Intelligent computer vision processing techniques for fall detection in enclosed environments

Detecting unusual movement (falls) for elderly people in enclosed environments is receiving increasing attention and is likely to have massive potential social and economic impact. In this thesis, new intelligent computer vision processing based techniques are proposed to detect falls in indoor environments for senior citizens living independently, such as in intelligent homes. Different types of features extracted from video-camera recordings are exploited together with both background subtraction analysis and machine learning techniques. Initially, an improved background subtraction method is used to extract the region of a person in the recording of a room environment. A selective updating technique is introduced for adapting the change of the background model to ensure that the human body region will not be absorbed into the background model when it is static for prolonged periods of time. Since two-dimensional features can generate false alarms and are not invariant to different directions, more robust three-dimensional features are next extracted from a three-dimensional person representation formed from video-camera measurements of multiple calibrated video-cameras. The extracted three-dimensional features are applied to construct a single Gaussian model using the maximum likelihood technique. This can be used to distinguish falls from non-fall activity by comparing the model output with a single. In the final works, new fall detection schemes which use only one uncalibrated video-camera are tested in a real elderly person s home environment. These approaches are based on two-dimensional features which describe different human body posture. The extracted features are applied to construct a supervised method for posture classification for abnormal posture detection. Certain rules which are set according to the characteristics of fall activities are lastly used to build a robust fall detection model

Computer vision based techniques for fall detection with application towards assisted living

In this thesis, new computer vision based techniques are proposed to detect falls of an elderly person living alone. This is an important problem in assisted living. Different types of information extracted from video recordings are exploited for fall detection using both analytical and machine learning techniques. Initially, a particle filter is used to extract a 2D cue, head velocity, to determine a likely fall event. The human body region is then extracted with a modern background subtraction algorithm. Ellipse fitting is used to represent this shape and its orientation angle is employed for fall detection. An analytical method is used by setting proper thresholds against which the head velocity and orientation angle are compared for fall discrimination. Movement amplitude is then integrated into the fall detector to reduce false alarms. Since 2D features can generate false alarms and are not invariant to different directions, more robust 3D features are next extracted from a 3D person representation formed from video measurements from multiple calibrated cameras. Instead of using thresholds, different data fitting methods are applied to construct models corresponding to fall activities. These are then used to distinguish falls and non-falls. In the final works, two practical fall detection schemes which use only one un-calibrated camera are tested in a real home environment. These approaches are based on 2D features which describe human body posture. These extracted features are then applied to construct either a supervised method for posture classification or an unsupervised method for abnormal posture detection. Certain rules which are set according to the characteristics of fall activities are lastly used to build robust fall detection methods. Extensive evaluation studies are included to confirm the efficiency of the schemes

Posture recognition based fall detection system for monitoring an elderly person in a smart home environment

We propose a novel computer vision based fall detection system for monitoring an elderly person in a home care application. Background subtraction is applied to extract the foreground human body and the result is improved by using certain post-processing. Information from ellipse fitting and a projection histogram along the axes of the ellipse are used as the features for distinguishing different postures of the human. These features are then fed into a directed acyclic graph support vector machine (DAGSVM) for posture classification, the result of which is then combined with derived floor information to detect a fall. From a dataset of 15 people, we show that our fall detection system can achieve a high fall detection rate (97.08%) and a very low false detection rate (0.8%) in a simulated home environment

Sensor fusion in smart camera networks for ambient intelligence

This short report introduces the topics of PhD research that was conducted on 2008-2013 and was defended on July 2013. The PhD thesis covers sensor fusion theory, gathers it into a framework with design rules for fusion-friendly design of vision networks, and elaborates on the rules through fusion experiments performed with four distinct applications of Ambient Intelligence

MUSCULOSKELETAL STRENGTH, FALL AND FRACTURE RISK IN EARLY POSTMENOPAUSAL WOMEN

Purpose: To evaluate the course of recovery in fall-risk and functional status over the first year following a distal radius fracture (DRF), and evaluate differences in fall and fracture risk factors in women over the age of 50 years with a DRF compared to their non-fractured peers. Methods: Two cohorts of participants volunteered in two sub-studies of the thesis. The first was seventy-eight women recruited from a DRF Clinic within the first week after their fracture, and followed up in concert with standard clinic appointments at week three, nine, 12, 26, and 52 post-fracture. The second cohort consisted of women aged 50 years or older, with and without a recent distal radius fracture, being at least 6 months post-DRF, but no more than 2 years post-fracture. Seventy-seven women age 50-78 with (Fx, n = 32) and without (NFx = 45) a history of DRF were assessed on two occasions within 4 weeks apart using a battery of fall and fracture risk tools, including balance, mobility, gait speed, fracture risk assessment, as well as bone quality assessment using peripheral quantitative computer tomography (pQCT) and dual x-ray absorptiometry (DXA). Results: Fall-risk status (strength, balance, mobility) gradually improved over the first year post-fracture, with balance confidence remaining high even immediately post-fracture. In the second study, women with a recent DRF, compared to women without, demonstrated higher fall and fracture risk. Women with a recent DRF had lower bone and muscle strength in both the upper and lower extremities compared to the non-fractured controls, with no differences in DXA derived aBMD at the femoral neck or spine. Significance of findings: The results of these studies will help clinicians understand the normal course of functional recovery post-fracture, and assist in determining appropriate fall risk assessment and interventions for post-menopausal women at risk of fragility fracture. Results demonstrate the importance of studying women at risk of DRF as an important first indicator of bone fragility and risk of future fracture. These findings also strengthen the notion that DXA alone may not be the best predictor for fracture risk

A Survey of Applications and Human Motion Recognition with Microsoft Kinect

Microsoft Kinect, a low-cost motion sensing device, enables users to interact with computers or game consoles naturally through gestures and spoken commands without any other peripheral equipment. As such, it has commanded intense interests in research and development on the Kinect technology. In this paper, we present, a comprehensive survey on Kinect applications, and the latest research and development on motion recognition using data captured by the Kinect sensor. On the applications front, we review the applications of the Kinect technology in a variety of areas, including healthcare, education and performing arts, robotics, sign language recognition, retail services, workplace safety training, as well as 3D reconstructions. On the technology front, we provide an overview of the main features of both versions of the Kinect sensor together with the depth sensing technologies used, and review literatures on human motion recognition techniques used in Kinect applications. We provide a classification of motion recognition techniques to highlight the different approaches used in human motion recognition. Furthermore, we compile a list of publicly available Kinect datasets. These datasets are valuable resources for researchers to investigate better methods for human motion recognition and lower-level computer vision tasks such as segmentation, object detection and human pose estimation