REU/ELEVATE SUMMER PROJECTS 2019
Name of Project: Flying wheelchair
Description: Wheelchair designers are continuously working to make chairs more stable and capable of traversing rough terrain and over steps and stairs. This project will be a little futuristic with researching potential technology that could allow a wheelchair to hover or glide over various surfaces and stairs
Project Level: Basic
Preferred Major/Background: Mechanical or Aerospace engineering
Required Skills/Courses: Physics
Submitted by: Jon Duvall, Rory Cooper
Name of Project: Table Tennis Wheelchair
Description: Participating in competitive table tennis can be difficult for manual wheelchair users. This project will be to design, fabricate, and test modifications to or a custom manual wheelchair to make it perform better for people competing in table tennis.
Project Level: Basic
Preferred Major/Background: Mechanical Engineer, Mechanical Design
Required Skills/Courses: CAD experience, design experience
Submitted by: Jon Duvall, Rory Cooper
Name of Project: Hybrid Power Wheelchair
Description: Traditionally, power wheelchairs use heavy batteries which have limited ranges and need to be charged everyday. This project will involve research into alternative hybrid approaches and designing a prototype for testing.
Project Level: Intermediate
Preferred Major/Background: Mechanical Engineering, Mechanical Design
Required Skills/Courses: Research and design process, prototype development
Submitted by: Jon Duvall, Rory Cooper
Name of Project: AgileLife bed with Group 3 Power Wheelchair
Description: The AgileLife robotic transfer system is bed that automatically transfers a person to and from a custom manual wheelchair. This project is to design and fabricate modifications and add-ons that will make the AgileLife bed compatible with a Group 3 power wheelchair with seating functions.
Project Level: Intermediate
Preferred Major/Background: Mechanical design, CAD experience
Required Skills/Courses: Experience in research process; design and fabrication; prototype initialization
Submitted by: Jon Duvall
Name of Project: Effectiveness of a Wheelchair Maintenance Training Program
Description: The wheelchair is one of the most enabling technologies a clinician can provide. Proper wheelchair function is also critical to participation and quality of life among end-users. Laboratory testing reveals many wheelchairs currently on the market do not meet durability standards and more than 60% of users report experiencing a breakdown in a 6-month period. Maintenance has been suggested as an intervention to mitigate consequences and wheelchair failure, however structured training has been lacking. The goal of this project is to evaluate the effectiveness of a multi-site randomized control trial focused on teaching wheelchair users and their caregivers to complete wheelchair maintenance.
Project Level: Basic
Preferred Major/Background: Interest in clinical interventions
Required Skills/Courses: Microsoft Office; effective communication skills
Submitted by: Lynn Worobey
Name of Project: WheelFit app – Feasibility and Acceptability
Description In the general population there has been an increased move towards the usage of mobile health applications (mHealth apps) in conjunction with fitness wearables. However, these same methods for promoting physically activity are not currently available for individuals with spinal cord injuries who use manual wheel chairs. In this project participants will use a custom mHealth app, WheelFit, with a wrist worn sensor and a sensor attached to the wheel of their wheelchair. Students will be assisting with subject testing, data collection, and data analysis.
Project Level: Intermediate
Preferred Major/Background: Biomedical Engineering, Computer Science, Health Sciences, Information Science
Required Skills/Courses: Experience with statistical analysis and effective communication skills
Submitted by: Akhila Veerubhotla, Dan Ding
Name of Project: Assessing Energy Expenditure in SCI using Doubly Labeled Water and Activity Monitors
Description: This study aims to validate a wearable sensor-based Energy Expenditure prediction algorithm against the community gold standard measure of doubly labeled water in wheelchair users. As part of this study, student will learn about wearable technology for physical activity monitoring, handling, and processing large data sets and large data visualization.
Project Level: Intermediate
Preferred Major/Background: Biomedical Engineering, Computer Science, Health Sciences/Informatics, Information Science
Required Skills/Courses: Processing large data files, data visualization, and data analysis
Submitted by: Akhila Veerubhotla, Dan Ding
Name of Project: Activity Monitoring in Spinal Cord Injury
Description: For individuals with moderate impairments after a spinal cord injury (SCI) current clinical prediction rules that use age, strength, and sensation are not sufficient in predicting ambulation. Using activity monitors to measure lower limb movement, we aim to create new clinical prediction rules that provide more information and are more accurate for predicting ambulatory ability for individuals with SCI.
