WO2024089479A1 - Procédé et système de reconnaissance précoce d'individus exposés à un accident par l'intermédiaire d'une sécurité neuronale dans les emplois impliqués dans des risques de sécurité extrêmes - Google Patents

Procédé et système de reconnaissance précoce d'individus exposés à un accident par l'intermédiaire d'une sécurité neuronale dans les emplois impliqués dans des risques de sécurité extrêmes Download PDF

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WO2024089479A1
WO2024089479A1 PCT/IB2023/057284 IB2023057284W WO2024089479A1 WO 2024089479 A1 WO2024089479 A1 WO 2024089479A1 IB 2023057284 W IB2023057284 W IB 2023057284W WO 2024089479 A1 WO2024089479 A1 WO 2024089479A1
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data
accident
api
safety
recognition
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PCT/IB2023/057284
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English (en)
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Majid ALIZADEH
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Alizadeh Majid
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Priority to PCT/IB2023/057284 priority Critical patent/WO2024089479A1/fr
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00—Scenes; Scene-specific elements
    • G06V20/50—Context or environment of the image
    • G06V20/52—Surveillance or monitoring of activities, e.g. for recognising suspicious objects
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
    • G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
    • G06V40/16—Human faces, e.g. facial parts, sketches or expressions
    • G06V40/174—Facial expression recognition
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
    • G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
    • G06V40/18—Eye characteristics, e.g. of the iris

Definitions

  • This invention relates to early ,reliable and timely recognition of accident-prone individuals (API) through Neural-Based Safety ⁇ (linking between safety science and neuroscience for studying the neural activity of the brain) (bridges the study of employee behavior with neuroscience) (as the fields of brain imaging, activities and function) ⁇ in combination with digital reality (VR ,AR and MR) and artificial intelligence )and Machine Learning, Artificial Neural Network and Deep Learning that employs that employs in the process of pre- and in-employment screening in the critical jobs, such as Airplane Pilot, Ship Captain, Oil tanker Driver and ...
  • this invention it relates to exploring the brain of the employee in which uses medical and emerging technologies such as AI based fMRI (brain imaging), EEG and MEG to study the brain's response to safety related stimuli and also preprocessing and processing acquired data for prediction of behavioral patterns of employees before accident occur due to personal factors and inherent characteristics of them.
  • medical and emerging technologies such as AI based fMRI (brain imaging), EEG and MEG to study the brain's response to safety related stimuli and also preprocessing and processing acquired data for prediction of behavioral patterns of employees before accident occur due to personal factors and inherent characteristics of them.
  • Embodiments herein provide a system and method for mapping traffic accidents, storing historical and real-time accidents data, other accidents related information.
  • the data resides in the central server and implemented for alerting a user about traffic accidents based on location and type of category.
  • the system and method comprises storing a plurality of historical and real-time traffic accidents for different categories of the users in a unified database. It further stores a plurality of other traffic accident related information, comprising reasons, time and data, category of the accident participants, further comprising non-commercial vehicles or commercial vehicles users, motorcyclists, bicyclists or pedestrians.
  • the system and method uses an analysis mechanism to search and analyze historical or real-time traffic accidents data based on the category the user belongs to specific geocoded location and display the analyzed advisory notification on the basis of the user's location.
  • a system for monitoring health and safety of workers in a manufacturing facility includes a user interface, a set of sensors, a database, management software and/or a health monitoring system, among other components.
  • the system is configured to monitor health of workers using biometric data gathered by the set of sensors.
  • the system also tracks the position and motion of the worker.
  • the data gathered is aggregated in a database for datamining purposes so as to facilitate health monitoring and mitigation of identified illness.
  • the system may be configured to screen and identify workers who are not in normal health.
  • the system may be configured to screen workers’ health when clocking into or out of the time keeping system.
  • the system may be configured to perform contact tracing.
  • a system to monitor vehicle accidents using a network of aerial based monitoring systems, terrestrial based monitoring systems and in-vehicle monitoring systems is described.
  • Aerial vehicles used for this surveillance include manned and unmanned aircraft, satellites and lighter than air craft.
  • Aerial vehicles can also be deployed from vehicles. The deployment is triggered by sensors registering a pattern in the data that is indicative of an accident that has happened or an accident about to happen.
  • the senor transmits an electric reaction signal to a central system unit, and is also excited by the central system unit by a diagnostic and/or excitation signal as a reversible electro-mechanical system.
  • This invention emphasizes the ethical principles in performing Neural-based safety and states that its results should not lead employee employment discrimination and employers should develop appropriate written morality policies in this regard.
