The BCI Music Player is an innovative system that will allow severely disabled patients such as those with amyotrophic lateral sclerosis (ALS) or spinal cord injuries, to listen to music without the need for muscle control. Currently, in the United States, ALS affects up to 30,000 people, with 5,000 new diagnoses each year [1]. These patients eventually lose all motor function, causing them to be locked in their own bodies and unable to function by themselves in their daily lives. In order to enable these patients to communicate independently, P300-based brain-computer interface (BCI) systems were designed. These systems rely on collecting electroencephalogram (EEG) signals during a standard oddball paradigm designed to evoke the P300 electrophysiological response, which is then detected and translated into spelled characters on the screen [3]. The P300 response is an event-related potential (ERP) evoked in the brain between 250 to 500 ms after a rare “target” stimulus occurs among high-probability occurring “non-target” stimuli [3]. The oddball paradigm is a commonly used visual task that measures attention in ERP studies wherein sequences of repetitive stimuli are interrupted by a rare stimulus. The P300 Speller uses flashing rows and columns of a celebrity’s face over the matrix as the repetitive stimuli and the participant counts when a face appears over the desired target character in the matrix as a rare stimulus. While most BCI systems focus on communication, the BCI Music Player allows ALS patients themselves to take control of their music experience in order to improve their mental health, decrease their perception of pain, and empower these patients with self-sufficiency. Additionally, evidence suggests that music enhances some cognitive functions due to its short-term positive effect according to the mood hypothesis [6]. There have also been studies with results suggesting that music not only improves cognitive performance, but that music can also increase the amplitude of P300 responses. [5,6]. With this in mind, our goal in this study is twofold: 1) to design a P300-based BCI music player that provides entertainment and self-sufficiency for patients with motor control disabilities. The system will control a music application and a disco ball set up to provide an imperative distraction and improve the patient’s mood and wellbeing. 2) to investigate the effect of music on the P300 response by analyzing the signal changes before and after participants engage with the system. We also hypothesize that listening to music will affect the amplitude and spatial localization patterns of the P300 responses.

Upper extremity amputations (UEA) represent approximately 3% of the US amputee population[1]. UEAs are commonly caused by traumatic accidents or congenital anomalies, infections or tumors[1]. As a result, many patients are prescribed a prosthetic device to perform daily activities. However, there is a high percentage of prosthetic limb rejection which occurs when a patient is dissatisfied with their prosthetic and refuses to use their prosthetic. Reasons for prosthetic rejection include discomfort of the device (e.g., poor fitting to the residual limb, weight), technical difficulties (e.g., malfunctioning joints, complex functionality) and development of one-handedness where the individual gets accustomed to life without their missing limb and do not see the need for the prosthetic device. In this study, the AR-mStrong gaming system was developed using Augmented Reality (AR) to effectively train individuals using an upper extremity prosthetic in a fun and interactive manner. Our main objective is to create a series of motivating and engaging game-based rehabilitation exercises for the Microsoft HoloLens 2 that mirror the physical training for UAEs to achieve mastery of their new prosthetic and therefore, decrease the potential of limb rejection.

There are 5.4 million individuals in the United States who live with paralysis [1]. Recently, invasive methods for assistive prosthetics went through clinical trials for such disabled people, particularly targeting tetraplegic patients [2]. As an alternative approach, devices have been developed to attend to mouse interfaces with tetraplegia paralysis, including head motion detectors, electromyography, electroencephalography [3], and camera/eye tracking [4]. The current study is aimed to develop a new and noninvasive head motion detector platform as a universal joystick to allow such individuals to control two-dimensional (2D) and three-dimensional (3D) prosthetic devices with a focus in user experience. Here, the platform is developed for a 2D cursor control task that can be mapped to an assistive robotic arm.

Neuromuscular diseases such as amyotrophic lateral sclerosis (ALS) are characterized by the degeneration of the neuromuscular pathways that control voluntary motor activity. Most neuromuscular disorders show a prevalence rate between 1 and 10 per 100,000 population [1]. There has been an increase in the exploration of assistive methods using brain-computer interfaces (BCIs). These devices acquire brain signals that can be analyzed and translated to control signals for external devices. One and two-dimensional (1D and 2D) applications for BCIs have been explored, and different studies have been carried out to help improve the overall design and implementation. 2D BCI systems provide greater freedom to the user in terms of the possible commands to the system when compared with 1D systems, but are more difficult to control. [2]. BCIs offer a chance for many patients who have lost a sense of autonomy to regain lost motor functions, which can help them attain some level of independence [3]. The motivation for this project is to design and test a BCI that allows for independent control of a NAO robot’s upper limbs that allow the user to perform nonverbal gestures, providing a BCI for nonverbal communication.

