Autonomous IoT-based emergency coordination device with predictive biomedical risk analysis and automated response control

Abstract

A structurally integrated, autonomous, IoT-based emergency coordination device configured for predictive biomedical risk analysis and automated response control, comprising the following: a rigid housing suitable for mechanical mounting on a machine structure or fixed infrastructure; an energy management unit located inside the housing, configured to receive an external electrical supply and provide regulated energy to internal components; at least one energy storage unit that is electrically connected to the energy management unit to ensure an emergency power supply; a biomedical signal acquisition unit consisting of analog input circuitry, impedance matching networks and high-resolution analog-to-digital converters configured to receive physiological data from at least one of the following sensors: a wearable sensor, a non-contact radar sensor, an optical photoplethysmograph sensor and an acoustic breathing sensor; an environmental sensor unit configured to measure at least temperature, gas concentration and particle density near the housing; a processing unit consisting of a multi-core processor and non-volatile memory for storing executable instructions; a communication unit comprising at least one wireless transceiver and at least one wired network interface; and An actuator interface unit with relay drivers and digitally addressable outputs for controlling at least one structural or machine-based actuator, wherein the processing unit is configured to receive biomedical and environmental data, generate a predictive biomedical risk score using time series analysis and probabilistic state modeling, and send control signals to the actuator interface unit when the predictive biomedical risk score exceeds an adaptive threshold.

Description

Application area of ​​the invention

[0001] The present invention relates generally to intelligent emergency response technologies and in particular to a structurally integrated, autonomous, Internet of Things-enabled emergency coordination device configured for predictive biomedical risk analysis and automated response control in real time within a machine or fixed structure. Background of the invention

[0002] Conventional emergency warning systems in buildings, industrial plants, and transportation infrastructure typically rely on manual activation or threshold-based sensor triggers such as smoke, temperature, or panic buttons. These systems do not integrate predictive biomedical analytics, multisensor correlation, or autonomous coordination with distributed actuators and external response networks. Furthermore, existing wearable medical alarms operate independently of the building infrastructure and do not allow for coordinated machine-level interventions, such as power outages, adjustments to environmental conditions, access control overrides, or automated alarm notification. Therefore, there is a need for an integrated, building-integrated device that continuously monitors biomedical and environmental parameters, performs predictive risk assessments using embedded artificial intelligence, and autonomously orchestrates coordinated emergency responses.

[0003] Historically, existing emergency coordination technologies have evolved in parallel but independent fields, including fire alarm systems, industrial safety interlocks, portable biomedical monitors, building management systems, and telemedicine platforms. Traditional fire alarm systems primarily rely on threshold-based smoke detectors, heat sensors, and manual call points connected to central fire alarm control panels. While these systems typically meet building safety standards and effectively detect combustion hazards, they operate with a deterministic triggering logic that only activates when a physical parameter exceeds a predefined threshold. Such reactive architectures do not incorporate predictive analytics or contextual biomedical monitoring.Therefore, they cannot detect pre-symptomatic physiological stresses in those present, such as cardiac arrhythmias or a decrease in blood oxygen levels, which can precede or worsen an emergency. Furthermore, conventional fire alarm control panels lack the integration of personal health data, preventing them from dynamically adapting evacuation or ventilation strategies to the health status of those present.

[0004] Industrial safety systems in production facilities and environments with heavy machinery are typically based on programmable logic controllers (PLCs) connected to emergency stop circuits, overload relays, vibration sensors, and gas detection modules. These systems are designed for deterministic machine protection and fail-safe shutdown upon detection of abnormal mechanical or chemical parameters. While they ensure the safety of the equipment, they generally do not consider individual, human-centered biomedical risk assessments. For example, while gas detection systems can trigger ventilation once a hazardous concentration threshold is reached, they do not assess the physiological effects on nearby workers in real time.Consequently, there is a time gap between the detection of an environmental hazard and the determination of a potential impact on human health. Furthermore, conventional industrial control systems often operate in isolated network environments with limited external connectivity, preventing coordinated communication with medical services or higher-level emergency infrastructures.

[0005] Another group of existing solutions includes emergency call boxes and panic alarms in public areas and industrial facilities. These devices allow manual activation by the user to request assistance. While helpful in certain situations, they respond exclusively to emergencies and rely on the affected person recognizing the emergency and activating the alarm mechanism. In cases of sudden cardiac arrest, fainting, or poisoning, the affected person may be incapacitated before activation. Furthermore, panic alarm systems do not perform predictive analysis or correlate with the environment and cannot automatically shut down machinery or adjust ventilation to prepare for a deterioration in the user's physiological condition.