  • This invention relates to early, reliable and timely recognition of accident-prone individuals (API) through Neural-Based Safety in combination with digital reality and artificial intelligence in the process of pre- and in-employment screening in the critical jobs involved extreme safety hazards in the dangerous industries and services to preventing major accidents and saving life, properties, environment and reputation as well.
  • API accident-prone individuals
  • MEG mobility-based fMRI
  • VR digital Reality technology
  • This invention relates to early, reliable and timely recognition of accident-prone individuals (API) through Neural-Based Safety in combination with digital reality and artificial intelligence in the process of pre- and in-employment screening in the critical jobs involved extreme safety hazards in the dangerous industries and services to preventing major accidents and saving life, properties, environment and reputation as well.
  • it relates to exploring the brain of the employee in which uses medical and emerging technologies such as AI based fMRI (brain imaging), EEG and MEG to study the brain's response to safety related stimuli (using Digital Reality technology like VR and its related software as well).
  • Integrated Intelligent Platform for Data Analytics to process acquired data (all data are encrypted) for prediction of behavioral patterns of employees and (dynamic and real-time) prediction of workplace safety risks base on brain mapping and biometric data before accident occur due to personal factors and inherent characteristics of them. It also uses eye tracking technology, galvanic skin response test and facial action coding system (as unconscious or automatic imitation of facial expressions) to acquiring some specific personal data for evaluating cognitive traits of accident- prone individuals (API).
  • Psychological test and assessment A psychological test, psychometrics, is often designed to measure and observe a person's behavior (unobserved constructs, also known as latent variables using questionnaires and interviews) and to determine a person's mental capacity to arrive at a diagnosis and guide treatment.
  • Psychological assessment is also more comprehensive assessment of the individual.
  • Psychological assessment can help diagnose conditions such as depression, anxiety, bipolar disorder, and attention deficit hyperactivity disorder (ADHD), among others. It can also be used to assess an individual's cognitive abilities, such as memory, problem-solving skills, and intellectual functioning.
  • Psychological assessment is a process that involves checking the integration of information from multiple sources, such as tests of normal and abnormal personality, tests of ability or intelligence, tests of interests or attitudes, as well as information from personal interviews. Typical types of focus for psychological assessment are to provide a diagnosis; to assess a particular area of functioning or disability; or to help assess job applicants or employees and provide career development counseling or training.
  • Neuropsychological assessment is a branch of psychology that is concerned with how the brain and the rest of the nervous system influence a person's cognition and behaviors. More importantly, professionals in this branch of psychology often focus on how injuries or illnesses of the brain affect cognitive functions and behaviors.
  • Neuropsychological assessment is the systematic evaluation of the brain-behavior relationships in an individual. Tools used to complete the NPA are measures of cognition and intelligence that have been standardized on a neurologically normal sample. The purpose of neuropsychological evaluation is currently multifaceted. A complete NPA helps referral source gain an understanding of the employee's cognitive processes such as memory, language, and perception. In addition, it can assist in diagnosis and identification of difficulties in cognition that might be related to psychiatric conditions and motivation.
  • Neuropsychological tests are specifically designed tasks used to measure a psychological function known to be linked to a particular brain structure or pathway. Tests are used for research into brain function and in a clinical setting for the diagnosis of deficits. According to Larry J. Seidman, the analysis of the wide range of neuropsychological tests can be broken down into four categories. First is an analysis of overall performance, or how well people do from test to test along with how they perform in comparison to the average score. Second is left-right comparisons: how well a person performs on specific tasks that deal with the left and right side of the body. Third is pathognomonic signs, or specific test results that directly relate to a distinct disorder. Finally, the last category is differential patterns, which are strange test scores that are typical for specific diseases or types of damage.
  • BBS Behavior Based Safety
  • BBS is defined according to the recognition of occupational hazards, risky behaviors, observing those attitudes, making decision to feedback and making fundamental reforms.
  • Recent studies have focused on investigating the relationship between common genetic polymorphisms and specific psychological characteristics for distinct candidate genes, with some studies showing that the neurotransmitters 5-hydroxytryptamine (5-HT) (serotonin), 5-hydroxytryptophan (5-HTP), and dopamine play an important role in personality traits.
