Description:FIELD OF THE INVENTION
The invention relates to medical Internet of Things (IoT) systems applied in defense and combat environments. It particularly concerns a bio-integrated wearable system with edge-based artificial intelligence, multi-sensor physiological monitoring, predictive trauma analysis, and decentralized mesh communication for real-time soldier health monitoring and predictive emergency detection.
BACKGROUND OF THE INVENTION
In contemporary combat fields, troops fight under high levels of physical and psychological stress with poor access to proximate medical aid. Early detection of life-threatening medical conditions like internal bleeding, dehydration, cardiac arrhythmias, or psychological trauma can greatly enhance survival and recovery rates. Contemporary solutions available for battlefield health monitoring are suboptimal in terms of capability, cost, and intelligence.
Traditional wearable devices either have simple fitness monitoring or depend a lot on centralized infrastructure, which tends to be unreliable or non-existent in field deployment. These systems have some serious shortcomings:
Lack of Predictive Intelligence: Current systems are reactive and cannot forecast physiological decline or screen for early signs of trauma like hemorrhage or PTSD onset.
Limited Sensor Integration: The majority of devices track a single or double parameter, not gleaning the complete physiological condition needed for effective battlefield triage.
Centralized Architecture Vulnerabilities: Relying on cloud servers creates single points of failure, which are unrealistic in combat environments with momentary connectivity or GPS denial.
Inadequate Personalization: Standard threshold-based alerts disregard individual differences, causing false positives or overlooked life-critical events.
High Cost and Limited Scalability: It is common for military-grade health monitoring systems to be costly and not scalable to cover large numbers of infantry units.
Therefore, there is a pressing need for a low-cost, decentralized, smart, and scalable medical IoT solution that can continuously monitor a soldier's health in real-time, forecast critical medical events, and function independently of conventional network infrastructures.
US20210186329A1: A monitoring system a user activity sensor to determine patterns of activity based upon the user activity occurring over time
US8449471B2: A heart monitoring system for a patient includes one or more wireless nodes forming a wireless network; a wearable appliance having a wireless transceiver to communicate with the one or more wireless nodes; and an analyzer to determine vital signs, the analyzer coupled to the wireless transceiver to receive patient data over the wireless network.
In modern combat fields, soldiers face life-threatening risks from undetected trauma, dehydration, cardiac events, and psychological stress while lacking immediate access to medical support. Existing wearable systems are reactive, cloud-dependent, and limited in physiological coverage. They are costly, prone to failure in GPS- or internet-denied zones, and lack predictive intelligence and personalization. The present invention solves these problems by providing a decentralized, low-cost, AI-enabled, multi-sensor system that functions autonomously, learns the physiological baseline of each soldier, predicts trauma before critical deterioration, and transmits alerts via secure mesh networks without relying on centralized infrastructure.
SUMMARY OF THE INVENTION
This summary is provided to introduce a selection of concepts, in a simplified format, that are further described in the detailed description of the invention.
This summary is neither intended to identify key or essential inventive concepts of the invention and nor is it intended for determining the scope of the invention.
The invention discloses a wearable, combat-ready health monitoring system integrating multiple physiological sensors such as heart rate, oxygen saturation, body temperature, galvanic skin response, hydration indicators, motion tracking, and bioimpedance. These sensors provide real-time monitoring of a soldier’s condition and feed into embedded edge-based artificial intelligence models.
The edge AI module learns individual baselines and performs localized predictive analysis to identify early signs of hemorrhage, arrhythmias, dehydration, electrolyte imbalance, concussions, or psychological trauma. Predictions are processed without requiring cloud support, ensuring functionality in disconnected battlefields.
The system employs a decentralized mesh network using low-power communication protocols, enabling each wearable to act as a relay node. This ensures reliable communication of health status and alerts even in GPS-denied environments. Emergency alerts include projected condition, severity, and, when available, geolocation for faster response by nearby medics or commanders.
The invention also incorporates bio-digital twin synchronization, whereby long-term soldier health data is periodically updated and accessible at command centers for triage planning and medical decision support. Data security is enforced through encryption, role-based access, and audit logging, ensuring privacy and integrity of sensitive health information.
