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We developed a basic lightweight powered wheelchair model using a participatory action design (PAD). We conducted two types of research to identify users’ desires and satisfaction with wheelchairs: one focused on identifying the problems and needs of manual or powered wheelchair users through focus group interviews (FGIs), and the other focused on capturing users’ post-experience feedback with the developed prototypes through usability testing (UT). We structured scenario-based usability tests and questionnaires to capture the participants’ experiences and needs, with the results indicating that both manual and powered wheelchair users emphasized the product’s lightness, design, cost, and battery and motor performance. This user-focused development resulted in higher satisfaction and usability. The significance of this study lies in the active involvement of wheelchair users throughout the research process, incorporating the obstacles and terrains they encounter in real community environments into the development. Future research should assess the usability of the final lightweight powered wheelchair, aiming to improve its affordability to wheelchair users through public benefits.

This technology can prevent accidents involving large vehicles, such as trucks or buses, by selecting an optimal driving lane for safe autonomous driving. This paper proposes a method for detecting forward-driving vehicles within road images obtained from a vehicle’s DashCam. The proposed method also classifies the types and colors of the detected vehicles. The proposed method uses a YOLO deep learning network for vehicle detection based on a pre-trained ResNet-50 convolutional neural network. Additionally, a Resnet-50 CNN-based object classifier, using transfer learning, was used to classify vehicle types and colors. Vehicle types were classified into four categories based on size whereas vehicle colors were classified into eight categories. During autonomous driving, vehicle types are used to determine driving lanes, whereas vehicle colors are used to distinguish the road infrastructure, such as lanes, vehicles, roads, backgrounds, and buildings. The datasets used for learning consisted of road images acquired in various driving environments. The proposed method achieved a vehicle detection accuracy of 91.5%, vehicle type classification accuracy of 93.9%, and vehicle color classification accuracy of 94.2%. It accurately detected vehicles and classified their types and colors. These can be applied to autonomous and safe driving support systems to enhance the safety of autonomous vehicles.

Introduction: Telemedicine across many specialties in clinical practice has been established in the literature regarding technology platforms, privacy issues, cost, and clinical effectiveness. However, the lack of data in these areas applicable to spinal cord injury telerehabilitation (teleSCI) still exists. The gaps in these knowledge areas continue to hinder its widespread implementation and serve as pathways for focused efforts in teleSCI research. Objective: This systematic review aims to substantiate the clinical effectiveness and potential barriers to teleSCI implementation by verifying the statistical significance of various clinical outcomes from randomized trials published within the recent past decade. Methods: A qualitative synthesis of randomized studies, conducted across various regions, was systematically reviewed after identifying relevant records from database search engines. Applied filters in the search included publication dates (2010?2020), humans, full-text, and no language preference. The 13 studies were selected per Preferred Reporting Items for Systematic Reviews and Meta-Analyses flow diagram, and the risk of bias across studies was evaluated by using the Physiotherapy Evidence Database scale of quality assessment. Results: Quantitative outcome measurements demonstrated positive impact across studies: 79.1% (34/43) of all measurements were statistically significant for positive outcomes and 18.6% (8/43) yielded no effect but were significant. Primary outcomes addressed various spinal cord injury (SCI) management areas; 38.5% (5/13) of studies also assessed secondary outcomes. Interventional platforms were conventional technologies used in telemedicine. One study (7.7%) achieved data encryption; no studies presented cost-analysis data. Conclusion: The majority of studies demonstrated significant positive outcomes to validate teleSCI clinical effectiveness through conventional technology. These results further expand our understanding of teleSCI's impact and its demonstrated potential for improving SCI individuals' lives. However, heterogeneity of selected studies limits the conclusive recommendations to address potential barriers to its widespread implementation. Moreover, the development of new data is warranted to promote ?buy-in? of widespread teleSCI implementation.

Objective. The aim of this study is to investigate the effect of occupation-based community rehabilitation on activity daily of living and health-related quality of life of people with disabilities after stroke at home. Method. In this study of three people with disabilities after stroke living at home, A-B-A single-subject design was used. The occupation-based community rehabilitation was implemented during the intervention phase. It included task oriented and feedback, related information education, home environment modification, and community resource network. After applying the intervention, changes in activities of daily living and health-related quality of life were evaluated by the Modified Barthel Index (MBI), the EuroQol-5 dimension (EQ-5D), and the Assessment of Motor and Process Skills (AMPS). Result. After applying the occupation-based community rehabilitation program, all three participants’ daily life activities and quality of life improved. In addition, the occupational performance skills in all participants were maintained. Conclusion. It was confirmed that individual occupational-based community rehabilitation had a positive effect on the activities of daily living and quality of life improvement of the people with disabilities after stroke at home.

