# An Image-Based Fall Detection System for the Elderly
###### Abstract  
Due to advances in medical technology, the elderly population has continued to grow. Elderly healthcare issues have been widely discussed--especially fall accidents--because a fall can lead to a fracture and have serious consequences. Therefore, the effective detection of fall accidents is important for both elderly people and their caregivers. In this work, we designed an Image-based FAIL Detection System (IFADS) for nursing homes, where public areas are usually equipped with surveillance cameras. Unlike existing fall detection algorithms, we mainly focused 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. 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 evaluate the effectiveness of IFADS, we not only simulated different fall scenarios, but also adopted YouTube and Giphy videos that captured real falls. Our experimental results showed that IFADS achieved an average accuracy of 95.96%. Therefore, IFADS can be used by nursing homes to improve the quality of residential care facilities.  
intelligent surveillance systems; elderly fall detection; object detection; image recognition Article  
An Image-Based Fall Detection System for the Elderly  
[PERSON], and [PERSON][PERSON]. [PERSON]  
Department of Computer Science and Information Engineering, National Yunlin University of Science and Technology, Yunlin 64002, Taiwan; [EMAIL_ADDRESS]  
Correspondence: [EMAIL_ADDRESS]; Tel.: +886-05-5342601  
[1]Received: 7 September 2018; Accepted: 16 October 2018; Published: 20 October 2018  
**Featured Application: By using image recognition and object detection, we presented the system named IFADS to detect the fall, especially the falls that occur while sitting down and standing up from a chair for nursing homes, where public areas are usually equipped with surveillance cameras.**  
**Abstract: Due to advances in medical technology, the elderly population has continued to grow. Elderly healthcare issues have been widely discussed--especially fall accidents--because a fall can lead to a fracture and have serious consequences. Therefore, the effective detection of fall accidents is important for both elderly people and their caregivers. In this work, we designed an Image-based FAIL Detection System (IFADS) for nursing homes, where public areas are usually equipped with surveillance cameras. Unlike existing fall detection algorithms, we mainly focused 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. 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 evaluate the effectiveness of IFADS, we not only simulated different fall scenarios, but also adopted YouTube and Giphy videos that captured real falls. Our experimental results showed that IFADS achieved an average accuracy of 95.96%. Therefore, IFADS can be used by nursing homes to improve the quality of residential care facilities.**  
**Keywords: intelligent surveillance systems; elderly fall detection; object detection; image recognition**