To further clarify advantages and features of the present invention, a more particular description of the invention will be rendered by reference to specific embodiments thereof, which is illustrated in the appended drawings. It is appreciated that these drawings depict only typical embodiments of the invention and are therefore not to be considered limiting of its scope. The invention will be described and explained with additional specificity and detail with the accompanying drawings.
The invention, BioSenseNet, presents a low-cost, AI-based, decentralized, and mesh-capable wearable IoT technology to predict, sense, and alert medical emergencies in soldiers while they are engaged in combat operations without depending on centralized infrastructure or cloud support.
BRIEF DESCRIPTION OF THE DRAWINGS
The illustrated embodiments of the subject matter will be understood by reference to the drawings, wherein like parts are designated by like numerals throughout. The following description is intended only by way of example, and simply illustrates certain selected embodiments of devices, systems, and methods that are consistent with the subject matter as claimed herein, wherein:
FIGURE 1: SYSTEM ARCHITECTURE
The figures depict embodiments of the present subject matter for the purposes of illustration only. A person skilled in the art will easily recognize from the following description that alternative embodiments of the structures and methods illustrated herein may be employed without departing from the principles of the disclosure described herein.
DETAILED DESCRIPTION OF THE INVENTION
The detailed description of various exemplary embodiments of the disclosure is described herein with reference to the accompanying drawings. It should be noted that the embodiments are described herein in such details as to clearly communicate the disclosure. However, the amount of details provided herein is not intended to limit the anticipated variations of embodiments; on the contrary, the intention is to cover all modifications, equivalents, and alternatives falling within the scope of the present disclosure as defined by the appended claims.
It is also to be understood that various arrangements may be devised that, although not explicitly described or shown herein, embody the principles of the present disclosure. Moreover, all statements herein reciting principles, aspects, and embodiments of the present disclosure, as well as specific examples, are intended to encompass equivalents thereof.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments. As used herein, the singular forms “a",” “an” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms “comprises,” “comprising,” “includes” and/or “including,” when used herein, specify the presence of stated features, integers, steps, operations, elements and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components and/or groups thereof.
It should also be noted that in some alternative implementations, the functions/acts noted may occur out of the order noted in the figures. For example, two figures shown in succession may, in fact, be executed concurrently or may sometimes be executed in the reverse order, depending upon the functionality/acts involved.
In addition, the descriptions of "first", "second", “third”, and the like in the present invention are used for the purpose of description only, and are not to be construed as indicating or implying their relative importance or implicitly indicating the number of technical features indicated. Thus, features defining "first" and "second" may include at least one of the features, either explicitly or implicitly.
Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which example embodiments belong. It will be further understood that terms, e.g., those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the relevant art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
The system comprises a flexible, wearable patch embedded with multiple biosensors. These sensors continuously capture vital parameters including heart rate, oxygen saturation, body temperature, galvanic skin response, hydration status through sweat electrolyte levels, body motion through accelerometry, and bioimpedance for detecting hidden injuries and tissue damage.
The invention introduces real-time adaptive baseline learning. Unlike rigid threshold-based alerts, the wearable uses lightweight AI to learn each soldier’s normal physiological range under varying conditions. This reduces false alarms and ensures that alerts are only generated when meaningful deviations occur.
The edge AI architecture employs ultra-low-power microcontrollers running compact machine learning models. These models predict possible emergencies such as hemorrhagic shock, dehydration, cardiac arrest, or psychological trauma, allowing early intervention in the field before deterioration becomes life-threatening.
The trauma prediction capability leverages bioimpedance sensing combined with deviations in vital signs to identify injuries invisible to external observation. This gives commanders and medics crucial insights into hidden internal conditions.
The system communicates via a decentralized mesh network using low-power long-range and short-range protocols. Each wearable functions both as a monitoring unit and as a relay node. In the event of an emergency, alerts are transmitted through the mesh to nearby soldiers or medics without requiring satellite or internet. This ensures continuous communication even in adversarial environments where connectivity is limited.
An emergency health alert prioritization mechanism is embedded in the device. Detected abnormalities are categorized based on severity, ensuring that critical cases are transmitted with higher urgency. When GPS is available, the alert includes location data to assist in rapid evacuation.
