Matching Items (5)
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- All Subjects: Biomedical Engineering
- All Subjects: accessibility
- Genre: Academic theses
Description
Through decades of clinical progress, cochlear implants have brought the world of speech and language to thousands of profoundly deaf patients. However, the technology has many possible areas for improvement, including providing information of non-linguistic cues, also called indexical properties of speech. The field of sensory substitution, providing information relating one sense to another, offers a potential avenue to further assist those with cochlear implants, in addition to the promise they hold for those without existing aids. A user study with a vibrotactile device is evaluated to exhibit the effectiveness of this approach in an auditory gender discrimination task. Additionally, preliminary computational work is included that demonstrates advantages and limitations encountered when expanding the complexity of future implementations.
ContributorsButts, Austin McRae (Author) / Helms Tillery, Stephen (Thesis advisor) / Berisha, Visar (Committee member) / Buneo, Christopher (Committee member) / McDaniel, Troy (Committee member) / Arizona State University (Publisher)
Created2015
Description
Presented below is the design and fabrication of prosthetic components consisting of an attachment, tactile sensing, and actuator systems with Fused Filament Fabrication (FFF) technique. The attachment system is a thermoplastic osseointegrated upper limb prosthesis for average adult trans-humeral amputation with mechanical properties greater than upper limb skeletal bone. The prosthetic designed has: a one-step surgical process, large cavities for bone tissue ingrowth, uses a material that has an elastic modulus less than skeletal bone, and can be fabricated on one system.
FFF osseointegration screw is an improvement upon the current two-part osseointegrated prosthetics that are composed of a fixture and abutment. The current prosthetic design requires two invasive surgeries for implantation and are made of titanium, which has an elastic modulus greater than bone. An elastic modulus greater than bone causes stress shielding and overtime can cause loosening of the prosthetic.
The tactile sensor is a thermoplastic piezo-resistive sensor for daily activities for a prosthetic’s feedback system. The tactile sensor is manufactured from a low elastic modulus composite comprising of a compressible thermoplastic elastomer and conductive carbon. Carbon is in graphite form and added in high filler ratios. The printed sensors were compared to sensors that were fabricated in a gravity mold to highlight the difference in FFF sensors to molded sensors. The 3D printed tactile sensor has a thickness and feel similar to human skin, has a simple fabrication technique, can detect forces needed for daily activities, and can be manufactured in to user specific geometries.
Lastly, a biomimicking skeletal muscle actuator for prosthetics was developed. The actuator developed is manufactured with Fuse Filament Fabrication using a shape memory polymer composite that has non-linear contractile and passive forces, contractile forces and strains comparable to mammalian skeletal muscle, reaction time under one second, low operating temperature, and has a low mass, volume, and material costs. The actuator improves upon current prosthetic actuators that provide rigid, linear force with high weight, cost, and noise.
FFF osseointegration screw is an improvement upon the current two-part osseointegrated prosthetics that are composed of a fixture and abutment. The current prosthetic design requires two invasive surgeries for implantation and are made of titanium, which has an elastic modulus greater than bone. An elastic modulus greater than bone causes stress shielding and overtime can cause loosening of the prosthetic.
The tactile sensor is a thermoplastic piezo-resistive sensor for daily activities for a prosthetic’s feedback system. The tactile sensor is manufactured from a low elastic modulus composite comprising of a compressible thermoplastic elastomer and conductive carbon. Carbon is in graphite form and added in high filler ratios. The printed sensors were compared to sensors that were fabricated in a gravity mold to highlight the difference in FFF sensors to molded sensors. The 3D printed tactile sensor has a thickness and feel similar to human skin, has a simple fabrication technique, can detect forces needed for daily activities, and can be manufactured in to user specific geometries.
Lastly, a biomimicking skeletal muscle actuator for prosthetics was developed. The actuator developed is manufactured with Fuse Filament Fabrication using a shape memory polymer composite that has non-linear contractile and passive forces, contractile forces and strains comparable to mammalian skeletal muscle, reaction time under one second, low operating temperature, and has a low mass, volume, and material costs. The actuator improves upon current prosthetic actuators that provide rigid, linear force with high weight, cost, and noise.
