Pearl, O. D. | bioRxiv Science

bioRxiv2023

Analyzing Drumming Technique with Trajectory Optimization

Percussion began as a global phenomenon over seven thousand years ago and has continued to develop and shape human culture to this day. While drumming techniques have been qualitatively optimized in highly competitive environments like international orchestral, jazz, and marching arts competitions, little quantitative optimization has been performed to maximize technique efficiency and few tools currently exist to make a thorough quantitative analysis possible. Here, I demonstrate how trajectory optimization can be applied to the domain of percussion to (1) identify areas of suboptimality in experimental drumming strokes, (2) search for alternative locally optimal techniques, (3) analyze the sensitivity of optimal drumming techniques to variation in a drummers body type, and (4) analyze trends across different stroke types to create generalizable drumming strategies for a more coherent and efficient approach to drumming. Each of these quantifiable outcomes is interpreted to provide teachable insights for percussionists that are difficult to distill simply using the human eye and qualitative feedback. I also provide an open-source codebase for efficiently performing trajectory optimization on a biomechanical drumming arm model so that others can adapt this methodology for further biomechanical analysis and pedagogical development.

biophysics↗

bioRxiv2022

Fusion of Video and Inertial Sensing Data via Dynamic Optimization of a Biomechanical Model

Inertial sensing and computer vision are promising alternatives to traditional optical motion tracking, but until now these data sources have been explored either in isolation or fused via unconstrained optimization, which may not take full advantage of their complementary strengths. By adding physiological plausibility and dynamical robustness to a proposed solution, biomechanical modeling may enable better fusion than unconstrained optimization. To test this hypothesis, we fused RGB video and inertial sensing data via dynamic optimization with a nine degree-of-freedom model and investigated when this approach outperforms video-only, inertial-sensing-only, and unconstrained-fusion methods. We used both experimental and synthetic data that mimicked different ranges of RGB video and inertial measurement unit (IMU) data noise. Fusion with a dynamically constrained model significantly improved estimation of lower-extremity kinematics over the video-only approach and estimation of joint centers over the IMU-only approach. It consistently outperformed single-modality approaches across different noise profiles. When the quality of video data was high and that of inertial data was low, dynamically constrained fusion improved estimation of joint kinematics and joint centers over unconstrained fusion, while unconstrained fusion was advantageous in the opposite scenario. These findings indicate that complementary modalities and techniques can improve motion tracking by clinically meaningful margins and that data quality and computational complexity must be considered when selecting the most appropriate method for a particular application.

biophysics↗