Scalable realtime mesh network with universal artificial intelligence safety devices

Abstract

A safety monitoring AI system comprising interconnected safety AI wearable devices and a remote monitoring AI server to handle real-time data prioritizing urgent safety signals to optimize response time, wherein the safety wearable devices comprise multiple sensors (GPS, temperature, heart rate, humidity, VOC etc), advanced communication capabilities (LTE 4G, NB-IoT, LTE-M, NTN) and can be worn in various versatile configurations without any modification (belt, armband, ankle, helmet, etc); and wherein the AI devices form a peer-to-peer mesh network architecture exchanging relay signals to eliminate single points of failure, share safety-critical data through neighboring devices and preprocess data at device level. The invention allows to enhance workplace safety across various industries by offering a scalable, integrated solution that can adapt to different wearing configurations and operational requirements while maintaining robust communication and monitoring capabilities.

Description

[0001] SCALABLE REALTIME MESH NETWORK WITH UNIVERSAL ARTIFICIAL INTELLIGENCE SAFETY DEVICES

[0002] OBJECT OF THE INVENTION

[0003] The present invention relates to the field of safety monitoring, and in particular, to devices for health and safety monitoring in multiple environments.

[0004] It is an object of the present invention a safety monitoring Artificial Intelligence (Al) network comprising multiple safety wearable devices able to optimize response time and to be adapted to different wearing configurations and operational requirements while maintaining robust communication and monitoring capabilities.

[0005] Another object of the invention is a safety Al monitoring system able to eliminate single points of failure and to provide a scalable integrated solution.

[0006] A further object of the invention is a safety monitoring method for managing the safety monitoring system of the invention.

[0007] BACKGROUND ART

[0008] The improvement of workplace safety represents a fundamental global obligation that transcends national boundaries. Recent international statistics demonstrate the critical nature and worldwide scope of this challenge:

[0009] • United States: Approximately 2.7-2.8 million non-fatal workplace injuries and 4,500-5,000 fatal work injuries annually (Bureau of Labor Statistics)

[0010] • European Union: Over 3.1 million non-fatal workplace accidents and approximately 3,332 fatal accidents (Eurostat)

[0011] • China: Reports indicate approximately 27,412 work-related deaths annually (National Bureau of Statistics)

[0012] • India: Estimates suggest over 48,000 workplace fatalities annually (International Labour Organization)

[0013] • Australia: Over 120,000 serious workers' compensation claims and 183 fatalities (Safe Work Australia) • Japan: Approximately 125,000 workplace accidents and 845 fatalities annually (Ministry of Health, Labour and Welfare)

[0014] • Brazil: Reports over 700,000 workplace accidents annually (Ministry of Labor and Employment)

[0015] • Canada: Records approximately 264,000 workplace injuries and 1 ,027 fatalities annually (Association of Workers' Compensation Boards)

[0016] These figures encompass various industries, with particularly elevated incident rates in sectors such as construction, manufacturing, transportation, mining, and chemical processing. The rates demonstrate a clear global need for improved safety monitoring and prevention systems, particularly in production plants, oil refineries, mining operations, public works, road maintenance, ports, construction sites, airfields, manufacturing facilities and chemical processing plants.

[0017] Current market solutions demonstrate significant limitations in addressing global safety challenges, such as:

[0018] 1. Single-Purpose Devices:

[0019] ° Helmets with basic light / alert systems

[0020] ° Simple electronic beacons

[0021] ° Basic position markers for rescue

[0022] ° Standalone monitoring devices

[0023] 2. Key Limitations of Existing Solutions:

[0024] ° Limited to single functions or specific uses

[0025] ° Restricted to specific industries or activities

[0026] ° Constrained connectivity options

[0027] ° Lack of integrated monitoring capabilities

[0028] ° Absence of early alert systems

[0029] ° Limited environmental monitoring

[0030] ° No mesh network capabilities

[0031] ° Lack of global deployment options

[0032] 3. Technical Gaps:

[0033] ° No integration of advanced alert systems

[0034] ° Limited man-down detection capabilities ° Basic environmental monitoring

[0035] ° Restricted communication protocols

[0036] ° Limited scalability

[0037] ° No cross-border operation support

[0038] 4. Integration Limitations:

[0039] ° Poor compatibility with external systems

[0040] ° Limited protocol support (4G, LTE, Bluetooth)

[0041] ° Restricted utility in large-scale deployments

[0042] ° No unified global monitoring capabilities

[0043] Document US2012311761 discloses a wearable device that is designed for safety and carrying purposes. It consists of an elongated band, securely fastened using Velcro pads, and a pocket designed to hold a cell phone.

[0044] Document US2017229004 discloses a wearable device that helps to keep the wearer safe and can track their location. The device has sensors that can detect changes in voice, pulse, emotions, impact, motion and device's state. The device can be manually operated by the wearer or remotely operated. In an emergency situation, the device can activate an alarm, electric shock, GPS tracking, and send messages to a rescue team. It also has a camera and audio recording capabilities and can store data locally or send it to a server or cloud storage.

[0045] Document US2017309158 discloses an intelligent wearable device with an alarm function. The device is able to detect abnormal situations, to collect audio, video, pictures, and characters and to send alarm signals. The device sends the alarm information and evidence collection information to a background operation server. The device can be activated by pressing an alarm button or by monitoring user's heart rate, environmental sound, position, or vibrations.

[0046] Nevertheless, none of the solutions provided in the art allows to obtain a comprehensive universal portable safety wearable device that provides comprehensive monitoring and universal application capable of establishing a scalable mesh Al network for global safety control and monitoring. DESCRIPTION OF THE INVENTION

[0047] The present invention relates to a scalable safety Artificial Intelligence (Al) monitoring network and a safety Al monitoring system able to improve, manage, report, and control safety in real-time

[0048] In the invention proposed, the intelligent centralized Al safety monitoring system is able to process signals emitted concurrently from a plurality of safety wearable devices, which comprises sensors. The safety Al wearable devices measure real-time parameters including:

[0049] • Ambient temperature

[0050] • Precise positioning

[0051] • Air quality

[0052] • Humidity

[0053] • Body temperature

[0054] • Heat exposure

[0055] • Gas levels

[0056] • Toxic chemical elements and compounds

[0057] • Sound levels

[0058] • Impact detection

[0059] • Motion analysis

[0060] • Moving elements and objects

[0061] • High temperature materials

[0062] The scalable Al safety monitoring network comprises: two or more safety wearable devices.

[0063] Each of the safety wearable devices comprises, in turn, a positioning module configured to obtain a location. Also, each device comprises one or more sensors configured to obtain safety data from the user.

