Description:FIELD OF THE INVENTION

[0001] The present invention relates to an automated fall prevention and protection system for buildings and a method thereof that is capable of continuously monitoring individuals in elevated or exposed zones, providing early recognition of hazardous situations for reducing risks of accidents and ultimately enhancing overall building safety and occupant protection.

BACKGROUND OF THE INVENTION

[0002] Accidental falls from elevated structures such as balconies and roof edges continue to cause serious injuries and fatalities, revealing clear shortcomings in conventional safety approaches. Static barriers and warning signs prove inadequate, as they cannot adapt to unpredictable human behavior or effectively protect distracted individuals, unattended children, or those in vulnerable conditions. Conventional methods also lack to distinguish ordinary activity from hazardous movements, resulting in delayed or ineffective response. Moreover, once a fall begins, most solutions fail to provide any form of real-time intervention, relying solely on passive prevention rather than active safeguarding. They also struggle to combine accurate detection, proactive prediction and protective action.

[0003] Traditional methods of preventing fall from buildings or elevated areas generally rely on tools such as fixed railings, safety harnesses, guard barriers and warning signage. While these provisions offer a basic level of security, they place the responsibility almost entirely on individuals to stay cautious and compliant, which is difficult in situations involving distraction, fatigue or human error. Safety harnesses require users to actively wear and secure them, while fixed railings and barriers cannot adapt to sudden hazardous behavior or accidental slips, leaving significant gaps in protection. Warning signage, though useful, is frequently ignored or overlooked, reducing its effectiveness in high risk situations. These manual approaches also lack monitoring or adaptability, makes nearly impossible to predict accidents before they occur or react quickly enough when incidents unfold. Consequently, traditional tools struggle to provide a dynamic, reliable solution for safeguarding individuals in complex real world scenarios.

[0004] US9528285B2 discloses a system for substantially enclosing the periphery of a building top with a netting system which is easily and efficiently movable or reconfigurable during the building construction process comprises a lightweight netting system for extending above a completed work area or floor, a strong lightweight structural support system for the netting, wherein the structural support system is vertically adjustable via slidable engagement with brackets attached to the floors which are already completed, provides enhanced safety for workers and for pedestrians below by preventing passage of workers or debris through the netting and enhances efficiency of construction by providing an easily reconfigurable, inexpensive and lightweight system for providing such enhanced safety. The netting structure may be supported by a rigid panel structure which is slidably engaged with vertical support members. Stopper mechanisms may also be provided for retaining the vertical members and panels in place.

[0005] KR100501740B1 discloses a safety net structure that prevents the fall of the operator when building the building, and prevents falling to the ground of the various accessories, the upper end of the vertical portion of the bracket fixed to the building The upper end of the base is welded so that the base is inclined downward with respect to the vertical part at a predetermined angle a, and the top of the vertical part of the bracket is " ∨ "-shaped frame is linked, the first member of the frame is supported by the support of the bracket, and the first member of the frame By the safety net is mounted on the wire connected to the upper end of the top and the free end of the second member of the frame, the safety net is being constructed in a spaced apart state from the frame Safety net structure, characterized in that the safety net is installed on the outer wall of the building.

[0006] Conventionally, many devices have been developed to prevent accidental falls and enhance building safety, but these devices lack the ability to predict hazardous human behavior in real time, differentiate normal actions from risky ones, and provide proactive warnings. These existing devices also fail to actively deploy protective measures during an actual fall, relying only on static prevention methods. This limitation reduces their effectiveness in safeguarding vulnerable individuals in unpredictable scenarios.

[0007] In order to overcome the aforementioned drawbacks, there exists a need in the art to develop a system that requires to be capable of continuously monitoring human presence, analyzing posture and behaviour to predict risks and delivering timely alerts before accidents occur. Additionally, the system also needs to be further capable of dynamically responding during an actual fall by deploying protective measures for reducing injuries, enhancing adaptability in real time, and providing reliable safeguarding for vulnerable individuals in high-risk areas.

OBJECTS OF THE INVENTION

[0008] The principal object of the present invention is to overcome the disadvantages of the prior art.

[0009] An object of the present invention is to develop a system that is capable of monitoring of individuals in high risk areas by sensing to ensure accurate detection of human presence, improving safety through fast recognition while minimizing false triggers caused by environmental conditions or external factors.

[0010] Another object of the present invention is to develop a system that is capable of analyzing human posture, behaviour and movement patterns using predictive sensing that assess potential risk situations in advance, for enabling proactive intervention to reduce accidents and enhancing the overall reliability of fall prevention systems in buildings.

[0011] Another object of the present invention is to develop a system that is capable of delivering timely warnings through alert that directly engage individuals in hazardous zones for ensuring heightened awareness, promoting safe behavior and ultimately reducing the likelihood of accidental falls through an immediate and localized feedback.

