Project Summary
Real-time embedded systems in rehabilitation robotics adjust deadlines and execution times to handle dynamic patient-robot interaction, ensuring safety and adaptive therapy. These systems use adaptive control to alter task periods (e.g., faster feedback for spasticity) and prioritize tasks based on safety-critical needs, often using adaptive scheduling (e.g., Earliest Deadline First (EDF), Least Laxity First (LLF), and others). Common scenarios include mode changes (e.g., transitioning from normal to emergency mode), processor overload, or balancing power/performance in soft real-time systems like video processing.

REU Student Involvement
This REU Summer project will address research on the above topic. Students will use computer modeling and simulation to investigate real-time embedded systems scheduling. They will need to design, implement, and test a program written in an object-oriented language, such as Java. Through this experience, students will gain skills in real-time embedded systems modeling and implementation by ensuring the technolgy aligns with real rehabilitation needs. 

Project Summary
Human movement relies on the precise coordination of multiple muscles, many of which cross more than one joint. These biarticular muscles play complex roles in generating and controlling motion. For example, the gastrocnemius can simultaneously influence both the knee and ankle joints, sometimes accelerating them in opposite directions. This project focuses on studying how biarticular muscles such as the biceps brachii and triceps brachii long head contribute to movement at the shoulder and elbow. Using ADAMS software for biomechanical modeling, the team will analyze how these muscles can produce motion patterns that may seem counterintuitive, deepening our understanding of upper-limb coordination. 

REU Student Involvement
Students will use computer modeling and simulation to investigate joint biomechanics and muscle coordination. They will apply biomechanical principles to simulate arm movement and evaluate how electrical stimulation could improve motor control for individuals with spinal cord injuries. Through this experience, students will gain skills in musculoskeletal modeling, data interpretation, and rehabilitation research. 

Project Summary
This project investigates how mobile over-ground harness systems can promote independent play and community participation among children with disabilities. Using OpenCap.ai, an open source markerless motion capture platform, researchers will collect and analyze kinematic data from children engaged in play within natural environments. This data will provide quantitative measures of movement patterns and physical engagement as indicators of participation quality. By synchronizing motion capture outputs with video-based event coding, the research team will examine how specific movement behaviors—such as reaching toward peers, orienting to play objects, and transitioning between locations—relate to play engagement and social interaction. This multimodal approach will contribute to developing objective, data-driven measures that can inform clinical interventions and assistive technology design. 

REU Student Involvement 
REU students will participate in the design and implementation of play-based data collection protocols, work directly with children using the mobility support systems, and process motion capture and video data for analysis. They will explore methods to synchronize datasets and identify links between movement behaviors and social participation outcomes. Through this experience, students will gain hands-on training in pediatric rehabilitation research, motion capture technology, data analysis, and human-centered research methods. 

Available Research Projects

Dr. Espy is offering two potential research projects for the 2027 REU program. Students interested in working with Dr. Espy may select either project based on their research interests. The selected student will participate in one of the two projects

Project 1 Summary
This project aims to identify biomechanical indicators of instability to support the development of more effective and clinically feasible balance-training interventions. Impaired balance limits safe, independent movement and increases fall risk, yet clinicians currently rely on observational or self-reported scales with no objective, instrumented measures of task intensity or instability. To address this gap, the research team analyzes full-body motion-capture data from individuals performing balance tasks of varying difficulty to determine how movement patterns shift as balance demands increase. The project also explores whether wearable sensors could serve as practical substitutes for full motion capture in clinical environments, improving access to data-driven balance assessment.

REU Student Involvement 
REU students will help develop hypotheses about potential indicators of instability, analyze existing balance-task motion capture datasets, and assist in collecting pilot data from new participants performing targeted balance activities. Students will learn to process kinematic data, compare movement characteristics across different challenge levels, and evaluate whether wearable sensor signals align with full-body motion capture outputs. Through this experience, they will gain hands-on skills in biomechanics, data analysis, human movement science, and the development of clinically relevant measures for balance assessment and rehabilitation.

Project 2 Summary
The aim of this project is to develop a low-tech, clinically useful method to deliver standardized and measurable perturbations in reactive balance training. Reactive or perturbation-based balance training (PBT) is one of the most effective interventions to address fall risk for older adults. It allows people to learn effective motor responses to unexpected perturbations to their postural control. It is often delivered using high-tech, high-cost, low-accessibility lab-based systems. The Slip Trainer (MASS Rehab) is a low-tech, low-cost, clinically feasible PBT system. Its uptake and optimal use clinically has been limited by the lack of a method to deliver standardized and known perturbation magnitudes for training. The goal of this study would be to develop the hardware (low cost, low tech, easy user interfaces) and the algorithms to allow this device to be easily and effectively used clinically.

