Assistive Devices For Individuals Research Articles

SIGN LANGUAGE DETECTION USING ACTION RECOGNITION WITH PYTHON

Sign language detection plays a crucial role in bridging the communication gap between individuals with hearing impairments and the rest of the population. This study presents a novel approach for sign language detection using action recognition and LSTM deep learning models implemented in Python. The proposed system aims to accurately recognize and interpret sign language gestures in real-time. The research focuses on leveraging the power of deep learning techniques, specifically the Long Short-Term Memory (LSTM) architecture, to effectively capture temporal dependencies and classify sign language gestures. A comprehensive dataset comprising a wide range of sign language gestures is collected and preprocessed to train and evaluate the LSTM model. The pipeline for sign language detection consists of several stages, including video acquisition, preprocessing, feature extraction, and model training. In the pre-processing stage, the acquired video data is segmented into individual frames, and various image processing techniques are applied to enhance the quality and remove noise. Next, robust features are extracted from the preprocessed frames using techniques like optical flow or deep learning-based feature extraction methods. The LSTM model is then trained on the extracted features to learn the temporal dynamics of sign language gestures. Transfer learning is also explored to leverage pre-trained models on large-scale action recognition datasets. The trained model is evaluated using a comprehensive set of metrics, including accuracy, precision, recall, and F1-score, to assess its performance. Experimental results demonstrate the effectiveness of the proposed approach in accurately recognizing sign language gestures. The LSTM-based deep learning model achieves a high accuracy rate, showcasing its capability to handle the temporal nature of sign language. The system exhibits real-time performance, enabling it to be deployed in various applications, such as real-time interpretation tools or assistive devices for individuals with hearing impairments

Home modifications and repurposing: perspectives on the accessibility, affordability, and attractiveness

Purpose Society has progressed in universal design guidelines and assistive devices for individuals with disabilities yet challenges due to affordability and attractiveness concerns remain to incorporate them into existing residences. Repurposing (i.e., replacing or adapting problematic fixtures or spaces, with others not originally intended for that purpose) may be the action to address the concerns of consumers. The purpose of this study was to elicit information on problems and solutions regarding home modifications and identify ways that consumers and professionals implement repurposing, that residents deemed accessible, affordable, and attractive. Methods This convergent parallel design study consisted of focus groups who rated images of repurposed spaces. The focus groups included consumers with functional mobility and visual limitations (n = 8); and professionals who devised or recommended home modifications (n = 8). Participants reviewed three images of home modification solutions and completed a Likert-scale rating based on accessibility and attractiveness. Results Focus group data indicated that high contrast, heights of fixtures, doors, and flooring – all pose threats to accessibility in the home. Consumers placed more value on attractiveness than professionals. Participants were aware and receptive to repurposing as a home modification technique but focused their discussion on adaptations. Conclusion Overall, adoption and implementation of home modifications promote accessibility, but professionals need to consider individualized needs and preferences, before suggesting modifications. IMPLICATIONS FOR REHABILITATION Repurposing existing spaces, features, and fixtures can serve as a method of home modification. Consumer home modification recommendations are perceived to be more effective when collaboratively and individually developed with professionals. Data gathered through focus groups can be valuable for informing practice and research in home modifications.

ARFurniture: Augmented Reality Interior Decoration Style Colorization

Augmented Reality (AR) can seamlessly create an illusion of virtual elements blended into the real world scene, which is one of the most fascinating human-machine interaction technologies. AR has been utilized in a variety of real-life applications including immersive collaborative gaming, fashion appreciation, interior design, and assistive devices for individuals with vision impairments. This paper contributes a real-time AR application, ARFurniture, which will allow the users to envision furniture-ofinterests in different colors and different styles, all from their smart devices. The core software architecture consists of deep-learning based semantic segmentation and fast-speed color transformation. Our software architecture allows the user prompt the system to colorize the style of the furniture-of-interest within the scene on their mobile devices, and has been successfully deployed on mobile devices. In addition, using eye gaze as a pointing indicator, a head-mounted user-centric augmented reality based indoor decoration style colorization concept is discussed. Related algorithms, system design, and simulation results for ARFurniture are presented. Furthermore, a no-reference image quality measure, Naturalness Image Quality Evaluator (NIQE), was utilized to evaluate the immersiveness and naturalness of ARFuniture. The results demonstrate that ARFurniture has game-changing value to enhance user experience in indoor decoration.

