Employee health and safety are essential to maintaining a productive business. This is intended not only to protect workers from potential harm, but also to reduce medical, administrative, insurance and health-related costs. absenteeism.
according to National Security Council (NSC)Employer losses due to work-related injuries will be $163.9 billion ($1,100 per worker) in 2020, equivalent to 99 million work days lost in the same year. This is why many employers are investing in technology that improves 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.
- Measures air pollution and warns workers about hazardous environments.
- Identify signs of mechanical failure that can cause injury.
- Monitor important tasks such as heart rate, respiration rate, and vibration.
- Collect data on external conditions such as humidity, air pressure and vibration that can affect worker health.
One of these technologies is Industrial Internet of Things (IIoT).
IoT is a network of physical devices that rely on sensors and other technology gadgets to collect and exchange data over internet connectivity. IoT-collected data provides insights that can be used to enhance worker health and safety and prevent workplace accidents. But to do that, we need to analyze the data.
Therefore, IoT devices require effective solutions. machine learning A (ML) model that allows a computer to learn from that data and predict risk.
How IoT and Machine Learning Can Improve Workplace Safety
IoT device contains sensors that collect several types of data, mainly physical and environmental conditions such as pressure, temperature, humidity, motion and vibration.
Adding IoT sensors to wearable devices can also monitor worker vitals such as heart rate, respiration rate, and body temperature.
However, IoT devices and Connected worker platform It’s not just about storing data, it’s about sharing it. A connected worker platform shares data with other employees and managers to enable better decision making. IoT devices send data to cloud-based platforms where it can be processed, stored, and analyzed by machine learning algorithms.
Machine learning algorithms detect trends and patterns in data and provide 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 workers are exposed to high levels of pollutants or excessive noise levels that can cause hearing impairment. can. When detecting these hazards, the sensors send warnings and recommendations to help workers avoid risks.
Integrating IoT sensors into machines allows you to monitor their performance. Machine learning algorithms can use IoT-collected data to detect when equipment is failing or predicting failure based on past and current readings. This allows administrators to schedule maintenance before machines actually fail, causing unplanned downtime and injuries.
This process is called predictive maintenance and is only possible through the use of IoT-generated data, predictive analytics, and machine learning algorithms that analyze large datasets to look for potential problems.
How to implement IoT and ML for worker health and safety
Business owners need to establish goals before starting to work with IoT. machine learning model. Only then can we choose the most suitable hardware and software system for our case.
The introduction of IoT assumes the installation of IoT devices equipped with IoT sensors. However, the types of IoT devices and sensors you need to acquire depend on your specific use case and its environment. For example, if you’re dealing with an environment with high levels of noise pollution, he might prioritize his IoT-based wearables with noise sensors to monitor and regulate workers’ exposure to excessive noise. You can
Manufacturing plants can use IoT sensors to monitor machine performance, detect anomalies, and prevent downtime and work accidents. In addition, his IoT sensor of choice must be able to measure temperature, pressure and other measurements that reveal machine performance.
In some industries, tracking chemical exposure and air quality is essential to protect worker health.
Overall, business owners should assess what risks exist in the workplace, what are the best metrics to track in each case, as well as the costs and benefits of acquiring IoT devices. Is it scalable, can it work with existing software and hardware, or does it require a specific integration process?
Finally, IoT sensors always act as data sources.
Data must be sent to a central server or cloud-based platform, which requires wireless connectivity (such as Wi-Fi or Bluetooth) and proper data processing and storage.
Implementing machine learning consists of selecting or developing a model that meets the company’s objectives. In this case, analyze worker health and safety.
Therefore, the machine learning model of choice should be trained considering the factors that affect worker health and safety. Alternatively, business owners can leverage out-of-the-box solutions such as Google IoT Core and his AWS IoT Core to extract and store data useful for employee health and safety monitoring.
In any situation, data must be in a form that machine learning models can process and recognize trends and patterns.
Before deploying a machine learning model to the cloud, evaluated for prejudice,accuracy, interpretability Reliability (the model’s ability to perform well on never-before-seen data). This can be a time-consuming process, but the use of historical data on monitored health and safety factors can speed the process. This makes the initial dataset more complex, gives the model more “training options”, and allows it to learn faster (compared to training from scratch).
Only then can machine learning models perform real-time data analysis and make more accurate predictions. Even after this initial stage, it may still need to be updated and retrained periodically to remain accurate and effective, especially when new data become available or business objectives change. I have.
Why industrial IoT devices are useful
Analyzing employee health and safety using IoT and machine learning is an effective way to protect your workforce.
The data itself is useless. Data that can be transformed into actionable insights. In this way, IoT and machine learning algorithms can help administrators implement preventative health and safety measures, such as adding ventilation to areas where toxic gases accumulate or introducing protective gear that was not previously considered. helps you learn. Data can be used to predict risks such as machine failure to prevent health problems and costly accidents.
Machine learning models can also be trained to automatically shut down machines if measurements indicate a problem, further improving worker health and safety.
Overall, the combination of IoT and machine learning is a powerful tool for analyzing and enhancing worker health and safety. This ultimately improves workplace morale, job satisfaction and productivity, while also reducing costs associated with health and safety issues.