Project Level: Intermediate
Preferred Major/Background: Engineering, Computer Science
Required Skills/Courses: Matlab or Python coding, familiarity with signal processing
Submitted by: Stephanie Rigot, Michael Boninger
Name of Project: Stair Climbing Wheelchair
Description: MEBot (Mobility Enhancement roBotic) wheelchair. This wheelchair has the potential to climb stairs and self-level on any terrain. This wheelchair uses electro-hydraulic actuators controlled by TI C2000 microprocessor. To detect different terrains, an external terrain detection system needs to be integrated.
Project Level: Advanced
Preferred Major/Background: Computer Science, Mechanical Engineering
Required Skills/Courses: Coding in C, C++, and Python; use of Texas Instrument Microcontrollers (Ideal) Electronics
Submitted by: Siv Sivakanthan, Rory Cooper
Name of Project: Softwheels In-Wheel Suspension Evaluation
Description: Wheelchair users are exposed to whole body vibration as a result of performing activities of daily living. Constant vibration exposure has been correlated to increased low back pain and may be a contributing factor to fatigue in manual wheelchair users. SoftWheel Ltd. has developed an innovative in-wheel suspension system that effectively absorbs shock while increasing energy efficiency. However, it is unknown how using these wheels affects wheelchair users over time. The objective of this project is to evaluate the SoftWheel's ability to reduce neck/back pain over time via a three-month home trial. A secondary purpose is to describe vibrations experienced by the SoftWheel users in comparison to standard wheels during common activities of daily living through in-lab testing.
Project Level: Intermediate
Preferred Major/Background: Engineering preferred, specifically bioengineering, mechanical engineering, or computer science/engineering.
Required Skills/Courses: Proficiency in Matlab is required, as is experience with Microsoft Excel. Experience with statistical software (SPSS, SAS, or other comparable statistical packages) is preferred, but not required. Excellent communication skills are required, as the student would be assisting with human subjects testing for data collection.
Submitted by: Hailee Kulich, Ali Koontz
Name of Project: Development of TrasnKinect for Wheelchair Transfer Assessment
Description: We propose to develop TransKinect, an automated transfer assessment system for clinical settings that can help therapists and their patients to identify improper transfer motions and provide guidance on how to improve their technique. The system will be based on the Transfer Assessment Instrument (TAI) which is a valid and reliable scale used to assess the quality of transfer technique but requires new therapists considerable time to learn and to make judgements on appropriate body positioning and mechanics. TransKinect will eliminate the need for background knowledge and training on the TAI and provide objective measurement of body and joint motions using a portable, low-cost markerless motion capture sensor. In prior work, we developed machine learning classifiers for TransKinect that can differentiate proper from improper techniques with an average accuracy of 94% using the sensor data recorded during a transfer. These classifiers will be embedded in system software designed to ‘watch’ a transfer, automatically compute the TAI score, present the results in real-time and provide education and training recommendations to therapists and their patients. The specific aims are to iteratively develop the TransKinect software platform involving input from an expert panel.
Project Level: Advanced
Preferred Major/Background: Computer Science, Biomedical Engineering
Required Skills/Courses: Machine Learning Coding, Python, Matlab, Programming Languages
Submitted by: Lin Wei, Ali Koontz
Name of Project: MEBot 3.0
Description: MEBot is an innovative power wheelchair able to perform a variety of applications to improve the mobility and safety of people who lack of motor functions. These include a self-leveling application for tip prevention in uneven terrains, step climbing and descending over architectural barriers, and wheel drive configuration to improve maneuvering in narrow spaces. We are currently developing the third iteration of MEBot to incorporate hydraulic actuators and different sensors to improve these applications.
Project Level: Intermediate
Preferred Major/Background: Interdisciplinary Engineering, Mechanical, Electronics, Computer Science; experience with microcontrollers, Python, C++ programming
Required Skills/Courses: Into to Programming, Basic CAD design, Linear Algebra or Calculus
Submitted by: Jorge Candiotti, Rory Cooper
Name of Project: Strong Arm
Description: Musculoskeletal injury is unfortunate but common side effect for health care personnel performing dependent wheelchair transfers. This in turn affects quality of life as well as work performance, thus creating a physical, mental, and economic burden for those who care for wheelchair users. Use of clinical equipment is ergonomically unfriendly, thus inducing more stress. The Strong Arm was designed to overcome the shortcomings of standard approaches to transfers (i.e. manual transfers, stand pivot transfers, and mechanical lift transfers). A robotic arm attached to a power wheelchair, this powerful device is joystick operated and foldable, thus reducing several of the demand required to perform a dependent transfer. We recently completed subject testing assessing the usability of Strong Arm to reduce task load and biomechanical demand in health care personnel as well as its usability among power wheelchair users. Data was collected analyzing kinematics and muscle activation and surveys were completed assessing usability.
