Wearable Sensing for Muscle Dynamics
Muscles enable our joints to perform movements that range from the slow and finely-grained, like surgery, to the fast and highly dynamic, like running. Joint torques depend on both the lever arm of the muscles and the tendon on bone (as a result of joint angle), musculotendon tissue compliance, and muscle force. Muscle force depends on the state of the muscle (length and velocity) and the amount of activation. We are using ultrasound imaging to directly measure the state of both surface and deep muscle, and electromyography to measure activation. We focus not only on applying these sensing modalities to human-machine interfaces but also ask how we can fundamentally enhance these tools for more accessible multimodal sensing of the human body. Drawing from expertise in the lab and an interdisciplinary approach fusing neuromechanics, computer vision, signal processing, electronics, and machine learning, we can then develop integrated systems with improved human intent estimation to enhance the response of human-robot interaction for both healthy and clinical users.
Researchers: Sebastian Roubert Martinez
3D Ultrasound Image Processing and Instrument Tracking
We continue to expand our 3D Ultrasound image processing toolbox to better enable new intracardiac surgical procedures. For example, we developed a detection technique that identifies the position of the instrument within the ultrasound volume. The algorithm uses a form of the generalized Radon transform to search for long straight objects in the ultrasound image, a feature characteristic of instruments and not found in cardiac tissue. When combined with passive markers placed on the instrument shaft, the full position and orientation of the instrument are found in 3D space. This detection technique is amenable to rapid execution on the current generation of personal computer graphics processor units (GPUs). Our GPU implementation detected a surgical instrument in 31 ms, sufficient for real-time tracking at the 25 volumes per second rate of the ultrasound machine.
Researchers: Paul Novotny, Marius George Linguraru and Petr Jordan
Ultrasound Imaging for Identifying Dynamics of Soft Tissue
Understanding the in vivo dynamics of soft tissue, including its interaction with adjacent tissues, is a key problem for many fields, because it is expected to improve the fidelity of computational models of the human body. For instance, a patient-specific model with corresponding soft tissue dynamics would be useful in preoperative surgical planning and training. Also, a passenger-specific model including internal organs’ dynamics would contribute to adaptive control of vehicular dynamics for relieving motion sickness and improving rider comfort. We are studying a non-invasive method to identify the in vivo dynamics. Ultrasound is a promising measurement modality due to its compact form, low cost, high sampling rate, and non-invasive nature. We combine an ultrasound imaging system with a whole-body vibration exciter. Using this system, the dynamic characteristics of soft tissues can be identified by associating the observed response with the input vibration. An optical tracker is used to compensate for the vibration-induced motion of the ultrasound probe. Combining multiple ultrasound images taken from different observation points, four-dimensional in vivo motion and deformation of soft tissue can be reconstructed.
Researchers: Daisuke Yamada and Alperen Degirmenci