Adapted Pool Cue

Our primary goal was to develop an adaptive device to allow a user with limited upper body mobility to more effectively play a game of pool. Within this goal it was important to design around the concept of a least restrictive environment, in order to provide the user with as close to a standard pool playing experience as possible. The device was designed to specifically meet the needs of John Lee, an Assistive Technology Specialist at Cal Poly\u27s Disability Resource Center. Mr. Lee has muscular dystrophy, which limits his upper and lower body mobility and currently requires his use of a wheelchair. Research into the specific needs of our customer, current products, and pool game elements was performed to better understanding of the project goals. From this research and discussion with John Lee, customer requirements and engineering specifications were developed. These were then used to guide the design development process. Several ideation techniques were used, and concepts for a bridge mechanism and supporting bases were created. A rough prototype was developed to test the concepts before our final design direction was decided. Our final design acts as a robust bridge device with a self-supporting base that constrains the path of the pool cue to a set path, allowing the user to focus their effort on providing power to the shot. The base can be rolled around the table, and stabilizing posts are lowered through a cable system when the shot is being made to prevent the base from sliding. The shooting mechanism features horizontal and vertical angle adjustment to allow for proper positioning of the cue, as well as a small cue stop to adjust the allowable travel of the pool cue. A vertical height adjustment is also present to allow the mechanism to reach over the edge of the pool table and for special high angle shots. The shooting mechanism itself features fixed and sliding rollers that act as a modified pool bridge, limiting travel of the pool cue to one axis of motion. The top part of the device can be separated from the base and clamped to the upright post for easier transport. These design choices allow the device to be compatible with any size regulation pool cue. Engineering analysis was performed to select materials and size components for the final device. Parts were specified and costs recorded to keep within the program budget. The device was then manufactured to the specifications shown in the included technical drawings, and testing was performed to verify that the final product met the design requirements. The overall experience was successful, and the final product received strong approval from John Lee. Recommendations for potential future improvement of both manufacturing and design of the device are included, as are as tips for device operation

Development of an Integrated Telerehabilitation Information Management System to Support Remote Wheelchair Prescription

Information technology (IT) is central in providing Telerehabilitation (TR), which enables people with disabilities access to limited number of qualified practitioners with specialty expertise, especially at rural areas. Prior to 2008, most TR utilized non-integrated IT systems to provide its basic infrastructure. Using this approach, data management has to be done manually over multiple non-integrated systems, increasing the possibility of outdated or missing data. An integrated system that is open, flexible, extensible, and cost-effective was designed and developed as a solution to mitigate this problem. The work described in this dissertation elaborates the process of developing such system, called the Versatile and Integrated System for Telerehabilitation (VISYTER). VISYTER was intended to become a platform that is capable of delivering any TR, and was first used to support Remote Wheelchair Prescription (RWP), a TR effort to support clinicians in rural Pennsylvania to prescribe wheeled mobility and seating devices. The development process of VISYTER consisted of three main phases: identification and verification of requirements, validation, and evaluation. The requirement identification and verification phase involved a group of expert clinicians from RWP with the purpose of identifying the requirement of the system to support RWP: a system that can provide real-time teleconsultation and documentation support for prescribing a wheeled mobility intervention. Validation studies were conducted with help from ten individuals, including physicians, clinicians, and suppliers participated to validate VISYTER in their workplaces. All participants agreed that VISYTER can be used to properly support both the teleconsultation and documentation phase of RWP. Afterward, the usability of VISYTER was evaluated through a comparison study with a commonly utilized videoconferencing system in TR, POLYCOM. Twenty-six clinicians participated in a counterbalanced experimental study to measure the difference in usability for completing client assessment tasks using both systems. The study found VISYTER to be more efficient and less prone to error when compared to POLYCOM. Based on these findings, the study concluded that an integrated system could improve the usability TR delivery when compared to non-integrated systems approach

Enhanced Low-Latency Detection of Motor Intention From EEG for Closed-Loop Brain-Computer Interface Applications

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Human robot interaction in a crowded environment

