Daniel Ludvig

Non-Invasive Tools to Measure Muscle Function

Assessing Muscle and Tendon Properties with Non-Invasive Techniques

Problem: It's a significant challenge to measure the mechanical function of individual muscles, which can be affected by aging or injury and limit mobility and independence. Current mechanical measures cannot distinguish the function of individual muscles from other muscles crossing the joint or the tendons they attach to while clinical imaging tools like ultrasound elastography lack a direct, validated relationship between what they measure and muscle properties.

Method: We have pioneered non-invasive techniques that combine robotic actuators with traditional ultrasound to measure the mechanical properties of individual muscles and tendons. In a complementary effort, we've validated ultrasound elastography by comparing its measurements to direct force and stiffness readings from muscles. We are also developing novel methods to compute both muscle force and stiffness from using shear-wave elastography.

Real-World Impact: These tools provide objective, non-invasive ways to assess muscle health, clarifying the role of muscle in stiffness-related impairments. For example, our techniques have shown that in older adults, reduced ankle stiffness is due to a decrease in muscle strength, not changes in the tissue itself. This enables new classes of biomechanical experiments that can lead to more targeted interventions for a wide range of pathologies, from sports injuries to age-related mobility issues.

Below is a selected list of my most impactful publications. For a complete list and citation metrics, please visit my Google Scholar page.

• Ludvig, D., & Perreault, E. J. (2012). "System Identification of Physiological Systems Using Short Data Segments." IEEE Transactions on Biomedical Engineering, 59(12), 3469-3476.

  This work presents a highly efficient method for analyzing complex biological data, which can facilitate more rapid and reliable data collection and analysis. This innovation could accelerate the development and validation of new technologies and interventions in fields related to human physiology.

  Link to Publication

• Jakubowski, K., Ludvig, D., Bujnowski, D., & Perreault, E. J. (2022). "Simultaneous Quantification of Ankle, Muscle, and Tendon Impedance in Humans." IEEE Transactions on Biomedical Engineering, 69(12), 3448–3456.

  This research introduces a groundbreaking non-invasive technology that can separately measure the mechanical contributions of muscles and tendons to joint function. This provides a precise, data-driven tool for diagnostics and for monitoring the effectiveness of interventions, offering a new level of quantitative insight into musculoskeletal health.

  Link to Publication

• Ludvig, D., Whitmore, M. W., & Perreault, E. J. (2022). "Leveraging Joint Mechanics Simplifies the Neural Control of Movement." Frontiers in Integrative Neuroscience, 16, 802608.

  This publication reveals a fundamental principle of human motor control: utilizing a joint's intrinsic mechanics can simplify the neural effort required for a task and improve overall performance. This insight is a significant consideration for the design of future medical devices, assistive technologies, and advanced robotics that need to work seamlessly with the human body.

  Link to Publication

• Kim, S. J., Wen, Y., Ludvig, D., Kucuktabak, E. B., Short, M. R., Lynch, K.,... & Pons, J. L. (2022). "Effect of dyadic haptic collaboration on ankle motor learning and task performance." IEEE Transactions on Neural Systems and Rehabilitation Engineering, 31, 416–425.

  This study validates a new, human-centric model for robot-assisted therapy. It demonstrates that physically linking a therapist and a patient through a robotic system leads to more effective motor learning and higher muscle activation compared to conventional robotic guidance. This approach offers a new paradigm for therapeutic technology by merging human expertise with robotic precision to achieve superior outcomes.

  Link to Publication

A detailed version of my academic and professional experience is available in my full CV as well as in this concise resume.

Courses Taught

• BME 312: Biomedical Applications in Machine Learning

  This course provides a comprehensive overview of machine learning applications in the biomedical field. It covers fundamental concepts in supervised and unsupervised learning, including models such as Linear/Logistic Regression, Random Forests, and Deep Neural Networks. Students learn to apply these techniques in Python to analyze and interpret various biomedical data, from heart disease to Parkinson's, and explore new frontiers in the field.

• BME 313: Wearable Devices: From Sensing to Biomedical Inference

  This course explores the transformation of biomedicine through wearable sensors like smartwatches and cutting-edge devices. It addresses the challenges of inferring health information from large datasets, covering signal processing, machine learning, and AI. The curriculum uses project-based learning to teach data acquisition, signal processing, and model-based inference for real-world applications in human movement and rehabilitation.

I am open to new opportunities and collaborations. Please feel free to reach out to me via email.