a secure bootloader and a hardware root-of-trust. During startup, the system performs a cryptographic integrity check using embedded one-time programmable (OTP) fuses, allowing only verified and signed firmware to execute. This mechanism effectively prevents unauthorized code execution, malware injection, or firmware manipulation—even under adverse or hostile conditions. Additionally, the device housing integrates tamper-evident seals and embedded intrusion detection sensors. If any unauthorized attempt is made to open or alter the casing, the firmware responds by disabling all communication interfaces, securely wiping stored cryptographic keys, and logging the incident locally. Reactivation is only possible through a trusted reset protocol that requires validated, user-specific credentials, thereby preserving the system’s integrity and preventing misuse.

[0058] To strengthen system integrity, a zero-trust architecture is implemented, requiring each mesh-connected device to undergo identity verification before participating in data exchange. Rather than relying on predefined trust levels, the architecture ensures that every session is individually authenticated. Encrypted keys are issued per transaction and expire post-operation, reducing the risk of spoofing, lateral movement, or unauthorized access. Key rotation and validation are anchored within secure hardware modules embedded in the device.

[0059] The server decrypts and parses this data, extracting essential fields and assembling a patient emergency dossier.

[0060] The system includes a blockchain-based consent escrow mechanism where users can pre-authorize specific entities (e.g., hospitals, relatives, paramedics) for health record access during emergencies. This escrow logic ensures that in the absence of manual consent, access can still be granted under predefined, legally compliant emergency clauses.

[0061]The consent policies are stored immutably, supporting audits and privacy law compliance under India’s DPDP Act and GDPR-like frameworks.

[0062] The system features a multi-channel notification mechanism wherein the application server concurrently disseminates emergency alerts and patient information to multiple recipient groups, including the user’s registered emergency contacts via SMS, voice call, or push notification; nearby hospitals and emergency medical services through secure APIs or HL7/FHIR interfaces; first responders via mobile applications and connected devices with real-time location, event specifics, and health summaries; public digital signage and IoT-enabled display boards using LoRaWAN or internet-based control APIs; and municipal emergency dashboards with geo-tagged incident mapping for centralized coordination and situational awareness.

[0063] Notification messages include patient identification, event type, location, summary health data, and secure access links to full medical records, where authorized.

[0064] To enhance accessibility, the system can auto-localize alerts into regional Indian languages based on GPS data—using NLP modules that translate in real time. This ensures that emergency messages reach citizens in a language they understand, increasing effectiveness during public crises. Geo-fencing and GPS-based logic identify the linguistic region of the event, triggering real-time translation of broadcast messages into local Indian languages (e.g., Hindi, Marathi, Tamil, Bengali) using embedded NLP modules. This enhances accessibility and comprehension during public emergency announcements.

[0065] The system incorporates failover mechanisms: If a LoRaWAN message is not acknowledged within a preset time, alternative paths via SMS or satellite modems (if available) are triggered.

[0066] In fully disconnected zones, the wearable or local gateway node temporarily enters an offline mode, wherein data packets—including biometric vitals and timestamps—are locally cached and timestamp-signed using embedded real-time clocks. Once connectivity is restored, the node performs a burst transmission to sync queued alerts, ensuring no loss of continuity.

[0067] Multi-hop mesh forwarding capability can be supported on certain LoRa devices to extend coverage in absence of fixed gateways.

[0068] AI-based analytics modules predict event severity and prioritize notifications based on biometric trends, event clusters, or historical patient vulnerability.

[0069] The system optionally implements a role-based escalation matrix, wherein different categories of emergency responders (e.g., paramedics, district medical officers, disaster command centers) receive differentiated levels of alerts based on event type, severity index, and geographic cluster density. This ensures that minor health deviations do not overwhelm high-tier resources, preserving bandwidth for critical interventions.