[0064] Each safety wearable device comprises also a processing module connected to the sensors and the positioning module and configured to obtain and process location data from the positioning module and safety data from the sensors, by performing edge computing. In addition, each safety wearable device comprises a communication module connected to the processing module and configured to automatically search for and connect to other safety wearable devices in a peer-to- peer basis. Also, the communication module is configured to transmit and receive processed safety data from the processing module and / or from other safety wearable devices based on proximity.

[0065] The connection into a peer-to-peer basis allows to automatically form the safety monitoring network by automatic safety wearable devices discovery.

[0066] All the elements of each safety wearable device are enclosed in a housing, such as the processing module, the sensors, the communication module and the positioning module.

[0067] Preferably, the housing could be ready to be worn without any further modification as: a belt mounting, vest attachment, ankle-band, helmet integration, neck-strap, leg band, wristband wearing, armband wearing, hand lanyard, etc.

[0068] Preferably, the communication module could be implemented in the form of a modem with advanced communication capabilities selected from: Bluetooth for mesh networking, LTE 4G and 5G for wide area connectivity, NB-loT and LTE-M for Internet of Things (loT) applications, and Non-Terrestrial Network (NTN) connectivity.

[0069] In some preferred embodiments, the positioning module could comprise a GPS for outdoor applications and / or is configured to use Bluetooth or Wi-Fi connectivity from the communication module for indoor applications.

[0070] The safety wearable devices are preferably connected using Bluetooth Low Energy (BLE), thus, forming the safety monitoring network, wherein each safety wearable device transmits and receives safety data from other safety wearable devices.

[0071] More preferably, the positioning module of each safety wearable device could be configured to obtain and / or refine a position by obtaining location data from other connected safety wearable devices (1) and by applying proximity-based location refinement techniques.

[0072] Also, the processing module could be configured to process the safety data by using pattern recognition or similar advanced Al algorithms.

[0073] In some preferred embodiments, each safety wearable device could be configured to perform edge-computing using Artificial Intelligence (Al) models for analysis of the safety data, priority-based signal routing, cross-device coordination, pattern recognition, alert generation and resource management.

[0074] Moreover, the connected safety wearable devices could be configured to distribute processing load between multiple processing modules. Also, the safety wearable devices could be configured to distribute data storage for data preservation between multiple processing modules.

[0075] Also, each safety wearable device could be configured to determine a received signal strength indicator (RSSI) from each of the safety wearable devices surrounding it.

[0076] Regarding the sensors of the safety wearable devices, they could be selected from: environmental temperature sensor; skin temperature sensor; proximity sensor; heart rate sensor; activity sensor; humidity sensor; and air quality sensor configured to detect volatile organic compounds (VOCs), particulate matter, harmful gases or toxic elements and oxygen levels.

[0077] Each safety Al wearable device could also comprise an emergency button configured to trigger an emergency alert.

[0078] Alternatively or complementary, the processing module of each safety wearable Al device could be configured to process safety data from the sensors and to trigger an emergency alert when predefined thresholds are exceeded. More preferably, the predefined thresholds could be adaptative. Also, in some embodiments, the sensors are activated automatically on a scheduled time previously defined.

[0079] Moreover, the safety Al wearable devices could further comprise a man-down module comprising a MEMS sensor. The man-down module could be configured to detect accelerometer data and to transmit said accelerometer data to the processing module, which could be configured to process and to determine unusual movement patterns by using pattern recognition or similar Al algorithms. More preferably, the unusual movement patterns could be sudden movements, impacts or prolonged inactivity.

[0080] When the emergency alert is activated, a transmission of the location and an emergency notification to a remote monitoring system and / or other safety wearable devices could be triggered.

[0081] Also, the safety wearable device could comprise alert signals, which are preferably selected from a RGB LED module, a buzzer, a speaker and emergency notifications.

[0082] In some embodiments, the RGB LED signals could comprise a LED cover, made of translucent material, and one or more LED lights covered by said LED cover.

[0083] The safety Al wearable device of the invention could further comprise a rechargeable battery.

[0084] In preferred embodiments, the communication module of each safety Al wearable device connects dynamically with different safety Al wearable devices, in a peer-to- peer connection, for defining new paths of the network. Thus, redundant paths are established. More preferably, low-power consuming paths are selected to transmit the safety information.

[0085] For optimizing the power management, also the processing module and the communication module could be configured to automatically set transmission power. Alternatively or complementary, the processing module and the communication module could be configured to optimize the coverage of the network. The coverage zones of the network are flexible due to its scalable configuration. Also, redundant coverage could be implemented in said scalable safety network.

[0086] The safety wearable devices could implement self-healing mechanisms, by automatically determining node failures and reconfiguring the network paths in case of node failures.

[0087] Preferably, in some embodiments, the communication module of the safety Al wearable devices could be configured to transmit low-bandwidth heartbeat synchro signals adjusted to a predefined frequency.

[0088] More preferably, the safety Al wearable devices could comprise optimized message hopping algorithms for low power consumption.

[0089] In some embodiments, when an alert is triggered by any of the safety Al wearable devices, a message propagation protocol is activated. The message propagation protocol transmits the safety data through multiple network paths. Also, said message propagation protocol could comprise message verification and priority handling mechanisms.

[0090] As explained, the present invention provides an all-in-one system capable of continuous monitoring, alerting, and real-time communication, particularly in challenging work environments across different regions and regulatory frameworks.

[0091] Also, the architecture of the system of the invention is robust against failures by having no single point of failure and implementing automatic failover mechanisms and redundant communication.

[0092] The invention also relates to a safety monitoring system. The safety monitoring system of the invention comprises the safety monitoring network as previously defined and a remote monitoring server.

[0093] The safety wearable devices are configured to be connected one to another, forming the safety monitoring network, to exchange safety data between them based on proximity and to pre-process the safety data to obtain priority-related data. In turn, the remote monitoring server is configured as a cloud backend and performs the steps of: o obtaining safety data from the wearable devices; o prioritizing the safety data according to priority-related Al data obtained from the safety wearable devices; o processing the safety data obtained based on the priority-related Al data; and o generating alerts as a result of the processing.

[0094] By using the safety monitoring network, concurrent signal processing is enabled, which allows to obtain real-time data analysis, multi-signal handling and priority-based processing.

[0095] Also, the invention provides advanced scalability features, allowing seamless scaling from two units to many thousands. This is achieved by using the remote monitoring server configured as a cloud backend server, thus providing consistent performance across scale, global deployment support and multi-site integration.

