Increasing competition, demanding customers, and uncompromised quality standards across industries have resulted in organizations ensuring 24x7 operations. Added to this is the possibility of double-shifts, monotonous work, or long travel distances where workers extend their hours of duty without taking adequate rest and relaxation. This inherently has the risk of workers getting fatigued and cause impairment to their decision making ability, therefore putting lives and machinery at risk.
Why is worker fatigue important?
The cost of fatigue is of increasing concern to organizations across the globe as fatigue-related accidents and losses are extremely high. It is observed that for every $1 spent on direct costs like medication, insurance claims, absenteeism, or property damages, there is almost double or sometimes triple the amount spent on indirect costs like presenteeism due to lost productivity, decreased cognition, near-miss accidents delays in production and sometimes even losing end customers. Around 30 percent of all fatal accident and 15 percent of serious accidents occur due to driver fatigue. Statistics show a loss of over $63.2B to the U.S. Companies due to sleeplessness, around $240M is lost in road accidents in NZ whereas over $A5B is lost in Australia due to poor alertness.
Fatigue detection methods
Fatigue can be defined as the feeling of tiredness, exhaustion or lack of energy to perform tasks in an efficient manner. This can further be categorized as physical or mental fatigue. Although physical fatigue can be addressed by refraining from performing the strenuous activity, the onset of mental fatigue is hard to detect. Table 1 lists the different types of fatigue detection methods that can help in these situations.
Table 1: Classification of methods for worker fatigue detection.
Visual detection using infrared camera
When a worker is tired or deprived of sleep, there is natural tendency for the eyes to close. This can be analyzed using a camera mounted on a dashboard of the vehicle. Active illumination imaging system with infrared (IR) camera is used to measure the eye closure in day light and dark conditions. The face region is analyzed to detect closure of eyes to estimate PERCLOS measure which gives an indication of fatigue. There are dashboard mounted imaging systems used for fatigue detection for drivers. Some of the products for visual driver fatigue detection are FaceLab, Optalert, Seeing machines (DSSR), Delphi (DSM) and SmartEye.
Heart rate based measures
It is known that variability in the heart rate can be used as a fatigue measure. This can be measured using ECG devices. A decrease in the heart rate variability is linked to increased fatigue. But reliable detection of heart rate of workers using ECG signal poses challenges--the worker scenario may involve adverse conditions such as heavy work which may cause body perspiration which can degrade the ECG signal quality. These challenges have to be met by advancement of sensors and sophisticated signal processing algorithms that can obtain a reliable heart rate measure needed for calculating fatigue level of workers.
Muscle activity based measures
For workers involved in heavy physical work, it may be useful to detect physical fatigue levels. This can be measured using electromyography (EMG). The EMG signal is localized to the muscle and can give an indication of muscle fatigue. There are a few possible locations on the human body where EMG sensors can be applied as a patch so as to measure activation of prominent muscles that are used by the worker in performing their tasks. The location of the patches can be decided based on the activity type. Currently most of the EMG based methods for detecting fatigue are used in the sports physiology, but it can be adapted for worker fatigue detection scenarios after ruggedization of sensors.
Brain wave based measures
Electrical activity of the brain, also called electroencephalogram (EEG), can also be used to detect the extent of fatigue. EEG is measured by placing electrodes on the scalp. Compared to other methods of fatigue detection, EEG based detection is relatively mature. This is targeted towards heavy vehicle drivers in the mining or transportation industry. It requires the workers to wear a cap or a head band.
Worker fatigue detection framework
A fatigue detection framework can potentially be integrated with a worker scheduling system. For example, a wearable sensor detects one or more physiological parameters which is transmitted via Bluetooth to the worker’s smart-phone. This data is sent to the cloud server running analytics to detect fatigue. Alternatively, the fatigue algorithm can be implemented to run locally on the smart-phone and communicate the results intermittently to the cloud. The supervisor dashboard can show real-time view of the fatigue levels of workers performing similar or different types of tasks. If the fatigue level of a worker exceeds above a safe range, the worker is advised rest or reassigned a less strenuous task. This framework can also be used to build fatigue buildup trends for different workers for various tasks.
Benefits to employers and workers
The use of automatic worker fatigue detection methods can ensure proactive prevention of accidents leading to loss of life or high-value assets. The benefits are:
- For Society: Increased GDP due to lesser number of accidents and costs as well as higher productivity, assurance to regulatory bodies on worker safety
- Employer: Safety of personnel and assets, Lesser financial risk in terms of potential reduction in accidents, Risk based premiums shall reduce insurance costs, training costs of other employees, reduced presenteeism, satisfied customer and better through put
- For workers: proactive assessment of worker’s ability to handle a strenuous tasks prevents dangerous situations that can lead to accidents, thereby increasing worker safety
The adoption of wearable sensor technology for fatigue detection can be realized by investment from employers, acceptance by workers. It can also receive a benefit if there is a legislative mandate for using fatigue monitoring technologies for high risk worker profiles.
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