[0006] Recent advances in the Internet of Things (IoT) have enabled distributed sensor networks that allow for environmental monitoring and cloud-based data aggregation. IoT gateways collect data from gas sensors, temperature sensors, and presence detectors and transmit it to central dashboards. While such systems improve asset health visibility, they typically use rule-based automation scripts rather than adaptive predictive models. Many IoT implementations also rely on cloud processing for analytics, which introduces potential latency and a dependency on continuous network connectivity. In emergency situations such as network or power outages, cloud-based architectures can become inoperable. Existing IoT security implementations may lack hardware-based cryptographic protection and are therefore vulnerable to unauthorized command injection or data manipulation.

[0007] Artificial intelligence is being used in certain areas of healthcare and industry to predict equipment failures or disease progression. Predictive maintenance systems use vibration and thermal data to anticipate mechanical failures, while healthcare analytics platforms analyze large datasets to identify cardiovascular risk factors. However, these systems are typically domain-specific and operate independently. Predictive maintenance systems do not integrate human biomedical data, and healthcare AI platforms do not control industrial actuators. The lack of cross-domain integration prevents comprehensive situational awareness in environments where machine hazards and human health risks are interdependent.

[0008] Another limitation of existing solutions is the lack of adaptive thresholds based on individual baseline profiles. Many conventional alarm systems use static thresholds, such as fixed heart rate limits or gas concentration values. Such fixed thresholds do not account for interindividual variability or contextual environmental influences. For example, a physically active worker may naturally have an elevated heart rate and respiration rate, which can lead to false alarms when static thresholds are used. Conversely, a gradual decline may go undetected if the thresholds are set too conservatively. Existing systems rarely implement dynamic probabilistic models that continuously update baseline physiological parameters and correlate them with environmental influences.

[0009] Power supply security and fail-safe operation represent additional vulnerabilities in many current implementations. Conventional emergency systems may rely solely on mains power with limited backup runtime. Portable devices are limited by small battery capacities and require regular recharging. Cloud-based IoT systems become inoperable during network outages. In high-risk industrial environments or during natural disasters, power and connectivity interruptions are frequent, compromising the reliability of existing emergency monitoring infrastructures.

[0010] Security vulnerabilities further complicate the implementation of networked security systems. Many older control systems were not designed with integrated cryptographic authentication or secure startup mechanisms. Therefore, integrating biomedical monitoring and control functions raises cybersecurity concerns. Unauthorized access could manipulate actuator commands or falsify sensor data. Current solutions rarely integrate hardware-based encryption accelerators or tamper detection circuits into structurally integrated emergency devices.

[0011] Existing emergency response technologies are characterized by functional fragmentation, reactive logic architectures, limited predictive biomedical analytics, insufficient integration with structural actuators, the use of static thresholds, reliance on external connectivity, and inadequate cybersecurity safeguards. These shortcomings collectively hinder the development of a unified, autonomous, and predictive emergency coordination device capable of simultaneously monitoring human physiological states and environmental conditions and implementing safe, automated response control within a machine or structural framework. Summary of the invention

[0012] The present invention relates to an autonomous, IoT-based emergency coordination device that is structurally integrated into a building, industrial machine, vehicle, or fixed infrastructure. The device comprises a housing that can be mechanically attached to a wall panel, a device chassis, a control cabinet, or a vehicle frame. The housing contains a multilayer printed circuit board, a circuit for acquiring biomedical signals, environmental sensor arrays, a multi-core processor unit, non-volatile memory, a communication transceiver array, a power management system, and an actuator interface circuit.

[0013] The device continuously acquires biomedical signals from one or more nearby individuals via wireless body network interfaces, contactless radar-based vital sign sensors, optical photoplethysmographs, and acoustic breath analyzers. Simultaneously, it acquires environmental data such as temperature, gas concentration, particle density, vibration intensity, structural stress, and electromagnetic interference. The processing unit performs a predictive biomedical risk analysis by extracting features from time series, applying adaptive filtering, and utilizing probabilistic state models to calculate individual risk scores. Upon detecting an elevated risk, the device autonomously initiates a response by sending control signals to connected machine systems or actuators. These include, but are not limited to, isolating relays, ventilation controllers, access control locks, audible and visual alarms, and external emergency communication systems.

[0014] The present invention aims to provide an autonomous, IoT-based emergency coordination device that is integrated into a machine or fixed infrastructure and continuously acquires biomedical signals in real time and performs predictive risk analyses to detect impending medical emergencies before a critical physiological breakdown occurs. The invention overcomes the limitations of reactive alarm systems by directly integrating artificial intelligence into the device, thus enabling the proactive detection of abnormal physiological trends based on dynamic modeling instead of static threshold triggers.

[0015] A further objective of the invention is to provide a unified device architecture that integrates biomedical monitoring, environmental sensing, embedded processing, secure communication, and actuator control into a single, structurally mountable package. This eliminates the fragmentation present in conventional systems, where wearable devices, industrial safety controllers, and building management platforms operate independently. The invention aims to establish synchronized coordination between human health parameters and surrounding environmental conditions, enabling the detection and coherent management of complex risk states, such as simultaneous exposure to toxic substances and decreasing oxygen saturation.

