Abstract: The problem that we are addressing are the high risk of ankle sprains. An estimated 23,000 ankle sprains occur every day in the United States and the ankle is at the highest risk of reinjury following the year immediately after the initial sprain [1] Our hypothesis is that a contributing factor to this high rate of reinjury is because the current rehabilitation programs do not take into account the change of the proprioceptors in the ankle after injury. The ankle proprioceptors are the muscles, ligaments, and tendons within your ankle that help with balance and these are damaged during an ankle sprain. There is no current quantitative method employed by physical therapists. Additionally, there is another side to this problem, the knee. The knee can compensate and make up for lack of strength or mobility in the ankle when the ankle rehabilitation is being completed. This can lead to a slower recovery overall. Understanding the amount the knee is moving may help focus the rehabilitation plan on the injured ankle due to this compensation risk. If the knee sensor shows zero to minimal movement, the ankle is doing the work. By effectively retraining tissues within the ankle that affect a person’s balance it may allow for a lower chance of a person’s ankle to reinjure. Thus far in our progress, we have established a way to track the knee movement but are continually working on a visual representation of that movement for the physical therapist and the patient to use as feedback.

Abstract: For a long time, the behavior of a neuron has been notoriously difficult to model in terms of both measurement and general applications due to the vast differences in functionality between the different cell types. Due to this, the approach of creating a model to represent the cell has been the main focus of research and development when attempting to understand the behavior of a neuron. However, in many previous studies, the efforts made apply only to the overall action potential of a specific neuron, and the underlying structures are far less understood, making it more difficult to apply to other cell types. Our planned solution to this problem is to attempt to create a model in which we focus on the smaller structures, specifically the ionic channels, that are commonly shared between neurons to allow for modifications to be made to represent multiple different types, as well as develop a further understanding of how different neurons behave. To accomplish this task, we have divided our team into different roles: a hardware engineer to design and build a physical circuit to represent our neuron, a software engineer to design and implement the MPLAB X code needed to create the proper action potential signal, and a team manager to assist anywhere necessary, help keep our thoughts/work organized, and communicate with our advisors. In our particular project, we have assigned the following roles: Hannah as hardware engineer, Brenden as software engineer, and Brayden as team manager. Our aim in doing this is to be able to both reduce the overall workload among individual members to allow for better efficiency by allocating different tasks to be worked on simultaneously, as well as more constructive collaboration when issues arise.

Abstract: The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2015 states that neurological disorders are the world's largest cause of disability, estimating around 1 billion people directly afflicted. There are diverse illnesses which span all age groups and the “burden of neurological disorders is large and increasing, posing a challenge to the sustainability of health systems” . When concerned with collecting signals from the brain and analyzing neural function, it is desirable to work non-invasively. Electroencephalography (EEG) is commonly used to diagnose neural abnormalities. Widely utilized in modern applications, there is room for improvement of disc electrodes. We have built a controlled system for the three-dimensional evaluation of electrode technologies. Human subject testing is expensive and inconsistent as a testing environment for this goal, so a compatible phantom is used. The goal of this project was to design a versatile system that controls an attached dipole with sub-mm precision. The system utilizes motors to move an attached dipole relative to the compatible phantom in a 3D positional array. This array will be user-defined in terms of point spacing as well as size. After recording, a software system will process and display results for further analysis. This allows for the three dimensional performance evaluation of disc electrodes. The standalone application will be able to record, process, and visualize a compatible phantom recording. 

Abstract: Ankle injuries, specifically ankle sprains, put athletes out of work everyday for an extended period of time. An ankle sprain occurs when one twists, rolls, or abornally turns their ankle. Tendons and ligaments stretch and/or tear causing severe pain, discomfort, swelling, and immobility at times. According to the Journal of Athletic Training, 7 in 1000 people sustain an ankle injury per year and about 2 million acute ankle sprains occur yearly in the United States [1]. The severity of the injury determines an individual's recovery time. There is a flaw in the recovery system which is that the patient's strength cannot be quantified, meaning the medical professional assessing their injuries, mostly physical therapists, cannot be fully confident when saying that the injury is fully healed. Because of this, athletes are being sent back into the game too early where they are at higher risk of reinjuring the same ankle, potentially worse than the initial one. Our team is working hard to prevent these secondary injuries by implementing force sensing resistors (FSR) onto the top surface of a wobble board that physical therapists use in everyday practice. This will allow for the physical therapist to see the exact amount of pressure a patient is applying in order to assess that patient's progress and recovery to be certain that they are fully healed and able to go back to work.

