Matching Items (10)
- All Subjects: deep learning
- All Subjects: Rehabilitation
- Creators: McDaniel, Troy
Development of a Wearable Haptic Feedback System for Use in Lower-Limb Prosthetics: Proof of Concept and Verification
Skin and muscle receptors in the leg and foot provide able-bodied humans with force and position information that is crucial for balance and movement control. In lower-limb amputees however, this vital information is either missing or incomplete. Amputees typically compensate for the loss of sensory information by relying on haptic feedback from the stump-socket interface. Unfortunately, this is not an adequate substitute. Areas of the stump that directly interface with the socket are also prone to painful irritation, which further degrades haptic feedback. The lack of somatosensory feedback from prosthetic legs causes several problems for lower-limb amputees. Previous studies have established that the lack of adequate sensory feedback from prosthetic limbs contributes to poor balance and abnormal gait kinematics. These improper gait kinematics can, in turn, lead to the development of musculoskeletal diseases. Finally, the absence of sensory information has been shown to lead to steeper learning curves and increased rehabilitation times, which hampers amputees from recovering from the trauma. In this study, a novel haptic feedback system for lower-limb amputees was develped, and studies were performed to verify that information presented was sufficiently accurate and precise in comparison to a Bertec 4060-NC force plate. The prototype device consisted of a sensorized insole, a belt-mounted microcontroller, and a linear array of four vibrotactile motors worn on the thigh. The prototype worked by calculating the center of pressure in the anteroposterior plane, and applying a time-discrete vibrotactile stimulus based on the location of the center of pressure.
Noninvasive and Accurate Fine Motor Rehabilitation Through a Rhythm Based Game Using a Leap Motion Controller: Usability Evaluation of Leap Motion Game
This paper presents a system to deliver automated, noninvasive, and effective fine motor rehabilitation through a rhythm-based game using a Leap Motion Controller. The system is a rhythm game where hand gestures are used as input and must match the rhythm and gestures shown on screen, thus allowing a physical therapist to represent an exercise session involving the user's hand and finger joints as a series of patterns. Fine motor rehabilitation plays an important role in the recovery and improvement of the effects of stroke, Parkinson's disease, multiple sclerosis, and more. Individuals with these conditions possess a wide range of impairment in terms of fine motor movement. The serious game developed takes this into account and is designed to work with individuals with different levels of impairment. In a pilot study, under partnership with South West Advanced Neurological Rehabilitation (SWAN Rehab) in Phoenix, Arizona, we compared the performance of individuals with fine motor impairment to individuals without this impairment to determine whether a human-centered approach and adapting to an user's range of motion can allow an individual with fine motor impairment to perform at a similar level as a non-impaired user.
This paper presents work that was done to create a system capable of facial expression recognition (FER) using deep convolutional neural networks (CNNs) and test multiple configurations and methods. CNNs are able to extract powerful information about an image using multiple layers of generic feature detectors. The extracted information can be used to understand the image better through recognizing different features present within the image. Deep CNNs, however, require training sets that can be larger than a million pictures in order to fine tune their feature detectors. For the case of facial expression datasets, none of these large datasets are available. Due to this limited availability of data required to train a new CNN, the idea of using naïve domain adaptation is explored. Instead of creating and using a new CNN trained specifically to extract features related to FER, a previously trained CNN originally trained for another computer vision task is used. Work for this research involved creating a system that can run a CNN, can extract feature vectors from the CNN, and can classify these extracted features. Once this system was built, different aspects of the system were tested and tuned. These aspects include the pre-trained CNN that was used, the layer from which features were extracted, normalization used on input images, and training data for the classifier. Once properly tuned, the created system returned results more accurate than previous attempts on facial expression recognition. Based on these positive results, naïve domain adaptation is shown to successfully leverage advantages of deep CNNs for facial expression recognition.
In recent years, the development of new Machine Learning models has allowed for new technological advancements to be introduced for practical use across the world. Multiple studies and experiments have been conducted to create new variations of Machine Learning models with different algorithms to determine if potential systems would prove to be successful. Even today, there are still many research initiatives that are continuing to develop new models in the hopes to discover potential solutions for problems such as autonomous driving or determining the emotional value from a single sentence. One of the current popular research topics for Machine Learning is the development of Facial Expression Recognition systems. These Machine Learning models focus on classifying images of human faces that are expressing different emotions through facial expressions. In order to develop effective models to perform Facial Expression Recognition, researchers have gone on to utilize Deep Learning models, which are a more advanced implementation of Machine Learning models, known as Neural Networks. More specifically, the use of Convolutional Neural Networks has proven to be the most effective models for achieving highly accurate results at classifying images of various facial expressions. Convolutional Neural Networks are Deep Learning models that are capable of processing visual data, such as images and videos, and can be used to identify various facial expressions. The purpose of this project, I focused on learning about the important concepts of Machine Learning, Deep Learning, and Convolutional Neural Networks to implement a Convolutional Neural Network that was previously developed by a recommended research paper.
