Utilizing Raspberry Pi-powered HMI reTerminal for Fall Detection System across Industries
This fall detection alert system project aims to use the Seeed Studio ReTerminal and Pi camera to detect and alert individuals or caregivers when a fall occurs. This system uses the Efficientnet V0 TensorFlow Lite model for image classification to detect falls, and alerts the designated caregiver or emergency services with a notification.
This project is beneficial for elderly or disabled individuals who may be prone to falls, as well as their caregivers who can receive alerts and provide immediate assistance. It can also be utilized in industries such as healthcare, nursing homes, and assisted living facilities to improve safety and response times in case of falls.
Smart home concept and Industrial Safety
Fall detection is an important issue for both the elderly and children. Falls can cause serious injuries such as fractures, head trauma, and even death, and can result in a loss of independence and decreased quality of life. For elderly individuals, falls are the leading cause of injury-related hospitalization and death, and can also lead to long-term disability. For children, falls are a common cause of injury, especially in younger children who are learning to walk or crawl.
Fall detection systems have the potential to significantly improve the safety and well-being of both elderly and young individuals. By quickly detecting falls and alerting caregivers or emergency services, these systems can provide immediate assistance and potentially prevent further injury. Fall detection systems can also be used in various industries such as healthcare, nursing homes, and assisted living facilities to improve safety and response times in case of falls. Overall, fall detection systems are an important tool for ensuring the safety and well-being of vulnerable individuals in various settings.
Fall detection can be a challenging task due to the variability in human motion and the complexity of detecting falls accurately. This is especially true for elderly individuals, who may have different patterns of motion compared to younger individuals. Additionally, falls can occur in different ways, such as slipping, tripping, or fainting, making it difficult to accurately detect all types of falls.
Despite these challenges, fall detection is an important issue as falls can have serious consequences, especially for vulnerable individuals such as the elderly or young children. Falls can result in injuries such as fractures, head trauma, and even death, and can lead to a loss of independence and decreased quality of life. Early detection of falls can help prevent further injury and ensure that appropriate medical assistance is provided as quickly as possible.
Fall detection is also important in industries such as healthcare, nursing homes, and assisted living facilities, where it is crucial to ensure the safety and well-being of residents. By implementing fall detection systems, these facilities can improve their response times and ensure that residents receive prompt medical attention if a fall occurs.
In this project, the Seeed Studio reTerminal and Raspberry Pi Camera 1.3v are utilized for fall detection. The EfficientNet V0 Lite pre-trained model is used to detect the presence of a person in the frame. With some modifications to the code, the system can then detect whether or not a person has fallen.
EfficientNet is a popular family of convolutional neural network models that have achieved high accuracy on a variety of image classification tasks, including person detection. Using EfficientNet V0 Lite pre-trained model, the system can quickly detect whether a person is present in the camera frame.
With some modifications to the code, the system can also detect if the person has fallen by analyzing the person’s posture in the frame. This can be achieved by checking the position and orientation of the person in the frame over time.
This advanced fall detection system combines the power of Seeed Studio ReTerminal and Raspberry Pi Camera 1.3v with the EfficientNet V0 Lite pre-trained model and little code modifications to accurately detect if a person has fallen or is standing normally. The system displays a green bounding box for normal standing and a red bounding box if a fall is detected, making it easy for individuals and caregivers to quickly respond and potentially prevent further injury or complications. By optimizing the resources of the Seeed Studio ReTerminal, our system is an innovative and reliable solution that can improve the safety and well-being of vulnerable individuals, including the elderly and young children.
In nursing homes and assisted living facilities, our fall detection system can help staff respond quickly and effectively to falls, ensuring that residents receive the appropriate care and attention. The system’s accurate and reliable detection capabilities make it an invaluable tool for maintaining a safe and secure environment for residents.
In noisy working environments such as construction sites, our fall detection system can be a lifesaver, providing an additional layer of safety for workers who are at risk of falling. The system’s advanced technology and intuitive design make it a practical and effective solution for preventing falls and promoting workplace safety.
This fall detection system is a powerful and reliable solution that can make our day-to-day lives easier and safer, particularly in environments where falls are a significant ris, offering individuals and organizations a crucial tool for ensuring the safety and well-being of their loved ones and employees.
Latest Update on reTerminal Series
We see, respect, and truly implement the needs of users. We keep iteratively upgrading the reTerminal series. About a month ago, we released an advanced version of reTerminal, which is named reTerminal DM. A 10.1″ open-source industrial HMI – an Integrated Device Master to unify data flow and manage the onsite device.
It’s equipped with a larger screen, stricter industrial protection levels (IP65 industrial-grade front panel), and richer industrial interfaces.