Fall detection in homes of older adults using the Microsoft Kinect (original) (raw)
Related papers
Systematic review of indoor fall detection systems for the elderly using Kinect
International Journal of Telemedicine and Clinical Practices
The fall of the elderly presents a major health problem as it may cause fatal injuries. To improve the life quality of the elderly, researchers have developed several fall detection systems. Several sensors have been used to overcome this problem. So far, Microsoft Kinect has been the most used camera-based sensor for fall detection. This motion detector can interact with computers through gestures and voice commands. In this article, we presented a comprehensive survey of the latest fall detection research using the Kinect sensor. We provide an overview of the main features of the two Kinect versions V1 and V2 and compare their performances. Then, we detailed the method used for the articles selection. We provided a classification of the fall detection techniques to highlight the main differences between them. Finally, we concluded that it is not enough to evaluate a system performance under simulated conditions. It is important to test these approaches on old people who are likely to fall.
Development of a Fall Detection System with Microsoft Kinect
Falls are the leading cause of injury and death among older adults in the US. Computer vision systems offer a promising way of detecting falls. The present paper examines a fall detection and reporting system using the Microsoft Kinect sensor. Two algorithms for detecting falls are introduced. The first uses only a single frame to determine if a fall has occurred. The second uses time series data and can distinguish between falls and slowly lying down on the floor. In addition to detecting falls, the system offers several options for reporting. Reports can be sent as emails or text messages and can include pictures during and after the fall. A voice recognition system can be used to cancel false reports.
Towards a Real-Time Fall Detection System using Kinect Sensor
International Journal of Computer Vision and Image Processing
Falls are the major health problem among older people who live alone in their home. In the past few years, several studies have been proposed to solve the dilemma especially those which exploit video surveillance. In this paper, in order to allow older adult to safely continue living in home environments, the authors propose a method which combines two different configurations of the Microsoft Kinect: The first one is based on the person's depth information and his velocity (Ceiling mounted Kinect). The second one is based on the variation of bounding box parameters and its velocity (Frontal Kinect). Experimental results on real datasets are conducted and a comparative evaluation of the obtained results relative to the state-of-art methods is presented. The results show that the authors' method is able to accurately detect several types of falls in real-time as well as achieving a significant reduction in false alarms and improves detection rates.
Evaluation of an Inexpensive Depth Camera for Passive In-Home Fall Risk Assessment
Proceedings of the 5th International ICST Conference on Pervasive Computing Technologies for Healthcare, 2011
We present an investigation of a new, inexpensive depth camera device, the Microsoft Kinect, for passive fall risk assessment in home environments. In order to allow older adults to safely continue living in independent settings as they age, the ability to assess their risk of falling, along with detecting the early onset of illness and functional decline, is essential. Daily measurements of temporal and spatial gait parameters would greatly facilitate such an assessment. Ideally, these measurements would be obtained passively, in normal daily activity, without the need for wearable devices or expensive equipment. In this work, we evaluate the use of the inexpensive Microsoft Kinect for obtaining measurements of temporal and spatial gait parameters as compared to an existing web-camera based system, along with a Vicon motion capture system for ground truth. We describe our techniques for extracting gait parameters from the Kinect data, as well as the advantages of the Kinect over the web-camera based system for passive, in-home fall risk assessment.
Automated In-Home Fall Risk Assessment and Detection Sensor System for Elders
The Gerontologist, 2015
Falls are a major problem for the elderly people leading to injury, disability, and even death. An unobtrusive, in-home sensor system that continuously monitors older adults for fall risk and detects falls could revolutionize fall prevention and care. A fall risk and detection system was developed and installed in the apartments of 19 older adults at a senior living facility. The system includes pulse-Doppler radar, a Microsoft Kinect, and 2 web cameras. To collect data for comparison with sensor data and for algorithm development, stunt actors performed falls in participants' apartments each month for 2 years and participants completed fall risk assessments (FRAs) using clinically valid, standardized instruments. The FRAs were scored by clinicians and recorded by the sensing modalities. Participants' gait parameters were measured as they walked on a GAITRite mat. These data were used as ground truth, objective data to use in algorithm development and to compare with radar a...
