Fall Detection for Construction Worker Safety
Today, fall detection is a major concern in the construction sector. Fall accidents are one of the leading causes of serious work-related fatalities and injuries in the construction industry. Falls are a leading cause of unintentional injuries and can result in devastating disabilities and fatalities when left undetected and not treated in time.
Safety is a major problem in construction works. There is no proper solution to solve the problem. People’s safety is not ensured in the construction works. In most cases, the problem occurs due to work stress or poor health conditions. Some of the accidents occur where people fall from heights and are left unnoticed which leads to death due to lack of medical attention. According to some reports, almost 48000 workers die every year due to workplace accidents and the construction site tops the list with 24.20 percent. However, most deaths that occurred were preventable.
This project aims to develop smart wearable devices such as bands and helmets using various sensors that will help in monitoring the health and safety of workers. The devices constructed using IoT help in detecting the fall of any workers and send SMS notifications for immediate aid.
Moreover, the worker's vitals such as heart rate and temperature are also monitored and warned regarding abnormal health conditions. The project aims to provide a secure and safer working environment for workers thus reducing the number of deaths happening in construction sites. The prototype developed was tested on various conditions and showed high accuracy in the performance.
Cause of Death from Falls in construction site
7 falls may occur on the construction site but Proper fall protection isn’t always in place at construction sites. This cause live risk for workers.
Introduction of Fall Detection Model
The number of fatal deaths happening in construction sites is soaring up every year. The safety and health of people are not ensured in construction sites. The workers face a lot of struggles and difficulties in the workplace. The AI-based model help to detect falls and can be monitored via web or mobile-based device.
The model help to detect Fall detection on Slipping, Tripping, Fainting, Fatigue, Health issue, Running up. This model helps construction site workers. The model is a wearable-based solution. The fall-detection monitoring solution that implements both accelerometer and sound-based detection algorithm. The accelerometer-based fall detection is instrumental in the detection of a valid fall occurrence.
However, it has been shown that using an accelerometer alone is insufficient to accurately detect a fall, as the accelerometer tends to misinterpret some of the daily motion activities and misclassified them as valid falls. The sound sensor is introduced to detect the sound pressure generated from a resultant fall, but sound pressure cannot by itself be used as a reliable indicator of a fall. Thus a fuzzy logic–based fall-detection algorithm is developed to process the output signals from the accelerometer and sound sensor, where a valid fall activity detected by the accelerometer, coupled with a detected sound pressure from the resultant fall, can infer an occurrence of a valid fall.
Solution Architecture
Scenarios of Fall
Block Diagram of Fall Detection
Fall Detection Works
Sensors — Fall detection systems use accelerometers, a type of low-power radio wave technology sensor, to monitor the movements of the user. State-of-the-art fall detection devices use three-axis accelerometers, like those that are used within smartwatches and smartphones. Some fall detection devices use a built-in tri-axial accelerometer with patented algorithms developed by BioSensics.
Fall detection — Fall alert detectors can measure when the user has suddenly fallen by detecting the abrupt changes in body movements. The technology can evaluate an individual’s body position, physical activity, and the smoothness of acceleration of movements, says the International Journal of Telemedicine and Applications. If the device determines that these variables are within the danger zone and a fall has occurred, it will automatically activate an emergency fall alert and call emergency response agents for assistance.
Precision. Most common movements related to falls are accurately detected through automatic fall detection technology. A fall detection algorithm is calibrated for use while the device is worn around your neck. However, certain slower movements like sliding off the bed or sofa onto the floor cannot be accurately detected and may require you to press the call button for assistance.
Urgent response. When applicable, a medical alert device can detect a fall and automatically initiate a call to urgent response agents. The agents can evaluate the situation through the two-way speaker on the device and will remain on the line with the user until help arrives.
Conclusion
Fall detection has become an important stepping stone in the research of action recognition — which is to train an AI to classify general actions such as walking and sitting down. What humans interpret as an obvious action of a person falling face flat is but a sequence of jumbled-up pixels for an AI. To enable the AI to make sense of the input it receives, we need to teach it to detect certain patterns and shapes and formulate its own rules