REU Student Involvement 
REU students will help develop the sensors and the feedback modalities (e.g., visual, auditory) to guide the user (clinician) to produce perturbations that are consistent and of the intended magnitude. This will involve overall design, hardware, processing algorithms, and iterative testing with the device operators to refine the system. Students will learn to design a low-tech clinical system for rehabilitation with specific user and system requirements; to build or adapt low-cost sensor systems; to incorporate signal processing to produce real time, usable outputs; and to conduct iterative system testing and refinement to optimize its use in clinical contexts. Through this experience, they will gain hands-on skills in biomechanics, data analysis, human movement science, and the development of clinically relevant rehabilitation systems for advanced balance training.

 Project Summary
This project explores how electromyography (EMG)-based interfaces and intelligent assistive robots can support individuals with motor impairments in daily activities and rehabilitation tasks. By integrating EMG signal processing, machine learning, and interactive user interfaces, the team aims to develop systems that recognize users’ muscle activity and motor intentions to enable intuitive communication and control of assistive robotic technologies. These systems will translate physiological signals into meaningful commands, allowing users to interact with robotic devices through natural muscle activation patterns. The research will investigate adaptive control strategies and personalized interfaces that improve accessibility, usability, and user engagement. Ultimately, this project contributes to the development of next-generation assistive technologies that enhance independence, motor rehabilitation, and human–robot interaction.

REU Student Involvement: 
The student will help design, implement, and evaluate EMG-based control and communication interfaces for assistive robotic systems. They will collect and analyze muscle-activity signals, develop signal-processing and machine-learning approaches to recognize motor intentions, and investigate how to translate these signals into robotic commands. The student will also help develop interactive user interfaces that provide real-time feedback and support intuitive communication between users and assistive devices. Through experimental evaluations, the student will assess system accuracy, responsiveness, usability, and effectiveness in rehabilitation-related tasks. The student may also collaborate with clinicians and individuals with motor impairments to inform design decisions and explore practical rehabilitation applications.

Collaborative Research Project with Dr. Eric Schearer

Project Summary 
This project focuses on developing a robotic system that can perform digital rectal stimulation, a daily bowel care procedure that many people with spinal cord injury depend on a skilled caregiver to complete. Building on our robot-assisted feeding platform already used in a hospital setting, the team is creating a semi-autonomous robot that combines AI-based computer vision, force-controlled motion, and voice- and switch-based interfaces so that a person with paralysis or an untrained family member can safely direct the robot. The work is developed together with people with spinal cord injury, caregivers, and nurses through monthly co-design reviews. The ultimate goal is to give people with spinal cord injury more independence, privacy, and dignity in their daily care while reducing reliance on an increasingly scarce caregiver workforce. 

REU Student Involvement 
The REU student will help develop and test the robot’s control and safety software on a commercial anatomical training simulator, including programming and evaluating motion, force-monitoring, and retraction behaviors. They will help run bench trials, log and analyze force and motion data, and support monthly co-design sessions where community members with spinal cord injury, caregivers, and nurses try the system and give feedback. Depending on interest, the student may also contribute to designing and testing the operator interface for users without hand function, or to evaluating the computer vision models used to guide the robot. The student will work with both the engineering and clinical teams at MetroHealth’s Old Brooklyn Health Center.

Project Summary 
This project integrates mechanical and biomedical engineering to advance next-generation tissue constructs and biomedical systems that address key challenges in regenerative medicine and disease modeling. The research team specializes in advanced biomanufacturing techniques—including extrusion-based bioprinting, fused deposition modeling (FDM), stereolithography (SLA), and digital light processing (DLP)—and has extensive expertise in designing soft, hydrogel-based biomaterials. 

These tools support collaborative projects across multiple institutions focused on peripheral nerve regeneration, modeling cancers such as glioblastoma and neuroblastoma, cardiovascular disease modeling, Fragile X syndrome modeling, and the fabrication of microgels for microbial delivery. The overarching aim is to develop innovative engineered tissues and platforms that improve our ability to study disease and support clinical translation. 

REU Student Involvement 
REU students will contribute to the design and fabrication of biomaterials and tissue-engineered constructs using state-of-the-art bioprinting and polymer processing techniques. They will prepare and characterize hydrogel systems, assist with experimental setups for disease modeling applications, and collaborate with multidisciplinary research partners. Students will gain hands-on experience in biomanufacturing, biomaterials development, and regenerative medicine research while building skills that prepare them for graduate studies and future careers in biomedical engineering. 

Project Summary
This project investigates and compares two promising artificial muscle technologies for rehabilitation robotics: pneumatically driven soft actuators and shape memory alloy/polymer-based actuators. The goal is to understand how actuator type, material composition, and geometry influence motion, force output, and control precision for assisting impaired limb function. Building on NASA-supported work on functional coatings for SMAs, the project also explores how engineered surface layers improve bonding between SMAs and polymer matrices to enhance actuation efficiency and durability. Finite element (FE) simulations in ABAQUS will model the coupled thermo-mechanical and fluid–structure interactions in each actuator type, while additive manufacturing will be used to fabricate soft polymer structures and composite interfaces.  

