Description:TECHNICAL FIELD
[001] The present invention relates to wearable or kit-based medical emergency devices, and more particularly to a wearable, biosensor-driven, AI-integrated, closed-loop therapeutic platform that autonomously detects snakebite envenomation, classifies venom type, and administers a stabilised, precise dose of antivenom at the point of injury.
BACKGROUND OF THE INVENTION
[002] Snakebite envenoming results in a significant global health burden, with an estimated 81,000 to 138,000 deaths annually according to the World Health Organization. Cumulative mortality from 2000 to 2024 exceeds 2 million deaths, disproportionately concentrated in tropical and subtropical regions. India alone has accounted for nearly half of this mortality, largely due to underreporting, lack of accessible emergency care, and delayed intervention. Despite its scale, snakebite remains critically neglected in public health policy, clinical infrastructure planning, and emergency response training. These preventable deaths underscore the need for scalable, autonomous, and point-of-injury diagnostic–therapeutic devices that eliminate dependence on infrastructure, transportation, or trained personnel.
[003] Snakebite envenoming represents a major yet under-addressed public health crisis, particularly in remote, agricultural, and forested regions where immediate access to clinical care is limited or absent. It remains one of the most lethal acute-onset medical emergencies in the tropics and subtropics, often affecting rural laborers, children, and field personnel. Despite advancements in healthcare, the initial phase of snakebite response continues to suffer from delayed detection, uncertainty in venom identification, and inappropriate or missed antivenom administration. Many patients succumb to envenomation before reaching a hospital, while others suffer permanent disability due to systemic toxin effects or organ damage caused by delayed intervention.
[004] Existing snakebite treatments rely on manual diagnosis based on physical symptoms, eyewitness species identification, or symptomatic progression—factors that are often unreliable and too slow to support early-stage, evidence-based intervention. Even when antivenom is available, its efficacy is time-sensitive and largely dependent on matching the venom family or type. There is a critical unmet need for a field-deployable, wearable device that can detect envenomation at the biochemical level, infer the venom characteristics using artificial intelligence, and autonomously deliver the appropriate antivenom dose—ensuring the earliest possible neutralisation of circulating venom components.
[005] No existing solution offers a closed-loop combination of biosensor-based venom detection, real-time AI classification, and precision antivenom injection in a portable or wearable form.
SUMMARY
[006] The present invention discloses an AI-enabled, wearable and kit-based medical intervention system for rapid and autonomous treatment of snakebite envenomation.
[007] The device comprises a modular architecture that integrates a dual-modality biosensor array, an AI-based real-time venom classification engine, a programmable microfluidic antivenom delivery platform, and a ruggedized, tamper-resistant housing and energy module, all linked via a secure communication and control module.
[008] The embedded artificial intelligence (AI) engine is trained on a large dataset of venom biomarker profiles and is capable of processing a fused 64-dimensional input vector derived from multi-modal sensor signals—including electrochemical, optical, and dielectric inputs. The AI executes a quantised 8-bit convolutional neural network on an edge processor, achieving >98% classification accuracy in real time. Based on the output vector, which includes predicted venom class and severity, the device selects the appropriate drug payload and triggers delivery. Over-the-air (OTA) model updates are cryptographically signed and verified using secure enclaves with PUF-rooted keys, enabling lifelong adaptation to new venom types or mutations.
[009] Upon sensing venom-specific biomarkers in interstitial fluid or blood, the device classifies the venom type within seconds and initiates delivery of a precise dose of validated species-specific antivenom.
[010] The device supports OTA-updatable firmware with cryptographic verification, fall-back logic in case of AI failure, and flexible wearable configurations for wrists, arms, legs, or integration into protective field kits.
[011] Also, this device validated for use with lyophilised monoclonal antibodies, peptides, aptamers, and nucleic acid–based payloads, the invention ensures rapid, evidence-based, and infrastructure-independent therapy in high-risk environments such as rural regions, combat zones, and wildlife habitats.
[012] This innovation closes the diagnostic–therapeutic gap with a closed-loop, field-deployable solution capable of life-saving intervention within seconds of exposure.
[013] The power and communication module support solar trickle charging, low-power wake circuits, and secure BLE/SAT communication with OTA-updateable firmware. Embedded tamper detection and forensic logging ensure device trustworthiness and enable auditability in field scenarios.
[014] In an alternative embodiment, the antivenom cartridge may store a polyvalent formulation when monovalent classification is not feasible, allowing for safe empiric coverage.
[015] This invention fills the gap in rural and combat medicine by offering a compact, intelligent, and adaptive device that reduces treatment latency and improves clinical outcomes.