[0016] Another objective of the invention is predictive biomedical risk analysis using adaptive baseline profiling and time series modeling. The device continuously updates individual physiological reference patterns and evaluates deviations using probabilistic inference models. This ensures emergency detection tailored to the individual and their context, thereby reducing false alarms and minimizing undetected deterioration. By integrating hardware to accelerate artificial intelligence into the device, the invention enables local edge computing solutions without dependence on cloud processing. This reduces latency and improves reliability during network outages.

[0017] A further objective of the invention is to provide automated response control mechanisms that can interact directly with actuators at the structural and machine levels, including isolating relays, ventilation control systems, braking systems, access control systems, and signal transmitters. Through this integration, the device is configured not only to issue warnings but also to independently initiate mechanical and environmental protective measures in response to detected risk situations. This ensures that emergency measures begin immediately after predictive detection, thereby reducing reaction time and minimizing subsequent hazards.

[0018] A further objective of the invention is to ensure operational reliability through integrated energy management and fail-safe functions. These include automatic switching to internal energy storage upon detection of an external power outage, intelligent energy optimization in stable monitoring states, and prioritized processing in critical situations. The invention thus aims to maintain continuous emergency monitoring and response capability even in the event of infrastructure failures or disaster scenarios.

[0019] A further objective of the invention is to provide secure communication and command authentication through hardware-based cryptographic mechanisms, secure boot firmware validation, and encrypted data transmission in IoT networks. This objective addresses cybersecurity concerns associated with networked security systems and ensures that actuator control commands cannot be manipulated by unauthorized persons. The invention is thus designed for reliable operation in industrial, commercial, and public infrastructure environments where system integrity is of critical importance.

[0020] A further objective of the invention is modular expandability through a multi-layered internal structure, which enables the integration of additional sensor boards or special detection components without requiring a complete system replacement. This modularity ensures adaptability to various operating environments, including industrial plants, traffic hubs, healthcare facilities, and hazardous materials handling plants, while maintaining a standardized core architecture.

[0021] A further objective of the invention is to reduce dependence on manual intervention through contactless biomedical monitoring using radar-based and optical sensor technologies. This enables the detection of physiological emergencies, even when affected individuals are unable to activate alarm devices. The invention thus aims to protect incapacitated or unconscious persons in risk situations by initiating emergency protocols autonomously and without user intervention.

[0022] The invention also aims to provide a hierarchical decision logic that prioritizes and coordinates multiple simultaneously occurring emergency situations. This ensures that automated responses do not conflict with each other and that system resources are allocated according to the severity of the risk. Through the intelligent orchestration of environmental controls, mechanical protection mechanisms, and communication channels, the device achieves coherent and context-aware emergency management.

[0023] A higher objective of the invention is ultimately to create an integrated, predictive, safe and structurally embedded emergency coordination device that goes beyond conventional reactive safety mechanisms by combining biomedical analytics, environmental information and automated mechanical control in a single autonomous system capable of making context-aware decisions in real time and taking protective measures. BRIEF DESCRIPTION OF THE IMAGE

[0024] These and other features, aspects and advantages of the present invention will be better understood if the following detailed description is read with reference to the accompanying drawing, in which the same symbols represent the same parts: Fig. Figure 1 shows a block diagram of a structurally integrated, autonomous, IoT-based emergency coordination device configured for predictive biomedical risk analysis and automated response control.

[0025] Furthermore, those skilled in the art will recognize that the elements in the drawing are simplified and not necessarily drawn to scale. For example, the flowcharts illustrate the process by highlighting the main steps to facilitate understanding of the present disclosure. With regard to the construction of the device, one or more components may be represented in the drawing by conventional symbols. The drawing may show only the specific details relevant to understanding the embodiments of the present disclosure, so as not to clutter the drawing with details that are already apparent to those skilled in the art from the description contained herein. Detailed description of the invention

[0026] To facilitate understanding of the principles of the invention, reference is made below to the embodiment shown in the drawing, which is described using specific terms. It is understood, however, that this does not limit the scope of protection of the invention. Rather, modifications and further developments of the depicted system, as well as further applications of the inventive principles shown therein, are conceivable, insofar as they would normally occur to a person skilled in the art in the field of the invention.

[0027] It will be clear to those skilled in the art that the foregoing general description and the following detailed description are exemplary and explanatory of the invention and are not to be understood as a limitation thereof.

[0028] References to “an aspect”, “another aspect”, or similar phrases in this description mean that a particular feature, structure, or property described in connection with the embodiment is included in at least one embodiment of the present disclosure. Therefore, phrases such as “in one embodiment”, “in another embodiment”, and similar expressions in this description may, but do not necessarily, all refer to the same embodiment.

[0029] The terms "includes," "comprehensive," or similar expressions denote non-exclusive inclusion. Thus, a procedure or method containing a list of steps does not only include those steps but may also include further steps not explicitly listed or inherent in the procedure or method. Likewise, the statement "includes..." for one or more devices, subsystems, elements, structures, or components, without further limitations, does not preclude the existence of other devices, subsystems, elements, structures, or components.

