The aim of the research is to present an approach of obstacle distance estimation and navigation for smart wheelchair. The smart wheelchair is an electric wheelchair equipped with camera and line laser to navigate and avoid an obstacle. The camera was used to capture images from the environment to sense the pathway condition. The line laser was used in combination with camera to recognize an obstacle in the pathway based on the shape of line laser image in certain angle. A blob method detection was applied on the line laser image to recognize the pattern of the detected obstacles. The line laser projector and camera were mounted in fixed-certain position to make sure a fixed relation between blobs-gaps and obstacle-to-wheelchair distance. A simple linear regression from 16 obtained data was used to represent this relation as the estimated obstacle distance. As a result, the average error between the estimation and actual distance was 1.25 cm from 7 data testing experiments. The experiments result indicates that the proposed method is able to estimate well the distance between wheelchair and obstacle. Later, the smart wheelchair needs to decide further action whether it should turn left, right or just walk straight when facing certain obstacle to avoid it.

People with physical disability such as quadriplegics may need a device which assist their mobility. Smart wheelchair is developed based on conventional wheelchair and is also generally equipped with sensors, cameras and computer based system as main processing unit to be able to perform specific algorithm for the intelligent capabilities. We develop smart wheelchair system that facilitates obstacle detection and human tracking based on computer vision. The experiment result of obstacle distance estimation using RANSAC showed lower average error, which is only 1.076 cm compared to linear regression which is 2.508 cm. The average accuracy of human guide detecting algorithm also showed acceptable result, which yield over 80% of accuracy.

Topic extraction is an essential tool to help gathering information from a vast amount of sources. This paper introduces a new approach to extract topics from a collection of text documents. In order to obtain the topics, preprocessing steps are conducted to remove unnecessary parts of the documents. Then, a term frequency-inverse document frequency is built to weight terms in documents. After that, SVD-based feature transformation is involved in building features used for clustering. Prior to clustering process, the Pillar algorithm is run to select initial centroids for K-Means clustering. Finally, weights of terms in clusters are calculated using term-cluster weight as a basis to choose topics from clusters. Based on the experimental result, it is concluded that the framework achieves satisfactory results by attaining the accuracy of 100%, 95.1%, 83,7%, and 68.7% for 4 topics obtained from Binary2, Multi5, Multi7, and Multi10 categories of 20Newsgroup dataset.

People with impairment and having difficulties to walk, even impossible to move due to illness, injury, or disability need assistance tool. One assistance tool to help those people is wheelchair. With current technological developments, conventional wheelchair can be improved. Conventional wheelchair which operated by hand cannot be used by people with hand-foot impairment, as well as electric-powered wheelchair that need to be controlled with hand. For those with hand-foot impairment, conventional wheelchair can be assisted by assistant to help pushing and to maneuver. One drawback with this approach is the assistant will have limited movement and will have fatigue from pushing a wheelchair. This research try to overcome this drawback so that the wheelchair can move semi-autonomously. Proposed approach incorporates human tracking algorithm that later will be used to make the wheelchair moving independently without assistant to push from behind. This paper propose a framework that combines keypoint descriptors for human tracking: ORB, KAZE, AKAZE, BRISK, SIFT, and SURF. Each keypoint descriptors are given a score which is used to choose which descriptor is used until the minimum number of keypoints is fulfilled. If the best in the method list does not suffice, then the second best will be selected to generate keypoints, and so on. The result of the framework obtained high precision, 0.93 and 0.89 from two videos with different environments.

Hue is an appearance parameter model which theoretically shows only color information. In cases where illumination varies from locations and conditions, the hue in HSV/HSL/HSI color space is theoretically remains alike. This study gives an experimental analysis on whether hue value is similar in various illuminations. Analysis of RGB and grayscale under the same situation are also presented. The experiment used objects which have primary colors of red, green and blue in several variation such as dark vs. light color, indoor vs. outdoor location and dark vs. bright illumination. The result shows that hue has standard deviation of <;16 (range 0°-360°) for all variations. It is very small compared to RGB which has standard deviation of > 52 (range 0-255) for all variations. Moreover, RGB values dropped drastically under a very low light condition. An analysis on grayscale was also performed. It results that objects' color cannot be differentiated in this color-space. Grayscale differentiates object well when based on brightness as its value on brighter objects or illuminations were larger than darker object or illumination.

This paper presents an approach of obstacle distance estimation for smart wheelchair. A smart wheelchair was equipped with a camera and a laser line. The camera was used to capture an image from the environment in order to sense the pathway condition. The laser line was used in combination with camera to recognize an obstacle in the pathway based on the shape of laser line image in certain angle. A blob method detection was then applied on the laser line image to separate and recognize the pattern of the detected obstacles. The laser line projector and camera which was mounted in fixed-certain position ensured a fixed relation between blobs-gap and obstacle-to-wheelchair distance. A simple linear regression from 16 obtained data was used to respresent this relation as the estimated obstacle distance. As a result, the average error between the estimation and the actual distance was 1.25 cm from 7 data testing experiments. Therefore, the experiment results show that the proposed method was able to estimate the distance between wheelchair and the obstacle.