What is Fall Detection?
Fall detection involves using different types of technology to detect when people fall. One way to do this is through sensors like watches and pendants that people wear. These devices use accelerometers to detect body movements and trigger an alert in the event of a fall. There are also computer vision systems with one or more cameras that monitor scenarios with people present. These systems use artificial intelligence to track and detect specific types of objects, people, and fall events.
Why is it Important?
Falling is a major source of serious injury and death, especially for the elderly. According to the CDC, over 800,000 patients a year are hospitalized because of a fall injury, most often because of a head injury or hip fracture. Falls are also the most common cause of traumatic brain injury.
The number of Americans over the age of 65 is expected to double by 2060. That will certainly increase the need for improved automatic fall detection solutions.
Fall Detection With Computer Vision
The ability to recognize human activity with computer vision allows us to create applications that can interact with and respond to a person in real-time. Vision-based fall detection systems use deep learning computer vision models to analyze video input from cameras. These CV models are trained to detect people and track their movements or body poses. Based on established criteria, the system can detect when a fall event occurs and send out an alert.
Pose Estimation for Fall Detection
A computer vision technique called pose estimation is used to recognize human activity. Pose estimation maps out a person’s physical frame in an image by assigning sets of coordinates known as key points to specific body parts. Once this map of key points is created, the application can determine a person’s activity based on their positions in a video stream.
A vision-based system with pose estimation has a number of advantages over user devices for detecting falls. Computer vision eliminates the possibility of human error when using the device. The user may wear it improperly, fail to set it up correctly, or not realize the battery is no longer working. With CV, the need for personal physical devices and sensors is eliminated. Computer vision simplifies the infrastructure and number of components needed to implement fall detection systems on a larger scale.
The Importance of Privacy
There are many scenarios like fall detection where it’s incredibly useful to monitor people for their protection, but the need for safety must be balanced with privacy. Computer vision applications that process images of real people can contain very personal information.
Many computer vision systems and applications run on the edge. With edge computing, inferencing and data processing happens locally on the device itself. That means no data is sent to the cloud or stored. The system simply detects a fall event and sends an appropriate alert. Another option is to only send data to an external source when an event occurs. Components of those images can then be automatically blurred to hide peoples' faces or other sensitive, identifiable information.
Fall Detection Applications and Use Cases
Worksite Safety. Industrial manufacturing and construction are two industries where workers face serious health and safety hazards on a daily basis. Workers on a job site or manufacturing floor can trip on materials and equipment or be struck by falling objects. A computer vision system can quickly detect falls and alert managers that aid is needed.
Public Safety. Fall detection systems also provide a layer of safety monitoring for any type of organization. Camera systems are already utilized in many businesses and public spaces. Large employers, retailers, and places like shopping malls can add computer vision capabilities to their cameras to detect possible adverse health events and trigger alerts.
Protect the Elderly. Computer vision fall detection is useful for residential care facilities with dozens of residents. Rooms and common areas can be monitored to detect falls without the need for every person to wear a personal device. Eliminating individual devices can simplify monitoring and avoid malfunctioning equipment - ensuring residents are protected at all times.
Patient Care. Many hospital patients are at risk of falls due to medications that cause dizziness or post-surgery recovery. Computer vision systems deployed in recovery areas provide an extra set of eyes to detect falls immediately and alert medical staff. Of course, privacy is a large concern in the medical field and HIPPA regulations must be followed. So it’s paramount to employ privacy features like facial blurring to protect patients and obscure identifiable information.
Public Transportation. The rise of connected smart cities will lead to an increase in the use of public transportation and autonomous vehicles. With the advent of driverless public buses and trams, system operators need a way to monitor the health and safety of passengers. In-cabin computer vision systems can detect falls and health emergencies. Once an event is detected, the vehicle can stop immediately or proceed to the safest stopping point to wait for emergency personnel.
Computer Vision on the Edge With alwaysAI
alwaysAI provides developers and enterprises a comprehensive platform for building, deploying, and managing computer vision applications on IoT devices. We make computer vision come alive on the edge - where work and life happen. The alwaysAI platform offers a catalog of pre-trained models, a low-code model training toolkit, and a powerful set of APIs to help developers at all levels build and customize CV apps. alwaysAI has an easy deployment process and a state-of-the-art run-time engine to accelerate computer vision apps into production quickly, securely, and affordably.