Tracking Joint Kinematics and Muscle Kinetics with Multimodal Wearable Sensors

Abstract

Smart wearables are becoming increasingly popular, taking forms such as watches, glasses, and even rings. Health tracking has emerged as one of their key functions, offering unobtrusive and continuous monitoring beyond the capabilities of traditional clinical tools. Current smart wearables primarily focus on general health metrics, such as heart rate, blood oxygen level, and calories burned. However, injuries and conditions often arise at the tissue level, specifically within muscles, bones, or joints. Tissue-specific tracking can thus provide more detailed and actionable health insights, making it a transformative feature of next-generation smart wearables. This dissertation aims to bridge the gap between wearable technologies and tissue-specific measurements by developing and evaluating a range of wearable sensing systems to track joint movements and muscle force outputs, paving the way for more precise and personalized health monitoring. This thesis begins by developing wearable joint kinematics estimation strategies. First, we present a soft sensing shirt designed to track 3D shoulder kinematics during both cyclic and random arm movements. The shirt incorporates eight textile-based capacitive strain sensors with high linearity, low hysteresis, and effective shielding against human parasitic capacitance, making it ideal for wearable applications. In a single-participant study, the sensing shirt achieved accurate tracking with an ensemble-based machine learning algorithm, yielding root mean square errors (RMSEs) below 4.5° for joint angle estimation and normalized root mean square errors (NRMSEs) below 4% for joint velocity estimation. Next, we propose a frame alignment method for inertial measurement units (IMUs), which are commonly validated against ground truth optical motion capture systems (OMC). Fair comparisons between IMU and OMC measurements require accurate frame alignment between the two systems. Unlike existing methods that address the local and global frame misalignments as separate issues, we present an assumption-free, data-based approach that simultaneously aligns both local and global frames via quaternion-based least squares optimization. Tested with data from six participants, this method achieved alignment errors under 1.5° and effectively isolated IMU drift during long-duration dynamic movements. Lastly, we develop a wearable system for long-duration 3D shoulder kinematics tracking by fusing soft sensor and IMU signals. Unlike existing wearable systems, our approach requires only a short calibration period and no external lab-based equipment. In tests with six participants, the system uses just 2.5 minutes of random arm movement calibration to achieve RMSEs of 4.5° across all degrees of freedom for over an hour of continuous functional activities, including desk work, dancing, walking, activities of daily living, and strength training. Furthermore, we demonstrate the system’s potential for real-world applications by showing that a simplified configuration using four soft sensors (instead of eight) and a shorter 90-second calibration could still achieve RMSEs of 5.1°. In addition to joint kinematics tracking, this thesis contributes to wearable sensing methods for muscle kinetics. First, we present a strategy for estimating corresponding joint torque from muscles with different architectures during various dynamic activities using wearable A-mode ultrasound. By tracking changes in muscle thickness with single-element ultrasonic transducers, we use muscle deformation data to estimate elbow and knee torque, achieving NRMSEs below 7.6% and coefficients of determination (R^2) exceeding 0.92 during controlled isokinetic contractions across 10 participants. We further demonstrate the feasibility of wearable joint torque estimation with 5 participants during dynamic real-world tasks, including weightlifting, cycling, and treadmill and outdoor locomotion. Lastly, we introduce a wearable muscle fatigue tracking strategy that combines A-mode ultrasound with electrical stimulation. This hybrid approach reveals that muscle deformation from electrically induced contractions correlates strongly with muscle fatigue. Using a muscle deformation index derived from A-mode ultrasound, we reliably track muscle fatigue with correlation coefficients (r) of 0.85 during dynamic, volitional fatiguing contractions on 8 participants using an isokinetic dynamometer. Together, this thesis advances the design and validation of wearable sensing systems for joint kinematics and muscle kinetics estimation, paving the way for practical applications in real-world, unconstrained environments.