[0012] Yet another object of the present invention is to develop a system that is capable of dynamically deploying protective arrangements that minimize injury risk during accidental falls by adjusting safety measures in real time according to body dimensions and weight for ensuring controlled impact absorption and providing maximum safeguarding efficiency for vulnerable individuals.

[0013] The foregoing and other objects, features, and advantages of the present invention will become readily apparent upon further review of the following detailed description of the preferred embodiment as illustrated in the accompanying drawings.

SUMMARY OF THE INVENTION

[0014] The present invention relates to an automated fall prevention and protection system for buildings and a method thereof, which identifies human activity through high precision thermal detection to predict unsafe behaviours before they escalate and delivers proactive interventions developed to prevent accidental falls and protect vulnerable individuals in critical areas.

[0015] According to an embodiment of the present invention, an automated fall prevention and protection system for buildings and a method thereof, comprises of a plurality of thermal cameras mounted on building edges and operatively coupled with motorized ball-and-socket joints are configured to capture thermal imaging data of individuals in high-risk zones, a plurality of passive infrared sensors positioned near balconies and roof edges detects human proximity in synchronization with the cameras and sends a detection signal to an embedded microcontroller which adjusts the orientation and operational parameters of the thermal cameras to focus on corresponding balconies or roof areas upon detection of human proximity, a plurality of LiDAR module integrated with the thermal cameras provides accurate distance and positional measurement, a plurality of piezoelectric transducers embedded in balcony floors and roof edges detects unusual vibrations and movements and differentiate between vibrations caused by human activity and environmental factors by means of a frequency-domain filtering protocol, a fall prediction module provided with the system and operatively coupled with the thermal cameras, LiDAR sensors, piezoelectric transducers, and PIR sensors analyzes thermal imaging data to differentiate humans from false triggers, evaluates posture and behavior of detected individuals, and predicts hazardous movements or fall-related behavior while prioritizing protection for children or vulnerable individuals and triggering the fall protection arrangement upon detecting their presence in high-risk zones, a haptic feedback and audio alert operatively coupled with the fall prediction module provides pre-emptive warnings to individuals approaching high-risk zones via vibration motors embedded in balcony railings for tactile warnings and directional speakers positioned at balcony edges for localized audio warnings.

[0016] According to another embodiment of the present invention, the system further comprises of a fall protection arrangement provided with the system comprises a horizontal slider railing positioned beneath each balcony and vertical sliding rails installed at building corners for controlled vertical movement adjoined with the horizontal slider, a barrel assembly having a scissor-extension arrangement operatively coupled with the curved plates and mounted on the horizontal slider houses a quick-extending rod guided by multiple electromagnetic coils and is configured to rapidly position itself beneath the falling individual via the horizontal slider and launch the extending rod using electromagnetic propulsion, curved plates and hinged extensions mounted on the distal end of the extending rod secure a flexible Kevlar safety net that deploys into a taut protective surface upon outward extension of the rod, the scissor-extension arrangement expands the Kevlar net to a wider surface area upon deployment, the Kevlar safety net reinforced with secondary mesh layers and cross-linked straps evenly distributes impact forces and multiple nets are sequentially deployable at different vertical levels secured to the curved plates of the extending rod via detachable connectors, a microcontroller operatively coupled with the LiDAR module dynamically controls the deployment of the Kevlar safety net by adjusting the tension and extension speed based on body proportions and approximate weight of the falling individual as determined by the LiDAR module, and a communication interface provided with the system transmits real-time alerts and video feeds to a display interface accessible by authorized personnel and generates incident reports for compliance with building safety regulations.

[0017] While the invention has been described and shown with particular reference to the preferred embodiment, it will be apparent that variations might be possible that would fall within the scope of the present invention.

BRIEF DESCRIPTION OF THE DRAWINGS

[0018] These and other features, aspects, and advantages of the present invention will become better understood with regard to the following description, appended claims, and accompanying drawings where:
Figure 1 illustrates a perspective view of an automated fall prevention and protection system for buildings and a method thereof;
Figure 2 illustrates a perspective view of a fall protection arrangement associated with the system; and
Figure 3 illustrates a flow chart depicting a method for detecting and preventing falls from high-risk zones.

DETAILED DESCRIPTION OF THE INVENTION

[0019] The following description includes the preferred best mode of one embodiment of the present invention. It will be clear from this description of the invention that the invention is not limited to these illustrated embodiments but that the invention also includes a variety of modifications and embodiments thereto. Therefore, the present description should be seen as illustrative and not limiting. While the invention is susceptible to various modifications and alternative constructions, it should be understood, that there is no intention to limit the invention to the specific form disclosed, but, on the contrary, the invention is to cover all modifications, alternative constructions, and equivalents falling within the spirit and scope of the invention as defined in the claims.