Basic aerobic exercise has been shown to improve psychomotor function in PD patients (Marusiak et al.). Since progression of the disease limits the ability of PD patients to engage in aerobic exercise, researchers and clinicians have designed and employed seated bicycle ergometers equipped with motors aimed to assist movement and restore neuromuscular pathways. Experimental human subject trials with these devices have shown promising results. PD patients who engage in ergometer based training experience improvements in gait function (Ni et al.) with one study finding this type of exercise comparable to treadmill training in its ability to enhance gait speed and endurance (Arcolin et al.). It is for these reasons that Theracycle is in the business of the development and distribution of motor assisted ergometers for patients with Parkinson’s disease and other conditions with similar challenges, including stroke, muscular sclerosis, muscular dystrophy, spinal cord injury, cerebral palsy and even arthritis. In order to enhance their design to incorporate more modern technology and features into their ergometer system, they have sourced engineering development to Dr. Dhaval Solanki’s laboratory at the University of Rhode Island. Working with both Dr. Solanki and company representatives, we have identified two main specifications of the current ergometer design that are in need of novel improvements.

Basic aerobic exercise has been shown to improve psychomotor function in PD patients (Marusiak et al.). Since progression of the disease limits the ability of PD patients to engage in aerobic exercise, researchers and clinicians have designed and employed seated bicycle ergometers equipped with motors aimed to assist movement and restore neuromuscular pathways. Experimental human subject trials with these devices have shown promising results. PD patients who engage in ergometer based training experience improvements in gait function (Ni et al.) with one study finding this type of exercise comparable to treadmill training in its ability to enhance gait speed and endurance (Arcolin et al.). It is for these reasons that Theracycle is in the business of the development and distribution of motor assisted ergometers for patients with Parkinson’s disease and other conditions with similar challenges, including stroke, muscular sclerosis, muscular dystrophy, spinal cord injury, cerebral palsy and even arthritis. In order to enhance their design to incorporate more modern technology and features into their ergometer system, they have sourced engineering development to Dr. Dhaval Solanki’s laboratory at the University of Rhode Island. Working with both Dr. Solanki and company representatives, we have identified two main specifications of the current ergometer design that are in need of novel improvements.

Our motivation is to improve today’s knee joint monitoring devices for patients with restricted range of motion (ROM) undergoing knee rehabilitation. Currently, over 600,000 people undergo knee replacement surgery every year in the United States [1]. The knee joint and its surrounding ligaments are at significant risk of being damaged due to its limited range of motion, the heavy load it carries, and the torque generated from the body’s twisting motions [2]. Currently, rehabilitation centers measure the patient’s knee ROM by using a manual goniometer. These devices require someone to hold it against the patient’s leg and manually adjust the angular positions. Another form of knee ROM measurement device is the Smart Knee Brace which can be worn for extended periods of time for continuous measurement [3]. The issue with the current technology used for knee joint movement monitoring is that these are not found in rehabilitation centers for patient use. Our textile-based smart brace eliminates that extra person and gives the patient more freedom to perform certain exercises (Shown in figure 1). This is done through the use of conductive ink and stainless steel fabric embedded into a comfortable, adjustable, wearable fabric. This combination ensures we are able to record and analyze precise knee angle measurements to create an optimal recovery plan for each patient.

Parkinson’s disease (PD) is a progressive neurodegenerative disease affecting over one million adults in the United States and over 10 million adults worldwide [1]. Those affected can experience many symptoms, including tremors, slowed movement (bradykinesia), and rigid muscles. This can escalate to sleep problems, depression, problems with eating, and many other problems with the everyday motor functions of the body. The symptoms of PD are caused by a loss of neurons that produce a chemical in the brain called dopamine. Dopamine is a neurotransmitter closely associated with movement and coordination. Studies suggest that by the time persons with PD begin exhibiting symptoms, they have already lost 60-80% of their dopamine producing cells [2]. There is no known cure for Parkinson’s disease. The Unified Parkinson Disease Rating Scale (UPDRS) is the clinical standard for assessment and treatment planning for those living with PD [3], but there are many limitations associated with this system such as its scalability and lack of validation with quantified data.

