Musculoskeletal conditions are the leading cause of disability worldwide, costing the United States’ economy $950 billion per year in direct and indirect costs. This burden is exacerbated by comorbidities that arise due to limited mobility, including obesity, cardiovascular disease, and diabetes. In this talk, I will share my work on key challenges toward precision rehabilitation from musculoskeletal injuries and diseases. I will first describe how we are combining deep learning with physics-based modeling to democratize motion capture for all researchers, clinicians, and patients, making it accessible from personal smartphones and inexpensive wearable sensors. These computational tools will strengthen the feedback loop between research and clinical practice, enabling large-scale research-grade data to be collected outside of specialized gait laboratories. I will then talk about the importance of understanding interactions between mechanical loading and biological factors, such as basal inflammation, when personalizing load-modifying interventions. To monitor the efficacy of these interventions (e.g., wearable haptic or assistive devices), we need new flexible and lightweight sensors that can capture meaningful biomechanical outcomes within clinically tolerable errors. I will last share our collaborative work on biomechanical characterization of a wearable capacitive sensing sleeve.