[0020] In any embodiment described herein, the open-ended terms "comprising," "comprises,” and the like (which are synonymous with "including," "having” and "characterized by") may be replaced by the respective partially closed phrases "consisting essentially of," consists essentially of," and the like or the respective closed phrases "consisting of," "consists of, the like.

[0021] As used herein, the singular forms “a,” “an,” and “the” designate both the singular and the plural, unless expressly stated to designate the singular only.

[0022] The present invention relates to an automated fall prevention and protection system for buildings and a method thereof that is capable of assessing real-time human movement, providing timely alerts to reduce accidents and dynamically deploying protective arrangements that minimize injury while maintaining an efficient and reliable building safety environment.

[0023] Referring to Figure 1 and 2, a perspective view of an automated fall prevention and protection system for buildings and a method thereof and a perspective view of a fall protection arrangement associated with the system are illustrated, respectively, comprising a plurality of thermal cameras 101 mounted on building and operatively coupled with motorized ball-and-socket joints 102, a set of vibration motors 103 embedded in balcony railings, a speaker 104 positioned at balcony, a horizontal slider railing 105 positioned beneath each balcony, a vertical sliding rails 106 installed at building corners, a barrel assembly 201 having multiple electromagnetic coils 202 equipped with a quick-extending rod 203 connected to curved plates 204 and hinged extensions 205 with a flexible Kevlar safety net 206 attached through a scissor-extension arrangement 207.

[0024] The system disclosed herein comprises of a plurality of thermal cameras 101 configured to securely mounted on building edges to continuously monitor human presence. The thermal cameras 101 are enclosed in weatherproof polymer housings with tempered glass lenses for ensuring durability, accuracy and continuous monitoring of human presence across varying environmental and lighting conditions.

[0025] A user is required to activate the system manually by pressing a button installed on the building and linked with an inbuilt microcontroller associated with the system. The button is a type of switch that is internally connected with the system via multiple circuits that upon pressing by the user, the circuits get closed and starts conduction of electricity that tends to activate the system and vice versa.

[0026] The thermal cameras 101 disclosed above are mounted on the edges of the building through a motorized ball and socket joints 102 that receives an activation signal from the microcontroller for monitoring continuous safety surveillance of human in high risk zones. The thermal camera 101 comprises an image capturing module including a lens assembly configured to focus incoming infrared radiation emitted from human body positioned within a balcony region of a building onto a focal plane array (FPA). The FPA comprises a plurality of temperature sensitive detector elements operative to convert incident infrared radiation into electrical signals proportional to body temperature. The electrical signals are conditioned and processed by a processor to generate thermal image data representing temperature distribution in the monitored balcony region.

[0027] A display module is operatively coupled to the processor to render the thermal image data using temperature dependent gradients and a memory unit is provided to store the thermal image data in the form of optical data. The processor is configured with an artificial intelligence protocol comprising predefined instruction sets operative to analyze the optical data, identify human thermal signatures and extract presence related information including body contours, movement or occupancy duration. The extracted information is converted into digital pulses and transmitted to a microcontroller to determine the presence of human in the balcony, assess positional parameters and generate control signals responsive to unauthorized entry or potential safety risks. Simultaneously, the microcontroller actuates the motorized ball-and-socket joint 102 to provide precise rotational movement across multiple axes, enabling the camera 101 to track human motion precisely. The motorized ball-and-socket joint 102 comprises a spherical ball element enclosed within a socket housing, the thermal camera 101 coupled to the ball element.

[0028] A plurality of electric motors is positioned orthogonally around the socket housing, each motor operatively linked to the ball element via a gear drive assembly. The gear drive assemblies are configured to selectively apply torque to the ball element along independent axes, for enabling controlled pivoting and rotational movement of the ball within the socket. When the motor is activated by the microcontroller, drives the gear drive to push or pull against the ball’s surface, causing the ball to pivot or rotate within the socket for allowing the thermal camera 101 to pivot, tilt and rotate for precise angular positioning of the thermal camera 101 to maximize field coverage, eliminate blind zones and enhance detection of human in monitored areas.

[0029] A plurality of passive infrared (PIR) sensors is positioned near balconies and roof edges of a building to provide reliable detection of human proximity by sensing variations in infrared energy emitted from the human body. The passive infrared (PIR) sensors work by receiving infrared radiation through a Fresnel lens that concentrates and segments the heat signatures of moving human bodies onto the sensing element. A pyroelectric element responds by generating tiny electrical charges whenever a change in thermal energy occurs within coverage zone. These weak charges are fed into a pre amplifier circuit that boosts the signal strength while filtering unwanted background fluctuations. The conditioned signal is then processed by a band pass filter to isolate the frequency range usually of human motion, reducing false activations from steady heat sources.

