Machine Learning and Biomechanical Sensing Toward Real-Time In-The-Loop Gait and Joint Health Optimization
Author(s)
Rosa, Luis G.
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Abstract
Despite advances in wearable sensing and assistive devices, current systems often rely on indirect or delayed signals that limit their ability to capture subcutaneous physiological dynamics in real-time. This challenge hinders progress across domains ranging from exoskeleton control to clinical monitoring in populations with movement or inflammatory disorders. This work aimed to expand corresponding biomechanical sensing capabilities by developing a novel sensing framework capable of extracting under-the-skin biomechanical signals from muscle and tendon structures using ultrasound and active acoustics. To achieve this, we (Aim 1) developed a machine learning pipeline for real-time estimation of muscle fascicle lengths from B-mode ultrasound images to enable “muscle-in-the-loop” feedback systems; (Aim 2) introduced and benchmarked an active acoustics sensor capable of measuring Achilles tendon loading in real-time with low latency across a wide range of locomotion tasks; and (Aim 3) applied our acoustics sensing approach in a pediatric arthritis cohort to quantify how inflammation-related physiological alterations affect machine learning task classification performance, highlighting its potential as a non-invasive biomarker for disease presence and severity. Collectively, these studies establish a new direction for non-invasive, task-relevant muscle-tendon sensing that could inform next-generation systems for rehabilitation, augmentation, and clinical assessment.
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Date
2025-12
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Text
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Dissertation (PhD)