[0030] Unless otherwise defined, all technical and scientific terms used herein have the same meanings generally known to those skilled in the art in the field to which this invention belongs. The systems, methods, and examples described herein serve only for illustration and are not to be understood as limiting.

[0031] Embodiments of the present disclosure are described in detail below with reference to the attached drawing.

[0032] Fig.Figure 1 shows a block diagram of a structurally integrated, autonomous, IoT-based emergency coordination device for predictive biomedical risk analysis and automated response control. The system 100 comprises: a rigid enclosure (102) for mechanical mounting on a machine structure or fixed infrastructure; an enclosed power management unit (104) for external power supply and regulated power supply to internal components; at least one energy storage device (106) for emergency power supply; a biomedical signal acquisition unit (108) with analog input circuitry, impedance matching networks, and high-resolution analog-to-digital converters for acquiring physiological data from at least one of the following sensors: wearable sensor, non-contact radar sensor (108a), optical photoplethysmograph, or acoustic respiratory sensor; and an environmental sensor unit (110) for measuring temperature, gas concentration, and particle density near the enclosure.A processing unit (112) with a multi-core processor and non-volatile memory for storing executable instructions; a communication unit (114) with at least one wireless transceiver and at least one wired network interface; and an actuator interface unit (116) with relay drivers and digitally addressable outputs for controlling at least one structural or machine-based actuator, wherein the processing unit is configured to receive biomedical and environmental data, generate a predictive biomedical risk score using time series analysis and probabilistic state modeling, and send control signals to the actuator interface unit when the predictive biomedical risk score exceeds an adaptive threshold.

[0033] In one embodiment, the biomedical signal acquisition unit (108) further comprises an adaptive filter circuit configured to suppress motion artifacts and reject electromagnetic interference prior to analog-to-digital conversion. The high-resolution analog-to-digital converters have a resolution of at least 24 bits and a sampling rate that is dynamically adjusted by the processing unit in response to the detected signal variability.

[0034] In one embodiment, the contactless radar sensor (108a) comprises a frequency-modulated continuous-wave radar transceiver arranged behind a non-metallic window of the housing, wherein the processing unit is configured to extract thoracic displacement signals corresponding to cardiac and respiratory activity by demodulating reflected radar frequency displacements and applying digital bandpass filtering to isolate physiological frequency components.

[0035] In one embodiment, the processing unit (112) is configured to segment incoming biomedical data into overlapping time windows, compute statistical, frequency domain-related and nonlinear dynamic features for each time window, normalize feature vectors relative to individualized base profiles stored in non-volatile memory, and determine a probabilistic classification of health status using a recurrent neural network architecture that is executed locally within the multi-core processor.

[0036] In one embodiment, the processing unit (112) is further configured to continuously update the individualized base profiles based on a continuous historical data set of physiological parameters acquired during non-emergency operating conditions, and to adjust the adaptive threshold based on variance and trend analyses derived from the continuous historical data set in order to reduce false positive and false negative emergency activations.

[0037] In one embodiment, the environmental sensor unit (110) further comprises a carbon monoxide sensor, a volatile organic compound sensor, a humidity sensor and a vibration sensor, wherein the processing unit is configured to calculate a multivariate correlation matrix between environmental measurements and biomedical parameters to identify combined risk states that indicate simultaneous environmental hazard and physiological stress.

[0038] In one embodiment, the actuator interface unit (116) is electrically connected to at least one of the following components: an electrical isolation relay, a ventilation control actuator, an automatic braking mechanism, an access control lock, and / or an acoustic or visual alarm device. The processing unit is configured to generate prioritized control commands based on a hierarchical decision logic that prevents conflicting actuator activations in the event of simultaneous emergency situations.

[0039] In one embodiment, the communication unit (114) comprises a cellular narrowband Internet of Things transceiver, a wireless local area network interface, a wireless short-range interface for body network communication, and an Ethernet interface, wherein the processing unit is configured to encrypt outgoing telemetry data and actuator control acknowledgments prior to transmission using a hardware-based cryptographic co-processor.

[0040] In one embodiment, the energy management unit (104) comprises an overvoltage protection circuit, voltage regulation circuits, a battery charging control circuit, and an automatic power source switchover configured to switch from the external power supply to the energy storage unit upon detection of a power interruption. The processing unit is configured to enter a power-saving mode during non-critical monitoring and to increase the sampling rate and computational intensity upon detection of a physiological anomaly.

[0041] In one embodiment, the rigid housing further comprises heat dissipation structures, vibration-damped mounting brackets, and seals to protect against the ingress of foreign matter. The internal components are arranged in a layered configuration, consisting of a lower power distribution board, a middle sensor acquisition board, and an upper processing board, interconnected via shielded board-to-board connectors to minimize electromagnetic interference from adjacent machines.

