Twitter @MSenseGroup

IMPROVING HUMAN HEALTH AND PERFORMANCE

M-Sense Research Group at the University of Vermont.

We develop wearable and mobile technologies for improving human health and performance.

Wearable and mobile technologies are poised to revolutionize assessment and intervention in a wide variety of populations. The M-Sense Research Group at the University of Vermont is at the forefront of this revolution, and is focused on developing methods for characterizing human biomechanics and physiology that employ these cutting-edge technologies. These methods are 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 use of wearable and mobile technologies for improving human health and performance. His work relies on expertise in human biomechanics, dynamics, signal processing, and machine learning 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 completed the contact form below and we will be in touch shortly.

Research Staff

Chris Petrillo

Research Technician

Steve Anderau

Research Technician

Graduate Student Researchers

Lukas Adamowicz

Graduate Research Assistant

MS, Mechanical Engineering, 2020

Reed Gurchiek

Graduate Research Assistant

PhD, Mechanical Engineering, 2023

Lindsey Tulipani

Graduate Research Assistant

PhD, Bioengineering, 2022

Undergraduate Student Researchers

Lara Weed

Undergraduate Research Assistant

BS, Biomedical Engineering, 2020

​

Currently co-op at MIT Lincoln Lab

Jordyn Scism

Undergraduate Research Assistant

BS, Biomedical Engineering, 2019

Kaseya Xia

Undergraduate Research Assistant

BS, Biomedical Engineering, 2020

Brett Meyer

Undergraduate Research Assistant

BS, Biomedical Engineering, 2020

Jon Ferri

Undergraduate Research Assistant

BS, Biomedical Engineering, 2020

​

Currently intern at LORD MicroStrain

Sarah Hampson

Undergraduate Research Assistant

BS, Biomedical Engineering, 2020

Caroline Duksta

Undergraduate Research Assistant

BS, Neuroscience, 2020

Gianna Barnhart

Undergraduate Research Assistant

BS, Neuroscience, 2021

Alumni

Ali Gohlke-Schermer

BS, Mechanical Engineering, 2018

​

Equipment Engineer at GLOBALFOUNDRIES

News

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.

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. Wearable Sensors and Machine Learning Diagnose Anxiety and Depression in Young Children. IEEE Conference on Biomedical and Health Informatics 2018: Las Vegas, NV, March 2018. Link

GET INVOLVED

NAME

E-MAIL

MESSAGE

Google Analytics is a web analysis service provided by Google and used to track activity on this site. Find Google's privacy policy here.

 (c) 2017