Krithika Swaminathan

All Publications

• Learning-based 3D human kinematics estimation using behavioral constraints from activity classification NATURE COMMUNICATIONS Kim, D., Jin, Y., Cho, H., Jones, T., Zhou, Y., Fadaie, A., Popov, D., Swaminathan, K., Walsh, C. J. 2025; 16 (1): 3454

  Abstract

  Inertial measurement units offer a cost-effective, portable alternative to lab-based motion capture systems. However, measuring joint angles and movement trajectories with inertial measurement units is challenging due to signal drift errors caused by biases and noise, which are amplified by numerical integration. Existing approaches use anatomical constraints to reduce drift but require body parameter measurements. Learning-based approaches show promise but often lack accuracy for broad applications (e.g., strength training). Here, we introduce the Activity-in-the-loop Kinematics Estimator, an end-to-end machine learning model incorporating human behavioral constraints for enhanced kinematics estimation using two inertial measurement units. It integrates activity classification with kinematics estimation, leveraging limited movement patterns during specific activities. In dynamic scenarios, our approach achieved trajectory and shoulder joint angle errors under 0.021 m and 6 . 5 ∘ , respectively, 52% and 17% lower than errors without including activity classification. These results highlight accurate motion tracking with minimal inertial measurement units and domain-specific context.

  View details for DOI 10.1038/s41467-025-58624-6

  View details for Web of Science ID 001465538200001

  View details for PubMedID 40216761

  View details for PubMedCentralID PMC11992036

• Estimation of joint torque in dynamic activities using wearable A-mode ultrasound NATURE COMMUNICATIONS Jin, Y., Alvarez, J. T., Suitor, E. L., Swaminathan, K., Chin, A., Civici, U. S., Nuckols, R. W., Howe, R. D., Walsh, C. J. 2024; 15 (1): 5756

  Abstract

  The human body constantly experiences mechanical loading. However, quantifying internal loads within the musculoskeletal system remains challenging, especially during unconstrained dynamic activities. Conventional measures are constrained to laboratory settings, and existing wearable approaches lack muscle specificity or validation during dynamic movement. Here, we present a strategy for estimating corresponding joint torque from muscles with different architectures during various dynamic activities using wearable A-mode ultrasound. We first introduce a method to track changes in muscle thickness using single-element ultrasonic transducers. We then estimate elbow and knee torque with errors less than 7.6% and coefficients of determination (R2) greater than 0.92 during controlled isokinetic contractions. Finally, we demonstrate wearable joint torque estimation during dynamic real-world tasks, including weightlifting, cycling, and both treadmill and outdoor locomotion. The capability to assess joint torque during unconstrained real-world activities can provide new insights into muscle function and movement biomechanics, with potential applications in injury prevention and rehabilitation.

  View details for DOI 10.1038/s41467-024-50038-0

  View details for Web of Science ID 001270732400017

  View details for PubMedID 38982087

  View details for PubMedCentralID PMC11233567