Abstract: Our team’s problem statement is “how can people affected by neuromuscular disorders such as amyotrophic lateral sclerosis (ALS) or Parkinson’s perform common household tasks with little to no assistance?” Neuromuscular disorder treatment in its current state can leave those afflicted with it feeling unempowered and dependent, and requires close attention and monitoring from medical professionals and caretakers. Our proposed solution involved developing a customizable smart home application to allow disabled patients to perform tasks such as turning lights on and off and controlling their television. We accomplished this by using two main components: the P300 Speller matrix and a Raspberry Pi microcontroller. The smart home menu modified in P300 Speller will determine the desired task the user wants to perform and interface with a Raspberry Pi to perform the physical task. Similar applications, such as a wireless bluetooth electroencephalogram (EEG) smart home and a smart home with features selected by having the user blink will be reviewed in the next section. The menu we have proposed will be developed from a Brain Computer Interface (BCI).  Our design choices offer alternatives  that allow us to circumvent some complications faced by other solutions, such as eye tracking encountering issues due to the loss of motor control suffered by neuromuscular patients, an issue BCI does not have. Additionally, choosing not to use a wireless EEG is another adaptation; the wireless headset would eventually need to be charged for several hours, a limitation our project will not have with a wired setup. Our team consists of DJ Nadeau, Isaac Michaud, and Tanner Wildfong. DJ is the project manager and one of the software engineers. Isaac is the hardware engineer. Tanner is the signal processing analyst as well as one of the software engineers.

Abstract: Our team is attempting to use a brain-computer interface (BCI) system while performing motor imagery (MI) to raise the arms of a fully programmable NAO robot. EEG (Electroencephalography) signals will be recorded from volunteers that will be analyzed for personal features that stand out while performing MI. Our goal when designing the system was to raise the right or left arm of NAO depending on right or left imagined movement, and to lower the arm when the brain signals show the participant in a state of rest. We wanted to take the target code when the cursor hits either the higher or lower target, and have NAO raise either the right or left arm accordingly. Programming the robot involved using Python in Choregraphe to send NAO a signal using a user datagram protocol (UDP) connection. The challenge faced: training one’s sensorimotor rhythms (SMRs) taking weeks to months [1]. We decided that our main area of focus will be how patients perform motor imagery (MI) (visualizing and/or imagining the feeling of a certain motion) to modulate the common frequencies of mu (μ) (8-13Hz) and beta (β) (15-30Hz) [2]. Our motivation was to observe whether someone is more adept at the visual or the kinesthetic (sensation) aspect of MI. We aim to gather the information with the intention of requiring fewer training periods for accurate use. This concept could be altered for communication or even to carry out simple daily tasks. Voluntary motor control using an external device for patients who can no longer carry out motor control themselves is what we are striving to accomplish with this design. Although this concept has been proven by research teams in the past, and topical EEG is not the most effective form of brain signal recording, we aim to provide a semi-unique alternative to invasive procedures and other EEG-BCI systems using the combination of Matab, BCI2000, Python, Choregraphe, and MI method. 

Abstract: Many people struggle with an impairment to their hand motility. This can be either as a result of an existing condition or due to either breaking or spraining parts of the hand and arm. When a hand or wrist is sprained or broken the patient must go through physical therapy to regain the strength they previously had and there is currently no way to measure progress other than how the patient feels themselves. Another issue for people with existing conditions is that they do not see their physicians or neurologists frequently so monitoring their condition is difficult. For example, patients with Parkinson’s disease see a neurologist every six months typically. There is no way for the physician to notice or recognize changes in the patient in between these visits, and as the progression of these illnesses happens slowly over time, it can be difficult for the patient to recognize that their condition is worsening. Our project is intended to assist people struggling with restoring or monitoring their abilities to move their hands properly.  The key to this system is a “sensor glove” that has flex sensors embedded into it that react to the amount of flexion in the fingers. This glove connects to an app that has patients perform various activities once a week to help assess their current abilities. The glove is intended to be connected to an app that was developed by a previous group. Data is collected on each of these movements through the sensors in the glove and uploaded to the app which can be opened on any web browser. Our unique data collection comparisons will allow physicians to monitor the patient's abilities as well as compare their results from week to week to be able to understand how the patient is doing and if they need to come in for an appointment earlier or even adjust their medication. In order to test this system, we created a 3D-printed robotic hand that serves to act as a testing medium for the glove. The robotic hand provides the advantage of having movements controlled by a computer, and so can make specific, repeatable movements for testing the sensor glove.

Abstract: Physical rehabilitation is crucial for patients who have undergone total hip replacement surgery. The success of the surgery ultimately depends on the success of the recovery process. Physical therapy programs can be completed independently by patients in their own homes, after initial instruction from a trained professional. However, a patient's willingness to comply plays a large role in the success of at-home exercise programs. If they are not encouraged to consistently participate, their long-term health and mobility will suffer. It has also been reported that many elderly patients who undergo this surgery experience postoperative cognitive decline and dysfunction due to the recovery period which often does not allow them to work and socialize within the community. Therefore, it is necessary to find an effective method of rehabilitation that accounts for both physical and cognitive strengthening that can be done at-home. Our solution utilizes augmented reality technology in order to increase the success of at-home rehabilitation for elderly hip replacement patients. Our game, “TheraPlay”, contains various tasks that will be used to motivate the patient while simultaneously fulfilling their exercise requirements. The difference between traditional rehabilitation techniques and our game, is that our game intends to assess and improve both the daily living activities such as walking as well as cognition in a fun and motivating manner. Our game consists of four modules, each mimicking an existing clinical assessment for hip replacement patients. We will be providing feedback and messages of encouragement through the Hololens2 head-mounted display. The American Physical Therapy Association has stated in a case study that “feedback provides information about the success of the action, it informs the learner about movement errors, and it is known to motivate the learner by providing information about what has been done correctly.” [1] Potentially, our solution may be useful for one physical therapist to remotely oversee the training progression of several patients in their own homes.