BRIEF DESCRIPTION OF DRAWINGS
[016] The accompanying drawings, which are incorporated in and constitute a part of this specification, show certain aspects of the subject matter disclosed herein and together with the description, help explain some of the principles associated with the disclosed implementations. In the drawing,
[017] Figure 1 illustrates the block diagram of device (100) which comprises all the components.
[018] Figure 2 illustrates the flow diagram of method (200) which comprises all the steps involved in working of the device (100).
[019] Figure 3 shows the OTA-secured method (300) with update unit (301), rollback (302), secure boot (303), AI fault logic (304), and fall-back logic (305).
[020] Figure 4 shows device (100) response time for 1,000 simulated envenomation events.
[021] Figure 5A shows impedance sensor response curves.
[022] Figure 5B shows optical sensor response curves.
[023] Figure 5C shows aptamer sensor response curves.
[024] Figure 6A shows the device (100) worn on the wrist.
[025] Figure 6B shows the device (100) worn on the arm.
[026] Figure 6C shows the device (100) worn on the leg.
DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS
[027] The present disclosure elaborates on various embodiments in detail. While specific implementations are presented, they serve only as illustrative examples. Those skilled in the relevant field will recognize that other configurations and components may be utilized without deviating from the essence and scope of the disclosure. Consequently, the descriptions and drawings herein are to be understood as illustrative rather than restrictive.
[028] Specific details are included to provide a comprehensive understanding of the disclosure, but well-known elements may be omitted to maintain clarity and conciseness. References to 'one embodiment,' 'an embodiment,' 'one aspect,' or similar phrases signify that the described feature, structure, or characteristic is applicable to at least one embodiment. The repeated use of such terms does not necessarily refer to the same embodiment, nor are different embodiments mutually exclusive. Certain features may appear in some embodiments but not in others.
[029] Terms used in this document generally carry their ordinary meanings within the relevant field, contextualized to the disclosure. Synonyms and alternative terminology may also be employed without implying additional limitations. Examples provided herein are purely illustrative and do not define or constrain the scope of the disclosure. Similarly, the scope of the disclosure is not restricted to the embodiments explicitly described. For clarification, examples of instruments, methods, or results based on these embodiments are included, with headings and subtitles used for convenience rather than to impose limitations. Unless explicitly defined, technical and scientific terms are interpreted as understood by those skilled in the art. In cases of conflict, definitions within this document prevail.
[030] Additional features and advantages will be evident from the following description or can be learned through the application of the principles disclosed. These features and benefits may be achieved using the methods and combinations specifically outlined in the appended claims, as well as through the practice of the described principles. The full scope of the disclosure will become clearer from the subsequent discussion and claims.
[031] The terms “device (100)” and “closed-loop snakebite detection and autonomous antivenom delivery wearable device (100)” are interchangeably used across the context.
[032] Figure 1 explains the block diagram of the device (100). It illustrates the core structural components of the device, including the sensor module (102), the processing unit (104), the programmable microfluidic drug delivery system (106), the ruggedized housing and power supply module (108), and the secure communication and telemetry module (110). These modules communicate over encrypted links and enable the full closed-loop functionality.
[033] The invention discloses a compact, field-deployable, and intelligent closed-loop device (100) that autonomously detects venom exposure, classifies the venom, and administers an appropriate antivenom. None of these modules are limited to any specific embodiment, and each may be realized through equivalent alternatives so long as they satisfy the core functionality defined herein.
[034] The sensor module (102) comprises a redundant, dual-modality biosensor array integrating electrochemical, optical, and dielectric sensing technologies. These include screen-printed electrochemical sensors, aptamer-fluorescence pads, and impedance spectroscopy circuits. Commercially validated implementations include DropSens DRP-110 for 3-electrode sensing, aptamer-based fluorescence recognition pads for venom-specific binding, and AD5940-based dielectric impedance circuits for confirming tissue disruption and fluid profile changes. These biosensors detect a validated panel of venom biomarkers including phospholipase-A₂ (PLA₂), neurotoxins, snake venom metalloproteases, hemotoxins, and pH/lactate shifts. Dual-modality confirmation minimizes false positives to below 0.1% and enhances diagnostic certainty regardless of bite depth or location. The biosensor array may also include on-board calibration references, signal amplifiers, and temperature-compensated baseline circuits to ensure robustness under varying environmental conditions.