Project Level: Basic
Preferred Major/Background: Bioengineering, Exercise Science, Rehab Sciences, Clinical Biomechanics, Software Programming
Required Skills/Courses: Applicants must have proficiency in biomechanics, software programming (particularly matlab), and statistics. Additionally, there must be a willingness to learn new material that is otherwise unfamiliar. Basic level familiarity with literature searches and scholarly paper writing skills is also necessary.
Submitted by: Mark Greenhalgh, Rory Cooper
Name of Project: PneuMobility
Description: The PneuMobility project is the development of air-powered mobility devices. Prototypes of a power wheelchair and scooter version have been created and improvements are continually being made.
Project Level: Intermediate
Preferred Major/Background: Mechanical Engineering
Required Skills/Courses: Component fabrication, mechanical assembly, wheelchair testing standards, research methods, and data collection/analysis.
Submitted by: Brandon Daveler, Rory Cooper
Name of Project: Technology Transfer Assistance Project
Description: Our lab is working with the VA technology transfer program office to help bring inventions made within the VA system to market. This includes prototyping, testing, preparing patent applications, and working with companies to license the technology
Project Level: Basic
Preferred Major/Background: Any background/major
Required Skills/Courses: Research, design, and fabrication skills
Submitted by: Jon Duvall, Garrett Grindle
Name of Project: Manual wheelchair Virtual Coach
Description: The manual Wheelchair Virtual Coach is a system that uses sensors in artificial intelligence algorithms to coach manual wheelchair users to perform effective pressure reliefs — thereby reducing their risk of developing pressure ulcers. Your primary focus will be assisting in making modifications to the smartphone app, which executes the algorithms and provides user interface to prepare the system for a clinical trial. You may also assist in analyzing data from the preclinical trial using statistical techniques
Project Level: Advanced
Preferred Major/Background: Bioengineering; Computer Science with significant coding experience
Required Skills/Courses: Instrumentation design, real-time data acquisition and analysis with machine learning techniques, smartphone app development, Android studio, Matlab, SPSS a plus
Submitted by: Andrea Sundaram, Dr. Rory Cooper
Name of Project: Visual feedback for gait retraining
Description: The goal of this ongoing research is to help people with lower limb impairments achieve better gait by providing customized visual feedback while they are walking. Our current prototype uses sensors in a limb prosthesis, as well as smart-glasses for the feedback. Part of the REU project objectives will be to adopt and optimize the design of both hardware and software of this prototype for use in a leg orthosis and to test the results with some actual lower limb orthosis users.
Project Level: Intermediate
Preferred Major/Background: Computer Science, Biomedical Engineering
Required Skills/Courses: C++, Java, App programming experience preferred, CITI training for human subjects research (may be obtained in first weeks of internship)
Submitted by: Krista Kutina, Goeran Fiedler
Name of Project: Sarcopenia study
Description: Sarcopenia is defined as the age-associated loss of skeletal muscle mass and function. The causes of sarcopenia are multifactorial and can include disuse, altered endocrine function, chronic diseases, inflammation, insulin resistance, and nutritional deficiencies. The effects of sarcopenia may be amplified in someone with a transtibial amputation. Residual limb volume is a key factor affecting prosthetic socket fit and limb health. A study by Fukumoto, et al (2012) shown that ultrasound has been used to measure the cross-sectional area of the quadriceps muscle successfully to determine muscle size and muscle quality. Furthermore, a study by Bochkezanian et al., used an extended-field-of-view ultrasound imaging protocol to measure increases in quadriceps muscle cross-sectional area after high-intensity knee extension NMES strength training. This project will be two-fold.
1) To develop methods to create a standardized test looking at muscle quality in able-bodied persons and transtibial amputees quantifying muscle quality via ultrasound.
2) Develop methods to standardize a knee extension test with the use of a hand held dynameter.
Project Level: Advanced
Preferred Major/Background: Bioengineering; Kinesiology; PT
Required Skills/Courses: The student should possess MAT lab skills and knowledge of imaging processing or have the desire to learn imaging processing.
Submitted by: Dr. Sara Peterson
Support for this program is provided by the National Science Foundation, Grant EEC 1852322.
Education > Undergraduates > ASPIRE > Projects > REU Projects 2019