[0096] By using the remote monitoring server a coordinated emergency response is enabled improving emergency protocols, also a cross-device coordination could be implemented.

[0097] The safety monitoring system of the invention also allows to increase adaptability by offering cross-industry compatibility, multiple wearing options, environmental adaptation, regional certification support and international standard compliance.

[0098] Preferably, the remote monitoring server could be configured to implement Artificial Intelligence models to perform pattern recognition, Al predictive analysis from the safety data, anomaly detection, managing of network topology and routing, and classification of the priority of the safety data according to the Al priority-related data and to context information.

[0099] In some cases, the remote monitoring server could be configured to activate collective safety alerts determined by using data mining and Machine Learning (ML). The present invention also relates to a safety monitoring method comprising the steps of: providing two or more safety wearable devices (1) and automatically connecting the safety wearable devices (1) in a scalable safety monitoring network according to any of claims 1 to 29; providing a remote monitoring server (2) according to claim 30; exchanging safety data between the safety wearable devices (1) based on proximity; pre-processing safety data locally by the safety wearable devices (1) through edge computing; determining network paths between safety wearable devices (1) and the remote monitoring server (2); calculating a shortest network path from each safety wearable device (1) to the remote monitoring server (2); and transmitting emergency alerts through the network when at least one safety wearable device (1) trigger an emergency alert.

[0100] By the characteristics explained the present invention allows to obtain a significant advance in safety monitoring technology, offering a compact, multi-functional Al safety wearable device on one end and a robust remote monitoring Al server, configured as a cloud back-end, on the other end. Therefore, worker safety across global operations is enhanced.

[0101] DESCRIPTION OF THE DRAWINGS

[0102] To complement the description being made and in order to aid towards a better understanding of the characteristics of the invention, in accordance with a preferred example of practical embodiment thereof, a set of drawings is attached as an integral part of said description wherein, with illustrative and non-limiting character, the following has been represented:

[0103] Figure 1.- schematically illustrates an embodiment of the safety Al wearable device of the invention. Figure 2.- schematically illustrates an embodiment of the safety Al wearable device of the invention showing its sensors.

[0104] Figure 3.- schematically illustrates an embodiment of the safety Al wearable device of the invention as a belt.

[0105] Figure 4.- schematically illustrates an embodiment of the safety Al wearable device of the invention as an ankle-band.

[0106] Figure 5.- schematically illustrates an embodiment of the safety Al wearable device of the invention as an armband.

[0107] Figure 6.- schematically illustrates an embodiment of the safety Al wearable device of the invention in a helmet.

[0108] Figure 7.- schematically illustrates the elements of an embodiment of the Al safety wearable device of the invention.

[0109] Figure 8.- schematically illustrates a flow diagram of the processing of an emergency alert by the safety monitoring system of the invention.

[0110] Figure 9.- schematically illustrates a flow diagram of the pre-processing of an emergency alert by the safety Al wearable device of the invention.

[0111] Figure 10.- schematically illustrates a flow diagram of the connection process carried out by safety Al wearable devices of the invention.

[0112] Figure 11.- schematically illustrates the elements of the safety Al monitoring system of the invention.

[0113] Figure 12.- schematically illustrates the elements of the safety monitoring Al system of the invention when a safety wearable device is not working.

[0114] Figure 13.- schematically illustrates a flow diagram of the processing of an emergency alert by the safety wearable device and the remote monitoring server of the invention. Figure 14.- schematically illustrates the multiple location options to wear safety wearable devices of the invention.

[0115] Figure 15.- shows a schematic representation of the flow in the monitoring system of the invention.

[0116] Figure 16.- shows a schematic representation of the flow in the Al safety wearable device.

[0117] Figure 17.- shows a schematic representation of the flow in case of an alert emergency generation.

[0118] PREFERRED EMBODIMENTS OF THE INVENTION

[0119] A set of preferred embodiments of the invention are presented and illustrated by the accompanying figures.

[0120] The present invention relates to a safety wearable device designed to provide continuous real-time monitoring and alerting in order to improve the safety of users, workers, etc. in large scale, open field, outdoors, industrial or hazardous environments.

[0121] This invention offers a compact, multi-functional device that enhances worker safety across a range of industries either using a single or multiple wearable devices simultaneously.

[0122] Figures 1 and 2 show an embodiment of the safety Artificial Intelligence (Al) wearable device (1) of the invention, which comprises a processing module (4) , in this case a microprocessor with a memory, a communication module (5), in this case a modem, a plurality of sensors (10), and a rechargeable internal battery (8) and a housing (6) suitable for global industrial environments, designed with universal adaptability for multiple wearing configurations. Figure 3 shows an embodiment of the safety wearable device (1) of the invention as a belt, Figure 4 shows an embodiment of the device (1) as an ankle-band, Figure 5 shows an embodiment as an armband and Figure 6 shows an embodiment placed in a helmet. Figure 14 shows more multiple locations to wear safety wearable devices (1).

[0123] The housing of the device (1) of the invention is suitable for industrial environments and could be designed to be worn as a belt, vest, armband, etc.

[0124] Figure 7 schematically illustrates the elements of an embodiment of the safety wearable device of the invention, which are now detailed.

[0125] Regarding the communication module (5), it is designed to provide global connectivity. In this case, it is achieved by including a multi-band modem for wide-area communication capabilities supporting LTE 4G global bands, NB- loT networks, LTE-M protocols, Non-Terrestrial Network (NTN) compatibility and 5G readiness. In this way, even in remote locations, the device (1) of the invention can maintain constant connectivity for signal alerts transmission. Thus, the communication module (5) comprises, in this case, a BLE antenna (26), a LTE antenna (27) and an optional Wifi module (29).

[0126] Also, the communication module (5) could be configured to fulfil regional frequency compliance in Europe (433 / 868 MHz), United States (915 MHz), China (470 MHz), Japan (920 MHz), Australia (915-928 MHz, even, some certification compliance could be incorporated, such as CE (Europe), FCC (USA), UKCA (UK), CCC (China) and TELEC (Japan).

[0127] Regarding the positioning module (3) global navigation support is provided by using GPS / GLONASS / Galileo / BeiDou compatibility, comprising a GPS antenna (28). In indoor environments it could use Bluetooth or Wi-Fi triangulation. The positioning module (3) of the invention achieves an accuracy of ±2m outdoor and ±5m indoor.

[0128] The update rate of the positioning module (3) is preferably set to 1 Hz in standard configuration and up to 10Hz in emergency configuration. The positioning module (3) provides real-time tracking capabilities by performing continuous position monitoring, allowing immediate assistance in case of emergencies. Even, the positioning module (3) together with the processing module could be configured to perform geofencing techniques and to define zone-based alerts and cross-border tracking.