[0030] A comparator stage follows, comparing the filtered signal against a preset threshold stored in a database to determine whether a valid motion event has occurred. Once confirmed, a digital pulse is generated and passed to the output driver, which formats for direct use by the microcontroller to process data and correspondingly adjusts the orientation and operational parameters of one or more thermal cameras 101 mounted on motorized ball-and-socket joint 102. By aligning camera orientation toward the specific balcony or roof region where human presence is indicated, to ensure focused and high resolution thermal imaging of areas while minimizing unnecessary scanning.

[0031] A plurality of LiDAR (Light Detection and Ranging) modules is integrated with thermal cameras 101 to deliver precise distance and positional measurements. The LiDAR (Light Detection and Ranging) modules works by using a laser emitter to generate rapid pulses of light directed toward the human body, while a beam steering arrangement, usually mirrors, scans these pulses across the field of view. When the light strikes a surface, reflects back and is collected by the photodetector, which converts the returning photons into electrical signals. A timing circuit working with a high resolution clock measures the time interval between emission and detection to calculate distance using the time of flight principle. These raw distance values are processed by a signal processor that filters noise, compensates for environmental conditions, and compiles multiple measurements into a dense three-dimensional point cloud representing the spatial geometry of surfaces, surrounding objects, or individuals. This data is then transmitted to the microcontroller, which synchronizes with the thermal image feed, enabling temperature variations to be overlaid directly onto accurate positional coordinates. This integration helps distinguish between objects at varying depths and ensures that human presence, movement, or anomalies are tracked with both thermal and spatial precision, to dynamically adjusts camera orientation, zoom factor and frame focus for reliable detection even in visually challenging environments.

[0032] A plurality of piezoelectric transducers embedded within balcony floors and roof edges to provide sensitive detection of unusual vibrations and movements. The piezoelectric transducers operate by converting vibrations into measurable electrical signals using layered structure of piezoelectric crystals sandwiched between conductive electrodes. When stress or deformation, such as a vibration or impact, is applied to the sensing surface, the piezoelectric crystal undergoes a change in internal lattice structure, resulting in displacement of electric charges across faces. This generates a voltage potential that is immediately captured by the electrode layers. The resulting signal is very weak, so passes through a charge amplifier that converts the high impedance crystal output into a stronger, usable voltage signal. Following amplification, signal conditioning circuits filter out noise and enhance specific frequency ranges of interest to distinguish relevant vibrations from background interference. These signals are then processed using a frequency domain filtering protocol, which are digitized through an analog to digital converter (ADC) and processed using a Fast Fourier Transform (FFT) module to decompose the waveform into constituent frequency components.

[0033] A digital filter stage, such as band pass, low pass or high pass filters, then isolates the frequency ranges associated with target events while suppressing unwanted bands caused by environmental noise. Human activity, such as footsteps, dragging movements or sudden impacts commonly occupies identifiable mid frequency ranges and displays distinct temporal patterns, whereas environmental influences like wind gusts, rain or structural resonance fall into separate bands with irregular or low amplitude characteristics. By normalizing the filtered spectrum and comparing with stored reference profiles, helps to reliably distinguish human induced vibration signatures from non human activity. The refined output is then translated by a decision module into a simplified detection signal and relayed to the microcontroller for enabling vibration analysis, pattern recognition or real time triggering of safety arrangement.

[0034] A fall prediction module is integrated within the system, developed to predict and avert accidents. The module comprises a machine learning protocol operatively coupled with thermal cameras 101, LiDAR module, piezoelectric transducers and PIR sensors, enabling to perform advanced multi modal data analysis in real time. thermal cameras 101 provide continuous imaging of body heat signatures, allowing to reliably distinguish humans from false triggers such as animals or heated objects. LiDAR modules enhance this by adding precise three dimensional positional and distance data, enabling the reconstruction of accurate body outlines and movement trajectories. Piezoelectric transducers embedded in structural surfaces further contribute by mapping vibration signatures from footsteps, shifts or sudden impacts, while PIR sensors add fast, low latency motion detection cues.

[0035] The fall prediction module is configured to prioritize protection for children and other vulnerable individuals by recognizing body size, movement patterns, and behaviour profiles that distinguish them from adults. Using inputs from thermal imaging, LiDAR based body proportion analysis, and vibration or motion cues from supporting modules, the system accurately identifies vulnerable individuals in high risk zones such as balconies or roof edges. Upon confirmation, the module lowers sensitivity thresholds, ensuring that even subtle imbalances, abrupt motions, or risky proximity near edges are flagged as potential hazards. This immediate recognition triggers the fall protection arrangement without requiring delay for secondary validation, enabling rapid deployment of safety measures such as net extension or alert systems. By prioritizing children and vulnerable individuals, the system ensures proactive intervention, offering a specialized layer of safety that adapts response to the user’s physical and behavioural profile.