[0070] To improve triage accuracy, the AI engine builds situational profiles based on multiple parameters like location, historical data, and time—allowing responders to prioritize effectively. These profiles influence triage priority and routing decisions. For example, a cardiac event at high altitude may be escalated faster than a similar event at sea level.

[0071] In one embodiment, a blockchain ledger is employed to maintain an immutable record of emergency events. However, in alternate configurations, this ledger may be implemented using a secure cloud-based database with access control auditing. The device supports tunable multi-band LoRa antenna configurations, enabling operation across a wide range of international ISM frequencies. These include, but are not limited to, 137 MHz, 169 MHz, 315 MHz, 433 MHz, 470–510 MHz, 779–787 MHz, 865–867 MHz, 868 MHz, 915 MHz, and 923–928 MHz. This flexibility allows the system to adapt to regional spectrum regulations and deployment environments across diverse geographies.

[0072] Integration with private or public 5G/6G networks is envisioned, enabling devices to dynamically select available communication paths based on real-time link quality assessment. To ensure data privacy without compromising intelligence, the system supports a federated learning model. Instead of uploading raw biometric data to a centralized server, each device trains localized models based on regional health patterns and user history. Periodically, only the model updates—not personal data—are synchronized with the server. This allows devices in a given geography to become smarter over time while respecting user sovereignty and regulatory constraints.

[0073] In a further embodiment, the system supports dynamic spectrum sharing and uplink switching. When ISM bands used for LoRaWAN experience interference or congestion, devices can fallback to 5G NR narrowband uplinks using licensed spectrum where available. A real-time link quality monitor determines optimal transmission paths.This may be based on signal strength (RSSI), battery status, channel congestion, or link latency, allowing intelligent fallback to available bandwidth-efficient links.

[0074] The wearable devices optionally incorporate on-device artificial intelligence (AI) or machine learning (ML) models, pre-trained to recognize early-stage biometric trend deviations predictive of health deterioration events such as cardiac arrest, respiratory failure, or hypoglycemia. The AI models run on low-power microcontrollers using quantized neural networks or lightweight anomaly detection algorithms, enabling predictive event alerts before critical thresholds are breached.

[0075] In a further embodiment, the system incorporates a crowdsourced emergency mapping feature. LoRaWAN-enabled devices operating within proximity to a detected emergency can voluntarily contribute real-time metadata such as location, signal health, and device status. These inputs are anonymously aggregated to generate a live emergency heatmap displayed on municipal and responder dashboards. Such peer-contributed intelligence enables situational awareness across affected zones and supports smarter allocation of emergency resources during mass casualty incidents, natural disasters, or infrastructure failures.

[0076] In an optional embodiment, wearable devices incorporate on-device federated learning models that allow the system to personalize biometric thresholds based on the user’s long-term physiological patterns. The system refines anomaly detection algorithms for individual users by training lightweight neural networks across a distributed cohort without exposing raw health data, thus preserving user privacy while enhancing prediction accuracy. The system utilizes low-power frameworks such as TinyML or TensorFlow Lite Micro, and training updates may occur during scheduled synchronization periods over Wi-Fi or cellular links when available.

[0077] The system supports continual on-device learning, enabling devices to update emergency response thresholds dynamically, which is particularly beneficial for aging populations or chronic disease patients whose baselines may shift over time.

[0078] Advanced biometric sensing and signal processing modules estimate the emotional state of the wearer using heart-rate variability, skin temperature, and motion signatures. In scenarios indicating high emotional distress (e.g., panic or fear), the device may preemptively trigger low-priority alerts for monitoring or initiate video capture for responder context. Recognizing that medical emergencies involve sensitive personal data, the system incorporates a dynamic consent framework. Users can pre-authorize specific actions, such as sharing vital signs with hospitals or notifying family members. Each consent instance is digitally signed, timestamped, and revocable, ensuring compliance with real-time health information exchange laws and ethical principles of patient autonomy.

[0079] Certain wearable devices are optionally equipped with miniature, body-worn cameras capable of recording and transmitting encrypted video streams of the surrounding environment during emergency events, enhancing situational awareness for first responders.