[0129] The device of the invention also comprises an emergency button (7). The emergency button (7) manually triggers emergency alerts. Thus, in case of immediate emergencies, the device (1) transmits wearer’s location and alert notification to a remote monitoring system, allowing for a rapid response in emergencies. The emergency button could have force-feedback confirmation.

[0130] Also, the emergency alerts could be triggered by multiple activation methods. Once triggered, the alert is distributed automatically. The location of the device and the current status are transmitted among multiple network paths, providing automatic escalation.

[0131] The safety wearable device (1) of the invention also provides automated incident detection. For that, the device (1) comprises a man-down module (11) which comprises a MEMS sensor with a 3D accelerometer, 16G detection range and a sampling rate of 100Hz. The processing module (4) could perform pattern recognition from the accelerometer data using pattern recognition algorithms. The pattern recognition algorithms could detect unusual movement patterns, such as sudden rapid movement of the person that is wearing the device (1), an impact or a prolonged inactivity, thus, triggering an automatic emergency alert. The accelerometer is calibrated to distinguish between normal movements and hazardous events.

[0132] The device of the invention also comprises other sensors (10) such as: proximity sensor (14) able to perform impact detection, fall detection and inactivity monitoring using pattern recognition; environmental temperature sensor (12) to perform temperature monitoring in a range of -40°C to +85°C and with an accuracy of ±0.1°C and a response time of <1 second, therefore, if the ambient temperature exceeds a predefined threshold, an emergency alert is sent, signaling potential heat exhaustion or potential fire hazards; skin temperature sensor (13) to perform temperature monitoring and issuing emergency alerts if dangerous heat levels are detected, the skin temperature sensor (13) monitors in a range of 32°C to 42°C and with an accuracy of ±0.1 °C and a response time of <1 second; humidity sensor (17) to measure humidity in a range of 0-100% RH, with an accuracy of ±2% RH and a response time: <8 seconds, the humidity sensor (17) triggers an emergency alert if abnormal rates are found; air quality sensor (18) configured to detect volatile organic compounds (VOCs), particulate matter, harmful gases (CO, CO2), oxygen levels and chemical vapor for providing early warning of hazardous conditions; heart rate sensor (15) to perform continuous monitoring of wearer’s vital signs in a range of 30-220 BPM, with an accuracy of ±1 BPM and including motion artifact rejection, therefore, if the heart rate falls outside the normal range, an emergency alert is triggered, indicating a potential health risk such as fatigue, exhaustion or possible cardiac anomaly health issues; and activity sensor (16) implementing step counting, location, path determination, heat maps for activity, energy expenditure, activity classification and fatigue detection.

[0133] The device (1) of the invention also comprises user interface elements, including visual indicators, tactile feedback and audio indicators. The visual indicators, in this case, are RGB LED module (20) with LED lights (21). The RGB LED module (20) could incorporate multi-color status indication, 360° visibility, automatic brightness adjustment and power-efficient operation. The RGB LED module (20) allows to provide information regarding current status, alerts, battery status and connection status. The RGB LED module (20) can comprise a LED cover made of translucent material and LED lights (21), creating a visual light ring continuous line effect from a discrete number or LEDs.

[0134] The tactile feedback is provided by a buzzer (22), which could incorporate multiple patterns, adjustable intensity, directional indication and power-efficient design. The tactile feedback allows to notify people around the device (1) even under noisy environments where visual indicators may not be noticed. The audio indications are provided by a speaker (23) with 100dB output capability, including multiple alert tones, voice message support and multi-language capability.

[0135] The safety wearable device (1) of the invention could be connected with other multiple devices (1) in a mesh network implementation. Also, the safety wearable devices (1) are connected to a remote monitoring server (2), called Al Halotech.

[0136] In this case, the firmware of each safety Al wearable device (1) is configured to automatically establish connection sending a exchanging peer-to-peer low-bandwidth heartbeat synchro signals for conforming a global safety mesh network.

[0137] Using said mesh network allows to provide the following advantages:

[0138] - robust: there is no single point of failure in the mesh network architecture;

[0139] - synchronization: depending on the industry or application the peer-to-peer heartbeat can be adjusted for the requested frequency;

[0140] - Al-based priority signal assignment, edge calculations in each device (1) lowers the latency of the alert signal in the remote monitoring server (2);

[0141] - quick scalability from a single unit to many thousand;

[0142] - concurrent signal processing for the entire network, by performing advanced functions in each device (1) the global capacity of the entire network handling currently alert signals increases;

[0143] - universal adaptability across industries by adjusting parameters in a memory of the device (1) by uploading the firmware of the device (1) tailored for the specific industry or activity. The parameters adjusted could be a synchronization interval, specific safety values, sensor thresholds parameters, etc.

[0144] These functions are supervised by the remote monitoring server (2), acting as a cloudbased monitor server that controls the entire plant in real-time, that coordinates and reports any collective safety alerts and variables using data mining. In this way, the remote monitoring server (2) can provide any KPI or reporting required for any industry or activity. Figure 11 shows an embodiment of the safety monitoring system of the invention comprising a remote monitoring system (2), with user interface, memory and cloud processing, and multiple safety wearable devices (1). In Figure 11 , the shortest path to the remote monitoring server (2) is shown. Figure 12, shows the safety monitoring system dynamic path reconfiguration when a safety wearable device (1) is not working properly.

[0145] Figure 8 schematically illustrates a flow diagram of the processing of an emergency alert by the safety monitoring system of the invention, including the steps of preprocessing and assigning priority by the safety wearable device (1), transmitting the safety data to the remote monitoring server (2), analyzing the safety data by said remote monitoring server (2) and generating alerts and actions as a result of the analysis.

[0146] By using the mesh network described, it is possible to process and monitor in realtime any number of devices (1) for controlling their safety being completely scalable from a few up to thousands of devices (1 ) simultaneously which is a great advantage for improving safety for any worker or user activity, public or private, and for companies or collectives of any dimension.

[0147] The connection between devices (1) is performed with a protocol with the following specifications: Bluetooth Low Energy BLE 5.0, mesh profile support, 128-bit AES encryption and dynamic key rotation.

[0148] Using a mesh network implementation allows to provide auto-formation capabilities, self-healing of the network, dynamic routing and load balancing.

[0149] The use of Bluetooth connection allows for seamless communication with portable electronic devices (e.g., smartphones or tablets), thus, enabling remote monitoring and data logging Bluetooth Connectivity. This allows for easy synchronization, data collection, and remote monitoring by supervisors, health professionals, assistance teams, or the global integrated safety system.

