Michal Kepski

Human fall detection on embedded platform using depth maps and wireless accelerometer

Since falls are a major public health problem in an aging society, there is considerable demand for low-cost fall detection systems. One of the main reasons for non-acceptance of the currently available solutions by seniors is that the fall detectors using only inertial sensors generate too much false alarms. This means that some daily activities are erroneously signaled as fall, which in turn leads to frustration of the users. In this paper we present how to design and implement a low-cost system for reliable fall detection with very low false alarm ratio. The detection of the fall is done on the basis of accelerometric data and depth maps. A tri-axial accelerometer is used to indicate the potential fall as well as to indicate whether the person is in motion. If the measured acceleration is higher than an assumed threshold value, the algorithm extracts the person, calculates the features and then executes the SVM-based classifier to authenticate the fall alarm. It is a 365/7/24 embedded system permitting unobtrusive fall detection as well as preserving privacy of the user.

Improving fall detection by the use of depth sensor and accelerometer

Since falls are a major cause of harm to older people, there is considerable demand for low-cost fall detection systems. To meet demands of the end-users we propose a new architecture for low cost and reliable fall detection, where an accelerometer is used to indicate a potential fall and the Kinect sensor is used to authenticate the eventual fall alert. In consequence, the depth maps are not processed frame-by-frame, but instead we download from a circular buffer a sequence of depth maps acquired prior to the fall and then process them to authenticate fall event. We determine features both in the depth maps and point clouds to extract discriminative fall descriptors. Since people typically follow typical motion patterns related to specific locations in home or typical daily activities, we propose to utilize k-nn classifier to implement an exemplar-based fall detector. We show that such a classifier is competitive on our publicly available URFD dataset in terms of sensitivity and specificity while being much more simple to implement on an embedded platform.

Fuzzy inference-based reliable fall detection using kinect and accelerometer

Falls are major causes of mortality and morbidity in the elderly. However, prevalent methods only utilize accelerometers or both accelerometers and gyroscopes to separate falls from activities of daily living. This makes it not easy to distinguish real falls from fall-like activities. The existing CCD-camera based solutions require time for installation, camera calibration and are not generally cheap. In this paper we show how to achieve reliable fall detection. The detection is done by a fuzzy inference system using low-cost Kinect and a device consisting of an accelerometer and a gyroscope. The experimental results indicate high accuracy of the detection and effectiveness of the system.

Fall detection on embedded platform using kinect and wireless accelerometer

In this paper we demonstrate how to accomplish reliable fall detection on a low-cost embedded platform. The detection is achieved by a fuzzy inference system using Kinect and a wearable motion-sensing device that consists of accelerometer and gyroscope. The foreground objects are detected using depth images obtained by Kinect, which is able to extract such images in a room that is dark to our eyes. The system has been implemented on the PandaBoard ES and runs in real-time. It permits unobtrusive fall detection as well as preserves privacy of the user. The experimental results indicate high effectiveness of fall detection.

Fuzzy inference-based fall detection using kinect and body-worn accelerometer

Graphical abstractDisplay Omitted HighlightsA new approach for reliable fall detection.In case of potential fall a threshold-based algorithm launches the fuzzy system to authenticate the fall event. The fuzzy system consists of two input Mamdani engines and a triggering alert Sugeno engine.The output of the first engine is a fuzzy set, which assigns grades of membership to the possible values of dynamic transitions, whereas the output of the second one is another fuzzy set assigning membership grades to possible body poses.Since the Mamdani engines perform fuzzy reasoning on disjoint subsets of the linguistic variables, the total number of the fuzzy rules needed for input-output mapping is far smaller. In this paper, we present a new approach for reliable fall detection. The fuzzy system consists of two input Mamdani engines and a triggering alert Sugeno engine. The output of the first Mamdani engine is a fuzzy set, which assigns grades of membership to the possible values of dynamic transitions, whereas the output of the second one is another fuzzy set assigning membership grades to possible body poses. Since Mamdani engines perform fuzzy reasoning on disjoint subsets of the linguistic variables, the total number of the fuzzy rules needed for input-output mapping is far smaller. The person pose is determined on the basis of depth maps, whereas the pose transitions are inferred using both depth maps and the accelerations acquired by a body worn inertial sensor. In case of potential fall a threshold-based algorithm launches the fuzzy system to authenticate the fall event. Using the accelerometric data we determine the moment of the impact, which in turn helps us to calculate the pose transitions. To the best of our knowledge, this is a new application of fuzzy logic in a novel approach to modeling and reliable low cost detecting of falls.