  • this invention relates to early ,reliable and timely recognition of accident-prone individuals (API) through Neural-Based Safety ⁇ (linking between safety science and neuroscience for studying the neural activity of the brain) (bridges the study of employee behavior with neuroscience) (as the fields of brain imaging, activities and function) ⁇ in combination with digital reality (VR ,AR and MR) and artificial intelligence )and Machine Learning, Artificial Neural Network and Deep Learning that employs( that employs in the process of pre- and in-employment screening in the critical jobs (such as Airplane Pilot, Ship Captain, Oil tanker Driver, Metro loco pilot, Control room operator, Air traffic control tower operator) involved extreme safety hazards in high complexity industries and services (such as Nuclear ,Chemical , Oil and Gas Refinery ,Petrochemical , Transportation) which are very dangerous work environment and able to prevent major accidents and saving life ,properties , environment and reputation as well.
  • API accident-prone individuals
  • Neural-Based Safety uses neural medical technologies include fMRI, EEG and MEG (as a non-invasive method for measuring and mapping brain activity) to show correlations between employee’s brain activities and safety stimuli and also to study the brain's response to safety stimuli.
  • the brain is responsible for all employee behaviors then this invention uses brain activity for this porpuse.
  • Neural-Based Safety combines, integrates and uses tools, achievements, and tests from the disciplines of safety, neuroscience (an interdisciplinary science where such fields of science as computer, mathematics, chemistry, medicine, and biology intersect), psychology and neurology to measure biological responses to safety related stimuli. It is applied to understand the cognition, emotions and behavioral patterns of risk-taking employees or those have phobia through the integration of biological and social sciences and leads to the identification of Accident-Prone Individuals. This invention helps safety professionals achieve employees' inner and subconscious tendencies and design their safety activities in a way that attracts their attention for safe behavior and is most effective.
  • Neural-Based Safety as a novel method employs brain imaging to detect how employees feel and how they respond to safety stimuli, and which part of the brain stimulates.
  • the brain has three main parts include Neocortex, Limbic System and Reptilian Brain.
  • the neocortex is the most external part of the brain, which is associated with higher thinking, preprocessing data, and deduction. The findings that this layer attains are then shared with the other layers of the brain.
  • the limbic system is middle brain that is associated with feelings and processes emotions. Of course, anything that this part of the brain feels is communicated with the other two brains.
  • the third brain is called the Reptilian Brain, which is where all decisions are ultimately made. This part of the brain sucks in all the information that the other two brains have gathered and uses the information to formulate the best decision.
  • the limbic system is the main part of the brain in this regard, which is basically responsible for human emotional and instinctive behavior. Emotions are one of the most powerful forces driving behavior.
  • Neural-Based Safety focuses on the limbic system, which is located on deep structure of cerebral membrane, is responsible for memory, learning and emotional status. Also, the main structures of the limbic system are the hippocampus, the amygdala, and the hypothalamus that are deeper structures of the human brain.
  • the limbic brain is the seat of the value judgments that employee make, often unconsciously, that exert such a strong influence on their behavior. In term of obtained principles of Neural-Based Safety, the limbic system produces instinctual and sensory responses which are later transported to the cerebral cortex.
  • hypothalamus works as the master control of our autonomic system. Sleep, hunger, thirst, body temperature and blood pressure are controlled and regulated by hypothalamus. Thalamus on the other hand regulates sensory information, attention and memory. Amygdalae originate our emotional response and hippocampus is the mainframe of our memory. Therefore, the amygdala is one of the main parts of the limbic system that is investigated for brain imaging in this invention. Hence, neuroimaging and neural signal recording devices have used limbic system and brain states to map the mind of an employees.
  • This invention employs Neural-Based Safety as a neuroscientific method so as to predicting, diagnosing and treating the at-risk, unsafe and risky behavior on accident-prone individuals like risk takers.
  • the new technological methods like neuroimaging have also used to examine the brain form the basis for this invention which can directly observe the brain activities of employee while engaging them with mental tasks using digital reality like VR.
  • neuroimaging as a part of this invention, is a brain scanning technique which produces the images of structures or functions of neurons. After recognition of API, a developed Individual Risk Control Protocol use for minimizing individual risk to ALARP level for example, through rTMS Therapy and other relevant intervention measures.
  • the invention provides a non-invasive psychoanalysis neural methods of measuring brain activity that include Functional Magnetic Resonance Imaging (fMRI), Electroencephalography (EEG), Magnetoencephalography (MEG), Event Related Potential (ERP), Eye tracking, Galvanic Skin Response (GSR) and Facial Coding (FC).