[0150] The devices (1) are connected in a peer-to-peer communication with the following signal characteristics: range of 100m line-of-sight, penetration of 20-30m indoor, power enough for adaptive transmission and channel hopping. Figure 10 shows a flow diagram of the connection process carried out by safety wearable devices (1) of the invention. The devices (1) find close neighbor devices (1) by scanning using the BLE connection and automatically connects with said devices (1) forming the mesh.

[0151] The connection between devices (1) of the invention allows multi-hop transmission, signal strengthening, path optimization and priority routing.

[0152] More specifically, the multiple devices (1) use Bluetooth Low Energy (BLE) connectivity to create a redundant safety monitoring network, functioning each device (1) as interconnected nodes. Each node continuously monitors specific safety parameters and simultaneously acts as both a sensor and relay point, capable of transmitting and receiving safety-critical data. Upon detection of hazardous conditions by any node, the mesh employs message propagation protocols for ensuring rapid dissemination of safety alerts through multiple network paths, incorporating message verification and priority handling mechanisms.

[0153] The mesh topology enables self-healing capabilities, where the network automatically reconfigures routing network paths in case of node failures, while maintaining low power consumption through optimized message hopping algorithms. This distributed architecture eliminates single points of failure and ensures reliable safety monitoring across extended physical areas, with each node capable of triggering localized safety responses while contributing to the broader network's emergency response capabilities.

[0154] Although the devices (1) are connected to the remote monitoring server (2), each safety wearable device (1) of the invention performs edge computing for local data analysis, pattern recognition, threshold monitoring and alert generation.

[0155] In such a way, the resources needed are reduced by including memory optimization, power management and thermal control.

[0156] The processing module (4) of each safety wearable device (1) and the remote monitoring server (2) could have Artificial Intelligence capabilities for pattern recognition, anomaly detection, predictive analysis and continuous learning. Using Machine Learning Models allows to obtain a lightweight implementation, edge- optimized, privacy-preserving and to set adaptive thresholds.

[0157] Regarding power management, each device (1) of the invention comprises a power circuit (19) with a charger and a battery (8). The battery has the following specifications: 2000mAh, Li-ion material, operating time >12 hours, charging time <2 hours and cycle life of >500 cycles.

[0158] The power circuit (19) used allows smart power management, including sleep mode optimization, emergency power reserve and battery health monitoring, thus ensuring that the device (1) remains functional throughout long working shifts.

[0159] The charger is a contact-based charger with USB-C compatibility and fulfils regional power standards.

[0160] In addition, a critical real-time software is integrated within the remote monitoring server (2) to create a comprehensive global safety system. This software is responsible for displaying key performance indicators (KPIs) related to safety on a global scale in every facility and region of operation.

[0161] Thus, the remote monitoring server (2) is configured to perform prioritization of urgent data traffic by processing multiple concurrent signals from global operations. It also identifies and prioritizes urgent safety-related data, thus ensuring that critical information is processed without delays. The remote Al monitoring server (2) also handles regional compliance requirements, supports multiple language protocols and maintains cross-border data handling standards.

[0162] In turn, the safety wearable devices (1) also perform signal edge processing, thus preprocessing the signals for reducing the computational load on the remote monitoring server (2) and ensuring faster real-time responses. The devices (1) could perform local data filtering and analysis, providing also regional threshold adaptation and local regulatory compliance checking. Figure 9 shows schematically a flow diagram of the pre-processing of an emergency alert by the safety wearable device (1) of the invention, including the steps of assigning priority to the emergency alert and classifying the signal obtained.

[0163] With the elements describes, the system of the invention provides a mesh fail-proof safety network, where the devices (1) are being linked to each other, exchanging peer-to-peer signals to its closest neighbor device (1) automatically. Thus, the network formed is a global fail-proof mesh safety network since in case one device (1) fails or at least one device (1) activates an emergency alert signal, the shortest path to the remote monitoring server (2) is determined. This key advantage of the system is accomplished by incorporating a single firmware in all the devices (1) of the invention. The mesh network embodiment also provides regional network optimization, cross-border operation support and regulatory-compliant communication protocols.

[0164] Figure 13 shows schematically a flow diagram of the complete processing of an emergency alert by the Al safety wearable device (1) and the remote monitoring server (2) of the invention.

[0165] Figure 15 shows a schematic representation of the monitoring system of the invention.

[0166] In said system, the remote monitoring server (2) comprises an Al layer configured to perform safety data processing, priority assignment and alert generation.

[0167] The safety data processing comprises multiple signal processing steps including: data validation, data enrichment, and deep analysis processing. Then, the resulting safety data is analyzed by using pattern recognition techniques, predictive analysis and risk analysis. This analysis allows to perform global priority assignment, alert generation and to develop a safety action planning, which feed the deep analysis step with updated control actions and thresholds.

[0168] In the safety wearable device, an edge Al Layer and a mesh Network layer are included. The edge Al Layer obtains data from the remote monitoring server regarding safety control actions and thresholds. The safety data obtained by the sensors (10) of the safety wearable device (1) is analyzed with a local Al Engine along with the safety control actions and thresholds obtained. The local Al Engine performs real-time analysis and generates emergency alerts.

[0169] Then, the edge Al Layer assigns priority to the analyzed safety data and emergency alerts. Then, the resulting safety data is preprocessed and transmitted to the mesh network layer.

[0170] The Mesh Network layer performs network management actions such as: node discovery, route optimization and failover handling.

[0171] Finally, the priority-based safety data is aggregated and transmitted to the remote monitoring server (2).

[0172] Figure 16 shows a schematic representation of the flow in the safety wearable device (1).

[0173] The safety wearable device (1) comprises the sensors (10), which obtain safety data. The safety data is preprocessed by applying data cleaning, data formatting and Al edge processing techniques.

[0174] Then, the analyzed safety data is priority-based analyzed. This analysis applies a priority function to classify each set of safety data into normal, warning or critical events.

[0175] Then, a queue management unit of the processing module (4) transmits the safety data through an update channel, an alert channel or an emergency channel depending on the classification previously assigned. Also, machine learning optimization could be applied to update the priority function with feedback data from the queue management unit.

[0176] Figure 17 shows a schematic representation of the flow in case of an alert emergency generation. The safety monitoring device, in this case, comprises a proximity sensor, a VOC sensor, a humidity sensor, a body temperature sensor, a MEMS sensor for acceleration and a pulse rate sensor. When a sensor detects values out of a predetermined range, an emergency alert is triggered, for example as a response to an impact, absence of movement, abnormal position or vital signs out of the range.

