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The intent of this project is to share early work that has been done to define means and methods used to instrument makerspaces for the purpose of collecting data about who is using what, and when they are using it. This capability is important to helping staff understand how students engage with their makerspaces. This poster discusses the link between goals, hardware, software, implementation and other choices/decisions that were used to instrument a makerspace. Having this example is important as these results can be adapted to other types of makerspaces, thereby enabling staff to better understand which tools students are using and impact of pedagogical interventions in makerspaces. 

Leveraging living muscle as an efficient and adaptive actuator for soft robots has been of increasing interest over the past decade, with a focus on proof‐of‐concept demonstrations of function. Reproducible design and scalable manufacturing of biohybrid machines requires methods to increase the stroke output of strain‐limited muscle actuators and enable accurate and precise quality control and performance monitoring. Compliant mechanical elements, termed flexures, are designed to enhance muscle contractile stroke to ≈5× previously reported values and decode contraction dynamics with high spatiotemporal resolution. Combining rigid and flexible elements within a linear elastic flexure enables us to outperform the sensitivity of gold standard elastomeric beam‐based measurements of muscle contraction at both low‐ and high‐frequency stimulations. Flexures are leveraged to make quantitative comparisons of force, work, and power outputs in muscle actuators, driving us to discover a new observation of frequency‐dependent fatigue in muscle, and also develop a novel method for tuning muscle contractile dynamics in a frequency‐independent manner. By enhancing the contractile stroke of muscle actuators and precisely tuning contractile dynamics and endurance with unprecedented precision, this study sets the stage for leveraging flexures to improve robust, reproducible, and predictive design and manufacturing of next‐generation biohybrid robots.