Myex: A M<small>ATLAB</small> Interface for the Tobii Eyex Eye-Tracker

Myex is a Matlab interface for the Tobii EyeX eye-tracker. It allows Matlab users to receive incoming data from the eye-tracker, by providing a data buffer that can receive data from the EyeX, and be queried by the user on demand. Myex enables Matlab users to take advantage of low-cost, portable eye-tracking technology, ideal for use in gaze-contingent psychophysical paradigms, or for users looking to develop assistive devices for individuals with impaired mobility.

Mobile Standards-Based Traffic Light Detection in Assistive Devices for Individuals with Color-Vision Deficiency

Considering the substantial population affected by some form of color-vision deficiency (CVD), reliable traffic control signal head light detection is an important problem for driver-assistance systems. While a large number of technologies can be used to localize traffic lights, without drastic changes in infrastructure, only visual information can be used in identifying the status of the light. In addition, traffic light detection is not currently integrated into any driver-assistance systems, making driving for individuals with CVD (where permitted) dangerous to other drivers, pedestrians, and themselves. This paper presents a robust, traffic-standards-based, and computationally efficient method for detecting the status of the traffic lights without relying on Global Positioning System, lidar, radar information, or prior (map-based) knowledge. To the extent of our knowledge, this is the first work to use official Institute of Transportation Engineers (U.S.) and British Standards Institute (European Union) standards for defining traffic light colors, as well as integrating a number of fail-safe mechanisms designed to prevent erroneous detection. The algorithm can be easily ported over to an embedded smart camera platform and used as a windshield-mounted driver-assistance device by individuals with CVD. The system can accurately identify the status of the light at 400 ft away from the intersection, reliably detecting solid, faulty, arrow, and high-visibility signal lights. Over 50 h of video (over 2000 intersections) were tested with the system, containing intersections with one to four traffic lights, governing different lanes of traffic, with 97.5% accuracy of solid light detection.

Exploration of the neural correlates of cerebral palsy for sensorimotor BCI control.

Cerebral palsy (CP) includes a broad range of disorders, which can result in impairment of posture and movement control. Brain-computer interfaces (BCIs) have been proposed as assistive devices for individuals with CP. Better understanding of the neural processing underlying motor control in affected individuals could lead to more targeted BCI rehabilitation and treatment options. We have explored well-known neural correlates of movement, including event-related desynchronization (ERD), phase synchrony, and a recently-introduced measure of phase dynamics, in participants with CP and healthy control participants. Although present, significantly less ERD and phase locking were found in the group with CP. Additionally, inter-group differences in phase dynamics were also significant. Taken together these findings suggest that users with CP exhibit lower levels of motor cortex activation during motor imagery, as reflected in lower levels of ongoing mu suppression and less functional connectivity. These differences indicate that development of BCIs for individuals with CP may pose additional challenges beyond those faced in providing BCIs to healthy individuals.

Neuro-cognitively inspired haptic user interfaces

Haptic systems and devices are a recent addition to multimodal systems. These devices have widespread applications such as surgical simulations, medical and procedural training, scientific visualizations, assistive and rehabilitative devices for individuals who have physical or neurological impediments and assistive devices for individuals who are blind. While the potential of haptics in natural human machine interaction is undisputable, the realization of such means is still a long way ahead. There are considerable research challenges to development of natural haptic interfaces. The study of human tactile abilities is a recent endeavor and many of the available systems still do not incorporate the domain knowledge of psychophysics, biomechanics and neurological elements of haptic perception. Development of smart and effective haptic interfaces and devices requires extensive studies that link perceptual phenomena with measurable parameters and incorporation of such domain knowledge in the engineering of haptic interfaces. This paper presents design, development and usability testing of a neuro-cognitively inspired haptic user interface for individuals who are blind. The proposed system design is inspired by neuro-cognitive basis of haptic perception and incorporates the computational aspects and requirements of multimodal information processing system. Usability testing of the system suggests that a biologically inspired haptic user interfaces may form a powerful paradigm for haptic user interface design.