Monoclinic-phase VO2 (VO2(M)) has been extensively studied for use in energy-saving smart windows owing to its reversible insulator–metal transition property. At the critical temperature (Tc = 68 °C), the insulating VO2(M) (space group P21/c) is transformed into metallic rutile VO2 (VO2(R) space group P42/mnm). VO2(M) exhibits high transmittance in the near-infrared (NIR) wavelength; however, the NIR transmittance decreases significantly after phase transition into VO2(R) at a higher Tc, which obstructs the infrared radiation in the solar spectrum and aids in managing the indoor temperature without requiring an external power supply. Recently, the fabrication of flexible thermochromic VO2(M) thin films has also attracted considerable attention. These flexible films exhibit considerable potential for practical applications because they can be promptly applied to windows in existing buildings and easily integrated into curved surfaces, such as windshields and other automotive windows. Furthermore, flexible VO2(M) thin films fabricated on microscales are potentially applicable in optical actuators and switches. However, most of the existing fabrication methods of phase-pure VO2(M) thin films involve chamber-based deposition, which typically require a high-temperature deposition or calcination process. In this case, flexible polymer substrates cannot be used owing to the low-thermal-resistance condition in the process, which limits the utilization of flexible smart windows in several emerging applications. In this review, we focus on recent advances in the fabrication methods of flexible thermochromic VO2(M) thin films using vacuum deposition methods and solution-based processes and discuss the optical properties of these flexible VO2(M) thin films for potential applications in energy-saving smart windows and several other emerging technologies.

The sulfurization reaction was investigated as a promising fabrication method for preparing metal sulfide nanomaterials. Traditional sulfurization processes generally require high vacuum systems, high reaction temperatures, and toxic chemicals, utilizing complicated procedures with poor composition and morphology controllability. Herein, a facile method is reported for synthesizing nanostructured copper sulfide using a sulfurization reaction with Na2S at room temperature under non-vacuum conditions. Moreover, we demonstrate that the morphology, composition, and optical properties of nanostructured copper sulfides could be controlled by the Na2S solution concentration and the reaction time. Nanostructured copper sulfides were synthesized in nanospheres, nanoplates, and nanoplate-based complex morphologies with various oxidation states. Furthermore, by comparing the optical properties of nanostructured copper sulfides with different oxidation states, we determined that reflectivity in the near infrared (NIR) region decreases with increasing oxidation states. These results reveal that the Na2S solution concentration and reaction time are key factors for designing nanostructured copper sulfides, providing new insights for synthesis methods of metal sulfide nanomaterials.

Objective The purpose of this study is to introduce a standardised assessment tool by verifying the reliability of the translated Korean version of the Feeding Abilities Assessment (K-FAA), which was developed to suit Korean culture. Methods The research subjects were 65 patients with dementia living in nursing homes. The K-FAA was completed by verifying the suitability of translation and reverse translation. The validity of the K-FAA was established through content validity, while its reliability was analysed based on internal consistency reliability for the items, test–retest reliability and inter-rater reliability. Results The content validity index determined, based on the assessment of professors, occupational therapists, and nurses, was more than .70. Cronbach’s α was more than .929, showing good internal consistency. A test–retest reliability of .884 was derived using Pearson’s correlation coefficient (p

A smart grid (SG) has attracted great attention due to recent environmental problems. SG technologies enable users, such as energy system operators and consumers, to reduce energy consumption and the emission of greenhouse gases, by changing energy infrastructure more efficiently. As a part of the SG, home energy management system (HEMS) has become increasingly important, because energy consumption of a residential sector accounts for a significant amount of total energy consumption. However, a conventional HEMS has some architectural limitations on scalability, reusability, and interoperability. Furthermore, the cost of implementation of a HEMS is very expensive, which leads to the disturbance of the spread of a HEMS. Therefore, this paper proposes an Internet of Things- (IoT-) based HEMS with lightweight photovoltaic (PV) system over dynamic home area networks (DHANs), which enables the construction of a HEMS to be more scalable, reusable, and interoperable. We suggest the techniques for reducing the cost of the HEMS with various perspectives on system, network, and middleware architecture. We designed and implemented the proposed HEMS and conducted a experiment to verify the performance of the proposed system.