Human Robot Interaction (HRI) is the primary means of establishing natural and affective communication between humans and robots. HRI enables robots to act in a way similar to humans in order to assist in activities that are considered to be laborious, unsafe, or repetitive. Vision based human robot interaction is a major component of HRI, with which visual information is used to interpret how human interaction takes place. Common tasks of HRI include finding pre-trained static or dynamic gestures in an image, which involves localising different key parts of the human body such as the face and hands. This information is subsequently used to extract different gestures. After the initial detection process, the robot is required to comprehend the underlying meaning of these gestures [3]. Thus far, most gesture recognition systems can only detect gestures and identify a person in relatively static environments. This is not realistic for practical applications as difficulties may arise from people‟s movements and changing illumination conditions. Another issue to consider is that of identifying the commanding person in a crowded scene, which is important for interpreting the navigation commands. To this end, it is necessary to associate the gesture to the correct person and automatic reasoning is required to extract the most probable location of the person who has initiated the gesture. In this thesis, we have proposed a practical framework for addressing the above issues. It attempts to achieve a coarse level understanding about a given environment before engaging in active communication. This includes recognizing human robot interaction, where a person has the intention to communicate with the robot. In this regard, it is necessary to differentiate if people present are engaged with each other or their surrounding environment. The basic task is to detect and reason about the environmental context and different interactions so as to respond accordingly. For example, if individuals are engaged in conversation, the robot should realize it is best not to disturb or, if an individual is receptive to the robot‟s interaction, it may approach the person. Finally, if the user is moving in the environment, it can analyse further to understand if any help can be offered in assisting this user. The method proposed in this thesis combines multiple visual cues in a Bayesian framework to identify people in a scene and determine potential intentions. For improving system performance, contextual feedback is used, which allows the Bayesian network to evolve and adjust itself according to the surrounding environment. The results achieved demonstrate the effectiveness of the technique in dealing with human-robot interaction in a relatively crowded environment [7]

\u3cem\u3eGRASP News\u3c/em\u3e: Volume 9, Number 1

The past year at the GRASP Lab has been an exciting and productive period. As always, innovation and technical advancement arising from past research has lead to unexpected questions and fertile areas for new research. New robots, new mobile platforms, new sensors and cameras, and new personnel have all contributed to the breathtaking pace of the change. Perhaps the most significant change is the trend towards multi-disciplinary projects, most notable the multi-agent project (see inside for details on this, and all the other new and on-going projects). This issue of GRASP News covers the developments for the year 1992 and the first quarter of 1993

How a Diverse Research Ecosystem Has Generated New Rehabilitation Technologies: Review of NIDILRR’s Rehabilitation Engineering Research Centers

Over 50 million United States citizens (1 in 6 people in the US) have a developmental, acquired, or degenerative disability. The average US citizen can expect to live 20% of his or her life with a disability. Rehabilitation technologies play a major role in improving the quality of life for people with a disability, yet widespread and highly challenging needs remain. Within the US, a major effort aimed at the creation and evaluation of rehabilitation technology has been the Rehabilitation Engineering Research Centers (RERCs) sponsored by the National Institute on Disability, Independent Living, and Rehabilitation Research. As envisioned at their conception by a panel of the National Academy of Science in 1970, these centers were intended to take a “total approach to rehabilitation”, combining medicine, engineering, and related science, to improve the quality of life of individuals with a disability. Here, we review the scope, achievements, and ongoing projects of an unbiased sample of 19 currently active or recently terminated RERCs. Specifically, for each center, we briefly explain the needs it targets, summarize key historical advances, identify emerging innovations, and consider future directions. Our assessment from this review is that the RERC program indeed involves a multidisciplinary approach, with 36 professional fields involved, although 70% of research and development staff are in engineering fields, 23% in clinical fields, and only 7% in basic science fields; significantly, 11% of the professional staff have a disability related to their research. We observe that the RERC program has substantially diversified the scope of its work since the 1970’s, addressing more types of disabilities using more technologies, and, in particular, often now focusing on information technologies. RERC work also now often views users as integrated into an interdependent society through technologies that both people with and without disabilities co-use (such as the internet, wireless communication, and architecture). In addition, RERC research has evolved to view users as able at improving outcomes through learning, exercise, and plasticity (rather than being static), which can be optimally timed. We provide examples of rehabilitation technology innovation produced by the RERCs that illustrate this increasingly diversifying scope and evolving perspective. We conclude by discussing growth opportunities and possible future directions of the RERC program

Optimizing Common Spatial Pattern for a Motor Imagerybased BCI by Eigenvector Filteration

One of the fundamental criterion for the successful application of a brain-computer interface (BCI) system is to extract significant features that confine invariant characteristics specific to each brain state. Distinct features play an important role in enabling a computer to associate different electroencephalogram (EEG) signals to different brain states. To ease the workload on the feature extractor and enhance separability between different brain states, the data is often transformed or filtered to maximize separability before feature extraction. The common spatial patterns (CSP) approach can achieve this by linearly projecting the multichannel EEG data into a surrogate data space by the weighted summation of the appropriate channels. However, choosing the optimal spatial filters is very significant in the projection of the data and this has a direct impact on classification. This paper presents an optimized pattern selection method from the CSP filter for improved classification accuracy. Based on the hypothesis that values closer to zero in the CSP filter introduce noise rather than useful information, the CSP filter is modified by analyzing the CSP filter and removing/filtering the degradative or insignificant values from the filter. This hypothesis is tested by comparing the BCI results of eight subjects using the conventional CSP filters and the optimized CSP filter. In majority of the cases the latter produces better performance in terms of the overall classification accuracy