  • fMRI Functional Magnetic Resonance Imaging
  • EEG Electroencephalography
  • MEG Magnetoencephalography
  • ERP Event Related Potential
  • GSR Galvanic Skin Response
  • FFC Facial Coding
  • the neural methods employ to investigate neurons activities and their responses in safety field for recognition of Accident-Prone Individuals that anticipates the human subconscious with specially selected sets of images or videos (using digital reality like VR according to a designed task) and leads a positive emotional response and activate hidden images, metaphors stimulating the safe or unsafe acts.
  • This invention combines EEG, MEG and fMRI (Neurophysiological tools) in order to optimize temporal, portability and spatial resolution issues and provide the added value of time stamping critical cognitive sequences at the enormous speed of just a few milliseconds.
  • the Neural-Based Safety predicts models of employee behavior and occurrence of future accidents. It applies for Brain Mapping and Biometric Based Risk Management and can theorization of emotional aspects of employee behavior. It uses technologies such as imaging to measure changes in different parts of the brain and uses brain scans to measure activity in a specific part of the brain to study the behavior of employees and decision-making areas in the brain. As such, it takes psychology, neuropsychology and behavior-based safety to a new level as it enables safety professionals to understand and manage the neural processes that drive human behavior. For better understanding, the invention is explained in the following seven sections. Each section may have specific modules, in which case, each module is described separately.
  • initial necessary data for instance: demographic data, medical history and examination records, person’s CBLN4 genes test result, employee biometrics records, Individual risk registry, accident and near-miss record of employee, critical job list , HTA and TLX NASA , workplace and environmental monitoring , RFID tag information for employee and predefined PPE,CPE and Danger Zones
  • initial necessary data for instance: demographic data, medical history and examination records, person’s CBLN4 genes test result, employee biometrics records, Individual risk registry, accident and near-miss record of employee, critical job list , HTA and TLX NASA , workplace and environmental monitoring , RFID tag information for employee and predefined PPE,CPE and Danger Zones
  • the most commonly used demographic factors are: age, gender, ethnicity, income, level of education, home ownership, sexual orientation, marital status, religion, occupation, family status and structure.
  • the second section is a very important part of the invention called the Data Acquisition System which includes tools and methods to collect environmental and individual data.
  • Environmental data include lighting, indoor and outdoor air quality, harmful agents of the work environment, environmental pollutants, noise, ergonomics conditions, heat stress and cold stress, and something like these.
  • individual data is collected clinically and non-clinically based on developed data collection protocols, which is explained in the following five modules.
  • Module No. 1 Worksite (Indoor and Outdoor Workplace)- Some real-time and dynamic data of individual and environment from both indoor and outdoor worksites are collected through fixed static camera, high resolution drone-based camera, (wearable and wireless) vital signs sensors of individual and environmental monitoring sensors. These data will be helpful to isolate the effects of personal characteristics due to heredity and inherent factors that will obtained through measuring brain activity as neural methods. There is also a 3 layers’ mechanism for authentication of employee using infrared laser (identification of people based on their heartbeat), RFID tag and image scanning. For example, a drone-based camera is to capture still images and video from a distance or at a high altitude to gather information about individuals. The obtained data are used for unsafe behavior detection and people authentication. The videos and images are analyzed in an AI-based video analytics tools as part of Integrated Data Analytics Platform.
  • AI-based video analytics tools as part of Integrated Data Analytics Platform.
  • Modules No. 2 and 3 The signals from the human body and brain are collected for understanding the origins and functions of Neural-Based Safety in the investigation of employees’ Safe (Unsafe) behaviors in Brain Imaging Lab and Biometric lab.
  • the human body scales include the facial expressions, eye movements, eye blink, startle reflex, behavioral responses, electro dermal activity, heart rate, blood pressure, pupil dilation, and respiration.
  • the human brain signals are captured by the blood oxygenation, electrical fields, and magnetic fields.
  • the human brain structure has three major parts include Basal ganglia, Limbic system and Neocortex. All of parts are functionally different, also interconnected.
  • Biometrics are about detection and measurement of autonomic or involuntary bodily changes triggered by nervous system responses to emotional impact within interaction events. Examples include changes in heart rate, respiration, perspiration, and eye pupil dilation. Changes in perspiration are measured by galvanic skin response measurements to detect changes in electrical conductivity.
  • the following three categories of applied neuroscience techniques are used with the purpose of Neural-Based Safety to measure the neural activity of the brain with specific applications in the field of safety.
  • they are techniques of measurement of brain response to developed safety stimuli that is displayed in the digital reality goggle include techniques to record the CNS activities such as: EEG, MEG, ERP, fMRI, techniques to record the PNS activities such as: GSR, FC, Techniques to record behavior such as ET.