This team is working to create a smart glove system that will serve as a solution to the many limitations of UPDRS. The DAQ Smart Gloves measure 3 finger flexion, thumb/index pressure, and pitch and roll via an embedded system to transmit real time data over bluetooth. While still in its validation phase, the gloves have application and potential to provide quantified hand motor function data to a clinician and help with the treatment planning process. This year in capstone, the team set out to further optimize a smart glove design that could someday be used to aid in treatment planning and disease progression for those living with Parkinson’s Disease. The team worked very collaboratively on many components of the project, but each assumed a specific role. Alex served as the software engineer to design and implement a software program to initialize, collect, and transmit data from the smart gloves to a personal computer. Quinn served as the lead hardware engineer to design the smart gloves and worked closely with Dana, who functioned as the project manager and assisted with hardware engineering, on fabricating the final glove design. Together, the team has implemented a working smart glove design and are able to analyze and validate the data collected using a gold standard motion-sensing system.

The purpose of SnapDiet, the Smart Diet Monitoring System is to help clinicians to observe the daily food intake of their patients for a research study. The study in question involves patients with Polycystic Kidney Disorder (PKD). PKD is an inherited disease in which cysts develop in and on the kidneys. Cysts are noncancerous sacs that fill up with liquid, making the kidneys much larger and interrupting kidney function (Figure 1). Autosomal Dominant Polycystic Kidney Disorder (ADPKD) is the most common type of PKD, with about 1:500 - 1:1000 individuals affected [5]. PKD does not have a cure or treatment, and since it is a progressive condition, early intervention is important. Some interventions that clinicians recommend are increasing water intake, reducing sodium intake, and lowering protein intake [6]. This project will be centered on technology to help with one of the main interventions of ADPKD, which is bodyweight control. In order to slow down the progression of PKD, it is crucial to maintain a healthy lifestyle and diet. In the “Weight Loss Trial in ADPKD” study by Nowak et al., it was shown that body mass index (BMI) correlates to kidney function, meaning that patients with a higher BMI were more at risk to decline in kidney function [5]. Studying the effects of weight loss on PKD patients can help to solidify the link between high BMI and decreased kidney function.

Gait abnormalities affect a wide variety of individuals. 30% of adults over the age of 60 exhibit some form of gait dysfunction, and the prevalence increases to 60-80% for adults of age 80 or greater [1]. While such issues are most commonly observed in older individuals, age is not the only relevant factor when considering populations affected by gait abnormalities. Many common diseases contribute to abnormal walking patterns. Those impacted include individuals with both neurological and non-neurological complications [1]. Parkinson’s disease, brain tumors, stroke, cerebral palsy, multiple sclerosis, and muscular dystrophy can each affect an individual's gait cycle. The scope and pervasiveness of these illnesses exemplifies the importance of this issue [2]. The classification of gait abnormalities is dependent upon the cause of the disorder. Propulsive, scissor, spastic, steppage, waddling, ataxic, and magnetic gait are some of the most frequently observed disorders, and each is caused by one of a variety of debilitating diseases or by trauma to the body [2]. Unfortunately, gait abnormalities have an everlasting effect on quality of life. They are the leading cause of falls in older adults, and can propagate additional injuries, disability, and loss of independence. It is incredibly important for those with gait abnormalities to afford proper treatment and rehabilitation so that they are able to maintain an independent and active lifestyle [3].

The purpose of Re-Gait is to use technology-assisted tools to improve physical therapy for gait correction, and to provide quantitative analysis of an individual's gait cycle. This is done with the hopes of creating a more accurate and personalized approach to the traditional rehabilitative process. In general, gait dysfunction is primarily combated in two ways; gait quantification and gait rehabilitation [4]. Gait quantification incorporates the use of sensors to collect data describing an individual’s gait cycle for the purpose of calculating specific parameters that a clinician can observe. Gait rehabilitation generally involves working with a physical therapist to employ exercises and activities which facilitate gait improvement. The Re-Gait system is intended to exceed previous methods in efficiency and reliability by combining quantification and rehabilitation into one product. To quantify gait in this project, swing time and stance time were chosen as the gait parameters of focus. Swing time refers to the period of time one foot is entirely suspended, while stance time is the duration of foot contact with the ground for one complete cycle. The gait cycle is the complete heel toe action of one foot spanning from initial heel contact to final heel contact over the course of one stride, and a balanced gait should include roughly 60% stance time and 40% swing time for the entire cycle [5]. The benefit of a fully integrated system is apparent here as the rehabilitation components can interact closely with the quantitative components.