Changes for README.md: 73 added lines, 63 removed lines.
Original line number
Diff line number
Diff line
# workout-training
# Boxing Training Data Collection Project
## Project Overview
This project is designed to systematically track and analyze boxing training sessions, focusing on core strength and conditioning workouts. By collecting both objective metrics and subjective assessments, the aim is to enhance training effectiveness and monitor performance improvements over time.
## Getting started
## Goals
To make it easy for you to get started with GitLab, here's a list of recommended next steps.
-**Monitor Training Progress**: Log each session to observe progress and identify areas needing attention.
-**Evaluate Performance**: Gather data on key performance indicators to assess training efficacy.
-**Inform Training Adjustments**: Utilize insights from the data to refine training routines and optimize performance.
Already a pro? Just edit this README.md and make it your own. Want to make it easy? [Use the template at the bottom](#editing-this-readme)!
## Assumptions
## Add your files
-**Data Collection Methods**:
-**Apple Watch**: Sessions are recorded using the Workout app on the Apple Watch, capturing key metrics such as heart rate, total calories burned, and session duration.
-**Manual Data Entry**: Pre and Post-session data such as exercises performed and subjective assessments are manually input into a Python script or Streamlit app.
-[ ] [Create](https://docs.gitlab.com/ee/user/project/repository/web_editor.html#create-a-file) or [upload](https://docs.gitlab.com/ee/user/project/repository/web_editor.html#upload-a-file) files
-[ ] [Add files using the command line](https://docs.gitlab.com/ee/gitlab-basics/add-file.html#add-a-file-using-the-command-line) or push an existing Git repository with the following command:
-**Streamlit and Python**: Data will initially be added manually to a database (e.g., PostgreSQL) through Python scripts or a Streamlit app interface for ease of entry and future analysis.
-**Consistency**: Data collection relies on consistent manual recording before and after each workout session. As such, there may be occasional lapses due to tiredness, forgetting to record, or other factors.
-**Bias in Subjective Metrics**: The subjective metrics such as perceived effort and satisfaction could vary depending on mood, fatigue, etc. Still, they are valuable for personal insights.
## Integrate with your tools
## Data Dictionary
-[ ] [Set up project integrations](https://mscds.doc.gold.ac.uk/gitlab/sgras001/workout-training/-/settings/integrations)
| Variable Name | Data Type | Format | Description |
| `session_id` | Integer | Unique ID | A unique identifier for each recorded session. |
| `date` | Date | YYYY-MM-DD | The date when the workout session took place. |
| `start_time` | Time | HH:MM | The time the workout session started. |
| `end_time` | Time | HH:MM | The time the workout session ended. |
| `workout_type` | String | Text | Type of workout, e.g., "Core," "Strength," etc. |
| `total_duration` | Float | Minutes | Total workout duration, including active and rest periods. |
| `total_calories_burned` | Float | Kilocalories| The total calories burned during the session (active + resting). |
| `average_heart_rate` | Integer | Beats/min | The average heart rate during the session (in beats per minute). |
| `max_heart_rate` | Integer | Beats/min | The maximum heart rate achieved during the workout. |
| `min_heart_rate` | Integer | Beats/min | The minimum heart rate during the workout (including rest periods). |
| `exercise` | String | Text | Specific exercises performed during the session (e.g., "Plank," "Squats"). |
| `health_condition` | Integer | 1-5 | Rate your health condition **before** the workout (1 = very poor, 5 = excellent). |
| `motivation_level` | Integer | 1-5 | Rate your motivation **before** this session (1 = low, 5 = high). |
| `perceived_effort` | Integer | 1-10 | Rate the perceived effort of the workout (1 = easy, 10 = extremely hard). |
| `overall_satisfaction` | Integer | 1-5 | Rate your overall satisfaction **after** the session (1 = not satisfied, 5 = very satisfied). |
| `notes` | String | Text | Any manual notes related to the session, such as intensity or observations. |
## Collaborate with your team
### Pre-Session Data
-[ ] [Invite team members and collaborators](https://docs.gitlab.com/ee/user/project/members/)
-[ ] [Create a new merge request](https://docs.gitlab.com/ee/user/project/merge_requests/creating_merge_requests.html)
-[ ] [Automatically close issues from merge requests](https://docs.gitlab.com/ee/user/project/issues/managing_issues.html#closing-issues-automatically)
These variables should be recorded **before** the session begins:
## Test and Deploy
-**Health Condition**: Rate your physical condition prior to the session, noting any muscle soreness or fatigue.
-**Motivation Level**: Reflect on your motivation to train before starting.
Use the built-in continuous integration in GitLab.
### Post-Session Data
-[ ] [Get started with GitLab CI/CD](https://docs.gitlab.com/ee/ci/quick_start/index.html)
-[ ] [Analyze your code for known vulnerabilities with Static Application Security Testing (SAST)](https://docs.gitlab.com/ee/user/application_security/sast/)
-[ ] [Deploy to Kubernetes, Amazon EC2, or Amazon ECS using Auto Deploy](https://docs.gitlab.com/ee/topics/autodevops/requirements.html)
-[ ] [Use pull-based deployments for improved Kubernetes management](https://docs.gitlab.com/ee/user/clusters/agent/)
-[ ] [Set up protected environments](https://docs.gitlab.com/ee/ci/environments/protected_environments.html)
The following variables should be recorded **after** the session:
***
-**Perceived Effort**: How hard did the session feel? This can be recorded after the workout to assess difficulty.