  • fMRI Fluorescence Magnetic Resonance Imaging
  • It is used to examine the brain function.
  • fMRI Magnetic Resonance Imaging
  • Employees are scanned while lying on their back into the tube of fMRI machine and watching a developed safety stimulus, by means of digital reality like virtual reality.
  • the fMRI is very reliable and the most accurate method of evaluating the brain activity, also about presenting a safety stimulus. Since, fMRI has an excellent spatial resolution, it is uses to detect activity in specific and small parts of the brain in Neural-Based Safety.
  • EEG Neural-Based Safety
  • EEG recording instrument detects the electrical generated signals through a group of activated surface neurons.
  • the activation of different neurons in disparate area depends on various developed safety stimuli, by means of digital reality like virtual reality.
  • MEG Magnetic electroencephalography
  • ERP Event Related Potential
  • EEG Event Related Potential
  • ERP is an electrophysiological method, which record the brain wave-pattern during the presentation a developed safety stimulus such as sensory, cognition or motor stimulation.
  • ERP is specific application of EEG.
  • ERP method has been measured the brain electrical activity at the level of scalp through some electrodes placed on employees’ head.
  • ERP is able to recognize the brain response to a single event which is not visible in EEG method, because of the EEG reflects to a group activity of neurons.
  • duration of the safety stimuli provided the mean brain responses recorded by the ERP, are measured.
  • the human Alongside brain regions associated with neural response, the human has a peripheral system which corresponds to cognitive and emotional processes. Eye movement, skin conductance, facial expression all are result of neural processes that the following is explained.
  • GSR Globalvanic Skin Response
  • EDA electro dermal activity
  • skin conductance indicates the amount of skin’s electrical activity which flows through the skin perspiration.
  • GSR instrument contains electrical conductance sensors, attach to the finger and measures the number of sweating changes in response to some developed safety stimuli. GSR is also used beside other neural methods to validate Neural-Based Safety findings. Unlike other aspects of measuring emotion, rapid changes in skin conductance are automatic, happening without conscious awareness or control, since it is part of our autonomic nervous system. While we might be able to control where we look and even our facial expressions, we cannot control our sweat glands. This makes GSR a very attractive tool because we can measure employee’s arousal level without requiring them to articulate their feelings, or potentially hiding them as well.
  • FC Ficial Coding
  • FC facial MEG
  • FC facial Coding
  • This instrument contains a camera to record the facial micro expressions. Therefore, does not need to place sensors on the face of employees. Hence, this device is not being able to detect generated electrical response by muscle contraction. It is an indirect recording technology.
  • ET Eye Tracking
  • Eye Tracking is based on personal behavior. This technique according to the position of pupil, records eye movements and gaze patterns of subjects. Identification of gaze patterns by ET is done to describe the visual path of response to a specific event.
  • the eye tracking devices equipped by infrared cameras to recognize the subject attention at a given moment.
  • Two types of eye-trackers are designed include eyeglass (Mobile) tracker to register the looking patterns in real situation because of mentioned cameras and stationary eye-tracker is used to laboratory or controlled areas. Eye tracking approve the theory of limiting human attention. This means that they are able to focus on a certain number of things at the moment.
  • ET supplies the information about temporal process with high resolution. Eye tracking is primarily considered as the physiological response in Neural-Based Safety; however, in this invention, eye tracking is used to activation of the visual cortex or a secondary neural response.
  • five sub-modules are applied that include Personality Test (include MMPI and NEO PI-R), Mood Tracking, Humorism, Temperament and other psychology test such as MBTI, Rorschach, TAT.
  • MMPI MMTI
  • Rorschach TAT
  • NEO PI-R Neurophysiological tests
  • test results are used to help understand the cause of problems with thinking and understanding of employees. For example, test results are used to determine if cognitive (mental) changes of employees are due to normal aging, a neurological illness, depression, anxiety or other causes.
  • the third section there is a data extraction system with various devices that refine and extract data obtained from previous sections.
  • a mechanism for pre-processing of the data that is done by devices or equipment such as video scanner, FRID tag reader and image scanner.
  • Vital sign data include body temperature, pulse rate, respiration rate, blood pressure.
  • extracted data is translated to psychological vital signs of anxiety, anger, fear, stress, depression and something like these. Basically, emotions motivate behavior and have a significant impact on psychological states of individuals that are very important in this invention.