Lecture Notes in Computer Science, 2014
The Choice Stepping Reaction Time (CSRT) task is time-based clinical test that has shown to reliably predict falls in older adults. Its current mode of delivery involves the use of a custom-made dance mat device. This mat is a measurement tool that can reliably obtain step data to discriminate between fallers and non-fallers. One of the pitfalls of this test is that the technology in use still imposes an obstacle on the degree of freedom to be able to perform adaptive exercises suitable for the elderly. In this paper, we describe a Kinect-based system that measures stepping performance through the use of a hybrid version of the CSRT task. This study focuses on assessing this system's capabilities to reliably measure a time-based clinical test of fall risk. Results showed a favorable correspondence and agreement between the two systems, suggesting that this platform could be potentially useful in the clinical practice.
Computer Vision Based Fall Detection Methods Using the Kinect Camera : A Survey
International Journal of Computer Science and Information Technology, 2018
Disabled people can overcome their disabilities in carrying out daily tasks in many facilities [1]. However, they frequently report that they experience difficulty being independently mobile. And even if they can, they are likely to have some serious accidents such as falls. Furthermore, falls constitute the second leading cause of accidental or injury deaths after injuries of road traffic which call for efficient and practical/comfortable means to monitor physically disabled people in order to detect falls and react urgently. Computer vision (CV) is one of the computer sciences fields, and it is actively contributing in building smart applications by providing for image\video content "understanding." One of the main tasks of CV is detection and recognition. Detection and recognition applications are various and used for different purposes. One of these purposes is to help of the physically disabled people who use a cane as a mobility aid by detecting the fall. This paper surveys the most popular approaches that have been used in fall detection, the challenges related to developing fall detectors, the techniques that have been used with the Kinect in fall detection, best points of interest (joints) to be tracked and the well-known Kinect-Based Fall Datasets. Finally, recommendations and future works will be summarized.
Comprehensive evaluation of skeleton features-based fall detection from Microsoft Kinect v2
Signal, Image and Video Processing, 2019
Most of the computer vision applications for human activity recognition exploit the fact that body features calculated from a 3D skeleton increase robustness across persons and can lead to higher performance. However, their success in activity recognition, including falls, depends on the correspondence between the human activities and the used joint/part features. To provide for this correspondence, we experimentally evaluate in this paper skeleton features-based fall detection by comparing fall detection performance for different combinations of skeleton features used in previous related works. We determine the skeleton features that best distinguish fall from non-fall frames, and the best performing classifier. In this endeavor, we followed the classical five steps of supervised machine learning: (1) we collected a learning data composed of 42 fall and 37 non-fall videos from FallFree; (2) we extracted and (3) preprocessed the skeleton data of the training set; (4) we extracted each possible skeleton feature; finally (5) we evaluated all extracted and selected features using two main experiments; one of them based on neighborhood component analysis (NCA). In this evaluation, we show that fall detection based on skeleton features has very encouraging accuracy that varies depending on the used features. More specifically, we recommend the following features: 12 features that resulted from NCA experiment, original and normalized distance from Kinect, and the seven features of the upper body part. These features ranked 1st, 2nd, 4th, and 8th on 22 feature sets, with accuracies 99.5%, 99.4%, 97.8%, and 94.5%, respectively. In addition, random forest is the best performing classifier. Keywords Fall detection • Skeleton features • Feature selection • Kinect v2 • Neighborhood component feature selection 1 Introduction Falls represent a major cause of morbidity and mortality among the elderly. In fact, statistics show that falls are the primary reason for injury-related death for seniors aged 79 B Salma Kammoun Jarraya
Sensors, 2021
Because of population ageing, fall prevention represents a human, economic, and social issue. Currently, fall-risk is assessed infrequently, and usually only after the first fall occurrence. Home monitoring could improve fall prevention. Our aim was to monitor daily activities at home in order to identify the behavioral parameters that best discriminate high fall risk from low fall risk individuals. Microsoft Kinect sensors were placed in the room of 30 patients temporarily residing in a rehabilitation center. The sensors captured the patients’ movements while they were going about their daily activities. Different behavioral parameters, such as speed to sit down, gait speed or total sitting time were extracted and analyzed combining statistical and machine learning algorithms. Our algorithms classified the patients according to their estimated fall risk. The automatic fall risk assessment performed by the algorithms was then benchmarked against fall risk assessments performed by cl...