Development of an artificial intelligence algorithm for the analysis of wheelchair movements

Monitoring wheelchair user movement is an essential task for assessing a wheelchair user’s mobility and helping them maintain an active lifestyle. Research has shown that increased mobility leads to healthier overall lifestyles, and that people with disabilities are at an increased risk for sedentary lifestyles and the health problems associated with that lifestyle, including cardiovascular disease, obesity, and the development of pressure ulcers (WHO, 2014). Existing technology for analyzing wheelchair user mobility data requires the use of external sensors that must be purchased and maintained (Warms & Belza, 2004). To improve the ease by which mobility data is maintained and analyzed, a wheelchair user can utilize existing technology, such as smart mobile devices, to gather and analyze motion data. This study will focus on the development of a recurrent neural network (RNN) that is trained using wheelchair user data collected from smart devices attached to the wheelchair or wheelchair user. The benefit of collecting data this way is that it does not require the use of additional sensors or equipment, as most wheelchair users will already have access to a smart device capable of collecting movement data. The study found that it was feasible to meaningfully analyze data gathered from a smart device using an RNN. The raw data is analyzed with the RNN to gather information about the mobility of a wheelchair user. The final analysis includes the total time spent moving, number of bouts of movement, and the longest bout of movement. This resulting data could be used by a wheelchair user or healthcare professional to help assess healthy lifestyle habits

Quantifying Electric Powered Wheelchair Driving Ability

Electric Powered Wheelchairs (EPWs) are complex rehabilitation technology indispensable for independent mobility of people with disabilities. Clinical driving assessments are critical for the provision of EPWs and training EPW users to promote safe mobility. Multiple studies have illustrated that problems with driving EPWs are associated with impairments in motor, sensory and cognitive functions. Existing EPW driving assessment tools provide rehabilitation professionals little insight into the selection of specific training strategies for this key activity based on the users’ impairments. The primary objective of this study is to develop clinical tools to quantify users’ motor, sensory and cognitive impairments that are commonly evaluated during an EPW driving evaluation. The secondary objective is to develop an assistance based scoring system to evaluate EPW driving in the clinic and develop a set of clinically-relevant objective metrics that can serve as an outcome measure of EPW driving evaluation and training. This motivated the development and content validation of two clinical tools, the Powered Mobility Screening Tool (PMST) and the Powered Mobility Clinical Driving Assessment (PMCDA). A set of objective variables termed Quantitative Driving Metrics (QDM) were developed as digital markers for user’s driving ability. Preliminary psychometric evaluations of these movement-based variables in an EPW driving simulator revealed high stability and construct validity. Content validity of QDM was established through expert interviews. Real-world QDM computed using two modalities (passive motion capture and inertial measurement unit with 9 axis motion sensors) revealed high concurrent validity between the two modalities. A pilot study demonstrated the feasibility of gathering data to compute QDM in a wheelchair clinic. Psychometric evaluation revealed that the PMCDA and QDM have acceptable measurement properties for use in a clinical setting

Context-Based Rider Assistant System for Two Wheeled Self-Balancing Vehicles

Personal mobility devises become more and more popular last years. Gyroscooters, two wheeled self-balancing vehicles, wheelchair, bikes, and scooters help people to solve the first and last mile problems in big cities. To help people with navigation and to increase their safety the intelligent rider assistant systems can be utilized that are used the rider personal smartphone to form the context and provide the rider with the recommendations. We understand the context as any information that characterize current situation. So, the context represents the model of current situation. We assume that rider mounts personal smartphone that allows it to track the rider face using the front-facing camera. Modern smartphones allow to track current situation using such sensors as: GPS / GLONASS, accelerometer, gyroscope, magnetometer, microphone, and video cameras. The proposed rider assistant system uses these sensors to capture the context information about the rider and the vehicle and generates context-oriented recommendations. The proposed system is aimed at dangerous situation detection for the rider, we are considering two dangerous situations: drowsiness and distraction. Using the computer vision methods, we determine parameters of the rider face (eyes, nose, mouth, head pith and rotation angles) and based on analysis of this parameters detect the dangerous situations. The paper presents a comprehensive related work analysis in the topic of intelligent driver assistant systems and recommendation generation, an approach to dangerous situation detection and recommendation generation is proposed, and evaluation of the distraction dangerous state determination for personal mobility device riders

Enabling the Development and Implementation of Digital Twins : Proceedings of the 20th International Conference on Construction Applications of Virtual Reality

Welcome to the 20th International Conference on Construction Applications of Virtual Reality (CONVR 2020). This year we are meeting on-line due to the current Coronavirus pandemic. The overarching theme for CONVR2020 is "Enabling the development and implementation of Digital Twins". CONVR is one of the world-leading conferences in the areas of virtual reality, augmented reality and building information modelling. Each year, more than 100 participants from all around the globe meet to discuss and exchange the latest developments and applications of virtual technologies in the architectural, engineering, construction and operation industry (AECO). The conference is also known for having a unique blend of participants from both academia and industry. This year, with all the difficulties of replicating a real face to face meetings, we are carefully planning the conference to ensure that all participants have a perfect experience. We have a group of leading keynote speakers from industry and academia who are covering up to date hot topics and are enthusiastic and keen to share their knowledge with you. CONVR participants are very loyal to the conference and have attended most of the editions over the last eighteen editions. This year we are welcoming numerous first timers and we aim to help them make the most of the conference by introducing them to other participants