  • Video Scanner system as part of present invention is a multipurpose device that grab multiple frames to monitor behavior of employees such as risk taking or no use of personal protective equipment (safety helmet or ear muff). It is an AI-based video analytics tool is to extract meaningful information from videos of all formats by using a set of complex algorithms that scan videos pixel-by-pixel and automatically detects, analyzes, processes, and interprets unsafe behavior with videos obtained from all existing fixed static cameras (indoor and outdoor workplace) and drone-based cameras that is called an unsafe behavior detection system.
  • This system enables automated observation on behavior to be surveyed in a real-time way by taking video signals, digitizes frames, and analyses the resultant pixels to determine the behavior types (Safe, At-risk or Unsafe behavior) in a specific software that is part of section of integrated Data Analytics Platform that is related to the areas of computer vision and machine learning.
  • This behavior detection system is designed based on the training sample through which the system is developed to learn the pattern of safe behavior, and later they detect the unsafe behavior in the workplace using the learned pattern using artificial intelligence and deep learning technology.
  • an unsafe behavior pool has been developed, which is added to the number of unsafe behaviors in this pool every year, through use of artificial intelligence, machine learning method and deep learning technology in various workplaces.
  • the authentication of employee is done through 3 following explained methods as part of monitoring plan of behavior of employee in the workplace.
  • the reason for using 3 methods of simultaneous authentication of employees is to increase the reliability of the findings of behavioral monitoring of employees.
  • the following methods are used to locate them in the workplace, which helps to find the exact location where employees engaged in unsafe behavior.
  • Face recognition system includes a pretrained model that is used for face recognition from video and also uses machine learning method and deep learning technology. Facial features of employees are extracted through a combination of the linear binary pattern histogram algorithm and convolutional neural network (CNN) methods.
  • CNN convolutional neural network
  • LDV Laser doppler vibrometer
  • the last method for authentication of people is a mixed, accurate and practical system for real-time people tracking and identification based on RFID and two previous methods.
  • RFID is also one of the wireless telecommunication techniques for recognizing employee as part of Internet of Things (IoT) technologies. People is detected by their RFID tag and using the position of the RFID reader antennas as reference points and then finding the best match between output of previous methods and RFID tags. There is a method for synchronizing all obtained data, which is captured regularly.
  • All clinical biometric data from real-time facial action coding, eye tracking and galvanic skin response, and brain images data from fMRI, EEG and MEG are collected, refined, extracted, harmonized and preprocessed in several stages.
  • Data Harmonization is also carried out as the process of integrating disparate data of varying types, sources, and formats.
  • preprocessing is conducted to eliminate different kinds of artifacts such as motion correction.
  • Pre- processing consists of spatial or temporal filtering of fMRI data and improving the image resolution.
  • signal detection to determine which voxels are activated by the stimulation is carried out.
  • the output of this stage is an activation map which indicates those parts of brain which have been activated in response to the safety stimuli.
  • obtained data, the brain map are sent to the platform for processing and analytical operation.
  • Mood tracking is to identify patterns in how employees’ mood (include anxiety, clinical depression, and bipolar disorder) varies. Wearable devices are used to track moods of employees throughout the day and receive personalized insights and recommendations based on their data. Data is sent from mood tracker of employee to a developed app. Then, the app predicts the mood by automatically aggregating the data into daily features. Then predicted mood of employees is transmitted to platform for analytics process alongside of other obtained data. Data of psychology and neuropsychology is analyzed by a data analysis procedure. Statistical analysis involves collecting and analyzing data to discover patterns and trends. Of course, statistical data analysis is carried out in platform based on some popular methods (such as ANOVA, ANCOVA, and MANOVA, multivariate analysis, Bayesian analysis, and confirmatory factor analysis) by statistical analysis software.
  • ANOVA ANCOVA
  • MANOVA multivariate analysis
  • Bayesian analysis and confirmatory factor analysis
  • FMRIB Software Library is a software library containing image analysis and statistical tools for functional, structural and diffusion fMRI brain imaging data or EEGLAB is a toolbox designated for EEG signal processing in MATLAB.
  • the data also produced from sensors will be continuous and in real time.
  • the system learns to recognize patterns directly from real data sets which can result from supervised and unsupervised learning.
  • the data is used to train the AI algorithm to recognize behavioral safety patterns and other related patterns.