[0042] The autonomous, IoT-based emergency coordination device operates with an integrated computing architecture that continuously collects, preprocesses, analyzes, and correlates biomedical and environmental data streams to generate predictive risk assessments that guide automated response control. The processing unit's technique is structured as a multi-stage pipeline that combines signal conditioning, feature extraction, individual baseline modeling, probabilistic state estimation, adaptive threshold control, and prioritized actuator command generation.

[0043] During system initialization, the Secure Boot firmware, stored in non-volatile memory, verifies the integrity of the executable instructions using hardware-based cryptographic validation. Upon successful validation, the processing unit activates the biomedical signal acquisition unit and the environmental sensing unit, synchronizing their sampling rates to ensure the temporal alignment of heterogeneous data sources. Biomedical input data can originate from wearable sensors via short-range wireless interfaces or from integrated, contactless radar and optical sensors within the housing. Environmental measurements are performed simultaneously by sensors for gas, temperature, humidity, vibration, and particles.

[0044] The first technical stage comprises adaptive signal processing. Raw biomedical signals are amplified analogously and then digitized using high-resolution analog-to-digital converters. The processing unit applies digital filter sequences, including FIR bandpass filters, configured to isolate physiological frequency bands corresponding to cardiac and respiratory activity. Motion artifacts are suppressed by correlating accelerometer data with electrocardiographic or photoplethysmographic waveform distortions. This enables the subtraction of motion-related components via adaptive least-mean-squares filtering. Electromagnetic interference is suppressed by spectral notch filtering targeting mains frequencies and harmonics.In radar-based signals, frequency demodulation methods convert reflected frequency shifts into displacement waveforms that represent thoracic micromovements. Subsequently, envelope detection is performed to extract cardiac and respiratory cycles.

[0045] After signal conditioning, the processing unit temporally segments the continuous data streams by dividing them into overlapping windows of predefined duration. Within each time window, a feature extraction routine computes a comprehensive set of descriptors. Time-domain features include mean amplitude, variance, RMS value, heart rate interval variability, and respiratory interval distribution. Frequency-domain features are derived using discrete Fourier transforms to quantify spectral power within predefined physiological frequency bands. Nonlinear dynamic features, including entropy measures and fractal dimension indicators, are calculated to capture subtle irregularities in physiological rhythms. Environmental data are processed analogously to derive statistical and trend-based descriptors, including rates of change for gas concentration and temperature gradients.

[0046] The extracted features are normalized using individual baseline profiles stored in non-volatile memory. During initial setup, the device undergoes a calibration phase in which baseline physiological parameters are acquired under stable operating conditions. These baseline profiles are continuously refined using a constantly updated dataset from ring buffers. The processing unit calculates moving averages and measures of variance to dynamically adjust the expected physiological ranges. This adaptive baseline modeling reduces sensitivity to interindividual differences and long-term physiological changes.

[0047] Predictive biomedical risk analysis is performed locally on the multi-core processor using a recurrent neural network architecture optimized for time-series forecasting. The neural network receives normalized feature vectors from successive time windows and estimates the probability distribution of future physiological states. To improve interpretability and stability, the neural network output is integrated with a probabilistic state transition model that encodes conditional dependencies between physiological and environmental variables. This probabilistic layer evaluates combined risk states by calculating joint probabilities, such as a simultaneous drop in oxygen saturation and an increase in carbon monoxide concentration.This identifies dangerous synergistic conditions that may not exceed the thresholds of individual parameters.

[0048] The system uses adaptive threshold control to detect when an emergency is imminent. Instead of fixed trigger limits, the device calculates a risk score derived from weighted probabilities of adverse conditions predicted by a neural network and a probabilistic model. The weighting factors are dynamically adjusted based on the severity of environmental conditions, historical false alarm patterns, and system confidence metrics. Subsequently, a decision threshold is calculated as a function of baseline variability and recent trend acceleration. If the risk score exceeds this adaptive decision threshold for a configurable period, the system transitions from monitoring to pre-alert mode. If the elevated risk persists or worsens, the system enters a confirmed emergency state.

[0049] Upon entering a confirmed emergency state, the processing unit executes a hierarchical response control procedure. This procedure accesses a stored response matrix that assigns specific risk classifications to corresponding actuator actions. The response matrix contains priority indices to prevent conflicting commands. For example, in a scenario involving toxic gas exposure and cardiovascular instability, the procedure prioritizes activating ventilation and electrical isolation, while simultaneously unlocking access control for evacuation. The processing unit sends digitally signed control signals to the actuator interface, which controls relay circuits or digital communication lines connected to the building systems. Acknowledgement signals from the actuators are monitored to verify successful execution.

[0050] Simultaneously, the communication unit generates encrypted telemetry packets containing time-stamped biomedical data, environmental measurements, risk assessments, and actuator status information. Hardware-based cryptographic acceleration ensures secure encryption before transmission via cellular narrowband or local network interfaces. In the absence of a network connection, the device stores event logs locally and autonomously continues executing the response protocols.