[0080] The application server is further configured with a role-based access control system ensuring that retrieved medical records are selectively accessible to authorized recipients based on predefined emergency role profiles (e.g., paramedics, hospital ER teams, municipal authorities), thus ensuring compliance with data privacy frameworks during incident triage.

[0081] The architecture includes developer-facing APIs and SDK toolkits for third-party integration. Authorized OEMs, healthcare app providers, or public safety agencies may securely plug into the system using tokenized onboarding and defined data scopes, enabling cross-platform functionality while maintaining data sovereignty and privacy.

[0082] In another embodiment, the system incorporates a context-aware incident reconstruction module that combines biometric sensor logs, timestamped motion data, optional video streams, and environmental sensor inputs to generate a detailed account of the moments leading up to an emergency. This reconstruction, processed either on-device or via cloud computation post-synchronization, allows medical staff to not only receive real-time vitals but also understand possible causes—such as whether a fall occurred from standing or walking, whether the patient showed signs of prior dizziness, or whether gas levels spiked nearby. Such evidence-based triage insights improve diagnostic decisions, facilitate post-incident investigations in cases of industrial accidents or elder abuse, and may be archived in the blockchain log for forensic-grade compliance. In high-risk public environments like elderly care homes, remote tribal clinics, or hazardous workplaces, this feature provides both real-time benefit and retrospective safety insights. The reconstruction engine respects privacy norms by working with anonymized or consented data under dynamically enforced access scopes tied to patient profiles and incident types.

[0083] The system allows configurable emergency response protocols specific to disaster scenarios such as earthquakes, floods, heatwaves, or industrial gas leaks, with customizable triage priorities, notification templates, and IoT data correlation triggers.

[0084] Disaster Scenario: The following examples illustrate how this invention operates in real-world emergencies—showcasing its ability to fill existing systemic gaps with life-saving precision. In a flood-affected village without cellular service, elderly patients wearing LoRa-enabled smartbands trigger automatic fall alerts. Messages are picked up by rescue boats carrying mobile LoRaWAN gateways mounted on rescue boats, relayed to hospitals with real-time medical history via DigiLocker.

[0085] Pandemic Monitoring: Health workers with LoRa wristbands reporting fevers auto-transmit temperature data and retrieve chronic conditions (e.g. COPD) from DigiLocker for physician review before triage. In disaster-prone zones, the system can auto-adapt its logic based on the type of hazard. For instance, in flood-prone areas, the system prioritizes drowning risk indicators like abrupt motion cessation or irregular breathing. In earthquake zones, it boosts fall detection and postural instability tracking. Such geo-contextual calibration enhances detection accuracy when infrastructure is under stress.

[0086] Elderly Fall Detection: A senior citizen falls at home; a wrist-worn device detects the event, triggers a LoRa alert, retrieves medication data, and notifies paramedics and family simultaneously.

[0087] Ambulance Pre-Notification: During ambulance transfer, a mobile LoRaWAN gateway receives the patient’s vital data and retrieves allergy records pre-arrival at the hospital, improving emergency care preparedness.

[0088] Remote Village Clinic: Health workers use rugged tablets with LoRa modules to transmit patient ECG readings and retrieve ABHA-linked records from DigiLocker for remote cardiology consultation via the LoRa mesh network.

[0089] Drowning Detection Use-Case: Wearable devices equipped with motion pattern recognition detect drowning movements in water bodies. On detecting abnormal aquatic motion, an emergency alert is triggered, optionally correlating with environmental sensor nodes measuring water parameters.

[0090] Industrial Site Hazard Anticipation: In factory or industrial sites, wearable devices capture biometric deviations in conjunction with IoT gas sensors or environmental monitors. AI modules on the wearable predict collapse or health deterioration caused by hazardous gas exposure or physical stress, triggering preemptive alerts.

