Fall Detection Analysis Using a Real Fall Dataset

International Conference on Soft Computing Models in Industrial and Environmental Applications (13th. 2018. San Sebastián

Depth-Based Fall Detection: Outcomes from a Real Life Pilot

With the increasing ageing population representing a challenge for society and health care systems, solutions based on ICT to prolong the independent living of older adults become critical. Among them, systems able to automatically detect falls are being investigated since several years, because many solutions that appear promising when tested in lab settings, fail when faced with the constraints and unforeseen circumstances of real deployments. In this paper, we present the outcomes resulting from the pilot installation of a fall detection system based on the use of depth sensors located on the ceiling of the monitored apartment, where a 75 years old woman lives alone. We highlight the system design process, moving from the research leading to an original algorithm working offline, preliminarily tested in a lab setting, to the real-time engineering of the software, and the physical deployment of the system. Testing the system in a real-life scenario allowed us to identify a number of tricks and conditions that should to be taken into account since the initial steps, but the lab experimentation alone can barely help to focus on

A Fall Detection/Recognition System and an Empirical Study of Gradient-Based Feature Extraction Approaches

Physically falling down amongst the elder helpless party is one of the most intractable issues in the era of ageing society, which has attracted intensive attentions in academia ranging from clinical research to computer vision studies. This paper proposes a fall detection/recognition system within the realm of computer vision. The proposed system integrates a group of gradient-based local visual feature extraction approaches, including histogram of oriented gradients (HOG), histogram of motion gradients (HMG), histogram of optical flow (HOF), and motion boundary histograms (MBH). A comparative study of the descriptors with the support of an artificial neural network was conducted based on an in-house captured dataset. The experimental results demonstrated the effectiveness of the proposed system and the power of these descriptors in real-world applications

Lecture Notes in Artificial Intelligence

Fall detection represents an important issue when dealing with Ambient Assisted Living for the elder. The vast majority of fall detection approaches have been developed for healthy and relatively young people. Moreover, plenty of these approaches make use of sensors placed on the hip. Considering the focused population of elderly people, there are clear differences and constraints. On the one hand, the patterns and times in the normal activities-and also the falls- are different from younger people: elders move slowly. On the second hand, solutions using uncomfortable sensory systems would be rejected by many candidates. In this research, one of the proposed solutions in the literature has been adapted to use a smartwatch on a wrist, solving some problems and modifying part of the algorithm. The experimentation includes a publicly available dataset. Results point to several enhancements in order to be adapted to the focused population