[0051] To ensure reliability, various energy management techniques operate in parallel. Under normal operating conditions, the system maintains energy-efficient sampling intervals. Upon detection of an anomaly, the processing unit instructs the energy management unit to increase the sampling frequency and computing power, thereby improving the temporal resolution of critical events. If an external power supply failure is detected, an automatic switching circuit transfers operation to the energy storage without interrupting monitoring or actuator control.

[0052] Throughout operation, continuous self-diagnostic processes monitor sensor integrity, signal quality indices, memory usage, and communication reliability. In the event of sensor malfunctions or reduced signal quality, the system recalibrates the weighting factors to prevent erroneous emergency classifications.

[0053] By integrating adaptive signal processing, individualized baseline modeling, prediction using recurrent neural networks, probabilistic state correlation, dynamic threshold control, and prioritized actuator control in a structurally integrated package, the device enables predictive biomedical risk analysis combined with automated response control. The technical architecture ensures low-latency decision-making, context-aware detection of complex hazards, reduced false alarm rates, and autonomous protective intervention in machine or infrastructure environments.

[0054] The autonomous emergency coordination device is housed in a robust enclosure made of flame-retardant polycarbonate or anodized aluminum alloy. The enclosure features a weatherproof front panel with transparent optical windows for photonic sensors and radar apertures. The rear panel is equipped with vibration-damped mounting brackets and attachment points for secure integration into a machine frame, industrial cabinet, or building wall.

[0055] Internally, the device features a multi-layered architecture. The bottom layer comprises a power supply board with overvoltage protection circuits, voltage regulators, battery charge controllers, and an additional energy storage system with lithium iron phosphate cells. The middle layer includes a data acquisition board with analog input amplifiers, impedance matching networks, analog-to-digital converters with at least 24-bit resolution for precise biomedical signal processing, and digital isolation barriers to protect against electromagnetic interference from nearby machines. The top layer comprises a processing board with a multi-core microprocessor featuring integrated neural network acceleration, hardware cryptography coprocessors, and memory for secure boot firmware.

[0056] The device features a multi-protocol communication system with cellular narrowband IoT transceivers, WLAN modules, Bluetooth Low Energy interfaces for integration into wearables, and a wired Ethernet interface for integration into industrial networks. The housing is also equipped with cooling fins and internal temperature sensors to ensure stable operation even in high-temperature industrial environments.

[0057] During operation, the device continuously records biomedical signals from authorized personnel within a predefined radius. The biomedical monitoring system captures heart rate variability, ECG waveform morphology, blood oxygen saturation, respiratory rate, body temperature, and movement patterns. Frequency-modulated continuous-wave radar is used for non-contact monitoring to detect thoracic micromovements associated with cardiac and respiratory cycles. The analog biomedical signals are filtered using adaptive bandpass filtering to eliminate motion artifacts and electromagnetic interference from nearby machines.

[0058] The processing unit performs predictive biomedical risk analyses using a multi-stage computational pipeline. First, continuous signals are segmented into overlapping analysis windows. Statistical descriptors, frequency domain components via fast Fourier transform, indicators of nonlinear dynamics (including entropy measures), and morphological waveform metrics are then calculated. The extracted feature vectors are normalized and fed into a probabilistic classification architecture, implemented using a hybrid recurrent neural network and a Bayesian state transition model, and stored in non-volatile memory.

[0059] The predictive model estimates the probability of medical emergencies such as cardiac arrhythmias, hypoxia, syncope, heatstroke, inhalation of toxic substances, and stress-related cardiovascular instability. Simultaneously, environmental parameters are correlated with biomedical indicators using multivariate covariance modeling to identify combined risk conditions, such as elevated carbon monoxide levels in conjunction with decreasing oxygen saturation.

[0060] If the calculated risk probability exceeds an adaptive threshold, determined using historical baseline data, the automatic response control system is activated. The device generates structured control signals that are transmitted to connected machine systems via relay drivers or digital communication interfaces. In a building, for example, the device can automatically unlock emergency exits, activate ventilation systems, illuminate escape routes, and send real-time telemetry data to a central emergency coordination server. In an industrial machine, the device can interrupt electrical supply lines, slow down mechanical rotating assemblies, depressurize fluid lines, and activate mechanical braking systems.

[0061] The automatic response control uses a hierarchical prioritization technique to prevent conflicting trigger commands. For example, if a fire alarm and a medical emergency occur simultaneously, the device coordinates evacuation signals while also ensuring smoke extraction. Redundant communication channels guarantee reliable notification of remote emergency responders via encrypted telemetry packets transmitted over cellular IoT networks.

[0062] The device also features secure cryptographic authentication protocols to prevent unauthorized command injection. All outgoing and incoming communication is encrypted using hardware-based cryptographic acceleration, and firmware integrity is verified at startup through secure boot validation.

