Models of physical phenomena can be developed using two distinct approaches: using expert knowledge of the underlying physical principles, or using experimental data to train a neural network. Here, our aim was to better understand the advantages and disadvantages of these two approaches. We chose to model cycling power because the physical principles are already well understood. Nine participants followed changes in cycling cadence transmitted through a metronome via earphones and we measured their cadence and power. We then developed and trained a physics-based model and a simple neural network model, where both models had cadence, derivative of cadence, and gear ratio as input, and power as output. We found no significant differences in the prediction performance between the models. The advantages of the neural network model were that, for similar performance, it did not require an understanding of the underlying principles of cycling nor did it require measurements of fixed parameters such as system weight or wheel size. These same features also give the physics-based model the advantage of interpretability, which can be important when scientists want to better understand the process being modelled.

To help educators deliver their physiology laboratory courses remotely, we developed an inexpensive, customizable hardware kit along with freely-available teaching resources. We based the course design on four principles that should allow students to conduct insightful experiments on different physiological systems. First, the experimental setup should not be constrained to laboratory environments. Second, students should be able to take this course without prior coding and electronics experience. Third, the hardware kit should be relatively inexpensive and all other resources should be freely-available. Fourth, all resources should be customizable for educators. The hardware kit consists of commercially-available electronic components, with a microcontroller as its hub (Arduino-friendly). All measurement systems can be assembled without soldering. The hardware kit is cost-effective (~cost of a textbook) and can be customized depending upon instructional needs. All software is freely-available and we share all necessary codes in open-access, online repositories for simple use and customizability. All lab manuals and additional video tutorials are also freely-available online and customizable. In our particular course, we have weekly asynchronous physiology lectures and one synchronous laboratory session, where students can get help with their equipment. In this paper, we will only focus on the novel and open-source laboratory part of the course. The laboratory includes four units (data acquisition, ECG, EMG, activity classification) and one final project. It is our intent that these resources will allow other educators to rapidly implement their own remote physiology laboratories, or to extend our work into other pedagogical applications of wearable technology.

Here we seek to control mechanical power output in outdoor cycling by adjusting commanded cadence of a cyclist. To understand cyclist’s dynamic behavior, we had one participant match their cadence to a range of commanded cadences. We then developed a mathematical model that predicts the actual mechanical power as a function of commanded cadence. The average absolute error between the predicted power of our model and the actual power was 15.9 ± 11.7%. We used this model to simulate our closed-loop controller and optimize for proportional and integral controller gains. With these gains in outdoor cycling experiments, the average absolute error between the target and the actual power was 3.2 ± 1.2% and the average variability in power was 2.9 ± 1.3%. The average responsiveness, defined as the required time for the actual power to reach 95% of the target power following changes in target power, was 7.4 ± 2.0 s.

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