Development of a Remote Wearables Laboratory Course
Bibliographic record
Abstract
Delivering a remote, hands-on lab course in human biomechanics can be challenging.During the COVID-19 pandemic, we developed an inexpensive, customizable hardware kit along with fully open-source teaching resources to help educators deliver their biomechanics lab courses remotely.These resources allow educators to teach students how to conduct hypothesis-driven biomechanical experiments, without the need for students to have previous coding or electronics experience.Additionally, all of our resources are fully customizable, allowing educators to fit the requirements of their respective biomechanics courses.Before developing our course resources, we defined multiple key principles.First, the whole system must be wearable.All measurement systems should be able to run battery powered and store data.Second, students should not require technical experience for taking the course.Setting up the hardware should work without soldering, and the number of wires and devices for a measurement system should be at a minimum.Students also should not require prior coding knowledge to set up software for the measurement systems and the data analysis.Third, all resources should be financially accessible.The hardware kit should be inexpensive (~cost of a textbook), all the software required should be open-source, and the instructional materials should be open-access.Lastly, the workload for educators to develop a similar course should be minimal.All hardware, software, and instructional materials should be customizable.Our hardware kit consists of commercially available electronic components, specified online [1], with a microcontroller as its base (Fig. 1).A microcontroller functions as a small computer and can read, alter, and output signals from and to other devices in the system.To make the hardware kit wearable, a 9V battery can power the system and a data logger can store the data on a micro SD, eliminating the need for a computer connection.To make the system solderless and minimize the number of wires, all devices in the kit directly connect to the microcontroller with no more than three wires.Moreover, our hardware kit is ~$120 US and can be customized with any device that communicates digitally, analog, or via a specific one-wire-communication system called "qwiic".To support students without prior coding experience, we share our codes in openaccess GitHub repositories [1].To make all software open-source, we chose Arduino IDE (Integrated Development Environment), a commonly used programming platform for microcontrollers, to program our hardware, and Python, the fastest growing programming language in the world, for data analysis
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.088 | 0.046 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".