For long-term health tracking, the system includes a bio-digital twin. This is a digital representation of a soldier’s health profile, updated periodically whenever connectivity allows. The command center can access these profiles to observe patterns, detect emerging risks, and plan triage or deployment strategies.
Security is achieved through encryption and role-based access controls. Every data access or modification event is logged locally and later synchronized to central records. The design ensures interoperability with standard medical records by supporting export formats such as HL7 or FHIR.
The invention further emphasizes cost efficiency and scalability. The wearable patch is designed with modular sensors and can be manufactured using off-the-shelf components and printable flexible substrates, making it feasible for large-scale deployment across entire units.
Unlike existing systems, the invention integrates predictive AI, decentralized communication, baseline personalization, and bioimpedance sensing within a single low-cost platform. This convergence enables combat troops to be continuously monitored, protected, and supported by real-time intelligence-driven health systems.
Best Method of Working
The best method of working involves deploying the wearable units directly to soldiers in combat environments. Each unit is equipped with multiple biosensors embedded on a flexible patch. Once worn, the device begins capturing physiological data and establishes individual baseline models within hours of use.
The embedded AI model runs locally, continuously comparing live data against baseline norms. On detecting anomalies such as abnormal heart rate variability, sudden electrolyte imbalance, or bioimpedance irregularities, the device triggers predictive alerts.
Communication occurs over a mesh network where each device automatically connects with nearby units. In case of an emergency, alerts are propagated through peer nodes, ensuring the message reaches medics even without central command connectivity.
The command center receives synchronized bio-digital twin data when intermittent internet or satellite connectivity is available. This allows commanders to visualize the health map of deployed units and make informed triage and evacuation decisions.
This method of working ensures the invention delivers maximum resilience, scalability, and predictive medical support in battlefield conditions.
The invention, BioSenseNet, presents a low-cost, AI-based, decentralized, and mesh-capable wearable IoT technology to predict, sense, and alert medical emergencies in soldiers while they are engaged in combat operations without depending on centralized infrastructure or cloud support.
Step 1: Multi-Modal Sensor Integration and Baseline Learning
The wearable patch uses several biomedical sensors to monitor in real-time core physiological parameters like heart rate (ECG/PPG), blood oxygen saturation (SpO₂), skin temperature, galvanic skin response (GSR), level of sweat electrolytes (hydration), body movement, and bioimpedance (for wound and internal trauma). The patches engage in real-time self-learning based on lightweight AI models to identify the soldier's physiological baseline in different conditions.
Step 2: Edge AI-Based Health Event Prediction
TinyML models are deployed onto ultra-low-power microcontrollers to perform inference locally, without the need for internet or cloud connectivity. These models identify deviations from the learned baseline and predict key events such as:
 Hemorrhagic shock
 Dehydration and electrolyte imbalance
 Arrhythmias or cardiac arrest
 Onset of PTSD (through HRV + GSR analysis)
 Concussion or trauma due to impact
 Alerts are automatically generated and prioritized by severity.
Step 3: Secure Mesh-Based Battlefield Communication
The device works within a secure, decentralized LoRa/BLE mesh network. During an emergency, the patch will automatically send the soldier's health status, projected condition, and GPS coordinates to units nearby or field medics no internet or satellite required. Each soldier's device is also a node in the mesh to provide extended range communication.
Step 4: Bio-Digital Twin Synchronization and Command Center Dashboard
Every soldier possesses a customized, cloud-syncable bio-digital twin that is updated with long-term health patterns and anomalies when periodic connectivity is available. A command center dashboard maps the health status of deployed forces, identifies emergencies, and aids in battlefield triage and evacuation planning.
Step 5: Secure Access, Audit, and Interoperability
Sensitive health information access is encrypted and limited by identity-based access control. Each access or data modification event is locally logged and synchronized to central command logs where practicable. The system is architected to interoperate with military medical systems and battlefield EHRs via modular export standards like HL7/FHIR.
NOVELTY:
The new invention BioSenseNet provides a novel, integrated approach that solves the key challenges of medical monitoring in battlefield environments. Unlike current systems, the new invention integrates advanced physiological sensing, embedded AI-driven prediction, decentralized mesh communication, and personalized baseline modeling within a low-cost, scalable, and infrastructure-independent platform.
