Twitter @MSenseGroup

Empowering Patients with Digital Health Technologies 

M-Sense Research Group at the University of Vermont.

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We develop digital biomarkers and therapeutics for improving human health and performance

Digital health technologies are poised to revolutionize healthcare. The M-Sense Research Group at the University of Vermont is at the forefront of this revolution. We pair cutting-edge wearable and mobile technologies with our expertise in biomedical signal processing, machine learning, biomechanics, and computational dynamics for the development and validation of novel digital biomarkers and therapeutics. These novel health technologies are developed and deployed in collaborative, cross-disciplinary research with colleagues in engineering, medicine, mental health, and movement science. This research is highly translational, and can lead to entrepreneurial opportunities for students that help improve people’s lives.  See the links below to learn more about what we do and to find out how to get involved.

People

The M-Sense Group is composed of Researchers from a variety of disciplines, reflecting the multidisciplinary nature of the work that we do.

Ryan S. McGinnis, PhD

Assistant Professor, Department of Electrical and Biomedical Engineering

Director, M-Sense Research Group

Research interests focus on the development of digital biomarkers and therapeutics. His work relies on technical expertise in biomedical signal processing, machine learning, biomechanics, and computational dynamics developed during past positions in both academia and industry. He is passionate about developing new technology-based solutions to pressing problems facing society. 



 


We’re always looking for passionate researchers to join our team! Please fill out the contact form below or send an email to  Ryan.McGinnis@uvm.edu to learn how you can get involved.


If you’d like to volunteer for one of our ongoing research studies, please complete the contact form below and we will be in touch shortly.

See Biosketch
See CV

Graduate Student Researchers

Lindsey Tulipani

Graduate Research Assistant

PhD, Bioengineering, 2021

Reed Gurchiek

Graduate Research Assistant

PhD, Mechanical Engineering, 2021

Brett Meyer

Graduate Research Assistant

MS, Biomedical Engineering, 2021

Jordyn Scism

Graduate Research Assistant

MS, Biomedical Engineering, 2020

Undergraduate Student Researchers

Lara Weed

Undergraduate Research Assistant

BS, Biomedical Engineering, 2020

Jon Ferri

Undergraduate Research Assistant

BS, Biomedical Engineering, 2020

Dale Larie

Undergraduate Research Assistant

BS, Biomedical Engineering, 2020

Anna Ursiny

Undergraduate Research Assistant

BS, Biomedical Engineering, 2022

Miles Welbourn

Undergraduate Research Assistant

BS, Biomedical Engineering, 2020

Melissa Seib

Undergraduate Research Assistant

BS, Mechanical Engineering, 2019

Connor Harrigan

Undergraduate Research Assistant

BS, Biomedical Engineering, 2020

Alumni

Chris Petrillo

BS, Mechanical Engineering, 2018


Knowledge Engineer at Amazon

Steve Anderau

BS, Mechanical Engineering, 2018


Data Management Research Tech Intermediate at Michigan Medicine

Ali Gohlke-Schermer

BS, Mechanical Engineering, 2018


Equipment Engineer at GLOBALFOUNDRIES

Lukas Adamowicz

MS, Mechanical Engineering, 2019


Data Scientist at Pfizer

News

  • • August 2019: Reed and Lindsey are headed to ASB/ISB in Calgary, AB, Canada to present their works titled Wearable Sensor-Based Remote Gait Analysis Detects Altered Duty Factor and Phase Specific Quadriceps Muscle Activation in Patients Recovering from ACL Reconstruction Surgery and Wearables demonstrate transition technique relates to balance confidence and fatigue in persons with multiple sclerosis, respectively. 
  • • July 2019: Congrats to Lindsey for being awarded a registration award for 9th International Symposium on Gait and Balance in Multiple Sclerosis: Technology for Assessment and Intervention.
  • • July 2019: McGinnis founds Allostatech, LLC to commercialize the WePanic digital therapeutic for treating panic attacks. 
  • • June 2019: Congrats to Reed for being award the 2019 McClure Research Award from the UVM Department of Orthopedics and Rehabilitation.
  • • May 2019: Congrats to Reed for being awarded a NSF registration award for the IEEE International Conference on Body Sensor Networks
  • • May 2019: Congrats to Reed for winning the Best Student Presentation Award at the IEEE International Conference on Body Sensor Networks
  • • May 2019: Congrats to Reed for being awarded a 2019 NASA Space Grant Research Fellowship 
  • • May 2019: Congrats to Lukas for accepting a position in Data Science at Pfizer
  • • May 2019: Congrats to Lukas for being awarded the Outstanding MS Dissertation Award from the UVM Graduate College 
  • • May 2019: Congrats to Lukas for being awarded the ME Department Research Award from the UVM Department of Mechanical Engineering 
  • • May 2019: Congrats to Lukas for successfully defending his MS thesis
  • • May 2019: Congrats to Brett for receiving a prestigious Summer Undergraduate Research Fellowships from UVM 
  • • January 2019: Congrats to Lindsey for being awarded an Educational Travel Grant to ACTRIMS in Dallas, TX

Projects

Below are several example projects currently being tackled by M-Sense Group Researchers. Projects are cross disciplinary and are often pursued by a team of researchers working in concert to develop innovative solutions.