[0036] The machine learning protocol fuses these heterogeneous data streams, to analyze posture dynamics such as slouching, stumbling, or imbalance and to evaluate behavior patterns linked with hazardous scenarios like leaning excessively over a balcony edge or making abrupt, unstable movements. By comparing real time data against learned behavioral models and reference profiles, the module predicts the likelihood of a fall event with high accuracy. Upon identifying risky behavior, the system generates alerts or engages automated interventions, such as warning signals to safety personnel. This predictive and preventative approach ensures not only rapid response but also proactive risk mitigation, effectively transforming fall detection into fall prevention.

[0037] The fall prediction module also analyzes environmental conditions and historical incident data to refine decision making. Sensors such as thermal cameras 101 and LiDAR provide information on lighting, obstacles and spatial constraints, while piezoelectric transducers capture unusual vibrations from the surrounding structure, all of which are factored into a real time environmental profile. Simultaneously, historical incident data stored within the microcontroller memory is processed by the embedded machine learning protocol to identify recurring patterns, seasonal influences or location specific risks that increase the probability of a fall. Based on these inputs, the microcontroller dynamically adjusts sensitivity level, essentially redefining the behavioral threshold that to be crossed before triggering a warning or activating fall protection measures. For example, on a slippery rainy day or in areas with a known history of prior falls, the threshold is lowered to detect even subtle imbalance or unstable movements, while under normal safe conditions, is elevated to reduce false alarms. This self optimizing sensitivity ensures highly reliable risk assessments, enabling proactive intervention while maintaining operational efficiency and minimizing unnecessary activations.

[0038] The fall prediction module further incorporates a deep learning protocol developed to continuously enhance accuracy by leveraging extensive historical data and real time contextual inputs. This protocol is trained on past fall related incidents, encompassing variables such as postural deviations leading to imbalance, behavioral precursors like hesitation or sudden shifts and outcome patterns of actual falls. Beyond human movement data, the deep learning protocol also integrates environmental conditions, including wind speed, surface wetness, time of day and lighting variations, which alter risk levels significantly. Additionally, user demographics such as age profiles, mobility patterns or physical tendencies are factored into the model to refine individualized risk assessments. By processing multi layered data, to identifies subtle correlations and predictive markers. Over time, with continuous retraining on new field data, the deep learning protocol adapts to the evolving environments and diverse populations. This not only detect hazardous movements in the moment but also forecast fall likelihood with heightened precision for ensuring proactive intervention that is both context aware and tailored to the specific risk environment.

[0039] A haptic feedback and audio alert means are operatively coupled with the fall prediction module, to provide proactive, context aware safety interventions for individuals approaching high risk zones such as balcony edges or rooftop boundaries. The haptic feedback consists of vibration motors 103 embedded in balcony railings, designed to deliver tactile warnings, while the audio alert consists of directional speaker 104 positioned at balcony edges to emit localized warnings. When the fall prediction module detects hazardous movement, unstable posture or unsafe proximity based on data from thermal cameras 101, LiDAR module, PIR sensors and piezoelectric transducers, transmits control signals to the microcontroller to activates vibration motors 103 to deliver haptic feedback that is quickly felt through touch. The vibration motors 103 consist of a rotary shaft fitted with an off-center weight. As the motor 103 rotates, the uneven distribution of the weight generates vibrations, which are transferred to the balcony railings and perceived by the user.

[0040] This provides a discreet yet warning cue, even in low visibility or noisy conditions, alerting the individual that they are nearing a danger zone. Simultaneously, the microcontroller actuates the speaker 104 to generate localized warning signals to the area of concern. The speaker 104 works by converting the electrical signal into the audio signal, consists of a cone known as a diaphragm attached to a coil-shaped wire placed between two magnets. When the electric signal is passed through the voice coil, generates a varying magnetic field that interacts with the magnet causing the diaphragm to move back and forth. This movement creates pressure variations in the surrounding air, generating sound waves in order to deliver targeted warning signals that are limited to the relevant area, ensure that individuals retreat from unsafe positions. This guarantees redundancy and effectiveness across different user conditions for instance, offering vibration feedback as a failure safe for individuals with limited hearing or sound alerts for those who do not directly perceive tactile feedback. Collectively, this reduces accidents by guiding individuals away from danger before an incident occurs, while maintaining user comfort and compliance in everyday building use.