Haptic interface based on tactile sensors for assistive devices

Tesis leída el 14 de febrero de 2018.Los países desarrollados deben hacer frente al creciente envejecimiento de su población. Un proceso de envejecimiento adecuado requiere capacidad funcional en las actividades del día a día. Así, las tecnologías de asistencia deben lidiar con uno de los principales problemas asociados con la edad: el deterioro de la movilidad. Los bastones y los andadores son prescritos para personas con movilidad reducida, pero aún con capacidad de andar. Sin embargo, hay un considerable número de personas en la tercera edad que necesitan otro tipo de ayuda. En este sentido, las sillas de ruedas eléctricas suponen un medio para el aumento de la participación y de la actividad de sus usuarios. Normalmente, estas sillas se conducen mediante un joystick alojado al final de uno de los reposabrazos. No obstante, este dispositivo no es adecuado para todo tipo de usuarios. Algunos de ellos lo encuentran difícil de usar y, para otros, su manejo no es posible y necesitan de la asistencia de otra persona (aquellos que padecen ciertas enfermedades del sistema nervioso, lesiones en la médula espinal, discapacidad mental, etc.). De esta manera, hay casos en que se requiere la ayuda de un cuidador que desplace la silla. Empujar una silla de ruedas de forma habitual produce distintos tipos de lesiones, por lo que es interesante que los asistentes o cuidadores también se beneficien de las ventajas de las sillas de ruedas eléctricas. En este caso, la solución más común consiste en otro joystick situado en la parte trasera de la silla. Como se ha apuntado anteriormente, este no es un dispositivo cómodo e intuitivo para muchos usuarios. Con respecto a la investigación, con frecuencia los dispositivos de asistencia propuestos basan su interfaz con el asistente en sensores de fuerza. Estos componentes son caros y suponen por tanto una barrera de cara a que el dispositivo llegue al mercado

Artificial Intelligence-based Smarter Accessibility Evaluations for Comprehensive and Personalized Assessment

The research focuses on utilizing artificial intelligence (AI) and machine learning (ML) algorithms to enhance accessibility for people with disabilities (PwD) in three areas: public buildings, homes, and medical devices. The overarching goal is to improve the accuracy, reliability, and effectiveness of accessibility evaluation systems by leveraging smarter technologies. For public buildings, the challenge lies in developing an accurate and reliable accessibility evaluation system. AI can play a crucial role by analyzing data, identifying potential barriers, and assessing the accessibility of various features within buildings. By training ML algorithms on relevant data, the system can learn to make accurate predictions about the accessibility of different spaces and help policymakers and architects design more inclusive environments. For private places such as homes, it is essential to have a person-focused accessibility evaluation system. By utilizing machine learning-based intelligent systems, it becomes possible to assess the accessibility of individual homes based on specific needs and requirements. This personalized approach can help identify barriers and recommend modifications or assistive technologies that can enhance accessibility and independence for PwD within their own living spaces. The research also addresses the intelligent evaluation of healthcare devices in the home. Many PwD rely on medical devices for their daily living, and ensuring the accessibility and usability of these devices is crucial. AI can be employed to evaluate the accessibility features of medical devices, provide recommendations for improvement, and even measure their effectiveness in supporting the needs of PwD. Overall, this research aims to enhance the accuracy and reliability of accessibility evaluation systems by leveraging AI and ML technologies. By doing so, it seeks to improve the quality of life for individuals with disabilities by enabling increased independence, fostering social inclusion, and promoting better accessibility in public buildings, private homes, and medical devices

Estudio y evaluación de plataformas de distribución de cómputo intensivo sobre sistemas externos para sistemas empotrados.

Falta palabras claveNowadays, the capabilities of current embedded systems are constantly increasing, having a wide range of applications. However, there are a plethora of intensive computing tasks that, because of their hardware limitations, are unable to perform successfully. Moreover, there are innumerable tasks with strict deadlines to meet (e.g. Real Time Systems). Because of that, the use of external platforms with sufficient computing power is becoming widespread, especially thanks to the advent of Cloud Computing in recent years. Its use for knowledge sharing and information storage has been demonstrated innumerable times in the literature. However, its core properties, such as dynamic scalability, energy efficiency, infinite resources... amongst others, also make it the perfect candidate for computation off-loading. In this sense, this thesis demonstrates this fact in applying Cloud Computing in the area of Robotics (Cloud Robotics). This is done by building a 3D Point Cloud Extraction Platform, where robots can offload the complex stereo vision task of obtaining a 3D Point Cloud (3DPC) from Stereo Frames. In addition to this, the platform was applied to a typical robotics application: a Navigation Assistant. Using this case, the core challenges of computation offloading were thoroughly analyzed: the role of communication technologies (with special focus on 802.11ac), the role of offloading models, how to overcome the problem of communication delays by using predictive time corrections, until what extent offloading is a better choice compared to processing on board... etc. Furthermore, real navigation tests were performed, showing that better navigation results are obtained when using computation offloading. This experience was a starting point for the final research of this thesis: an extension of Amdahl’s Law for Cloud Computing. This will provide a better understanding of Computation Offloading’s inherent factors, especially focused on time and energy speedups. In addition to this, it helps to make some predictions regarding the future of Cloud Computing and computation offloading