Vision based interface system for hands free control of an intelligent wheelchair

<p>Abstract</p> <p>Background</p> <p>Due to the shift of the age structure in today's populations, the necessities for developing the devices or technologies to support them have been increasing. Traditionally, the wheelchair, including powered and manual ones, is the most popular and important rehabilitation/assistive device for the disabled and the elderly. However, it is still highly restricted especially for severely disabled. As a solution to this, the Intelligent Wheelchairs (IWs) have received considerable attention as mobility aids. The purpose of this work is to develop the IW interface for providing more convenient and efficient interface to the people the disability in their limbs.</p> <p>Methods</p> <p>This paper proposes an intelligent wheelchair (IW) control system for the people with various disabilities. To facilitate a wide variety of user abilities, the proposed system involves the use of face-inclination and mouth-shape information, where the direction of an IW is determined by the inclination of the user's face, while proceeding and stopping are determined by the shapes of the user's mouth. Our system is composed of electric powered wheelchair, data acquisition board, ultrasonic/infra-red sensors, a PC camera, and vision system. Then the vision system to analyze user's gestures is performed by three stages: detector, recognizer, and converter. In the detector, the facial region of the intended user is first obtained using Adaboost, thereafter the mouth region is detected based on edge information. The extracted features are sent to the recognizer, which recognizes the face inclination and mouth shape using statistical analysis and <it>K</it>-means clustering, respectively. These recognition results are then delivered to the converter to control the wheelchair.</p> <p>Result & conclusion</p> <p>The advantages of the proposed system include 1) accurate recognition of user's intention with minimal user motion and 2) robustness to a cluttered background and the time-varying illumination. To prove these advantages, the proposed system was tested with 34 users in indoor and outdoor environments and the results were compared with those of other systems, then the results showed that the proposed system has superior performance to other systems in terms of speed and accuracy. Therefore, it is proved that proposed system provided a friendly and convenient interface to the severely disabled people.</p

Intention to Use Long-Term Care Facilities: Differences between Korean Pre-elderly and Korean Baby-boomers

With the rapidly increasing number of older adults, dealing with long-term care (LTC) needs becomes an emerging issue in South Korea. This study aims to examine factors affecting the intention to use longtermcare facilities with two groups of young-old adults: (1) Korean pre-elderly (KPE) and (2) Korean babyboomers (KBB). Guided by Andersen's behavioral model of health service use and prior research, predisposing characters, enabling resources, need factors, availabilities of informal care and self-care activities were used as predictors. In the final analyses, 803 KPE and 966 KBB were included. The results of logistic regression analyses showed different findings in two groups. Age, education, spouse's physicalhealth, and self-care activities for relationship with family and friends are significantly associated with intention to use LTC facilities among KPE. However, income, physical health of respondents, and relationship satisfaction with children are significantly related to intention of use LTC facilities in the group of KBB. This study suggests different LTC needs between KPE and KBB. Health care professionals and policy makers need to consider such differences to provide quality LTC care for them

Probing transport in quantum many-fermion simulations via quantum loop topography

Quantum many-fermion systems give rise to diverse states of matter that often reveal themselves in distinctive transport properties. While some of these states can be captured by microscopic models accessible to numerical exact quantum Monte Carlo simulations, it nevertheless remains challenging to numerically access their transport properties. Here we demonstrate that quantum loop topography (QLT) can be used to directly probe transport by machine learning current-current correlations in imaginary time. We showcase this approach by studying the emergence of superconducting fluctuations in the negative-U Hubbard model and a spin-fermion model for a metallic quantum critical point. For both sign-free models, we find that the QLT approach detects a change in transport in very good agreement with their established phase diagrams. These proof-of-principle calculations combined with the numerical efficiency of the QLT approach point a way to identify hitherto elusive transport phenomena such as non-Fermi liquids using machine learning algorithms.Comment: 7 pages, 5 figure