This paper presents the design and evaluation of a haptic interface for augmenting human-human interpersonal interactions by delivering facial expressions of an interaction partner to an individual who is blind using a visual-to-tactile mapping of facial action units and emotions. Pancake shaftless vibration motors are mounted on the back of a chair to provide vibrotactile stimulation in the context of a dyadic (one-on-one) interaction across a table. This work explores the design of spatiotemporal vibration patterns that can be used to convey the basic building blocks of facial movements according to the Facial Action Unit Coding System. A behavioral study was conducted to explore the factors that influence the naturalness of conveying affect using vibrotactile cues.
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.
Humans have a great ability to recognize objects in different environments irrespective of their variations. However, the same does not apply to machine learning models which are unable to generalize to images of objects from different domains. The generalization of these models to new data is constrained by the domain gap. Many factors such as image background, image resolution, color, camera perspective and variations in the objects are responsible for the domain gap between the training data (source domain) and testing data (target domain). Domain adaptation algorithms aim to overcome the domain gap between the source and target domains and learn robust models that can perform well across both the domains.
This thesis provides solutions for the standard problem of unsupervised domain adaptation (UDA) and the more generic problem of generalized domain adaptation (GDA). The contributions of this thesis are as follows. (1) Certain and Consistent Domain Adaptation model for closed-set unsupervised domain adaptation by aligning the features of the source and target domain using deep neural networks. (2) A multi-adversarial deep learning model for generalized domain adaptation. (3) A gating model that detects out-of-distribution samples for generalized domain adaptation.
The models were tested across multiple computer vision datasets for domain adaptation.
The dissertation concludes with a discussion on the proposed approaches and future directions for research in closed set and generalized domain adaptation.
Low-Intensity Blood Flow Restriction Training as a Preoperative Rehabilitative Modality to Improve Postoperative Outcomes for Anterior Cruciate Ligament Reconstruction
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.
Classification of Fabric Based Soft Actuators and Feedback Controller for At-home Hand Rehabilitation
With an aging population, the number of later in life health related incidents like stroke stand to become more prevalent. Unfortunately, the majority those who are most at risk for debilitating heath episodes are either uninsured or under insured when it comes to long term physical/occupational therapy. As insurance companies lower coverage and/or raise prices of plans with sufficient coverage, it can be expected that the proportion of uninsured/under insured to fully insured people will rise. To address this, lower cost alternative methods of treatment must be developed so people can obtain the treated required for a sufficient recovery. The presented robotic glove employs low cost fabric soft pneumatic actuators which use a closed loop feedback controller based on readings from embedded soft sensors. This provides the device with proprioceptive abilities for the dynamic control of each independent actuator. Force and fatigue tests were performed to determine the viability of the actuator design. A Box and Block test along with a motion capture study was completed to study the performance of the device. This paper presents the design and classification of a soft robotic glove with a feedback controller as a at-home stroke rehabilitation device.
The impact of Artificial Intelligence (AI) has increased significantly in daily life. AI is taking big strides towards moving into areas of life that are critical such as
healthcare but, also into areas such as entertainment and leisure. Deep neural
networks have been pivotal in making all these advancements possible. But, a well-known problem with deep neural networks is the lack of explanations for the choices
it makes. To combat this, several methods have been tried in the field of research.
One example of this is assigning rankings to the individual features and how influential
they are in the decision-making process. In contrast a newer class of methods focuses
on Concept Activation Vectors (CAV) which focus on extracting higher-level concepts
from the trained model to capture more information as a mixture of several features
and not just one. The goal of this thesis is to employ concepts in a novel domain: to
explain how a deep learning model uses computer vision to classify music into different
genres. Due to the advances in the field of computer vision with deep learning for
classification tasks, it is rather a standard practice now to convert an audio clip into
corresponding spectrograms and use those spectrograms as image inputs to the deep
learning model. Thus, a pre-trained model can classify the spectrogram images
(representing songs) into musical genres. The proposed explanation system called
“Why Pop?” tries to answer certain questions about the classification process such as
what parts of the spectrogram influence the model the most, what concepts were
extracted and how are they different for different classes. These explanations aid the
user gain insights into the model’s learnings, biases, and the decision-making process.