[0063] To ensure operational reliability, the energy management system automatically switches to battery operation in the event of a power outage. Energy optimization techniques reduce the sensor sampling frequency under stable conditions and increase the resolution when anomalies are detected, thereby extending the operating time during a power failure.

[0064] The device's structural configuration allows for modular expansion. Additional sensor boards can be inserted via internal expansion ports to support specific industrial applications, such as modules for the spectroscopy of toxic gases or high-precision strain gauge arrays for structural monitoring.

[0065] This autonomous, IoT-based emergency coordination device is suitable for smart buildings, industrial plants, hazardous materials processing facilities, mines, transportation hubs, healthcare facilities, and environments with heavy machinery. By integrating predictive biomedical analytics with automated structural response mechanisms, the device reduces emergency response time, increases employee safety, and minimizes consequential damage to facilities and infrastructure.

[0066] The present invention provides a structurally integrated, autonomous device that enables continuous biomedical monitoring, predictive risk calculation, and coordinated, automated response control in machines or buildings. By combining IoT connectivity, integrated artificial intelligence, secure communication, and actuator interfaces in a robust mechanical housing, the device enables proactive emergency management and represents a significant improvement over conventional reactive alarm systems.

[0067] The structurally integrated, autonomous, IoT-based emergency coordination device is implemented as a physically realized electromechanical assembly, in which each component corresponds to a specific hardware structure within a robust housing. The housing is made of impact-resistant polymer or metal and features mounting holes, threaded inserts, and vibration dampers for secure mounting to a machine frame or infrastructure surface. The power management unit is implemented as a discrete voltage regulation circuit on a dedicated printed circuit board and includes surge protection, rectifier stages, switching or linear regulators, current limiting circuits, and battery charge controllers, constructed from semiconductor switching elements, inductors, transformers, capacitors, and fuses.The energy storage unit is a rechargeable battery or an electrochemical storage device that is electrically coupled via conductors and protective circuits to ensure an uninterrupted power supply in the event of a power outage.

[0068] The biomedical signal acquisition unit is implemented as an analog hardware subsystem and comprises low-noise instrumentation amplifiers, impedance matching networks consisting of resistive and capacitive elements, hardware anti-aliasing filters, and high-resolution analog-to-digital converters in semiconductor devices with at least 24-bit quantization. Adaptive filtering is achieved using programmable amplifiers and analog filter stages that attenuate motion artifacts and electromagnetic interference before digitization. When using a contactless radar sensor, this consists of a physically integrated, frequency-modulated continuous-wave radar transceiver with a microwave oscillator, antenna structure, mixer circuitry, and intermediate frequency processing stages. These components are located behind a non-metallic window in the housing to enable electromagnetic transmission and reception.Optical photoplethysmography is implemented using light-emitting diodes and photodiode detectors on a sensor substrate, while acoustic respiratory detection utilizes a microelectromechanical microphone or a pressure transducer in an acoustic connector. The environmental sensor unit comprises discrete temperature sensors, gas detection cells with electrochemical or semiconductor-based gas-sensitive elements, particle measurement chambers with optical scatter detection, capacitive humidity sensors, and vibration sensors, each physically mounted and electrically connected to the sensor data acquisition board.

[0069] The processing unit is implemented as an integrated multi-core microprocessor or microcontroller, soldered onto the upper processing board and electrically connected to non-volatile memory such as flash memory chips for storing executable instructions and individual base data sets. The recurrent neural network calculations, probabilistic state modeling, feature extraction, time series segmentation, and adaptive threshold calculations are performed by physical processing units within the processor cores using hardware timers, DMA controllers, and cache subsystems. Continuous updating of individual base profiles is achieved through repeated writes to non-volatile memory sectors, controlled by the memory management circuitry integrated into the processor architecture.

[0070] The communication unit comprises hardware for wireless and wired interfaces, including integrated RF transceivers, antenna elements, impedance matching networks, physical layer Ethernet transceivers, magnet modules, and short-range radio communication chips mounted on the processor board. Encryption operations are performed by a hardware-based cryptographic coprocessor, implemented as a dedicated semiconductor circuit, enabling secure key storage and hardware-accelerated cryptographic transformations. The actuator interface is implemented as an output driver circuit comprising opto-isolated relay drivers, transistorized switching stages, digitally addressable outputs, and protection diodes, and is physically connected to external actuators such as isolating relays, brake solenoids, ventilation motors, access control locks, and alarm devices.The hierarchical decision logic is executed within the processor, but leads to the actual electrical actuation via these driver circuits by supplying controlled current or voltage to the connected mechanical or electromechanical actuators.

