Design of a Power-Assist Hemiplegic Wheelchair

Current one-handed manual wheelchairs are difficult to propel because one arm can only provide half the power that is ascertained in a two-handed manual wheelchair. A power-assisted hemiplegic (one-sided paralysis) wheelchair was developed that can effectively be propelled with one arm while remaining maneuverable, lightweight, and foldable. An existing manual wheelchair was minimally modified and fitted with powerassisted components that could alternatively be attached to a wide range of manual wheelchairs. The design implements a motor and gear train to power the wheel on the users affected side, encoders on both rear wheels to track wheel position, and a heel interface on the footrest to control steering. A controls program was developed that analyzes wheel position and steering to respond to the motion of the hand-driven wheel. Extensive testing was performed to ensure design integrity. Testing results showed that the prototype successfully met and exceeded predetermined design specifications based on industry standard testing procedures. The design has the potential to deliver increased freedom to a considerable consumer base

CES-514 Market Evaluation for Colchester Catalyst on the use of Robotic Wheelchairs

1.2 What is a Robotic Wheelchair?........................... 1 1.3 Type of Marketing Research used and sources of data...............

Human-Centric Detection and Mitigation Approach for Various Levels of Cell Phone-Based Driver Distractions

abstract: Driving a vehicle is a complex task that typically requires several physical interactions and mental tasks. Inattentive driving takes a driver’s attention away from the primary task of driving, which can endanger the safety of driver, passenger(s), as well as pedestrians. According to several traffic safety administration organizations, distracted and inattentive driving are the primary causes of vehicle crashes or near crashes. In this research, a novel approach to detect and mitigate various levels of driving distractions is proposed. This novel approach consists of two main phases: i.) Proposing a system to detect various levels of driver distractions (low, medium, and high) using a machine learning techniques. ii.) Mitigating the effects of driver distractions through the integration of the distracted driving detection algorithm and the existing vehicle safety systems. In phase- 1, vehicle data were collected from an advanced driving simulator and a visual based sensor (webcam) for face monitoring. In addition, data were processed using a machine learning algorithm and a head pose analysis package in MATLAB. Then the model was trained and validated to detect different human operator distraction levels. In phase 2, the detected level of distraction, time to collision (TTC), lane position (LP), and steering entropy (SE) were used as an input to feed the vehicle safety controller that provides an appropriate action to maintain and/or mitigate vehicle safety status. The integrated detection algorithm and vehicle safety controller were then prototyped using MATLAB/SIMULINK for validation. A complete vehicle power train model including the driver’s interaction was replicated, and the outcome from the detection algorithm was fed into the vehicle safety controller. The results show that the vehicle safety system controller reacted and mitigated the vehicle safety status-in closed loop real-time fashion. The simulation results show that the proposed approach is efficient, accurate, and adaptable to dynamic changes resulting from the driver, as well as the vehicle system. This novel approach was applied in order to mitigate the impact of visual and cognitive distractions on the driver performance.Dissertation/ThesisDoctoral Dissertation Applied Psychology 201

Get Involved: A Program for Kindergarten Students, Parents, & Teachers to Promote the Development of Motor Skills for Daily School Occupations

In the educational setting today, children are asked to acquire advanced academic skills at a faster pace and earlier age. Children entering the first years of school exhibit variances in their physical maturity levels, which affects both fine and gross motor skills and their performance in daily school occupations. Frequently, young children are unable to keep up with their kindergarten curriculum. Currently, the programs available to work on motor development in kindergarten age students focus solely on pre-writing skills and handwriting instruction. These programs lack information on motor skill development, home program activities, and specific occupations that kindergarten students are expected to complete during a typical school day. An extensive literature review was conducted and pertinent information was gathered to help illustrate the need for the product and guide the development of the product. A manual was developed to provide parents and teachers of kindergarten age students with information and resources for motor skills training and to give children opportunities to practice motor skills in the school and home environments. The manual is divided into three appendices. Appendix A is a parent manual on motor skills development. Appendix B is a teacher manual to focus on motor skills development in the school environment. . Information in the manual includes the developmental progression of motor skills, a quick motor skills screening tool, resource lists for parents and teachers, background information on motor skills areas, occupations children complete in school, an activity guide for the motor skills areas, and a complete reference list. Appendix C was included in the manual to provide teacher workshop information on how to use the manual in the classroom setting. This manual will be a valuable reference tool for parents and teachers of kindergarten age students to assist with motor skill development for occupations completed in the school environment

Human-in-the-Loop Cyber Physical Systems: Modular Designs for Semi-Autonomous Wheelchair Navigation

This project involves the design and development of a prototyping platform and open design framework for a semi-autonomous wheelchair to realize a human-in-the-loop cyber physical system as an assistive technology. The system is designed to assist physically locked-in individuals in navigating indoor environments through the use of modular sensor, communication, and control designs. This enables the user to share control with the wheelchair and allows the system to operate semi-autonomously with human-in-the-loop. The Wheelchair Add-on Modules (WAMs) developed for use in this project are platform-independent and facilitate development and application of semi- autonomous functionality