[0091] Drone-Based Temporary LoRaWAN Network: In disaster-affected zones with collapsed infrastructure, mobile LoRaWAN gateways mounted on drones or ground-based emergency vehicles such as ambulances, buses, or municipal response vans, providing temporary communication coverage in disaster-affected zones. This allows triage decisions to be made even without cloud access, significantly improving time-to-response in disaster scenarios.

[0092] In an alternate embodiment, the system may be embedded into automotive dashboards, especially in ambulances, emergency vehicles, or elderly transport vans. This allows direct vehicle-level sensing of occupant distress, roadside accident alerts, and integration with hospital navigation systems for route-based triage.

[0093] The system can be configurable for integration with future communication technologies, including but not limited to private and public 5G/6G networks, satellite IoT links, and smart city grid infrastructure. The system also supports tunable multi-band antenna systems and multi-hop mesh networking protocols to extend operability across diverse geographies and emergency scenarios.

[0094] Encryption protocols employed include AES-128 for LoRaWAN payload encryption and TLS 1.3 for API-level communication security. The LoRaWAN network architecture supports Class A, B, and C end device specifications as defined by the LoRa Alliance.

[0095] The system further conforms to India’s ABDM sandbox and data privacy frameworks, employing dynamic OAuth 2.0 authorization and consent-driven health data access.

[0096] In alternate embodiments, the system can employ wireless protocols including but not limited to Bluetooth Mesh, Wi-Fi HaLow, NB-IoT, or ZigBee in place of, or alongside, LoRaWAN. Similarly, additional biometric sensing technologies such as galvanic skin response (GSR), wearable EEG sensors, or hydration-level monitors may be integrated.
These variations are within the spirit and scope of the invention and intended to future-proof the system against evolving standards and form factors.

LEGAL AND TECHNICAL ADVANTAGES OF THE INVENTION

[0097] No prior art integrates infrastructure-independent multi-network emergency communication with authenticated ABHA record retrieval, AI event clustering, blockchain logging, and multi-hop LoRa mesh. For broader institutional adoption, the system is compatible with electronic health records (EHR) and clinical trial platforms. APIs are provided to integrate with eSanjeevani, ABHA-linked hospitals, and global systems like Epic or Cerner. This not only supports operational use in hospitals but also enables longitudinal studies and medical device validations under regulatory guidelines.

[0098] The Inventive step combines multiple individually known elements (LoRaWAN, health records, AI, blockchain) in an unconventional, operationally synergistic architecture.

[0099] The disclosed invention is not a mere algorithm or standalone software program; rather, it constitutes a hardware-software integrated system that operates across both physical and digital layers, involving embedded sensors, communication modules, and real-time processing units to enable tangible, infrastructure-independent emergency response.

[0100] The system architecture adheres to prevailing data protection standards, including India's Digital Personal Data Protection Act, 2023, GDPR-equivalent international norms, ABDM sandbox security protocols, and the ABHA consent-driven data exchange framework, ensuring lawful, ethical, and privacy-preserving handling of personal health information.

[0101] In jurisdictions outside India, the system is adaptable to equivalent digital health ecosystems such as Blue Button (USA), My Health Record (Australia), or NHS Spine (UK), with local privacy, consent, and data residency frameworks mapped accordingly. This facilitates international patent prosecution under PCT and improves licensing scope.

[0102] The system introduces federated learning for personalized event detection, enhancing device intelligence while preserving privacy.The system utilizes low-power frameworks such as TinyML or TensorFlow Lite Micro, and training updates may occur during scheduled synchronization periods over Wi-Fi or cellular links when available.

[0103] The claims are structured to avoid single-point design-around risks by incorporating optional but significant features (e.g., blockchain, edge AI, and consent-based record access).

[0104] As cybersecurity paradigms continue to evolve, the invention is designed to remain future-proof by anticipating integration with quantum-resistant encryption protocols. Such forward compatibility ensures that sensitive medical communications remain secure even in the face of quantum computing threats, thereby reinforcing the system’s long-term suitability for deployment in public health, defense, emergency response, and other critical infrastructure domains.