[0041] A fall protection arrangement is integrated into the system, developed to safeguard individuals during accidental falls from balconies or rooftop zones. The fall protection arrangement includes a barrel assembly 201 mounted on a horizontal slider railing 105 positioned directly beneath each balcony and adjoined with a vertical sliding rails 106 positioned at building corners, together forming a guidance arrangement that enables controlled multi directional positioning. The barrel assembly serves as the primary deployment arrangement and houses a quick-extending rod 203 actuated by a series of electromagnetic coils 202, providing instant propulsion for high extension speeds with precision control. The rod 203 is equipped with curved plates 204 and hinged extensions 205, to which a flexible Kevlar safety net 206 is securely fastened at the perimeter.

[0042] This net 206 is fastened through a scissor-extension arrangement 207 that expands outward to dynamically spread the net 206 into a taut protective surface, which is capable of absorbing and redistributing the kinetic energy of the fall. When the fall prediction module identifies a fall event, the microcontroller actuates both horizontal and vertical slider railing in synchronized way, allowing to traverse laterally beneath the balcony with high precision and speed. The horizontal slider railing 105 consists of a motor which drives a pinion gear that meshes with a linear rack mounted on guiding rails, translating rotational energy into linear displacement of a carriage attached to the barrel assembly 201 across the balcony edge. The vertical slider railing works in similar manner as horizontal slider railing 105, enabling controlled upward movement while maintaining stability. Both directions interconnected through a rigid frame and low-friction bearings, ensuring synchronized, vibration-free movement to achieve optimal positioning during a fall.

[0043] Simultaneously, the microcontroller actuates the quick-extending rod 203 through activation of electromagnetic coils 202 by propelling the rod 203 outward with precision and speed to position the attached curved plates 204 beneath the falling individual, ensuring accurate alignment for subsequent safety net 206 deployment. The quick-extending rod 203 works by utilizing multiple electromagnetic coils 202 arranged sequentially along length barrel assembly length, functioning as a linear electromagnetic launcher. When triggered, high current pulses are delivered to the coils 202 in a controlled sequence by a power driver circuit, creating strong, rapidly changing magnetic fields. These magnetic fields induce thrust forces that push the ferromagnetic core of the rod 203 forward to position the curve plate 204 at optimal positon, ensuring swift acceleration without delay. As the rod 203 fully extends and the curved plates 204 precisely positioned, the microcontroller actuates the scissor- extension arrangement 207 to extend outward for dynamically deploying the net 206 into a firm and protective surface. The scissor- extension arrangement 207 consists of cross linked scissor arms pivotally joined in a lattice formation, driven by an electric motor coupled to a rack and pinion arrangement.

[0044] When the motor activates, the rack and pinion arrangement translates rotary motion into linear push or pull, forcing the scissor arms to expand outward in a synchronized manner. This expansion drives the distal ends of the scissor arms, which are directly fastened to curved plates 204 and hinged extensions 205, thereby pulling the net 206 along perimeter. As expansion progresses, the net 206 is stretched uniformly outward, creating a wide, tensioned surface capable of absorbing and distributing impact forces. The Kevlar safety net 206 is developed as a multi layered impact absorbing structure, reinforced with secondary mesh layers and cross linked straps that interweave across surface to evenly distribute impact forces and prevent localized stress points. The cross linked straps work in tandem with the high tensile Kevlar mesh to maintain structural integrity under sudden, heavy loads, ensuring energy from a fall is dispersed across the full surface rather than concentrated at a single point.

[0045] To enhance adaptability, the microcontroller uses multiple nets 206 arranged at different vertical levels, each capable of sequential deployment based on the detected fall trajectory and elevation. These nets 206 are secured to the curved plates 204 using detachable connectors, enabling rapid attachment during deployment and safe disengagement after load absorption or servicing. By combining reinforcement and multi level redundancy, the arrangement ensures progressive, controlled deceleration, minimizing injury risk while providing fail safe protection in diverse fall scenarios. The sequential deployment is controlled, with the fall protection arrangement by dynamically adjusting parameters such as net 206 surface area and extension force based on the falling individual’s approximate body proportions and weight, as determined by the LiDAR module’s precise measurements. For lighter individuals, the deployment tension is reduced to cushion the fall without rebound, while for heavier subjects, the microcontroller reinforces the extension strength to maintain safety integrity.

[0046] A display interface installed within the computing unit to facilitate authorized personnel by providing real-time alerts, video feeds and generate incident reports for compliance with building safety regulations. The user interacts with the display interface through a touch screen or other input methods available on the computing unit. The computing unit mentioned herein includes, but is not limited to smartphone, tablet or laptop that comprises a processor that receives data from a microcontroller of the system, stores, processes and retrieves the output in order to display on the computing unit.