REU student involvement
Students will design actuator geometries, develop FE models to simulate actuation and stress distribution, and perform comparative analyses of pneumatic versus SMA-driven performance. They will also participate in 3D printing and experimental testing of soft actuators, collect strain and pressure data, and use simulation feedback to improve designs. Through this project, students will gain hands-on experience in computational mechanics, soft robotics, additive manufacturing, and human rehabilitation technologies. 

Collaborative Research Project with Dr. Gina Kubec

Project Summary
This project focuses on developing a robotic system that can perform digital rectal stimulation, a daily bowel care procedure that many people with spinal cord injury depend on a skilled caregiver to complete. Building on our robot-assisted feeding platform already used in a hospital setting, the team is creating a semi-autonomous robot that combines AI-based computer vision, force-controlled motion, and voice- and switch-based interfaces so that a person with paralysis or an untrained family member can safely direct the robot. The work is developed together with people with spinal cord injury, caregivers, and nurses through monthly co-design reviews. The ultimate goal is to give people with spinal cord injury more independence, privacy, and dignity in their daily care while reducing reliance on an increasingly scarce caregiver workforce..

REU Student Involvement
The REU student will help develop and test the robot’s control and safety software on a commercial anatomical training simulator, including programming and evaluating motion, force-monitoring, and retraction behaviors. They will help run bench trials, log and analyze force and motion data, and support monthly co-design sessions where community members with spinal cord injury, caregivers, and nurses try the system and give feedback. Depending on interest, the student may also contribute to designing and testing the operator interface for users without hand function, or to evaluating the computer vision models used to guide the robot. The student will work with both the engineering and clinical teams at MetroHealth’s Old Brooklyn Health Center.

Project Summary
Research in the Sikder Lab focuses on developing advanced biomaterials and 3D bioprinting strategies to repair and regenerate musculoskeletal tissues, including bone and skeletal muscle. The team engineers “smart” scaffolds that convert mechanical motion into biological signals, promoting healing and restoring functional tissue performance. This work integrates principles from engineering, biology, and medicine to create next-generation regenerative technologies.

REU Student Involvement
The student will participate in hands-on research related to materials synthesis, 3D bioprinting, and cell culture. Working closely with the interdisciplinary team, the student will help design, fabricate, and test biomaterial scaffolds that support tissue regeneration. The experience will provide practical training at the interface of engineering and biological sciences while contributing to ongoing development of innovative regenerative medicine tools.

Project Summary
Peripheral nerve injuries (PNI) affect millions worldwide and often result in long-term disability despite current treatment options. This project aims to develop a biodegradable, implantable platform that delivers custom MSC-derived exosomes (Exos) alongside wireless electrical stimulation (WES) to support nerve repair. Because Exos and WES may work synergistically to enhance regeneration, the platform enables both targeted Exos release and local stimulation of injured nerves. The work also explores how electrical stimulation influences Exos therapeutic content, with the goal of creating personalized treatments for a range of PNI conditions.

REU Student Involvement
The student will help develop and test the implantable platform, working with biomaterials, Exos preparation, and wireless stimulation systems. They will participate in experiments that assess Exos release, characterize the effects of electrical stimulation, and evaluate early indicators of nerve regeneration. This role provides hands-on experience at the intersection of biomaterials engineering and regenerative medicine while contributing to a promising new approach for PNI treatment.

Project Summary
This project investigates human performance in human-in-the-loop (HITL) systems, where people work with automated or semi-automated technologies to complete complex tasks. As automation expands in fields such as transportation, healthcare, and defense, it is critical to understand how human operators perceive, respond to, and influence these systems. The research examines how factors such as system design, autonomy level, and task complexity shape cognitive workload, decision-making, trust, and overall performance, with the goal of improving safety and efficiency in human-centered technologies.

REU Student Involvement
The student will assist in behavioral experiments and simulation studies that evaluate human performance under different operational conditions. Responsibilities include collecting and analyzing data related to cognitive workload, trust, and task accuracy, and working with tools such as neural haptics for sensory stimulation of the hand in a teleoperated bimanual robotics system. The student will collaborate with a multidisciplinary team of engineers, physiologists, phenomenologists, and ethicists while gaining hands-on experience in data collection, analysis, and research design.

Project Summary
The REAL Rehab Lab at Cleveland State University is developing a novel wearable monitoring system to detect and characterize voice production, swallowing, and coughing during everyday activities. Our goal is to investigate whether small, inexpensive, low power sensors placed on the neck can capture subtle tissue vibrations and movements associated with these behaviors. If successful, this technology could enable comfortable, low-cost, long-term monitoring of patients with voice, swallowing, and respiratory disorders without requiring conventional microphones or bulky sensors.

REU Student Involvement
The student will work alongside faculty and graduate researchers to develop and test prototype monitoring systems. Activities will include optimizing sensor placement, collecting preliminary signals during speaking, swallowing, and coughing, developing signal-processing methods to identify and distinguish these behaviors, and comparing signals against established reference sensors (e.g., accelerometers and microphones). Depending on the student's interests and experience, the project could emphasize hardware design, wireless sensing, signal processing, or machine learning.