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Many individuals who are in need of mobility assistance do not have access to the proper wheelchair for their type of mobility disability. There is growing research towards creating smart wheelchairs using a variety of methods, such as biopotential signals or eye tracking for input and LiDAR, ultrasonic sensors, or using a camera to create a map or track position. There have been other methods, such as voice control, sip and puff, and hand gestures, but there are disadvantages of these that can limit their usefulness. Smart wheelchairs should account for collisions, but also emphasize the safety and comfort of the user. In this paper, we review and classify state-of-the-art research in smart wheelchairs. Many machine learning models are used for various parts of wheelchairs, from mapping and signal processing to input classification. Smart wheelchairs rely on various hardware devices, such as eye trackers, electrode caps, EMG armbands, RPLidar, RGB-cameras, and ultrasonic sensors. Some hybrid models use a combination of methods to account for some of their limitations. Some research has leaned towards training games to help teach users. Future work should include improvement of classification methods for various input signals and improvement on the accessibility of the technology. 

Assistive robots are robots that are designed to help people with disa-bilities in their daily lives. These robots often are either too simple, and thus can-not meet the needs of the user, or are too complex, and thus become overly ex-pensive or unreliable. To mitigate this problem, we propose a system of multi-robot coordination that allows several heterogeneous robots to work coopera-tively to assist an individual. We design care task workflows that cater to the needs of a specific patient, outlining the tasks needed for the user as well as the robots that can complete each task. We then build a ROS simulation to test our coordination methods on two example workflows for PTSD care routines. Our experiments show that our coordination successfully navigates each robot to its task location at the proper time, thus showing the feasibility of these methods for multi-assistive-robot coordination. 

Robotic cooking can alter both home and commercial kitchens by automat-ing and improving a variety of cooking operations. The incorporation of modern technology, such as robot manipulation, computer vision, deep learning, modal sensors, and other machine learning techniques, allows these robots to perform difficult culinary operations with accuracy and consisten-cy. However, several challenges still exist in adapting robotic systems to the diverse tools and techniques used in cooking. Robots need to use a wide ar-ray of kitchen tools designed for humans, such as knives, spatulas, and whisks. This requires not only the ability to grasp and manipulate these tools but also the adaptability to switch between them efficiently and use them correctly in different cooking contexts. This paper reviews the latest devel-opments in robotic cooking platforms, examining their design, performance, and public perception. It also covers various technologies critical for building robotic chefs, categorizing these advancements into need and importance, emerging technologies, different techniques, and future challenges. Further-more, it addresses the technical and practical obstacles that currently hinder their widespread implementation. 

The concept of Smart Glasses has evolved since the introduction of early prototypes of the 1990s, only to gain wide acceptance recently. The global smart glasses market has recently experienced significant growth and is pre-dicted for further expansion. They can be tailored to many types of consum-er classes and industries, with several benefits. Like other IoT devices, Smart Glasses may be embedded with sensors to gather data which is then shared with other devices. Machine learning algorithms can be used for processing, sensor fusion, or classification and decision-making. Smart Glasses can also enhance productivity for medical practitioners, allowing them to view patient records or assist them during surgical procedures. New apps utilization such eyewear can also be an assistive technology, enhancing the quality of life for People with Disabilities. The paper provides a review of recent research on the applications of smart glasses, address critical research challenges, and the status of current smart glasses on the market. It also highlights key research issues that should be addressed in the short and long term to bring these powerful tools into mainstream usage. 

Millions of people with hearing disabilities use sign language for communication, creating a communication gap with those who are not fluent in ASL (American Sign Language). This paper aims to introduce an ASL interpreter system using a smart-glasses-based augmented reality system. We begin by introducing and comparing two models that translate spoken language into ASL poses. The first system translates spoken text to ASL Gloss, an intermediate representation, before generating ASL poses. The second system directly translates the text to ASL poses. Our analysis shows that using ASL Gloss as an intermediate step significantly improves the translation speed. We then explore a system of encoding ASL pose videos for display on smart glasses. The chosen translation method has a BLEU score of 66.5801 and a rate of 1.825 milliseconds per gloss translation. Our algorithm for mapping gloss text to ASL videos obtained a mean squared error of 0.05, indicating that our system has good translational accuracy and a low mapping error. 

This survey paper aims to provide an overview of the current state of EEG (Electroencephalography) signal technology as it relates to people with disabilities. It will highlight the various methods and techniques employed, discussing their advantages and disadvantages. The paper will also examine the applications of EEG technology in assisting individuals with disabilities, specifically focusing on Brain-Computer Interfaces (BCIs) and assistive device control. By understanding the current state of EEG signal technology, we can identify the opportunities and challenges involved in utilizing this technology to improve the lives of people with disabilities