[0177] In this case, the user is notified, if the user confirms that there is no emergency, the monitoring continues and the safety data that triggered the emergency alert is analyzed to update the thresholds of the sensors.

[0178] If the user cannot confirm that there is no emergency, an alert protocol is activated. As a result, the alert signals are activated, a notification is sent to other connected safety wearable devices (1), also location, history and sensor logs are transmitted to the remote monitoring system (2), acting as a cloud system, thus, creating a new emergency event. Finally, a notification of the emergency alert is sent to a notification Team.

[0179] The remote monitoring system (2), in turn, process the safety data obtained. On one hand, the remote monitoring system (2) performs location tracking of the safety wearable device (1) which sent the emergency alert and records in a database the obtained safety data.

[0180] Also, the remote monitoring system (2) performs machine learning, pattern recognition and data mining techniques to analyze the safety data and to generate reports.

[0181] With the features disclosed, the system of the invention allows to manage, control, and enhance safety in different environments.

[0182] Now, some examples of different types of plants or industrial environments where the system of the invention could be implemented are presented: a) Manufacturing Plants: In high-risk environments such as automotive, electronics, or heavy machinery manufacturing, the system can monitor worker safety, detect hazardous conditions, and prevent accidents by prioritizing safety signals in real-time; the system:

[0183] • Monitors worker safety through real-time tracking

[0184] • Detects hazardous conditions using regional thresholds

[0185] • Prevents accidents through predictive analytics

[0186] • Prioritizes safety signals according to local regulations

[0187] • Supports multiple language interfaces

[0188] • Complies with regional manufacturing standards (ISO, EN, ANSI) b) Chemical Processing Plants: With the presence of toxic substances, the system could monitor air quality, chemical leaks, and worker movements to prevent exposure and ensure rapid emergency response. The system:

[0189] • Monitors air quality against international standards

[0190] • Detects chemical leaks using global threshold databases

[0191] • Tracks worker movements in hazardous areas

[0192] • Ensures rapid emergency response

[0193] • Complies with international chemical safety protocols

[0194] • Supports regional emergency response systems c) Mining Operations: Underground or open-pit mining operations could use this system to track workers, monitor environmental conditions such as gas levels and temperature, and ensure rapid evacuation if dangerous conditions are detected, the system:

[0195] • Tracks workers in multiple mining environments

[0196] • Monitors environmental conditions (gas levels, temperature)

[0197] • Ensures rapid evacuation responses

[0198] • Adapts to regional mining regulations

[0199] • Supports multiple communication protocols

[0200] • Interfaces with local emergency services d) Oil and Gas Refineries: In this high-risk environment, the system could monitor for fire hazards, gas leaks, or equipment malfunctions while keeping track of worker health and safety metrics, the system: • Monitors for fire hazards using international standards

[0201] • Detects gas leaks across facility operations

[0202] • Tracks equipment malfunctions

[0203] • Monitors worker health and safety metrics

[0204] • Complies with international petrochemical standards

[0205] • Supports global emergency response protocols e) Power Plants: Whether nuclear, coal, or renewable energy plants, Al HALOTECH could ensure workers are protected from radiation, electrical hazards, and extreme temperatures, while responding quickly to emergency situations, the system

[0206] • Protects workers from radiation exposure

[0207] • Monitors electrical hazards

[0208] • Tracks temperature thresholds

[0209] • Responds to emergency situations

[0210] • Complies with international nuclear safety standards

[0211] • Supports multiple regional requirements f) Construction Sites: Given the dynamic and dangerous nature of construction work, the system can monitor worker locations, machinery usage, prevent accidents and structural integrity to avoid accidents and manage safety in real-time, the system

[0212] • Monitors worker locations across complex sites

[0213] • Tracks machinery usage patterns

[0214] • Prevents accidents through predictive analytics

[0215] • Manages safety in real-time

[0216] • Complies with international construction standards

[0217] • Supports multiple regional requirements g) Logistics and Warehousing: In large-scale warehouses or distribution centers, the system could track equipment operation, worker locations, and environmental hazards (e.g., heavy machinery) to ensure safety compliance, the system: the system:

[0218] • T racks equipment operation • Monitors worker locations

[0219] • Manages environmental hazards

[0220] • Ensures safety compliance

[0221] • Supports international logistics standards

[0222] • Adapts to regional warehouse regulations h) Pharmaceutical Manufacturing: To safeguard against exposure to chemicals or biological materials, Al HALOTECH could monitor worker conditions, sterile zones, and other critical factors in real time, the system:

[0223] • Monitors worker conditions

[0224] • Tracks sterile zone parameters

[0225] • Manages critical factors in real-time

[0226] • Ensures GMP compliance

[0227] • Supports international pharma standards

[0228] • Adapts to regional requirements i) Food and Beverage Processing: For plants where temperature control, hygiene, and machinery safety are critical, this system could prioritize signals related to machinery malfunction, temperature control, and safety conditions in food production lines, the system

[0229] • prioritizes machinery malfunction alerts

[0230] • Monitors temperature control

[0231] • Tracks safety conditions

[0232] • Ensures HACCP compliance

[0233] • Supports international food safety standards

[0234] • Adapts to regional requirements j) Steel and Metal Foundries: In environments with high heat, toxic fumes, and heavy machinery, the system could be used to monitor workers’ health and equipment conditions, ensuring safety protocols are maintained, the system

[0235] Monitors worker health conditions

[0236] Tracks equipment status • Ensures protocol compliance

[0237] • Manages safety standards

[0238] • Supports international metallurgy standards

[0239] • Adapts to regional requirements k) Public Works: Workers in road maintenance, waterworks, and sanitation can benefit from safety monitoring to detect gas leaks, accidents, dangerous equipment conditions, and work zone hazards, the system:

[0240] • Monitors road maintenance activities

[0241] • T racks waterworks operations

[0242] • Manages sanitation safety

[0243] • Detects hazardous conditions

[0244] • Complies with regional public works standards

[0245] • Supports multiple jurisdictional requirements l) Police and law enforcement: Al HALOTECH could be used to monitor officers’ safety in high-risk situations, such as during riots, or in dangerous areas. It could track their location, health status, and environmental conditions, the system Al HALOTECH® enhances officer safety globally through:

[0246] • Real-time monitoring during high-risk situations

[0247] • Tactical operation coordination

[0248] • Environmental hazard detection

[0249] • Location tracking in dangerous areas

[0250] • Health status monitoring

[0251] • International law enforcement standards compliance

[0252] • Cross-border operation support

[0253] • Multi-agency coordination capabilities m) Firefighters and Emergency Response Teams: Al HALOTECH can monitor real-time vitals, air quality, temperature, and GPS location of responders in dangerous environments like fires, collapsed buildings, or hazardous material incidents, Real-time vital signs

[0254] Air quality parameters • Temperature conditions

[0255] • GPS location tracking

[0256] • Team coordination

[0257] • Equipment status

[0258] • International emergency response standards

[0259] • Regional protocol compliance n) Ambulance and Medical First Responders: The system can prioritize signals related to patient health, equipment safety, and the responders’ own safety while in motion or during high-risk medical interventions. The system prioritizes:

[0260] • Patient health monitoring

[0261] • Equipment for safety tracking

[0262] • Vehicle status monitoring

[0263] • Response team safety

[0264] • High-risk intervention support

[0265] • International medical transport standards

[0266] • Regional healthcare compliance

[0267] • Cross-border operation protocols o) Search and Rescue Operations: For teams involved in disaster recovery, Al HALOTECH can monitor team member safety in hazardous environments (e.g., after earthquakes or floods) and prioritize critical signals related to air quality, temperature, and structural integrity. For teams involved in disaster recovery, the system monitors:

[0268] • Team member safety in hazardous environments

[0269] • Post-disaster condition assessment

[0270] • Structural integrity monitoring

[0271] • Environmental hazard detection

[0272] • International rescue operation standards

[0273] • Cross-border coordination

[0274] • Multi-agency integration p) Military and Defense: Al HALOTECH can be deployed to monitor soldiers in the field, track environmental hazards, and process vital data in real-time to prioritize life-saving actions during missions. The system supports:

[0275] • Field personnel monitoring

[0276] • Environmental hazard tracking

[0277] • Real-time vital data processing

[0278] • Mission-critical safety operations

[0279] • International defense standards

[0280] • Secure communication protocols

[0281] • Cross-border operation capability q) Transportation and Public Transit: For monitoring train, subway, or bus operators and workers in high-traffic areas, ensuring operational safety and preventing accidents through real-time feedback, Monitoring capabilities for:

[0282] • Train operator safety

[0283] • Subway system operations

[0284] • Bus fleet management

[0285] • High-traffic area safety

[0286] • International transport standards

[0287] • Regional safety compliance Cross-border operations r) Public Utilities (Gas, Water, Electric): The system can help detect gas leaks, electrical faults, or water contamination, ensuring workers and public safety during repair and maintenance activities. The system enables:

[0288] • Gas leak detection

[0289] • Electrical fault monitoring

[0290] • Water system safety

[0291] • Infrastructure maintenance

[0292] • International utility standards

[0293] • Regional compliance requirements

[0294] • Emergency response coordination s) Agricultural Operations: planting, recollecting, harvesting in environments with heavy machinery, pesticide applications, and varied weather conditions, the wearable system could monitor equipment safety, chemical exposure levels during spraying operations, and environmental factors like heat stress during harvest. It could track worker locations related to operating machinery, ensure proper PPE Personal Protective Equipment usage during chemical applications, and alert for hazardous conditions like accidents, excessive sun exposure or dehydration risks during peak summer fieldwork Supporting activities including:

[0295] • Planting operations safety

[0296] • Harvesting equipment monitoring

[0297] • Chemical application safety

[0298] • Environmental condition tracking

[0299] • International agriculture standards

[0300] • Regional compliance requirements

[0301] • Cross-border operation support

[0302] In each of these environments, the system of the invention would ensure rapid prioritization of critical safety data, providing real-time protection for workers and first responders in hazardous or high-stakes situations.

[0303] Each of these industries benefits from having real-time safety monitoring, signal prioritization, and Al-driven decision-making to prevent accidents and ensure rapid response to hazardous conditions.

[0304] The system of the invention improves exponentially over time through continuous training of its model, leveraging real-world data collected from various sensors across the wearable devices and backend systems. As more signals, safety incidents, and environmental conditions are processed, the Al will learn to better recognize patterns, optimize decision-making, and predict potential hazards with increasing accuracy. This ongoing refinement allows the system to become more efficient at prioritizing critical safety signals and responding to emerging threats.

[0305] With each data input and system update, the system of the invention becomes smarter, capable of adapting to new scenarios, improving predictive capabilities, and enhancing overall safety performance, ensuring faster and more reliable protection for workers and responders in dynamic, high-risk environments. This cycle of data accumulation and learning accelerates the system’s ability to provide proactive, real-time interventions, leading to exponential improvements in safety outcomes.

[0306] Regarding communication protocols specially in the field of security, tactical, law enforcement, military or rescue operations the use of PQC (Post-Quantum Cryptography) over network protocols can also be implemented in the communication module (5) of the safety wearable device of the invention, to provide an added layer or security. Quantum computers have the potential to break many of the cryptographic techniques currently in use, such as RSA and ECC, due to their ability to solve complex mathematical problems exponentially faster than classical computers. PQC protocols are designed to develop cryptographic algorithms that are resistant to quantum attacks, ensuring that data can be transmitted securely even in a future where quantum computers are widespread.

[0307] Thus, the system provides comprehensive protection against current cryptographic vulnerabilities, future quantum computing threats, data security risks, communication interception, international security threats and regional security challenges.

[0308] In each of these environments, the system of the invention ensures rapid prioritization of critical safety data, providing real-time protection for workers and first responders in hazardous or high-stakes situations worldwide.

Claims

AMENDED CLAIMS received by the International Bureau on 03 September 2025 (03.09.2025)1. A scalable safety monitoring network comprising:• two or more safety wearable devices (1), each device comprising: a positioning module (3) configured to obtain a location; one or more sensors (10) configured to continuously obtain safety data from the user; a processing module (4) connected to the sensors (10) and the positioning module (3) and configured to obtain and process, by performing edge computing, location data from the positioning module (3) and safety data from the sensors (10); a communication module (5) connected to the processing module (4) configured to: o automatically search for and connect to other safety wearable devices (1) in a peer-to-peer basis; and o to transmit and receive processed safety data from the processing module (4) based on proximity; and a housing (6) configured to house the processing module (4), the sensors (10), the communication module (5) and the positioning module (3);• a remote monitoring server (2) connected to the safety wearable devices (1); wherein the processing module (4) and the communication module (5) are configured to: o configure the network, defining multiple redundant paths and eliminating single points of failure; o automatically determine the shortest network path to the remote monitoring server (2), o implement self-healing mechanisms, by automatically determining node failures and reconfiguring the network paths in case of node failures (original claim 25) the processing module (4) of each safety wearable device (1) is configured to:o process safety data by using machine learning algorithms; (original claim 7) o perform edge-computing using Artificial Intelligence (Al) models for safety data analysis, priority-based signal routing, cross-device coordination, pattern recognition, alert generation and resource management; (original claim 8) o process sensors data to trigger an emergency alert when predefined thresholds are exceeded; (original claim 13) o the predefined thresholds are adaptative; (original claim 14) the safety wearable device (1) comprises: an edge Al layer which is configured to obtain data from the remote monitoring server regarding safety control actions and thresholds; (page 21 , lines 1-5) a mesh Network layer configured to perform network management actions such as: node discovery, route optimization and failover handling; (page 21 , lines 14-15), the remote monitoring server (2) is configured to implement Artificial Intelligence models to perform: pattern recognition, predictive analysis, and priority classification of safety data, with anomaly detection, managing of network topology and routing, and classification of the priority of the safety data based on priority-related data and context information, (original claim 31) (page 19, lines 1-5), (page 20, lines 25-26).