[035] The processing unit (104) incorporates an Arm Cortex-M55 microcontroller paired with an Ethos-U55 AI accelerator, capable of executing a quantised 8-bit convolutional neural network (~0.9 MB) that delivers over 98% venom classification accuracy. The classifier processes a fused 64-dimensional input vector derived from biosensor data streams. The firmware supports OTA updates using TLS 1.3 encryption with secure enclave verification rooted in a physical unclonable function (PUF), ensuring protection against adversarial model manipulation. The processor is supported by vendor-validated toolchains such as CMSIS-NN and TensorFlow Lite Micro, and also handles peripheral control including ADC multiplexing, data pre-processing, real-time clock, device alerts, and fall-back overrides.
[036] In certain embodiments, the AI-based classifier may be replaced or supplemented with a rule-based or hybrid classification engine capable of operating on the same fused sensor inputs. The classifier processes a fused 64-dimensional input vector derived from biosensor data streams, or applies rule sets to detect signature biomarker patterns.
[037] In embodiments where no satellite patch is worn, the device relies on (i) the rapid systemic propagation of venom biomarkers through interstitial fluid and dermal micro vasculature and (ii) an integrated capillary wicking strap (CWS) coupled to the sensor bay. Upon detection of a puncture induced pressure transient (> 50 kPa) on the strap, the CWS draws ~2 µL of lymph toward the biosensor module within ≤ 8 s at 32 °C, ensuring near source analyte sampling even when the bite occurs distal to the device. The fused sensor data therefore include both systemic biomarker spikes and any wicking derived local concentrate, enabling accurate venom family classification with > 95 % confidence at distances up to 50 cm from the bite site.
[038] The AI runtime implements a confidence gated dosing algorithm: where classification confidence C < 0.80 at t = 10 s post trigger, the processor initiates a weight adjusted one third dose of WHO approved polyvalent antivenom while continuing inference. Upon C ≥ 0.95, an incremental top up of the matched monovalent (or family specific) antivenom is delivered, thus safeguarding against mis specification without delaying therapy.
[039] The AI-based classification engine is trained using supervised learning techniques on a curated dataset containing thousands of labelled venom biomarker profiles across multiple snake families. Input to the model consists of a fused 64-dimensional vector constructed from real-time electrochemical impedance, optical fluorescence, and aptamer-binding sensor modalities.
[040] The training dataset includes neurotoxic, hemotoxic, and cytotoxic venom profiles, enabling the model to perform direct comparative classification across these categories. The processing pipeline includes ADC signal conditioning, normalization, temporal filtering, and dimensional encoding before inference. The quantised CNN model or rule-based logic evaluates these features to output a venom classification label (e.g., viperid, elapid, or hydrophiid) along with a confidence score and severity rank. Based on this output, the system initiates the correct antivenom protocol using the matched payload stored within the cartridge. Unlike conventional static systems, this adaptive engine responds dynamically to evolving data and geographic variations in venom type.
[041] OTA-updateable AI or rule-based classifier ensures future adaptability to new venom types without requiring hardware modification. The engine may operate on-device or, in alternate embodiments, be securely tethered to a cryptographically validated external processor. Classification results are used to determine species-specific dose selection and initiate the correct therapeutic response.
[042] The antivenom delivery module (106) includes a programmable microfluidic cartridge system comprising multiple N₂-sealed lyophilised payload chambers constructed from Zeonor/COC polymer, each paired with a sterile diluent pouch and controlled by solenoid micro-valves. Dosing is actuated by a Bartels MP6 piezoelectric micropump driving the reconstituted payload to subdermal tissue via retractable microneedles. Each chamber may carry protein-based antivenoms, LTNF peptide analogs, monoclonal antibodies (IgG, Fab), antisense oligonucleotides, or small-molecule enzyme inhibitors (e.g., PLA₂ blockers). Reconstitution completes within two minutes, and bioavailability of each formulation is validated through pre-clinical profiling. In an alternate embodiment, a single chamber containing a polyvalent antivenom may be used when rapid broad-spectrum neutralisation is warranted. The microfluidic cartridge is validated for a 24-month shelf life at 40 °C as per ICH Q1A stability guidelines, with hermetic sealing confirmed by <10⁻⁶ mbar·L/s helium leak rate post-drop (2000 × 1m).
[043] The housing module (108) is a ruggedised, IP65+ casing featuring internal µ-metal EMI shielding per MIL-STD-461G. It accommodates all internal components with resistance to physical shocks, environmental stress, and tampering. A flex-based tamper detection membrane triggers an automatic response including AES key wipe, injection lockout, and SHA-256 event logging. Manual override via an external lever allows fallback fixed-dose delivery if automatic functions are compromised. The housing may include external interface windows for visual status indicators, thermal monitoring, firmware access ports, and skin-contact sensing electrodes.