Systems engineering approaches to safety in transport systems

openDuring driving, driver behavior monitoring may provide useful information to prevent road traffic accidents caused by driver distraction. It has been shown that 90% of road traffic accidents are due to human error and in 75% of these cases human error is the only cause. Car manufacturers have been interested in driver monitoring research for several years, aiming to enhance the general knowledge of driver behavior and to evaluate the functional state as it may drastically influence driving safety by distraction, fatigue, mental workload and attention. Fatigue and sleepiness at the wheel are well known risk factors for traffic accidents. The Human Factor (HF) plays a fundamental role in modern transport systems. Drivers and transport operators control a vehicle towards its destination in according to their own sense, physical condition, experience and ability, and safety strongly relies on the HF which has to take the right decisions. On the other hand, we are experiencing a gradual shift towards increasingly autonomous vehicles where HF still constitutes an important component, but may in fact become the "weakest link of the chain", requiring strong and effective training feedback. The studies that investigate the possibility to use biometrical or biophysical signals as data sources to evaluate the interaction between human brain activity and an electronic machine relate to the Human Machine Interface (HMI) framework. The HMI can acquire human signals to analyse the specific embedded structures and recognize the behavior of the subject during his/her interaction with the machine or with virtual interfaces as PCs or other communication systems. Based on my previous experience related to planning and monitoring of hazardous material transport, this work aims to create control models focused on driver behavior and changes of his/her physiological parameters. Three case studies have been considered using the interaction between an EEG system and external device, such as driving simulators or electronical components. A case study relates to the detection of the driver's behavior during a test driver. Another case study relates to the detection of driver's arm movements according to the data from the EEG during a driver test. The third case is the setting up of a Brain Computer Interface (BCI) model able to detect head movements in human participants by EEG signal and to control an electronic component according to the electrical brain activity due to head turning movements. Some videos showing the experimental results are available at https://www.youtube.com/channel/UCj55jjBwMTptBd2wcQMT2tg.openXXXIV CICLO - INFORMATICA E INGEGNERIA DEI SISTEMI/ COMPUTER SCIENCE AND SYSTEMS ENGINEERING - Ingegneria dei sistemiZero, Enric

The effect of haptic guidance, aging, and initial skill level on motor learning of a steering task

In a previous study, we found that haptic guidance from a robotic steering wheel can improve short-term learning of steering of a simulated vehicle, in contrast to several studies of other tasks that had found that the guidance either impairs or does not aid motor learning. In this study, we examined whether haptic guidance-as-needed can improve long-term retention (across 1 week) of the steering task, with age and initial skill level as independent variables. Training with guidance-as-needed allowed all participants to learn to steer without experiencing large errors. For young participants (age 18–30), training with guidance-as-needed produced better long-term retention of driving skill than did training without guidance. For older participants (age 65–92), training with guidance-as-needed improved long-term retention in tracking error, but not significantly. However, for a subset of less skilled, older subjects, training with guidance-as-needed significantly improved long-term retention. The benefits of guidance-based training were most evident as an improved ability to straighten the vehicle direction when coming out of turns. In general, older participants not only systematically performed worse at the task than younger subjects (errors ∼3 times greater), but also apparently learned more slowly, forgetting a greater percentage of the learned task during the 1 week layoffs between the experimental sessions. This study demonstrates that training with haptic guidance can benefit long-term retention of a driving skill for young and for some old drivers. Training with haptic guidance is more useful for people with less initial skill

Feasibility and validity of a low-cost racing simulator in driving assessment after stroke

There is a myriad of methodologies to assess driving performance after a stroke. These include psychometric tests, driving simulation, questionnaires, and/or road tests. Research-based driving simulators have emerged as a safe, convenient way to assess driving performance after a stroke. Such traditional research simulators are useful in recreating street traffic scenarios, but are often expensive, with limited physics models and graphics rendering. In contrast, racing simulators developed for motorsport professionals and enthusiasts offer high levels of realism, run on consumer-grade hardware, and can provide rich telemetric data. However, most offer limited simulation of traffic scenarios. This pilot study compares the feasibility of research simulation and racing simulation in a sample with minor stroke. We determine that the racing simulator is tolerated well in subjects with a minor stroke. There were correlations between research and racing simulator outcomes with psychometric tests associated with driving performance, such as the Trails Making Test Part A, Snellgrove Maze Task, and the Motricity Index. We found correlations between measures of driving speed on a complex research simulator scenario and racing simulator lap time and maximum tires off track. Finally, we present two models, using outcomes from either the research or racing simulator, predicting road test failure as linked to a previously published fitness-to-drive calculator that uses psychometric screening