  • a large number of outputs is generated from developed computing platform that include 1-brain mapping repository (Non-Accident-Prone Individual and Accident-Prone Individual), 2-Biometric and Vital Signs Information of Employees (BVSIE) ( Biometric Profile for Accident-Prone Individual and Vital Signs Profile for Accident-Prone Individual), 3-Behavioral Safety and Personality Profile of API (BSPP) ( Personalized Personality Profile for Risk-Taking Individual on Safety and Personalized Behavioral Safety Profile (PBSP), 4-Brain Mapping and Biometric Based Risk Management (Individual Risk Assessment module and Individual Risk Control Protocol) , 5-Prediction of Future Unsafe Behavior (Future Unsafe Behavior Profile of Individual), 6-Future Incident (Accident and Near Miss) Prediction (Future Accident Prediction and Future Near-Miss Prediction).
  • 1-brain mapping repository Non-Accident-Prone Individual and Accident-Prone Individual
  • BVSIE Biometric Profile for Accident-Prone Individual and Vital Signs Profile for Accident-Prone Individual
  • BSPP 3-Behavioral Safety and
  • a data warehouse (hardware and cloud storage) will be stored all obtained data and findings and then after data decryption, data are sent to a module named Integrated Data Store. This information is stored in the current databases as a record-keeping mechanism.
  • API accident-prone individuals
  • brain mapping repository Biometric Information of Employees, Personalized Behavioral Safety Profile, Brain Mapping and Biometric Based Risk Management, Prediction of Future Unsafe Behavior, Future Incident Prediction, Future Accident Prediction and Future Near-Miss Prediction.
  • this invention includes 7 main sections include data base management system (100), data acquisition system (200), data extraction system (300), cryptography (400), integrated intelligent data analytics platform (500), processed and analyzed data outputs (600) and final obtained data warehouse (700).
  • an API Recognition System is outlined in several sections that as follows Section 100 Data Base Management System, Section 200 Data Acquisition System, Section 300 Features Extraction System, Section 400 Cryptography, Section 500 Integrated Data Analytics Platform, Section 600 Processed & Analyzed Data Outputs and Section 700 Acquired Data Warehouse.
  • Section 1 as a data base management system include some defined basic data for recognition of Accident-Prone Individuals (API) that are store in an Integrated Data Store (110).
  • API Accident-Prone Individuals
  • Section 2 as a data acquisition system comprised 5 modules as follows Worksite (201), Brain Imaging Lab. (230), Biometric Lab. (240), Behavioral Safety Center (250), Psychology and Neuropsychology Clinic (260).
  • indoor workplace (210) there are two parts include indoor workplace (210) and indoor workplace (220), which includes tools to collect environmental and individual data.
  • Environmental data are acquired by environmental monitoring equipment (217, 227) and individual data is also collected through fixed static camera (211 a, b, c, d and 221a, b, c, d), high resolution drone-based camera (228), wearable (213 ,223) and wireless (215 ,225) vital signs sensors.
  • the applied neuroscience techniques include fMRI (231), EEG/ ERP (232), MEG (233), ET (241), GSR (242), FC (243) that are used with the purpose of Neural-Based Safety to measure the neural activity of the brain.
  • they are techniques of measurement of brain response to developed safety stimuli that is displayed in the digital reality goggle (234 ,244).
  • EEG cap 232,242 for EEG test.
  • Module 250 consist of Behavioral Safety Center, there are 6 sub-modules include BBS Observation Record (251), API Questionnaire (252), Unsafe Behavior Pool (253), IUBI-Unsafe Behavior Index for Individuals (254), Risk Propensity Scale of Occupational Safety (255) and Risk-Taking Scale of Occupational Safety (256).
  • Module 260 consists of Psychology (261) and Neuropsychology Clinic (267) to evaluate and test the behavior, cognition and mind of employees by using psychological and neuropsychological tests.
  • Section 3 as a data extraction system (300) consists of 8 sub-modules that refine and extract data obtained from previous sections includes Vital Signs Data (310), Behavior Data (320), People Authentication (330), Health Hazard Monitoring (340), Clinical Biometric Data (350), Neuroimaging (360), Mood Data (370), Psychology and Neuropsychology Data (380).
  • the devices and methods for data extraction as follows portable (311) and wireless (312) vital signs scanner.
  • the Video Scanner system (321) is a multipurpose device that grab multiple frames to monitor behavior of employees. The data on authentication of employee are extracted by Infra-Red Laser scanner (331), Image Scanner (332) and RFID Reader (333).
  • Harmful Agents Data Reader 341) and Environmental Parameters Data Reader (342). All clinical biometric data from real-time facial action coding (351), eye tracking (352) and galvanic skin response (353), and brain images data (361) from fMRI, EEG/ERP and MEG are collected, refined, extracted, harmonized and preprocessed in several stages.