[044] The power and communication module (110) comprise a 350 mAh lithium-ion pouch cell, supplemented with solar trickle charging and NFC-based low-power activation. The module communicates over BLE 5.x and UWB, secured by AES-GCM encryption protocols. Alert signals and telemetry can be sent via SMS, GSM, or SAT link. Firmware architecture includes sensor polling routines, CMSIS-NN or TensorFlow Lite Micro inference engines, microfluidic pump drivers, and OTA firmware management services. Additionally, the device logs operational data in a ring buffer stored on tamper-evident memory, supporting post-event analysis and device revalidation.
[045] Figure 2 explains the method (200) implemented by the device (100). The method includes:
a) Step-1 (201): acquiring (201) interstitial fluid or blood analyte data using a dual-modality biosensor array (102);
b) Step-2 (203): processing sensor data in real time using an AI-based classifier in the processing unit (104) to determine venom type and severity;
c) Step-3 (205): selecting and reconstituting a matched antivenom payload from a lyophilised multi-chamber cartridge
d) Step-4 (207): delivering the antivenom via a programmable microfluidic actuator and subdermal injection mechanism (106); and
e) Step-5 (209): transmitting telemetry and status data through the communication channel (110).
Optional signal transmission to emergency contacts or medical responders is handled by the communication module (110). The device (100) continuously operates in a low-power surveillance mode, and transitions to active diagnostic mode upon sensing venom-related biochemical activity exceeding threshold values.
[046] Figure 3 explains the secure closed-loop update and fall-back mechanism (300). The OTA update (301) system allows remote deployment of model improvements and firmware patches. The rollback mechanism (302) is rooted in a physical unclonable function (PUF) that supports cryptographic key integrity and prevents unauthorized updates. The secure enclave and tamper detection logic (303) ensure device trustworthiness by enforcing boot integrity and initiating key wipe upon physical intrusion.
[047] If the AI model runtime fails (304), the fall-back threshold logic (305) determines the appropriate default dose based on predefined rules and field parameters. The tamper detection module (303) connects to both the fall-back logic (305) and AI runtime monitor (304), creating a secure and resilient execution pipeline.
[048] Figure 4 shows the cumulative timeline of device workflow for 1,000 simulated envenomation events across different venom families including Viperidae, Elapidae, and Hydrophiidae. Each step in the closed-loop sequence—puncture detection, fluid sampling, biosensor readout, AI/ML classification, cartridge reconstitution, injector activation, and initial bolus delivery—is captured with both median and 95th percentile curves.
[049] The results demonstrate consistent end-to-end system latency below 10 seconds for over 95% of cases. Median cumulative times from bite to full antivenom bolus range from 17 to 27 seconds across venom classes. Alert and telemetry steps are completed within 8–12 seconds after envenomation. These results validate the device's ability to reliably initiate life-saving intervention within the golden window, minimizing systemic venom spread.
Table (1)
[050] Figures 5A, 5B, and 5C show sensor response graphs for impedance, optical, and aptamer modalities, respectively. Each graph presents signal intensity over time for the three venom families. Median (solid lines) and 5th–95th percentile bands (shaded) demonstrate consistent and discriminative signal profiles across all modalities. Notably:
• Median event-to-antivenom initiation time is 6–8 seconds across all venom families.
• 95th percentile time remains under 11 seconds in all simulated cases.
• Full bolus delivery occurs within 17–27 seconds.
• Emergency telemetry is triggered within 8–12 seconds of envenomation.
• Classifier performance achieves AUC of 0.97 in multi-class validation.
• No failure events were recorded in >3,000 simulations.
The device includes automatic sensor fusion, error checking, and hardware failsafe triggering in case of outlier signals or signal dropout.
[051] The validated payloads include lyophilised formulations of venom-specific monoclonal antibodies (e.g., anti-PLA₂ IgG for viper venom), LTNF peptide analogs (targeting Elapidae neurotoxins), and aptamer-linked antidotes for marine venoms (Hydrophilidae). Each formulation is engineered to retain bioactivity under field conditions without cold-chain support, ensuring real-time therapeutic readiness. The combination of rapid sensor response, real-time AI processing, and multi-target antivenom formulations supports broad regional deployment with minimal risk of error or delay. showing the response of each sensor modality versus time—impedance (5A), optical (5B), and aptamer (5C).
[052] Each graph illustrates median response curves and 5th–95th percentile variability bands. Key metrics include: median event-to-antivenom initiation time of 6–8 seconds for all venom classes, full bolus delivery within ~17–27 seconds, emergency telemetry alert issued within 8–12 seconds, classifier AUC of 0.97 across multiclass models, and no failed simulation runs. The device maintains robustness even under high noise or extreme conditions and performs automatic self-tests and failsafe alerting for hardware anomalies.
