Worker health and safety are essential for sustaining a productive business. It’s not only about protecting workers from potential harm, but also about reducing costs associated with medical and administrative expenses, insurance costs and health-related absenteeism.
According to the U.S. National Safety Council (NSC), occupational injuries cost employers $163.9 billion in 2020 ($1,100 per worker), and accounted for 99 million days of work lost during that year. This is why many business owners are investing in technologies that improve worker health and safety.
5 Ways Industrial IoT Devices and Machine Learning Can Improve Worker Safety
- Track noise levels to regulate worker exposure to high-decibel areas.
- Measure air pollution and alert workers to hazardous environments.
- Identify signs of a malfunctioning machinery that can cause injury.
- Monitor work vitals like heart rate, respiratory rate and vibrations.
- Collect data on external conditions like humidity, air pressure and vibrations that can impact worker health.
One of these technologies is the industrial internet of things (IIoT).
IoT is a network of physical devices that rely on sensors and other tech gadgets to gather and exchange data over an internet connection. IoT-collected data can provide insights that can be used to enhance worker health and safety and prevent accidents in the workplace. But in order to do that, the data must be analyzed.
That is why IoT devices require an effective machine learning (ML) model that allows computers to learn from that data and predict risks.
How IoT and Machine Learning Can Improve Workplace Safety
IoT devices contain sensors that collect several types of data, mainly physical and environmental conditions such as pressure, temperature, humidity, motion and vibrations, among others.
If added to wearable devices, IoT sensors can also monitor the workers’ vitals, such as heart rate, respiratory rate and body temperature.
But IoT devices and connected worker platforms are meant to share data — not just store it. Connected worker platforms share data with other workers and managers, enabling better decision making. IoT devices send the data to cloud-based platforms, where it can be processed, stored and analyzed by machine learning algorithms.
Machine learning algorithms find trends and patterns in the data, providing actionable insights to improve worker health and safety. For example, IoT sensors can measure air quality and noise levels. Machine learning algorithms can be trained to take that information and identify when the workers are exposed to high levels of pollutants or when they are exposed to excessive levels of noise that may lead to hearing damage. After detecting these hazards, sensors can send out alerts and recommendations to help workers avoid the risks.
When integrated into machines, IoT sensors can monitor their performance. Using IoT-collected data, machine learning algorithms can detect when equipment is failing or expected to fail according to historical and current readings. This allows managers to schedule maintenance before the machine actually breaks down and causes unplanned downtime or injury.
This process is called predictive maintenance, and it is only possible through the use of IoT-generated data, predictive analytics and machine learning algorithms that analyze large sets of data in search of potential issues.
How to Implement IoT and ML for worker health and safety
Business owners must establish their objectives before they start working with IoT and machine learning models. Only then can they choose the most appropriate hardware and software systems for their case.
IoT implementation is based on the installation of IoT devices equipped with IoT sensors. But the type of IoT devices and sensors they will have to acquire will depend on the specific use case and its environment. For example, if they are dealing with an environment with high levels of noise pollution, they may want to prioritize IoT-based wearables with noise sensors to monitor and regulate the workers’ exposure to excessive noise.
In a manufacturing plant, IoT sensors can be used to monitor the performance of machines to detect anomalies and prevent downtime and work accidents. On top of that, the selected IoT sensors must be able to measure temperature, pressure and other readings that reveal the machines’ performance.
In some industries, tracking chemical exposure or air quality is essential to protect worker health.
Overall, business owners must evaluate what risks are present in the workplace and what metrics are best to track in each case, as well as the costs and benefits of the IoT devices to acquire. Are they scalable? Can they work with the existing software and hardware, or will a specific integration process be required?
Finally, IoT sensors will always act as data sources.
The data must be sent over to a central server or a cloud-based platform, which requires wireless connectivity (such as Wi-Fi or Bluetooth) and adequate data processing and storage.
Implementing machine learning consists of selecting or developing models that serve the company’s objectives. In this case, analyzing worker health and safety.
Therefore, the chosen machine learning models must be trained to account for factors that affect worker health and safety. Alternatively, business owners can leverage out-of-the-box solutions, such as Google IoT Core or AWS IoT Core, to extract and store data that help monitor worker health and safety.
In any situation, the data must be in a format that enables the machine learning model to process it and recognize trends and patterns.
Before they are deployed in the cloud, machine learning models need to be evaluated for bias, precision, interpretability and reliability (the model’s ability to perform well on data that it has never seen before). This can be a lengthy process, but it can be sped up using historical data on the health and safety factors that will be monitored. That way, the initial data set will be more complex, providing more “learning options” to the model and helping it learn faster (compared to having it learn from scratch).
Only then can a machine learning model perform real-time data analysis and make increasingly accurate predictions. Even after this initial phase, they may need to be updated and retrained periodically to ensure that they remain accurate and effective, especially as new data becomes available or as business goals change.
Why Industrial IoT Devices Are Useful
Using IoT and machine learning to analyze worker health and safety is an effective way of protecting your workforce.
Data by itself isn’t useful; data that can be translated into actionable insights is. This is how IoT and machine learning algorithms can help managers take proactive health and safety measures, such as adding ventilation to an area that accumulates toxic fumes or introducing protective equipment that wasn’t considered before. They can use data to prevent health problems and costly accidents by predicting risks like machine failures.
Machine learning models can even be trained to automatically shut down a machine if the readings indicate an issue, further improving worker health and safety.
Overall, the combination of IoT and machine learning is a potent instrument for analyzing and enhancing worker health and safety. This can ultimately improve morale, job satisfaction, and productivity in the workplace — reducing costs related to health and safety issues at the same time.