Human Fall Detection Using Kinect Sensor

Falls are major causes of mortality and morbidity in the elderly. The existing CCD-camera based solutions require time for installation, camera calibration and are not generally cheap. In this paper we show how to achieve automatic fall detection using Kinect sensor. The person is segmented on the basis of the updated depth reference images. Afterwards, the distance of the person to the ground plane is calculated. The ground plane is extracted by the RANSAC algorithm. The point cloud belonging to the floor is determined using v-disparity images and the Hough transform.

Unobtrusive Fall Detection at Home Using Kinect Sensor

The existing CCD-camera based systems for fall detection require time for installation and camera calibration. They do not preserve the privacy adequately and are unable to operate in low lighting conditions. In this paper we show how to achieve automatic fall detection using only depth images. The point cloud corresponding to floor is delineated automatically using v-disparity images and Hough transform. The ground plane is extracted by the RANSAC algorithm. The detection of the person takes place on the basis of the updated on-line depth reference images. Fall detection is achieved using a classifier trained on features representing the extracted person both in depth images and in point clouds. All fall events were recognized correctly on an image set consisting of 312 images of which 110 contained the human falls. The images were acquired by two Kinect sensors placed at two different locations.

Embedded system for fall detection using body-worn accelerometer and depth sensor

This paper presents an embedded system for fall detection using accelerometric data and depth maps. A real-time processing of motion data and depth maps is realized on a low-cost PandaBoard platform. In order to achieve detection of human falls with low computational cost the system performs a depth-based inferring about the fall event when persons movement is above some preset threshold. The performance of the system has been evaluated on our publicly available dataset consisting of synchronized depth maps and motion data. To investigate the detection accuracy in depth maps from different camera views the image sequences were simultaneously recorded by two Kinect sensors, where one of them was placed in the front of the scene, whereas the second one was located on the ceiling. The motion data were acquired by a body-worn accelerometer and transmitted wirelessly to the processing unit, responsible for both synchronization and recording or processing of the data.

Detecting human falls with 3-axis accelerometer and depth sensor

Previous work demonstrated that Kinect sensor can be very useful for fall detection. In this work we present a novel approach to fall detection that allows us to achieve reliable fall detection in larger areas through person detection and tracking in dense depth map sequences acquired by an active pan-tilt 3D camera. We demonstrate that both high sensitivity and specificity can be obtained using dense depth images acquired by a ceiling mounted Kinect and executing the proposed algorithms for lying pose detection and motion analysis. The person is extracted using depth region growing and person detection.

Fall Detection Using Kinect Sensor and Fall Energy Image

One of the main reasons for low acceptance by seniors the available technology for automatic fall detection is that the existing devices generate too much false alarms. Additionally, the camera-based devices do not preserve the privacy adequately. In our approach an accelerometer is utilized to indicate a potential fall. A fall hypothesis is then verified in the second stage in which we employ a depth image, which was shot at the moment of the potential fall. A detector that was trained in advance on features extracted both from depth images and points cloud is responsible for verification whether a person is lying on the floor. After all, to reliably distinguish the fall from fall-like activities we perform final verification, in which we employ the proposed fall energy image. The fall energy image expresses the distribution of the person’s motion in the set of images preceding the fall.