-**Overall Satisfaction**: Rate your satisfaction with the session once it's completed.
# Editing this README
### Exercise Library
When you're ready to make this README your own, just edit this file and use the handy template below (or feel free to structure it however you want - this is just a starting point!). Thanks to [makeareadme.com](https://www.makeareadme.com/) for this template.
Initially, the following exercises for core strength and conditioning are tracked:
## Suggestions for a good README
-**Core**:
- Planks
- Crunches
- Leg Raises
-**Strength**:
- Squats
- Deadlifts
- Pushups
Every project is different, so consider which of these sections apply to yours. The sections used in the template are suggestions for most open source projects. Also keep in mind that while a README can be too long and detailed, too long is better than too short. If you think your README is too long, consider utilizing another form of documentation rather than cutting out information.
This list will be regularly updated as new exercises are incorporated into the training routine. Additional exercises can be added to either category (Core or Strength), or new categories can be created based on the type of workout performed. Each time a new exercise is added to the routine, the exercise library should be updated accordingly.
## Name
Choose a self-explaining name for your project.
**How to update**:
-**Add New Exercises**: As you introduce new exercises, simply append them to the appropriate section (Core, Strength, or new categories such as "Cardio" or "Boxing").
-**Create New Categories**: If the exercise does not fit into an existing category, create a new section with a relevant name (e.g., "Boxing Drills" or "Cardio") and list exercises under it.
-**Document Variations**: If variations of existing exercises are used (e.g., weighted squats, side planks), they can be added under their respective categories to ensure all variations are tracked.
## Description
Let people know what your project can do specifically. Provide context and add a link to any reference visitors might be unfamiliar with. A list of Features or a Background subsection can also be added here. If there are alternatives to your project, this is a good place to list differentiating factors.
This approach ensures the exercise library remains dynamic and adaptable as your training evolves.
## Badges
On some READMEs, you may see small images that convey metadata, such as whether or not all the tests are passing for the project. You can use Shields to add some to your README. Many services also have instructions for adding a badge.
## Implementation Plan
## Visuals
Depending on what you are making, it can be a good idea to include screenshots or even a video (you'll frequently see GIFs rather than actual videos). Tools like ttygif can help, but check out Asciinema for a more sophisticated method.
1.**Data Recording**: Use the Apple Watch to start the appropriate workout type (e.g., "Mixed Cardio" or "Other") to prevent auto-pause during boxing sessions. This ensures continuous data collection.
2.**Pre-Session Input**: Before the workout, manually log your health condition and motivation level.
3.**Post-Session Input**: After the session, manually log subjective metrics such as perceived effort and overall satisfaction into a Python script or a Streamlit app.
4.**Data Storage**: The collected data will be stored in a structured database (e.g., PostgreSQL) for future analysis.
5.**Data Review and Analysis**: Regularly review the dataset to identify trends, areas of improvement, and training progress.
## Installation
Within a particular ecosystem, there may be a common way of installing things, such as using Yarn, NuGet, or Homebrew. However, consider the possibility that whoever is reading your README is a novice and would like more guidance. Listing specific steps helps remove ambiguity and gets people to using your project as quickly as possible. If it only runs in a specific context like a particular programming language version or operating system or has dependencies that have to be installed manually, also add a Requirements subsection.
## Considerations
## Usage
Use examples liberally, and show the expected output if you can. It's helpful to have inline the smallest example of usage that you can demonstrate, while providing links to more sophisticated examples if they are too long to reasonably include in the README.
-**Consistency Issues**: As the data collection is partially manual, there may be inconsistencies or missed entries. This is expected, given that data collection requires discipline and can sometimes be affected by fatigue or other factors.
-**Subjective Data**: Subjective assessments such as motivation and perceived effort, while useful, can be influenced by external factors like mood, weather, or fatigue. This data is intended for personal insights rather than scientific accuracy.
## Support
Tell people where they can go to for help. It can be any combination of an issue tracker, a chat room, an email address, etc.
## Future Enhancements
## Roadmap
If you have ideas for releases in the future, it is a good idea to list them in the README.
-**Automated Data Integration**: In the future, exploring ways to automate the transfer of data from Apple Watch directly to the database could reduce manual entry and potential errors.
-**Expand Exercise Library**: As more exercises are incorporated into the routine, the exercise library will expand, allowing for richer data collection.
-**Advanced Analysis**: Future analysis might include more sophisticated methods such as trend analysis, correlations between subjective and objective metrics, or even machine learning models to predict performance improvement.
## Contributing
State if you are open to contributions and what your requirements are for accepting them.
---
For people who want to make changes to your project, it's helpful to have some documentation on how to get started. Perhaps there is a script that they should run or some environment variables that they need to set. Make these steps explicit. These instructions could also be useful to your future self.
You can also document commands to lint the code or run tests. These steps help to ensure high code quality and reduce the likelihood that the changes inadvertently break something. Having instructions for running tests is especially helpful if it requires external setup, such as starting a Selenium server for testing in a browser.
## Authors and acknowledgment
Show your appreciation to those who have contributed to the project.
## License
For open source projects, say how it is licensed.
## Project status
If you have run out of energy or time for your project, put a note at the top of the README saying that development has slowed down or stopped completely. Someone may choose to fork your project or volunteer to step in as a maintainer or owner, allowing your project to keep going. You can also make an explicit request for maintainers.
This README file will serve as the project's reference document, explaining the scope, goals, and the structured approach to data collection and analysis. As this project evolves, the README will be updated to reflect changes in data collection methods, additional metrics, or new insights.