  • Wearable devices are used to track moods of employees throughout the day and receive personalized insights and recommendations based on their data.
  • Data is sent from mood tracker of employee to a developed app (371). Then, the app predicts the mood by automatically aggregating the data into daily features.
  • Data of psychology (381) and neuropsychology (382) are analyzed by a data analysis procedure. Statistical analysis involves collecting and analyzing data to discover patterns and trends.
  • a large number of outputs is generated from developed computing platform that include 1-brain mapping repository (610) ⁇ Non-Accident-Prone Individual (611) and Accident- Prone Individual (612) ⁇ , 2-Biometric and Vital Signs Information of Employees (620) ⁇ Biometric Profile for Accident-Prone Individual (621) and Vital Signs Profile for Accident-Prone Individual (622) ⁇ , 3-Behavioral Safety and Personality Profile of API (630) ⁇ ( Personalized Personality Profile for Risk-Taking Individual on Safety (631) and Personalized Behavioral Safety Profile (632) ⁇ , 4-Brain Mapping and Biometric Based Risk Management (640) ⁇ (Individual Risk Assessment module (641) and Individual Risk Control Protocol(642) ⁇ , 5-Prediction of Future Unsafe Behavior(650) ⁇ Future Unsafe Behavior Profile of Individual (651) ⁇ , 6-Future Incident Prediction (660) ⁇ Future Accident Prediction(661) and Future Near-Mis
  • Section 7 as a data extraction system (700) consists of a data warehouse ⁇ hardware (711) and cloud storage (712) ⁇ that all obtained data are store on this system and then after data decryption, data are sent to a module named Integrated Data Store (110).
  • the invention provides a Personalized Behavioral Safety Profile (PBSP), Brain Mapping and Biometric Based Risk Management (BRM), Prediction of Future Unsafe Behavior (BFB), Future Incident Prediction-FIP that are focusing on the inherent characteristics of people, using real-time, reliable, accurate, fair and dynamic neural data, biometric data and various related data in an AI-based analytical platform to recognition of accident-prone individual.
  • PBSP Personalized Behavioral Safety Profile
  • BRM Brain Mapping and Biometric Based Risk Management
  • BFB Prediction of Future Unsafe Behavior
  • Future Incident Prediction-FIP Future Incident Prediction-FIP that are focusing on the inherent characteristics of people, using real-time, reliable, accurate, fair and dynamic neural data, biometric data and various related data in an AI-based analytical platform to recognition of accident-prone individual.
  • the present invention has a module for Brain Mapping Repository that includes obtained record of Non-Accident-Prone Individual (nAPI) and Accident-Prone Individuals (API) from a developed Integrated Data Analytics Platform.

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Abstract

La présente invention concerne la reconnaissance précoce, fiable et opportune d'individus exposés à un accident (API) par l'intermédiaire d'une sécurité neuronale {(liaison entre la science de sécurité et la neuroscience pour étudier l'activité neuronale du cerveau) (relie l'étude du comportement d'employé avec la neuroscience) (en tant que champs d'imagerie, d'activités et de fonction cérébrales)} en combinaison avec une réalité numérique et une intelligence artificielle) et un apprentissage automatique, un réseau neuronal artificiel et un apprentissage profond qui utilisent ceux-ci dans le processus de sélection avant et pendant utilisation dans des emplois critiques, tels que pilote d'avion, capitaine de navire, conducteur de pétrolier et concerne l'exploration du cerveau de l'employé dans lequel sont utilisées des technologies médicales et émergentes telles que fMRI à base d'IA, EEG et MEG pour étudier la réponse du cerveau à des stimuli liés à la sécurité et également prétraiter et traiter des données acquises pour la prédiction de modèles comportementaux d'employés avant qu'un accident se produise en raison de facteurs personnels et de caractéristiques inhérentes à ceux-ci.
PCT/IB2023/057284 2023-07-17 2023-07-17 Procédé et système de reconnaissance précoce d'individus exposés à un accident par l'intermédiaire d'une sécurité neuronale dans les emplois impliqués dans des risques de sécurité extrêmes WO2024089479A1 (fr)

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US20220284566A1 (en) * 2021-03-08 2022-09-08 Justin Starr Computer-vision based workplace safety

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CN118570665A (zh) * 2024-07-31 2024-08-30 国网安徽省电力有限公司合肥供电公司 基于卫星遥感的高压输电线路施工安全监测系统及方法

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