Pressure ulcer is a prevalent complication for bed-bound patients who are not able to shift their body weights over time. Continuous monitoring of patient's postures in the bed can be helpful for caregivers in order to keep track of patient's movements and quality of their repositioning during a day. This information allows hospitals to plan an effective repositioning schedule for each patient. In this paper, a high speed and robust posture classification algorithm is proposed that can be employed in any pervasive patient's monitoring system. First, a whole-body pressure image is recorded using a commercial pressure mat system. Image enhancement is then applied to the raw pressure images and a binary signature for each different posture is constructed. Finally, using a binary pattern matching technique, a given posture can be classified to one of the known posture classes. Our extensive experiments show that the proposed algorithm is able to predict in-bed postures with more than 97% average accuracy.

Pressure ulcer is a major problem for bed-bound and wheelchair-bound individuals specially in regions like sacrum, buttocks, hip, heels, back and head. Once developed, it is extremely uncomfortable and costly. Identification and monitoring of high-risk regions and their pressure distributions help nurses to have information about risk in each specific area of body and reposition patient efficiently. In this paper, we propose an algorithm to detect regions that are under high stress. Because of low resolution nature of pressure image and changes in shape of human body parts in various images, we adopted image processing algorithms. The image of human body is segmented using Delaunay triangulation. The extracted tree is compared to defined template for each posture. Then, signal processing and graph matching algorithms are used to label the tree according to the template. Pressure values of each specific region are collected for other phases of ulcer management such as risk assessment and reposition schedule. The experimental results indicate that our method can detect 9 (6) regions in supine (side) postures with average accuracy of 85.7%.

Driving simulators offer several advantages in driver behavioral research, including low cost, repeatable experiments, and the ability to simulate scenarios that would be too dangerous for real cars. An effective simulation must elicit the same responses from drivers as a real car. Several factors contribute to this realism including a good physics engine, a realistic environment, and expected behavior from simulated traffic and drivers. In this paper, we present a novel method of generating a wide variety of realistic roads called Layered Semi-Markov Model. These roads are tested on five drivers with a driver distraction detection experiment. The results of this experiment matched the expected behavior and subjective feedback showed a favorable response.

Pressure ulcer is a significant problem for bedridden and wheelchair-bound patients, diabetics, and the elderly. These patients need to be regularly repositioned to prevent excessive pressure on a single area of their body, which can lead to ulcers. In this work, we develop a software platform that facilitates monitoring at-risk patients and suggests preventive steps that all can lead to less pressure ulcer formation incidents in the hospitals.

Pressure ulcer is an age-old problem imposing a huge cost to our health care system. Detecting and keeping record of the patient's posture on bed, help care givers reposition patient more efficiently and reduce the risk of developing pressure ulcer. In this paper, a commercial pressure mapping system is used to create a time-stamped, whole-body pressure map of the patient. An image-based processing algorithm is developed to keep an unobtrusive and informative record of patient's bed posture over time. The experimental results show that proposed algorithm can predict patient's bed posture with up to 97.7% average accuracy. This algorithm could ultimately be used with current support surface technologies to reduce the risk of ulcer development.

This paper, presents a new cephalometric landmark localization method based on combining two classifier results. Initially, a classifier based on histograms of oriented gradients makes a first estimation of the potential windows, and then a second classifier, based on histograms of gray profile, classifies the detected windows. By combining the results of these two classifiers, final decision is made about the landmark window location. HOG features gather edge profiles in the image and making decision for the most proper window in some detection windows needs more information than just edge profiles. By adding the gray profile features to the system and combining the results in a proper manner, detection performance increases significantly for a range of hard to easy landmarks.

In this paper, we explored the use of certain image features, block-wise histograms of local orientations. They are used in many current object recognition algorithms, for the task of locating cephalometric landmarks on X-ray images. After reviewing existing cephalometric landmark detection systems, we show experimentally that grids of Histograms of Oriented Gradients (HOG) descriptors significantly perform well on this task. The influence of bin and detection window size on performance for three landmarks has been studied. It has been shown that, fine orientation binning, large enough detection windows which contain whole the features around landmark are all important for good estimation of landmark position.