[053] Figures 6A, 6B, and 6C illustrate different wearable configurations of the device (100), including deployment on the wrist, upper arm, and lower leg respectively. These form factors are designed for high-risk anatomical regions and ensure ergonomic integration into everyday clothing, field uniforms, or tactical gear. Alternative embodiments include chest patches, boot inserts, and modular PPE pod configurations where sensor and injection units are distributed across the body but wirelessly linked to function as a single device.
[054] Applications for the device (100) include but are not limited to military and jungle patrols, veterinary emergency kits, rural health deployments, school safety programs in snake-endemic regions, and anti-poaching operations. The device (100) may be embedded in various form factors including forearm-worn bands, upper-arm inserts, calf wraps, wearable patches, or clothing-integrated kits to suit different user groups and field environments. This flexibility in deployment ensures consistent proximity to the likely bite site while accommodating ergonomic and operational considerations for long-duration wear.
[055] The invention offers a fully autonomous, closed-loop snakebite response device that operates within seconds of venom exposure. It reduces treatment delay, eliminates empiric polyvalent dosing, and minimizes risks of anaphylaxis. It is designed for rugged environments, requires no cold chain, and is OTA-updatable for evolving venom profiles. The device incorporates fail-safe mechanisms, secure firmware, and forensic logging, ensuring high reliability and adaptability.
[056] The implementation set forth in the foregoing description does not represent all implementations consistent with the subject matter described herein. Instead, they are merely some examples consistent with aspects related to the described subject matter. Although a few variations have been described in detail above, other modifications or additions are possible. In particular, further features and/or variations can be provided in addition to those set forth herein. For example, the implementation described can be directed to various combinations and sub combinations of the disclosed features and/or combinations and sub combinations of the several further features disclosed above. In addition, the logic flows depicted in the accompanying figures and/or described herein do not necessarily require the particular order shown, or sequential order, to achieve desirable results. Other implementations may be within the scope of the following claims.
, Claims:1. A closed-loop snakebite detection and autonomous antivenom delivery wearable device (100), comprises:
a biosensor module (102) configured to detect venom-specific biomarkers in interstitial fluid or blood;
a processing unit (104) configured to classify venom type and severity based on sensor data; and
a programmable microfluidic drug delivery unit (106) configured to reconstitute and inject a matched antivenom based on the classified venom type.
2. The device as claimed in claim 1, characterized in that the biosensor module (102) includes at least one sensor modalities selected from electrochemical, optical, or dielectric impedance sensors.
3. The device as claimed in claim 1, characterized in that the processing unit (104) with classification unit, wherein the classification unit comprises an AI-based or rule-based classifier executing a quantised convolutional neural network trained on venom biomarker datasets.
4. The device as claimed in claim 1, characterized in that the programmable microfluidic drug delivery unit (106) includes a multi-chamber or single-chamber lyophilised cartridge, sterile diluent pouch, solenoid valves, and a piezoelectric micropump.
5. The device as claimed in claim 1, characterized in that it further comprises a housing module (108) with tamper detection membrane, manual override lever, and EMI shielding.
6. The device as claimed in claim 1, characterized in that it further comprises a communication subsystem (110) for encrypted OTA updates and telemetry transmission over BLE, GSM, or satellite, wherein the telemetry transmission includes sending location, venom classification result, dose status, and device integrity logs.
7. The device as claimed in claim 1, characterized in that it further comprises a capillary wicking strap configured to draw interstitial or lymphatic fluid from a bite site located up to 50 cm away from the biosensor module (102).
8. A method (200) for autonomous detection and treatment of snakebite envenomation using a wearable closed-loop device (100),
characterized in that the method comprises the steps of:
a) Step-1 (201): acquiring (201) interstitial fluid or blood analyte data using a dual-modality biosensor array (102);
b) Step-2 (203): processing sensor data in real time using an AI-based classifier in the processing unit (104) to determine venom type and severity;
c) Step-3 (205): selecting and reconstituting a matched antivenom payload from a lyophilised multi-chamber cartridge;
d) Step-4 (207): delivering the antivenom via a programmable microfluidic actuator and subdermal injection mechanism (106); and
e) Step-5 (209): transmitting telemetry and status data through the communication channel (110).
9. The method as claimed in claim 8, characterized in that the processing step (203) includes fusing a 64-dimensional feature vector from the biosensor signals and classifying venom type using the AI model.
10. The method as claimed in claim 8, characterized in that the wearable configuration of the device (100) used in the method is selected from wristband, armband, leg wrap, chest patch, boot insert, or modular PPE pod.