Adding the Predictive P into Personal Protective Equipment
- Samantha J. Horseman (DBL) | Colin M. Sloman (Saudi Aramco) | Steven A. Seay (Saudi Aramco) | Mohammed A. Al Abdrabbuh (Saudi Aramco) | Yasser Alem (Saudi Aramco)
- Document ID
- Society of Petroleum Engineers
- SPE International Conference and Exhibition on Health, Safety, Security, Environment, and Social Responsibility, 16-18 April, Abu Dhabi, UAE
- Publication Date
- Document Type
- Conference Paper
- 2018. Society of Petroleum Engineers
- Human - Machine Interface, Human Sensory, Personal Protective Equipment, Predictive, Health, Safety & Environment (HSE)
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OSHA has reported in 2016 that the upstream industry has one of the highest rates of severe injuries, in some measures, it actually has the highest. Therefore, imagine a world where these accidents, injuries and diseases could be predicted before they actually happened. Such a system has been developed and tested that will redefine the HSE industry thinking. The Human Sensory Predictive Personal Protective Equipment (PPPE) system which is founded on a plurality of machine, predictive methods, and supervisory safety alert system was developed by the efactory (Saudi Aramco: Innovation lab). This game-changing system measures human sensory central and peripheral signals, via human – machine interface - namely brain signals measured by electroencephalography (EEG), and biometrics (heart rate, stress response, temperature, body position and location) measured by specialized sensors built into personal protective equipment (e.g. hard hats, safety glasses, gloves, and belts). The real time outputs from the PPPE could produce anticipated alerts and supervisory instructions to workers and worksite personnel. This innovative system predictively determines risk and alert levels associated to worksite tasks involving, personnel, equipment, and the environment. This system redefines the industry's current thinking through five core value propositions: situational awareness, knowledge and skill retention, biofeedback loops, predictive analytics, and safety alert system. Firstly, it is capable of identifying human awareness states (e.g. disengaged, boredom, fatigue, sleep deprivation) from a collection of brain signals, which is further validated by biometrics. These biological measurements are associated to an adaptive biofeedback system to the worker. The predictive analytics system contributes through a knowledge proposition of the potential of gathering sensory information based on ‘predictive opportunities'. In one work shift there are a total of 86,400 seconds of predictive power/employee that until now, have been left undiscovered. This further challenges and contributes to the well-known safety industry paradigms such as the Henrich - accident triangle. This biofeedback system leverages human machine interface collecting both the brain signals and biometrics. Currently, HSE alert systems do not provide methods and models to utilize awareness through the human computer interface (HCI). This intelligent human sensory system rises to the challenge in developing an innovative platform that could be capable of providing early detection and indication of any hazardous scenario in O&G operations.
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