[0047] A communication interface for establishing a wireless connection between the controller and a computing unit is inbuilt within the controller. The communication module used herein includes, but not limited to Wi-Fi (Wireless Fidelity) module, Bluetooth module, GSM (Global System for Mobile Communication) module. The communication interface used herein is preferably a Wi-Fi module that is a hardware component that enables the controller to connect wirelessly with the computing unit. The Wi-Fi module works by utilizing radio waves to transmit and receive data over short distances. The core functionality relies on the IEEE 802.11 standards, which define the protocols for wireless local area networking (WLAN). Once connected, the communication interface allows the controller to send and receive data through data packets.

[0048] Referring to Figure 3, illustrates a flow chart depicting a method for detecting and preventing falls from high-risk zone.

[0049] The present invention further provides a method for detecting and preventing falls from high risk zones by integrating a multi layered sensing, prediction, and protection framework that safeguards individuals in hazardous balcony or rooftop areas. The method includes capturing 301 continuous thermal imaging data of individuals using thermal cameras 101 mounted at building edges, while simultaneously measuring 302 precise distance and positional information through LiDAR modules integrated with the cameras to generate accurate spatial mapping. The method further includes monitoring 303 real time vibrations via piezoelectric transducers embedded in balcony floors and roof edges to recognize footsteps, impacts, or unusual movements, and detecting 304 human proximity near critical zones through passive infrared (PIR) sensors with fast response. All input data streams are then transmitting to a centralized fall prediction module powered by machine learning protocols, which is analysing 305 thermal, positional, vibration, and motion cues to differentiate humans from false triggers, evaluating posture and movement behaviors, and predicting fall related risks. Upon detecting hazardous movements, the system is providing 306 pre emptive warnings by activating vibration motors 103 embedded in balcony railings to deliver tactile alerts, and by emitting localized audio cues through directional speakers 104 installed at balcony edges.

[0050] If the risk escalates into an imminent fall, the microcontroller is activating 307 a fall protection arrangement: repositioning a barrel assembly 201 mounted on a horizontal slider beneath the falling individual, launching a quick extending rod 203 via electromagnetic propulsion, and deploying curved plates 204 with hinged extensions 205 to spreading a Kevlar safety net 206 into a taut protective surface. The net 206, reinforced and expandable through a scissor extension arrangement 207, is deploying sequentially at multiple vertical levels and adjusting dynamically according to the body proportions and approximate weight identified by the LiDAR module. Meanwhile, the system is transmitting 308 real time alerts, thermal video feeds, and incident data through a communication interface to authorized personnel for command oversight, and generating automatic reports for compliance with building safety standards. Critically, the module is prioritizing 309 children and other vulnerable individual by lowering detection threshold and triggering protective deployment automatically, thereby ensuring rapid and tailored interventions that transform fall prevention from reactive rescue into proactive safety assurance.

[0051] Although the field of the invention has been described herein with limited reference to specific embodiments, this description is not meant to be construed in a limiting sense. Various modifications of the disclosed embodiments, as well as alternate embodiments of the invention, will become apparent to persons skilled in the art upon reference to the description of the invention. , Claims:1) An automated fall prevention and protection system for buildings and a method thereof, comprising:

i) a plurality of thermal cameras 101 mounted on building edges and operatively coupled with motorized ball-and-socket joints 102, configured to capture thermal imaging data of individuals in high-risk zones;
ii) a plurality of LiDAR (Light Detection and Ranging) module integrated with the thermal cameras 101 for accurate distance and positional measurement;
iii) a plurality of piezoelectric transducers embedded in balcony floors and roof edges to detect unusual vibrations and movements;
iv) a plurality of passive infrared (PIR) sensors positioned near balconies and roof edges to detect human proximity in synchronization with the cameras 101;
v) a fall prediction module provided with the system, the module comprises machine learning protocol operatively coupled with the thermal cameras 101, LiDAR sensors, piezoelectric transducers, and PIR sensors, configured to:
a) analyze thermal imaging data to differentiate humans from false triggers;
b) evaluate posture and behavior of detected individuals, and
c) predict hazardous movements or fall-related behavior;
vi) a haptic feedback and audio alert operatively coupled with the fall prediction module, configured to provide pre-emptive warnings to individuals approaching high-risk zones via audio alert; and
vii) a fall protection arrangement provided with the system for safeguarding individuals during fall, the arrangement comprises:
a) a horizontal slider railing 105 positioned beneath each balcony;
b) vertical sliding rails 106 installed at building corners for controlled vertical movement adjoined with the horizontal slider;
c) a barrel assembly 201 having a scissor- extension arrangement 207 operatively coupled with the curved plates 204, the assembly 201 is mounted on the horizontal slider, housing a quick-extending rod 203 guided by multiple electromagnetic coils 202, the barrel assembly 201 is configured to:
i) rapidly position itself beneath the falling individual using the horizontal slider;
ii) launch the extending rod 203 via electromagnetic propulsion;
d) curved plates 204 and hinged extensions 205 mounted on the distal end of the extending rod 203; and
e) a flexible Kevlar safety net 206 secured at its perimeter to the curved plates 204 and hinged extensions 205, configured to deploy into a taut protective surface upon outward extension of the rod 203, wherein the fall protection arrangement is configured with sequential deployment capability and the deployment being dynamically controlled based on body proportions and approximate weight of the falling individual as determined by LiDAR module.

