Moreover, in [ 4 ] the authors point out that the common fall detectors, which are usually attached to a belt around the hip, are inadequate to be worn during the sleep and this results in the lack of ability of such detectors to monitor the critical phase of getting up from the bed. In general, the solutions mentioned above are somehow intrusive for people as they require wearing continuously at least one device or smart sensor.
There have been several attempts to attain reliable human fall detection using single CCD camera [ 1 ] [ 11 ] , multiple cameras [ 3 ] or specialized omni-directional ones [ 9 ] ]. The currently offered CCD-camera based solutions require time for installation, camera calibration and they are not generally cheap.
Typically, they require a PC computer or a notebook for image processing. The existing video-based devices for fall detection cannot work in nightlight or low light conditions. In addition, in most of such solutions the privacy is not preserved adequately. Video cameras offer several advantages in fall detection, among others the ability to detect various activities. Additional advantage is low intrusiveness and the possibility of remote verification of fall events.
However, the lack of depth information may lead to many false alarms. The existing technology permits reaching quite high performance of fall detection. However, as mentioned above it does not meet the requirements of the users with special needs. Our literature survey show that most of the approaches offers incremental improvements that can not lead to technology breakthrough, and which have insufficient potential for cutting edge scientific breakthroughs to make the life of people with special needs more fulfilling.
Our work brings new insight into fall detection by the use of a wireless wearable device and Kinect, which is a central component of our system for fall detection. The Kinect is a revolutionary motion-sensing technology that allows tracking a person in real-time without having to carry sensors. Unlike 2D cameras, Kinect allows tracking the body movements in 3D. In order to achieve reliable and unobtrusive fall detection, our system employs both the Kinect and a wearable motion-sensing device, which complement each other.
The fall detection is done by a fuzzy inference system using low-cost Kinect and the wearable motion-sensing device consisting of an accelerometer and a gyroscope.
The fuzzy inference system is a central ingredient of our fall detection prototype, and it is based on expert knowledge and demonstrates high generalization abilities [ 8 ]. We show that the low-cost Kinect contributes toward reliable fall detections. Using both devices, our system can reliably distinguish the falls from activities of daily living, and thus the number of false alarms is reduced.
In context of fall detection the disadvantage of Kinect is that it only can monitor restricted areas. In the areas where the depth images are not available we utilized only a wearable motion-sensing device consisting of an accelerometer and a gyroscope. On the other hand, in some ADLs during which the use of this wearable sensor might not be comfortable, for instance during changing clothes, wash, etc.
An advantage of Kinect is that it can be put in selected places according to the user requirements. Moreover, the system operates on depth images and thus preserves privacy for people being monitored. In this context, it is worth noting that Kinect uses infrared light and therefore it is able to extract depth images in a room that is dark to our eyes.
The system runs in real-time and has been implemented on the PandaBoard ES, which is a low-power, low-cost single-board computer development platform. This section is devoted to presentation of the main modules of the embedded sys-tem for fall detection.
At the beginning the system architecture will be outlined. The wearable device will be presented later. Then, the usefulness of the Kinect for fall detection is discussed in detail.
Afterwards, the extraction of the object of interest in depth images on the computer board with limited computational resources is presented. Our fall detection system uses both data from Kinect and motion data from a wearable smart device containing accelerometer and gyroscope sensors. The system runs under Linux operating system. Check out the FAQ! Hi there! Please sign in help. The Kinect only has a 2-axis accelerometer for tilt correct?
Eric Perko edit. Ah, that makes sense if it's true. Is that correct? In other words, I think we need gyros, not an accelerometer, right? Pi Robot edit. Thanks Eric. I think that answers all my questions.
Just where we use the SDK has been expanded as well -- in addition to promised Chinese support, Kinect input is an option for Windows 8 desktop apps. Programmers who find regular hand control just too limiting can hit the source for the download link and check Microsoft's blog for grittier detail.
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