Abstract: Oftentimes, prosthetic users have limited proprioception (accumulation of sensory feedback), thus lack the ability to recognize uneven surfaces/ terrain, and/ or sloped surfaces.  This lack of sensory feedback can cause discomfort in mobility for people with prosthetics.  It can also lead to a loss of balance for the person.  Our lab is exploring extracorporeal distal pressure sensors to convey lower limb prosthetic's vector (relative to the floor as a plane) information through vibrotactile feedback.  By using a textile band filled with several small vibrotactile motors to be worn on the thigh, information such as sensor data from pressure sensors placed on the prosthetic can be ascertained by the amputee in real-time.  This sensory feedback information is intended to increase the comfort and awareness of the people with prosthetics while walking.  Throughout this year, we were able to develop a functioning feedback system that collects the information through the pressure sensor and relays it up to the vibrotactile band through an Arduino with additional hardware components.  These hardware components are housed in a waist pouch which is worn by users.  The pressure mapping grid was fully developed and tested, initially through the use of a Graphic User Interface (GUI) to visualize the location and intensity of the pressure and later relayed directly to the vibrotactile band.  The vibrotactile band was used from an existing project.  Our team generated code which is able to process the data coming in from the pressure mapping system and send the appropriate signals to the corresponding vibrotactile motors to provide the user with the sensory input based on the ground below their feet. 

Abstract: Every year more than half a million infants are admitted to a Neonatal Intensive Care Unit. These babies are less than a month old, are pre-term, have serious health concerns, or a birth weight below five and one half pounds [1]. In the current environment, the infant is isolated during a crucial formative time that is normally spent with their parents. The excess of wires attached to the infant create a barrier between the infant and their caregivers. This prevents skin-to-skin contact which can greatly improve infant outcomes after the NICU. Additionally, the electrodes in the current monitoring system can be abrasive and damaging to their sensitive skin. The overarching goal of our project is to improve NICU conditions for the neonates, their parents, and the NICU medical team. Our solution to this problem is to utilize an e-textile chest belt, along with a user interface to create a more comfortable and advanced NICU monitoring system. Our design consists of a soft, adjustable chest belt that uses six textile pressure sensors to measure chest movements as they relate to respiration rate. In an effort to minimize the amount of wires, we have implemented the use of conductive thread to seamlessly hardwire the sensors. The chest belt was manufactured using an industrial embroidery machine to ensure the quality of our belt. This semester there was a large focus on improving our chest belt and testing the system we created.

Abstract: COVID-19 has quickly spread around the globe, resulting in the deaths of over one million people. According to the Center for Disease Control (CDC), “ the principal mode by which people are infected with SARS-CoV-2 (the virus that causes COVID-19) is through exposure to respiratory droplets carrying infectious virus.” [1] Transmission of respiratory droplets can result from breathing, coughing, speaking, or sneezing. Social distancing is one of the most effective ways to slow the spread of COVID-19. However, healthcare professionals work closely with patients and cannot always remain socially distant in the workplace. Healthcare professionals protect themselves and their patients by wearing face masks, which help prevent the spread of respiratory droplets between individuals. Nurses, physicians, and other healthcare professionals work directly with patients who have tested positive for the virus. Therefore, monitoring the health of these frontline workers is vital. Based on a survey conducted by our team, only 11.2% of healthcare professionals reported that they undergo mandatory COVID-19 testing. Consistent symptom monitoring is necessary to keep both the patients and healthcare workers safe. Our team’s proposed solution is to design a mask that will effectively protect individuals while simultaneously tracking respiratory actions to detect potential COVID-19 symptoms. Our team has added sensors to our mask frame and has collected motion data to distinguish between respiratory actions. Healthcare professionals wear masks and other personal protective equipment (PPE) for extended periods of time. The current PPE available for healthcare professionals causes several problems. Forty nine percent of survey participants reported that the masks they wear do not fit properly. Masks are most effective when they cover the nose and mouth completely. Therefore, our team also used 3D scanning technology by Bellus3D to create the most effective, comfortably fitting mask possible. Over 75% of survey participants complained of discomfort on skin and their glasses fogging while wearing a mask. A custom fit mask helps secure the mask comfortably on the face to prevent irritation and reduce the amount of escaped heat that causes glasses to fog.