[0071] The automatic power supply switching uses comparator circuits and semiconductor or electromechanical switching elements that switch the load supply between the external input and the energy storage device in the event of a power interruption. Heat dissipation structures such as heat sinks, thermal pads, and conductive housing parts are attached to the heat-generating integrated circuits, while gaskets and O-rings provide protection against external influences. The internal, multi-layered configuration comprises physically separate circuit boards: a lower power distribution board with high-current lines, a middle data acquisition board with analog circuitry and electromagnetic shielding, and an upper processing board with digital computing and communication components. These are interconnected via shielded connectors and grounded shielding pads to minimize electromagnetic interference. REFERENCES 100 A structurally integrated, autonomous, IoT-based emergency coordination device configured for predictive biomedical risk analysis and automated response control. 102 rigid case 104 Energy Management Unit 106 at least one energy storage unit 108 biomedical signal acquisition unit 108a contactless radar sensor 110 Environmental sensor unit 112 processing units 114 Communication unit 116 Actuator interface unit

Claims

A structurally integrated, autonomous, IoT-based emergency coordination device configured for predictive biomedical risk analysis and automated response control, comprising: a rigid enclosure suitable for mechanical mounting on a machine structure or fixed infrastructure; an enclosure-integrated power management unit configured to receive an external electrical supply and provide regulated power to internal components; and at least one energy storage unit electrically connected to the power management unit to ensure emergency power supply.a biomedical signal acquisition unit, consisting of analog input circuitry, impedance matching networks, and high-resolution analog-to-digital converters, configured to receive physiological data from at least one of the following sensors: a wearable sensor, a non-contact radar sensor, an optical photoplethysmograph sensor, and an acoustic respiratory sensor; an environmental sensor unit configured to measure at least temperature, gas concentration, and particle density near the enclosure; a processing unit consisting of a multi-core processor and non-volatile memory for storing executable instructions; a communication unit comprising at least one wireless transceiver and at least one wired network interface;and an actuator interface unit with relay drivers and digitally addressable outputs for controlling at least one structural or machine-based actuator, wherein the processing unit is configured to receive biomedical and environmental data, generate a predictive biomedical risk score using time series analysis and probabilistic state modeling, and send control signals to the actuator interface unit when the predictive biomedical risk score exceeds an adaptive threshold. Device according to claim 1, wherein the biomedical signal acquisition unit further comprises an adaptive filter circuit configured to suppress motion artifacts and to reject electromagnetic interference prior to analog-to-digital conversion, and wherein the high-resolution analog-to-digital converters have a resolution of at least 24 bits and a sampling rate that is dynamically adjusted by the processing unit in response to the detected signal variability. Device according to claim 1, wherein the non-contact radar sensor comprises a frequency-modulated continuous-wave radar transceiver arranged behind a non-metallic window of the housing, and wherein the processing unit is configured to extract thoracic displacement signals corresponding to cardiac and respiratory activity by demodulating reflected radar frequency displacements and applying digital bandpass filtering to isolate physiological frequency components. Device according to claim 1, wherein the processing unit is configured to segment incoming biomedical data into overlapping time windows, compute statistical, frequency domain-related and nonlinear dynamic features for each time window, normalize feature vectors relative to individualized base profiles stored in non-volatile memory, and determine a probabilistic health status classification using a recurrent neural network architecture executed locally within the multi-core processor. Device according to claim 1, wherein the environmental sensor unit further comprises a carbon monoxide sensor, a volatile organic compound sensor, a humidity sensor and a vibration sensor, and wherein the processing unit is configured to calculate a multivariate correlation matrix between environmental measurements and biomedical parameters to identify combined risk states indicative of simultaneous environmental hazard and physiological stress. Device according to claim 1, wherein the actuator interface unit is electrically connected to at least one of the following elements: an electrical isolation relay, a ventilation control actuator, an automatic braking mechanism, an access control lock and / or an acoustic or optical alarm device; and wherein the processing unit is configured to generate prioritized control commands based on a hierarchical decision logic that prevents mutually contradictory actuator activations in the event of simultaneous emergency situations. Device according to claim 1, wherein the communication unit comprises a cellular narrowband Internet of Things transceiver, a wireless local area network interface, a wireless short-range interface for body network communication and an Ethernet interface, and wherein the processing unit is configured to encrypt outgoing telemetry data and actuator control acknowledgments prior to transmission using a hardware-based cryptographic co-processor. Device according to claim 1, wherein the energy management unit comprises an overvoltage protection circuit, voltage regulation circuits, a battery charging control circuit and an automatic power source switching circuit configured to switch from the external power supply to the energy storage unit upon detection of a power supply interruption, and wherein the processing unit is configured to enter a power-saving mode during non-critical monitoring and to increase the sampling frequency and computation intensity upon detection of a physiological anomaly. Device according to claim 1, wherein the rigid housing further comprises heat dissipation structures, vibration-damped mounting brackets and seals to protect against the ingress of foreign bodies, and wherein the internal components are arranged in a layered configuration consisting of a lower power distribution board, a middle sensor sensing board and an upper processing board, which are interconnected via shielded board-to-board connectors to minimize electromagnetic interference from adjacent machines.

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