The Development Stages of AI-Powered EHS Open-Air Applications
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The Development Stages of AI-Powered EHS Open-Air Applications

By CIOReview | Monday, June 21, 2021

A new world of safety, powered by artificial intelligence (AI), opens up a slew of possibilities for asset-intensive businesses, including increased asset performance, quality control, and environmental and employee safety.

FREMONT, CA: Various technologies like AI, IoT, computer vision, cloud, and edge computing, must be used to construct an EHS (Environment, Health and Safety) open-air solution. These enable digital safety processes on-site, including controlling various health and well-being hazards, anticipating high-potential events, and forecasting tiredness risk levels. Following are the development stages of building AI-powered EHS open-air solutions.

Data Collection

AI-based software goes through numerous stages of development from a technical standpoint. First, the required data must be gathered, which begins with identifying the various data types and their collection from both internal and external sources, such as data from IoT sensors, video footage, mobile devices, and other sources. It is required to establish a database for AI-powered EHS applications, feeding it with the company's internal data and then augmenting it with external data such as weather, geolocation, air pressure, and social media information. For the EHS sector, the data collected must be broad enough to allow AI systems' prediction capabilities to adapt to several scenarios.

Model Training

Following the creation of the database, a training model must be defined. It includes creating neural networks, choosing an acceptable machine learning method, and 'feeding' it with massive amounts of different data at this point. In essence, based on preset risk parameters, suitable identification algorithms and procedures are chosen and then used in the AI system.Massive volumes of labeled training data are required to train big neural networks. It is advised that supervised learning methods, semi-supervised, and reinforcement learning approaches be used to maximize the application's capacity to produce correct predictions. For example, a voice checklist can be used to train data on the field because of its practicality–employees' hands are typically busy, and they can engage with AI software using their voice. As a result, data may be trained in the field regularly with little effort.

Predictive Analysis

Predictive analysis is the final stage of AI application development. It is most beneficial to use both cloud and edge computing to provide speedy processing of real-time data. The received data is processed at the edge in the AI applications section, bringing the calculations closer to the risk spots, while the remainder of the data is transferred to the cloud for model training. If a user needs immediate instructions and direction from supervisors or real-time insights into corporate performance, edge computing is a good option. For example, if a worker on a construction site did not wear a helmet while working in a hazardous area, the AI system would send the anomalous data to the edge nodes. The system would quickly recognize the risk and send an instant notification to a supervisor.