ContributorsLathers, Steven (Author) / La Belle, Jeffrey (Thesis advisor) / Vowels, David (Committee member) / Lockhart, Thurmon (Committee member) / Abbas, James (Committee member) / McDaniel, Troy (Committee member) / Arizona State University (Publisher)
Created2017
Description
One of the long-standing issues that has arisen in the sports medicine field is identifying the ideal methodology to optimize recovery following anterior cruciate ligament reconstruction (ACLR). The perioperative period for ACLR is notoriously heterogeneous in nature as it consists of many variables that can impact surgical outcomes. While there has been extensive literature published regarding the efficacy of various recovery and rehabilitation topics, it has been widely acknowledged that certain modalities within the field of ACLR rehabilitation need further high-quality evidence to support their use in clinical practice, such as blood flow restriction (BFR) training. BFR training involves the application of a tourniquet-like cuff to the proximal aspect of a limb prior to exercise; the cuff is inflated so that it occludes venous flow but allows arterial inflow. BFR is usually combined with low-intensity (LI) resistance training, with resistance as low as 20% of one-repetition maximum (1RM). LI-BFR has been used as an emerging clinical modality to combat postoperative atrophy of the quadriceps muscles for those who have undergone ACLR, as these individuals cannot safely tolerate high muscular tension exercise after surgery. Impairments of the quadriceps are the major cause of poor functional status of patients following an otherwise successful ACLR procedure; however, these impairments can be mitigated with preoperative rehabilitation done before surgery. It was hypothesized that the use of a preoperative LI-BFR training protocol could help improve postoperative outcomes following ACLR; primarily, strength and hypertrophy of the quadriceps. When compared with a SHAM control group, subjects who were randomized to a BFR intervention group made greater preoperative strength gains in the quadriceps and recovered quadriceps mass at an earlier timepoint than that of the SHAM group aftersurgery; however, the gains made in strength were not able to be maintained in the 8-week postoperative period. While these results do not support the use of LI-BFR from the short-term perspective after ACLR, follow-up data will be used to investigate trends in re-injury and return to sport rates to evaluate the efficacy of the use of LI-BFR from a long-term perspective.
ContributorsGlattke, Kaycee Elizabeth (Author) / Lockhart, Thurmon (Thesis advisor) / McDaniel, Troy (Committee member) / Banks, Scott (Committee member) / Peterson, Daniel (Committee member) / Lee, Hyunglae (Committee member) / Arizona State University (Publisher)
Created2022
Description
Over the past decade, advancements in neural networks have been instrumental in achieving remarkable breakthroughs in the field of computer vision. One of the applications is in creating assistive technology to improve the lives of visually impaired people by making the world around them more accessible. A lot of research in convolutional neural networks has led to human-level performance in different vision tasks including image classification, object detection, instance segmentation, semantic segmentation, panoptic segmentation and scene text recognition. All the before mentioned tasks, individually or in combination, have been used to create assistive technologies to improve accessibility for the blind.
This dissertation outlines various applications to improve accessibility and independence for visually impaired people during shopping by helping them identify products in retail stores. The dissertation includes the following contributions; (i) A dataset containing images of breakfast-cereal products and a classifier using a deep neural (ResNet) network; (ii) A dataset for training a text detection and scene-text recognition model; (iii) A model for text detection and scene-text recognition to identify product images using a user-controlled camera; (iv) A dataset of twenty thousand products with product information and related images that can be used to train and test a system designed to identify products.
This dissertation outlines various applications to improve accessibility and independence for visually impaired people during shopping by helping them identify products in retail stores. The dissertation includes the following contributions; (i) A dataset containing images of breakfast-cereal products and a classifier using a deep neural (ResNet) network; (ii) A dataset for training a text detection and scene-text recognition model; (iii) A model for text detection and scene-text recognition to identify product images using a user-controlled camera; (iv) A dataset of twenty thousand products with product information and related images that can be used to train and test a system designed to identify products.
ContributorsPatel, Akshar (Author) / Panchanathan, Sethuraman (Thesis advisor) / Venkateswara, Hemanth (Thesis advisor) / McDaniel, Troy (Committee member) / Arizona State University (Publisher)
Created2020
Description
Individuals with voice disorders experience challenges communicating daily. These challenges lead to a significant decrease in the quality of life for individuals with dysphonia. While voice amplification systems are often employed as a voice-assistive technology, individuals with voice disorders generally still experience difficulties being understood while using voice amplification systems. With the goal of developing systems that help improve the quality of life of individuals with dysphonia, this work outlines the landscape of voice-assistive technology, the inaccessibility of state-of-the-art voice-based technology and the need for the development of intelligibility improving voice-assistive technologies designed both with and for individuals with voice disorders. With the rise of voice-based technologies in society, in order for everyone to participate in the use of voice-based technologies individuals with voice disorders must be included in both the data that is used to train these systems and the design process. An important and necessary step towards the development of better voice assistive technology as well as more inclusive voice-based systems is the creation of a large, publicly available dataset of dysphonic speech. To this end, a web-based platform to crowdsource voice disorder speech was developed to create such a dataset. This dataset will be released so that it is freely and publicly available to stimulate research in the field of voice-assistive technologies. Future work includes building a robust intelligibility estimation model, as well as employing that model to measure, and therefore enhance, the intelligibility of a given utterance. The hope is that this model will lead to the development of voice-assistive technology using state-of-the-art machine learning models to help individuals with voice disorders be better understood.
ContributorsMoore, Meredith Kay (Author) / Panchanathan, Sethuraman (Thesis advisor) / Berisha, Visar (Committee member) / McDaniel, Troy (Committee member) / Venkateswara, Hemanth (Committee member) / Arizona State University (Publisher)
Created2020