# An Image-Based Fall Detection System for the Elderly
## 1 Introduction  
Because of declining birth rates and advances in medical technology, many countries are already, or soon will be, aged societies. Thus, providing high-quality elderly care is becoming increasingly important. According to a report of the World Health Organization, falls are one of the leading causes of accidental or unintentional deaths worldwide. Each year, an estimated 646,000 individuals die from falls globally, of which over 80% are in low- and middle-income countries [1]. Therefore, the effective detection of falls is important for both elderly people and their caregivers. Many methods have been employed to detect falls in elderly people. We categorize them into three types: wearable-device-based, environmental-sensor-based, and image-based methods. For wearable-device-based methods, most of them utilize triaxial accelerometers to detect the sudden changes of acceleration that are caused by a fall. For example, [PERSON] et al. [2] used sensors distributed over the body to determine the injury level when a fall occurs. Similarly, [PERSON] et al. [3] used an accelerometer and a gyroscope in a smartphone to detect a fall. However, wearable devices are inconvenient for older adults, because they often suffer from forgetfulness. On the other hand, environmental-sensor-based methods use various kinds of sensors to sense and interpret fall accidents in the surrounding environment. For example, [PERSON] et al. [4] used a smart floor that was embeddedwith pressure-sensitive fiber sensors to detect a fall. However, pervasively deploying sensors is costly and impractical. For the image-based methods, [PERSON] et al. [5] first constructed a three-dimensional (3D) shape of an elderly person using multiple cameras and then analyzed the changes in the shape along the vertical axis. Similarly, [PERSON] et al. [6] evaluated the distance between the centroid of a human 3D model and the ground in order to detect a fall accident. However, it is impractical and challenging to track a person continuously by multiple cameras, especially in public areas. Recently, [PERSON] et al. [7] first used a machine learning method [8] to identify people in a given image and then adopted a fuzzy-logic-based recognition method to detect fall events. Unfortunately, their method may not be workable when there are overlaps between an elderly person and surrounding objects in the environment. In this paper, we propose an Image-based Fall Detection System (IFADS) to detect falls in the elderly in a timely and accurate manner. IFADS can detect falls that occur while walking forward, sitting down, and standing up, which well-represents the most common types of falls in a long-term care environment. Unlike the existing fall detection algorithms, we particularly focus on falls that occur while sitting down and standing up from a chair, because the two activities together account for a higher proportion of falls than forward walking [9]. IFADS first applies an object detection algorithm to identify people in a video frame. Then, a posture recognition method is used to keep tracking the status of the people by checking the relative positions of the chair and the people. An alarm is triggered when a fall is detected. In order to investigate the effectiveness of IFADS, we not only simulated different fall scenarios, but also adopted YouTube and Giphy videos that captured real falls. We then evaluated the effects of camera angles and multiple objects on the method's accuracy. Our experimental results showed that IFADS achieved an average accuracy of 95.96% in detecting fall accidents. We also compared IFADS with other existing image-based methods, and the results showed that IFADS is a practical solution to help caregivers, security staff, and responders quickly detect falls. IFADS can be used by nursing homes to improve the quality of residential care facilities. Furthermore, IFADS can be easily extended and applied to any place as long as there is a camera, such as smart homes, parks, and libraries. The rest of the paper is organized as follows.