[0105] Unlike existing solutions which treat emergency detection, communication, and health data access as isolated layers, this invention’s synergistic convergence of decentralized communication, real-time medical data automation, and intelligent triage escalation offers a unified, robust, and life-saving intervention platform that cannot be trivially substituted by mere software or conventional networks.

, Claims:
1. A Communication System for Emergency Healthcare Transmission, comprising:
(a) one or more LoRaWAN-enabled wearable or portable devices, each embedded with biometric sensors configured to detect emergency events including, but not limited to, falls, cardiac anomalies, respiratory distress, or manual triggers;

(b) a network infrastructure comprising LoRaWAN gateways and network servers configured to receive and forward encrypted alerts; and

(c) a cloud-hosted application server, integrated with national digital health platforms such as DigiLocker and ABHA, configured to retrieve authenticated health records, compile a medical emergency dossier, and initiate multi-channel dissemination of alerts to registered emergency contacts, medical services, and public response infrastructure.
2. The system as claimed in Claim 1, wherein said application server is further configured to dispatch emergency alerts through multiple simultaneous communication channels, including SMS, voice calls, push notifications, and secure healthcare data formats such as HL7 or FHIR APIs.

3. The system as claimed in Claim 1, wherein said cloud-hosted server retrieves and compiles patient-specific health records through authenticated API access to DigiLocker or ABHA, based on tokenized consent protocols and digital ID integration.
4. The system as claimed in Claim 1, wherein all data transmissions are secured under a zero-trust architecture, incorporating dynamic device authentication, session-specific encryption keys, and hardware-based cryptographic validation modules.
5. The system as claimed in Claim 1, wherein an embedded AI inference engine evaluates biometric patterns in real-time to prioritize emergency events based on severity, enabling higher triage accuracy for conditions such as cardiac arrest, respiratory failure, or unconsciousness.
6. A Method for Infrastructure-Independent Emergency Health Response, comprising the steps of:
(a) detecting a biometric anomaly or manual trigger event through a LoRaWAN-enabled device;

(b) generating an encrypted alert packet comprising a user identifier, event type, severity code, timestamp, optional GPS data, and device health indicators;

(c) transmitting said alert to a LoRaWAN gateway;

(d) routing said alert to a cloud-hosted server integrated with national health platforms;

(e) retrieving health records using authenticated access protocols; and

(f) concurrently disseminating the compiled emergency dossier to multiple stakeholders through mobile networks, public alert systems, and hospital integration channels.
7. The method as claimed in Claim 6, wherein the application server performs multilingual localization of emergency alerts using natural language processing (NLP) algorithms based on the recipient's location, language preference, or configured system defaults.
8. The method as claimed in Claim 6, wherein the device dynamically selects its communication channel by switching between LoRaWAN, cellular, satellite, or Wi-Fi protocols based on real-time signal strength, link reliability, and energy availability.
9. The method as claimed in Claim 6, wherein a smartphone acts as a temporary LoRaWAN gateway through the use of an embedded or connected LoRa module, thereby enabling emergency packet relay in the absence of fixed gateway infrastructure.
10. A Wearable Device for Emergency Alert Transmission, comprising:
(a) a LoRaWAN communication module;

(b) a sensor array including at least one of: accelerometer, gyroscope, PPG heart-rate sensor, SpO₂ monitor, or electrocardiogram module;

(c) a manual distress trigger (panic button);

(d) a battery management subsystem configured for low-power operation; and

(e) firmware configured to autonomously transmit encrypted alerts when a biometric anomaly or manual trigger is detected.
11. The device as claimed in Claim 10, wherein said emergency alert is simultaneously routed to public alert infrastructure, including smart signage boards, municipal dashboards, and IoT-connected devices, via LoRaWAN or internet-based APIs for community-wide broadcast and situational awareness.