Tracking Symptom Progression in Neurological Disorders

This project focuses on the design, development, and deployment of a wearable sensor system for monitoring free living activities, mobility, and sleep in people with neurological disorders.  Researchers are developing statistical models for tracking these behavioral indicators of symptom progression using concepts from machine learning (including deep learning) and signal processing. The system will be deployed to large populations of patients to track symptom progression and its effects on human behavior longitudinally. This approach will enable a holistic, objective measure of intervention efficacy, and could potentially provide a means for improving the diagnosis and treatment of these disorders.

  • • McGinnis, RS, Mahadevan, N, Moon, Y, Seagers, K, Sheth, N, DiCristofaro, S, Silva I, Jortberg, E, Wright, J, Ceruolo, M, Pindado, JA, Ghaffari, R, Patel, S.  A Machine Learning Approach for Gait Speed Estimation using Skin-mounted Wearable Sensors: From Healthy Controls to Individuals with Multiple Sclerosis. PLOS One: (2017) 12, e0178366. Link
  • • Moon, Y, McGinnis, RS, Motl, RW, Seagers, K, Sheth, N, Wright, J, Ghaffari, R, Sosnoff, JS. Monitoring of Gait in Multiple Sclerosis with Novel Wearable Motion Sensors. PLOS One: (2017) 2, e0171346. Link

WE-Panic  

This project focuses on development and validation of a mobile application to provide biofeedback therapy for treating panic attacks. Researchers are developing computationally efficient algorithms for extracting heart and respiratory rate from the sensing modalities available on today’s mobile devices. These estimates are provided to users in real time while they are having a panic attack thereby enabling biofeedback therapy, an evidence-based approach to treating this pressing mental health problem.  The We-Panic app aims to bring this effective, evidence based treatment out of the lab and directly to users, wherever and whenever their panic attacks occur.

  • • McGinnis, RS, McGinnis, EW, Petrillo, CJ, Price, M. Mobile Biofeedback Therapy for the Treatment of Panic Attacks: A Pilot Feasibility Study. IEEE Conference on Body Sensor Networks 2019: Chicago, IL, May 2019. Link
  • • This project is being commercialized by M-Sense Group spin-off company Allostatech, LLC

Measuring Human Biomechanics Outside the Laboratory  

This project focuses on the development and validation of a wearable sensor system for accurately monitoring musculoskeletal joint kinematics and kinetics non-invasively outside of laboratory environments. Researchers on the project are applying advanced techniques from signal processing, human biomechanics, and dynamics to accurately characterize joint motion as compared to gold-standards including  optical motion capture and dual fluoroscopy. Outcomes from this research have direct implications for applications in orthopedics, physical therapy, sports training, and sports medicine where they can be used to identify and track injury risk, rehabilitation, and performance.     

  • • McGinnis, RS, Cain, SM, Tao, S, Whiteside, D, Goulet, GC, Gardner, EC, Bedi, A, Perkins, NC. Validation of a Novel IMU-based Three-dimensional Hip Angle Measurement in Diagnostic Tests for Femoroacetabular Impingement.  IEEE Transactions on Biomedical Engineering: (2015) 62, 1503-1513. Link
  • • McGinnis, RS, Cain, SM, Davidson, SP, Vitali, R, Perkins, NC, McLean, SG.  Quantifying the Effects of Load Carriage and Fatigue under Load on Sacral Kinematics during Countermovement Vertical Jump with IMU-based Method.  Journal of Sports Engineering: (2016) 19, 21-34. Link
  • • McGinnis, RS, Hough, J, Perkins, NC. Accuracy of wearable sensors for estimating joint reactions. ASME Journal of Computational and Nonlinear Dynamics: (2017) 12, 041010. Link

Improving Mental Health Assessment for Young Children  

This project focuses on the development and validation of wearable and mobile technologies to provide biomarkers for accurately characterizing risk for developing mood and anxiety disorders in young children. Researchers on the project are applying advanced techniques from signal processing, human biomechanics, and machine learning to develop statistical models for predicting risk for developing a disorder.  These technologies, if successful, have the potential to drastically reshape mental health assessment in this population. Ultimately this will help children get the care they need early which improves the efficacy of prevention and intervention efforts in this population. Check out an interview McGinnis did on this topic!

  • • McGinnis, RS, McGinnis, EW, Hruschak, J, Ip, K, Morlen, D, Lawler, J, Lopez-Duran, NL, Fitzgerald, K, Rosenblum, KL, Muzik, M. Wearable Sensors Detect Childhood Internalizing Disorders During Mood Induction Task. PLoS One: (2018) 13, e0195598. Link
  • • McGinnis, RS, McGinnis, EW, Fitzgerald, K, Muzik, M, Perkins, NC, Rosenblum, K. Movements indicate threat response phases in children at-risk for anxiety. IEEE Journal of Biomedical and Health Informatics: (2017) 21, 1460-1465. Link
  • • McGinnis, RS, McGinnis, EW, Hruschak, J, Lopex-Duran, NL, Fitzgerald, K, Rosenblum, K, Muzik, M. Rapid detection of internalizing diagnosis in young children enabled by wearable sensors and machine learning. PLoS One: (2019) 14,  e0210267. Link

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