2. The scalable safety monitoring network according to claim 1 , wherein the housing (6) is configured to be used as: belt mounting, vest attachment, ankle-band, helmet snap-on integration, neck-strap, wristband, legband, armband and handy lanyard.

3. The scalable safety monitoring network according to claim 1 , wherein the communication module (5) is a modem supporting wireless protocols selected from: Bluetooth Low Energy (BLE), LTE 4G, 5G, NB-loT, LTE-M and NonTerrestrial Network (NTN) connectivity.

4. The scalable safety monitoring network according to claim 3, wherein the positioning module (3) comprises a GPS for outdoor location and / or is configured to use Bluetooth or Wi-Fi data from the communications module (5) for obtaining the location indoor (RTLS).

5. The scalable safety monitoring network according to claim 3, wherein the safety wearable devices (1) are connected using a Bluetooth Low Energy (BLE), wherein each safety wearable device (1) transmits and receives safety data from other safety wearable devices (1).

6. The scalable safety monitoring network according to claim 1 , wherein the positioning module (3) of the safety wearable devices (1) is configured to refine a position by obtaining location data from connected safety wearable devices (1) and by applying proximity-based location refinement techniques.

7. The scalable safety monitoring network according to claim 1 , wherein processing load is distributed between the connected safety wearable devices (1).

8. The scalable safety monitoring network according to claim 1 , wherein the safety wearable devices (1) are configured to implement distributed data storage for data preservation.

9. The scalable safety monitoring network according to claim 1 , wherein each safety wearable device (1) is configured to determine a received signal strength indicator (RSSI) from surrounding wearable devices (1)10. The scalable safety monitoring network according to claim 1 , wherein the sensors (10) include one or more of: environmental temperature sensor (12); skin temperature sensor (13); proximity sensor (14); heart rate sensor (15); activity sensor (16);humidity sensor (17); and air quality sensor (8) configured to measure Volatile Organic Compounds (VOC), particulate matter, harmful gases or toxic elements and oxygen levels.

11. The scalable safety monitoring network according to claim 1 , wherein the sensors are activated automatically on a predefined schedule.

12. The scalable safety monitoring network according to claim 1 , further comprising a man-down module (11) comprising a MEMS sensor configured to detect accelerometer data and transmit it to the processing module (4) for Al-driven movement pattern analysis.

13. The scalable safety monitoring network according to claim 1 , further comprising an emergency button (7) configured to trigger an emergency alert.

14. The scalable safety monitoring network according to claim 1 , wherein the emergency alert triggers a transmission of location and an emergency notifications to the remote monitoring system (2) and / or other safety wearable devices (1).

15. The scalable safety monitoring network according to claim 1 , further comprising alert signals selected from:- RGB LED module (20);- buzzer (22); speaker (23); and emergency notifications.

16. The scalable safety monitoring network according to claim 1 , further comprising a rechargeable battery (8) with Al driven power management17. The scalable safety monitoring network according to claim 15, wherein the RGB LED module (20) comprises a translucent LED cover and one or more LED lights, providing a continuous light ring effect.

18. The scalable safety monitoring network according to claim 1 , wherein the communication module (5) connects dynamically with different safety wearable devices (1), in a peer-to-peer manner to define new optimized network paths19. The scalable safety monitoring network according to claim 1 , wherein low- power consuming paths are selected for safety data transmission20. The scalable safety monitoring network according to claim 1 , wherein the processing module (4) and the communication module (5) automatically set transmission power.

21. The scalable safety monitoring network according to claim 1 , wherein the processing module (4) and the communication module (5) optimize a network coverage.

22. The scalable safety monitoring network according to claim 1 , wherein the communication module (5) of each safety wearable device (1) transmits low- bandwidth heartbeat synchronization signals in a predefined frequency.

23. The scalable safety monitoring network according to claim 1 , wherein the safety wearable devices (1) employ optimized message hopping algorithms for low power consumption.

24. The scalable safety monitoring network according to claim 1 , wherein when an alert is triggered by any of the safety wearable devices (1), a message propagation protocol is activated which transmits the safety data through multiple network paths.

25. The scalable safety monitoring network according to claim 24, wherein the message propagation protocol includes message verification and priority handling mechanisms.

26. The scalable safety monitoring network according to claim 1 , wherein the communication module (5) employs post-quantum cryptography (PQC) protocols for highly secure data transmission.

27. The scalable safety monitoring network according to any preceding claim wherein the remote monitoring server (2) is configured as a cloud backend to: o obtain safety data from the safety wearable devices (1); o prioritize the safety data based on priority-related data from the safety wearable devices (1); o process safety data using Al-models; and o generate alerts based on the processed data .

28. The safety monitoring system according to claim 25, wherein the remote monitoring server (2) activates collective safety alerts determined using data mining or Machine Learning (ML) techniques29. A safety monitoring method comprising the steps of: providing two or more safety wearable devices (1) and automatically connecting the safety wearable devices (1) in a scalable safety monitoring network according to any of claims 1 to 26; providing a remote monitoring server (2) according to claim 1 ; exchanging safety data between the safety wearable devices (1) based on proximity; pre-processing safety data locally by the safety wearable devices (1) through edge computing; determining network paths between safety wearable devices (1) and the remote monitoring server (2); defining multiple redundant paths and eliminating single points of failure determining node failures and reconfiguring the network paths in case of node failures calculating a shortest network path from each safety wearable device (1) to the remote monitoring server (2); andtransmitting prioritized emergency alerts through the shortest network path, when at least one safety wearable device (1) triggers an alert.

Images