The innovation is the convergence of five innovative elements, which have not been implemented together in any prior art known to date:
1.AI-Driven Multi-Modal Health Prediction on the Edge:
Whereas most systems do post-event health analysis, this invention uses TinyML models that learn an individual soldier's physiological baseline and conduct real-time predictive analytics locally, detecting early warning signs of trauma (e.g., internal bleeding, PTSD onset, heatstroke) without cloud support.
2.Bioimpedance-Based Detection of Internal Injuries
The application of bioimpedance sensing in a small wearable to identify tissue damage or internal hemorrhage in real-time is a new addition to battle medical devices, giving information on injuries not externally apparent.
3.Adaptive, Decentralized Battlefield Mesh Network:
The solution utilizes a redundant, peer-to-peer mesh network through LoRa/BLE, providing secure broadcast of emergency alerts in GPS- or internet-forbidden environments. Wearables are used as relay nodes to extend coverage and robustness.
4.Bio-Digital Twin Synchronization:
A digital twin framework is used to model the health profile of each soldier and is updated constantly by real-time wearable data to inform longitudinal tracking, remote triage, and individualized medical decision-making.
5.Personalized Deviation Detection via Baseline Modeling:
Rather than being based on fixed clinical thresholds, the system adapts and learns each person's physiological norms. Alerts are triggered when contextual anomalies are identified, cutting down significantly on false positives and enhancing diagnostic accuracy under combat stress-heavy conditions. 
, Claims:1. A system for predictive trauma detection and real-time health monitoring of soldiers, comprising:
a) a wearable module equipped with multi-modal biosensors including heart rate sensors, oxygen saturation sensors, temperature sensors, galvanic skin response sensors, hydration sensors, motion sensors, and bioimpedance sensors;
b) an embedded edge-based artificial intelligence module configured to learn baseline physiological parameters and predict anomalies including hemorrhage, dehydration, arrhythmias, trauma, and psychological stress;
c) a trauma prediction unit combining bioimpedance-based analysis with vital sign deviations to classify severity and generate alerts;
d) a decentralized mesh communication module configured for peer-to-peer data relay using low-power protocols to operate without centralized infrastructure;
e) a bio-digital twin framework configured to synchronize soldier health profiles for command-level triage and monitoring;
f) a security and access control mechanism configured for encrypted data transmission, identity-based authorization, and audit logging;
wherein all modules operate in integration to provide continuous monitoring, predictive trauma detection, and reliable alerting in combat environments.
2. The system as claimed in claim 1, wherein the wearable module is configured with flexible, low-cost sensors optimized for combat use.
3. The system as claimed in claim 1, wherein the artificial intelligence module operates locally on ultra-low-power microcontrollers without dependence on cloud connectivity.
4. The system as claimed in claim 1, wherein the mesh communication module enables continuous connectivity in GPS-denied or internet-restricted environments.
5. The system as claimed in claim 1, wherein the bio-digital twin framework periodically synchronizes longitudinal health data to command centers for mission-wide triage support.
6. A method for predictive trauma detection and soldier health monitoring, comprising the steps of:
a) monitoring physiological parameters through wearable biosensors including heart rate, oxygen saturation, temperature, galvanic skin response, hydration, motion, and bioimpedance;
b) learning an individual soldier’s physiological baseline through adaptive edge-based artificial intelligence;
c) predicting anomalies including hemorrhagic shock, dehydration, cardiac arrest, concussion, or psychological trauma by comparing live data with baseline norms;
d) transmitting alerts and health data through a decentralized mesh communication network;
e) synchronizing soldier health profiles with command systems via a bio-digital twin framework when connectivity is available;
wherein the method ensures predictive trauma detection and reliable communication under combat conditions.
7. The method as claimed in claim 6, wherein anomaly detection is performed locally without reliance on external cloud servers.
8. The method as claimed in claim 6, wherein alerts include severity prioritization and geolocation when available.
9. The method as claimed in claim 6, wherein communication occurs through each wearable acting as a relay node in the mesh.
10. The method as claimed in claim 6, wherein health data is secured through encryption and role-based access control with interoperability for electronic health record systems.