2) The system as claimed in claim 1, wherein the PIR sensors configured to send a detection signal to an embedded microcontroller and the microcontroller adjusts the orientation and operational parameters of the thermal cameras 101 to focus on corresponding balconies or roof areas upon detection of human proximity.

3) The system as claimed in claim 1, wherein the haptic feedback comprises vibration motors 103 embedded in balcony railings, configured to deliver tactile warnings, and the audio alert comprises directional speaker 104 positioned at balcony edges to emit localized warnings.

4) The system as claimed in claim 1, wherein the barrel assembly 201 further comprises a scissor- extension arrangement 207 operatively coupled with the curved plates 204, the scissor- extension arrangement 207 is configured to expand the Kevlar net 206 to a wider surface area upon deployment.

5) The system as claimed in claim 1, wherein the Kevlar safety net 206 is reinforced with secondary mesh layers and cross-linked straps to evenly distribute impact forces, and multiple nets 206 are sequentially deployable at different vertical levels, each secured to the curved plates 204 of the extending rod 203 via detachable connectors.

6) The system as claimed in claim 1, wherein a communication interface is provided with the system for transmitting real-time alerts and video feeds to a display interface accessible by authorized personnel and generate incident reports for compliance with building safety regulations.

7) The system as claimed in claim 1, wherein the deployment of the Kevlar safety net 206 is dynamically controlled by a microcontroller operatively coupled with the LiDAR module, the microcontroller being configured to adjust the tension and extension speed of the safety net 206 based on the body proportions and approximate weight of the falling individual as determined by the LiDAR module.

8) The system as claimed in claim 1, wherein the piezoelectric transducers are further configured to differentiate between vibrations caused by human activity and environmental factors, by means of a frequency-domain filtering protocol.

9) The system as claimed in claim 1, wherein the fall prediction module is configured to prioritize protection for children or vulnerable individuals, and automatically triggers the fall protection arrangement upon detecting their presence in high-risk zones.

10) A method for detecting and preventing falls from high-risk zones, comprising the steps of:

i) capturing 301 thermal imaging data of individuals using a plurality of thermal cameras 101 mounted on building edges;
ii) measuring 302 distance and positional information of individuals using a plurality of LiDAR modules integrated with the thermal cameras 101;
iii) monitoring 303 vibrations in balcony floors and roof edges using a plurality of piezoelectric transducers;
iv) detecting 304 human proximity near balconies and roof edges using a plurality of passive infrared (PIR) sensors;
v) analyzing 305 the thermal imaging data, LiDAR data, piezoelectric transducer data, and PIR detection signals using a fall prediction module comprising a machine learning protocol to:
a) differentiate humans from false triggers;
b) evaluate posture and behavior of detected individuals; and
c) predict hazardous movements or fall-related behavior;
vi) providing 306 pre-emptive warnings to individuals approaching high-risk zones by activating vibration motors 103 embedded in balcony railings and directional speaker 104 positioned at balcony edges;
vii) activating 307 a fall protection arrangement upon detection of a fall risk, the arrangement comprising:

a) positioning a barrel assembly 201 mounted on a horizontal slider railing 105 beneath the falling individual;
b) launching a quick-extending rod 203 from the barrel assembly 201 via electromagnetic propulsion;
c) deploying curved plates 204 and hinged extensions 205 mounted at the distal end of the extending rod 203; and
d) extending a flexible Kevlar safety net 206 secured at its perimeter to the curved plates 204 and hinged extensions 205 into a taut protective surface, wherein the deployment is sequentially configurable at different vertical levels and dynamically controlled based on body proportions and approximate weight of the falling individual as determined by the LiDAR modules;
viii) transmitting 308 real-time alerts and video feeds to a display interface accessible by authorized personnel, and generating incident reports for compliance with building safety regulations; and
ix) prioritizing 309 protection for children or vulnerable individuals by automatically triggering the fall protection arrangement upon detecting their presence in high-risk zones.