Toward a wearable gait lab for the quantitative assessment of musculoskeletal function

Donatella Simonetti

Research output: ThesisPhD Thesis - Research UT, graduation UT

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Understanding a person's musculoskeletal function, which involves assessing the mechanical forces exerted by individual muscles and the resulting joint torques, is crucial for comprehending movement mechanisms and designing effective training and rehabilitation strategies. In the case of neurologically impaired individuals like stroke survivors, the primary rehabilitation goal is to restore locomotion function, significantly impacting their quality of life.
In clinical settings, swiftly measuring the forces produced by individual muscles in a quantitative and non-invasive manner is challenging. Musculoskeletal assessment often relies on fast, standardized observational tools for qualitative evaluations of endurance and functional capability. Alternatively, fully equipped laboratories with multiple sensors provide quantitative evaluations of musculoskeletal performance at the joint and muscle levels, but this comes at the cost of time and complexity. Unfortunately, neither approach efficiently combines simplicity, rapidity, and quantitative evidence of muscle strength, essential requirements in standard clinical rehabilitation.

This work aims to obtain rapid and quantitative measures of musculoskeletal function by combining wearable sensors, advanced musculoskeletal modeling, and signal-processing techniques. Three studies systematically reveal key aspects for developing a smart wearable tool. Firstly, we introduce a fully automated muscle localization algorithm to eliminate manual labor in identifying muscle sites. We combine such techniques with an EMG-sensorized leg garment and an EMG-driven model for the estimation of dorsi-plantar flexion ankle torque during a variety of dynamic tasks performed by healthy participants. The pipeline is then enhanced to generalize across different anatomies and neuromuscular control strategies of healthy participants and post-stroke individuals. A novel EMG-equipped garment and improved muscle localization algorithm are introduced. Lastly, laboratory-based technologies are replaced with five IMU sensors, enabling a fully wearable technology for non-invasive estimation of musculoskeletal parameters.
In conclusion, this dissertation demonstrates that wearable and automated technologies offer a viable alternative to standard laboratory techniques, potentially saving experimental time crucial in clinical settings. These technologies could facilitate the use of advanced EMG-driven modeling pipelines in clinics, as well as in recreational and occupational domains.
Original languageEnglish
QualificationDoctor of Philosophy
Awarding Institution
  • University of Twente
  • Sartori, Massimo, Supervisor
  • Koopman, Bart F.J.M., Supervisor
Award date19 Jan 2024
Place of PublicationEnschede
Print ISBNs978-90-365-5884-6
Electronic ISBNs978-90-365-5885-3
Publication statusPublished - 19 Jan 2024


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