The range of solutions on the market is growing almost as fast as the technology that underpins them. They’re leveraging data, the ubiquity of mobile devices and the increasing ability of these technologies to combine with one another.
The most common among those are backed by GPS or sensors and fall under the broad umbrella known as the Industrial Internet of Things (IIoT). By collecting and analysing real-time data from connected devices using GPS tracking or information from sensors, modern companies are unlocking new ways of keeping their employees safe and out of harm.
Moreover, these technologies are presenting clear opportunities to build safer, smarter working environments. So, what’s out there?
The application of the IIoT to safety workflows has expanded dramatically in the last five years. The tech uses a range of sensors to monitor the status of tools, equipment, machinery, and environmental controls, predominantly in industrial settings. The sensors communicate with apps and central control systems to create alerts and flag incidents. They’re able to do so in real-time and unobtrusively.
The rise in mobile technology has also seen an increase in the number of safety management apps and software systems (SMS). These tend to focus more on the actual workforce and allow for staff to be monitored and ensure that regulations are met. These types of systems are flexible not only because they can be made program-specific, but also because they leverage mobile data. It allows the organisation to set measurable goals, track process and make evidence-based decisions when an incident has occurred – albeit with a limited degree of accuracy.
With a lot of the workforce increasingly on the move, GPS-based systems are coming into more widespread use. For workforces that operate off site, they provide peace of mind in dangerous, isolated, or volatile situations. Users are able send real-time location tracking to contacts, engage in conversation with them and make emergency calls if required.
The AI evolution
While very useful, GPS and sensors backed solutions aren’t without their limitations – at least in isolation. The reliance on GPS, mobile data or Wi-Fi means that SMS solutions are only as good at recording and monitoring incidents as the signal that supports the mobile device; in the case of IIoT that’s less of a problem, but battery life, outages and faults are common problems with sensors – all of which affect the accuracy of what’s being monitored.
These issues and the dramatic increase in the power of artificial intelligence (AI) have given rise to a new breed of health & safety monitoring solutions – some of which are leveraging old technologies to bring new and powerful solutions to workplaces. Chief among those is Computer Vision (CV).
CV techniques are revolutionary but work off perfunctory tech like CCTV. By integrating with IP cameras, for example, CV can be trained to respond to digital images in real-time. In warehouses or factories, it can be used to assess the current state of equipment or machinery, to check if a worker is wearing a hard hat or if staff are maintaining social distance. By integrating with an IIoT ecosystem, it can be set up to monitor specific locations. By feeding data from sensors into predictive AI algorithms it can learn to detect patterns and flag potential hazards ahead of time.
What sets CV apart?
The technology continues to develop. Away from the factory floor, drone mounted cameras are being used to conduct remote inspections of hazardous environments like construction sites, malfunctioning equipment, or inaccessible domains. By using CV, they’re able to spot hazards, incidents, and danger from the sky – giving a new vantage point and reducing the need to put human inspectors at risk.
That example gives rise to the notion that in-field and remote workers could be tracked and traced using a combination of GPS and CV-backed video drones, combining the strengths of one technology with another to protect their workforce. At the rate computer vision is developing such a solution wouldn’t be a surprise in the very near future.
Indeed, the technology is eminently scalable, which is a major differentiating to those in more widespread. Its ability to learn from both human feedback and from the environment it surveys makes CV a safer bet from a safety perspective, and as a long-term investment. By learning from scenarios, it can reduce false alarms, mitigate future risks, and predict with a high degree of accuracy how and why an incident might be about to happen.
In fact, it is this versatility that sets it apart. Because it is relatively simple to apply CV AI models from one scenario to another it has a potentially limitless number of use cases and points of customisation. Take the pandemic as an example. Let’s say a manufacturer wanted to monitor social distancing and mask wearing among its office workers. That’s something CV AI could be trained to do for them using input and data from its CCTV network.
That same company could expand the remit of that model to check whether its plant engineers are wearing safety goggles and helmets, to look for fire hazards or for chemical spills. It’s just a matter of training the model to do so and actually requires no additional hardware because it is working with the same camera/CCTV system the original model is working with. To that end, it’s not only more versatile and scalable, but also far more cost effective in the long run because it requires no investment in hardware.
We are all aware that cultivating safe workspaces is now far more important than it has ever been. The events of the last year were unforeseen but have shone a light on the need to take proactive measures when it comes to health and safety. We can’t be certain that another Black Swan event will take place, but we can be sure that health & safety should always improve. By leveraging new and existing technologies, that is most certainly possible.
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