{"id":19710377,"url":"https://github.com/aditya-xq/har-test","last_synced_at":"2026-06-09T02:33:45.575Z","repository":{"id":199282016,"uuid":"576942053","full_name":"aditya-xq/har-test","owner":"aditya-xq","description":"A test project to explore smartwatch data simulation and human activity recognition","archived":false,"fork":false,"pushed_at":"2023-10-09T14:07:08.000Z","size":4,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-02-27T19:32:50.020Z","etag":null,"topics":["human-activity-recognition","python","simulation","wearable-sensors"],"latest_commit_sha":null,"homepage":"","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/aditya-xq.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null}},"created_at":"2022-12-11T13:45:33.000Z","updated_at":"2023-10-09T13:48:06.000Z","dependencies_parsed_at":null,"dependency_job_id":"0d7754d6-8170-44d8-9376-cd9dc0553434","html_url":"https://github.com/aditya-xq/har-test","commit_stats":null,"previous_names":["aditya-xq/har-test"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/aditya-xq/har-test","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/aditya-xq%2Fhar-test","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/aditya-xq%2Fhar-test/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/aditya-xq%2Fhar-test/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/aditya-xq%2Fhar-test/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/aditya-xq","download_url":"https://codeload.github.com/aditya-xq/har-test/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/aditya-xq%2Fhar-test/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":34089328,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-05-26T15:22:16.424Z","status":"online","status_checked_at":"2026-06-09T02:00:06.510Z","response_time":63,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["human-activity-recognition","python","simulation","wearable-sensors"],"created_at":"2024-11-11T22:07:13.518Z","updated_at":"2026-06-09T02:33:45.554Z","avatar_url":"https://github.com/aditya-xq.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# SmartWatch Activity Simulator 🕰️\n\nHello and welcome to the **SmartWatch Activity Simulator**! This fun little test project simulates the data generation of a smartwatch's accelerometer and gyroscope. But that's not all; it also predicts human activity based on this data! \n\n## How it works:\n1. **SmartWatchSimulator.py:** This file contains a class `Smartwatch` which can generate random accelerometer and gyroscope data, simulating the kind of data a real smartwatch might capture.\n\n2. **App.py:** This is where the action happens! It uses the `Smartwatch` class to generate data and then predicts the activity using a simple heuristic. The results are printed out for you to see.\n\n## How to use:\n1. Run the `App.py` script.\n2. Watch the console! You'll see generated data from the smartwatch's sensors and then a prediction of the human activity based on that data.\n3. It will keep running, generating a new prediction every second. If you want to stop, simply press `CTRL + C` or close the console.\n\n## Future Directions:\n🚀 **Machine Learning Integration:** Instead of using simple heuristics, we can integrate a machine learning model to predict activities based on the data for even more accurate predictions!\n\n🎨 **GUI Implementation:** A simple graphical user interface could be added to visualize the data and predictions in a more user-friendly manner.\n\n🌍 **Additional Sensors:** We can simulate other sensors such as heart rate monitors or GPS to generate even richer datasets.\n\n🕵️ **Anomaly Detection:** Beyond just predicting activities, we can detect unusual patterns in the data, potentially useful for health monitoring or fall detection.\n\n## Conclusion:\nThis project is ideal for anyone new to programming and wanting a sneak peek into how smartwatch data works or for those wanting to experiment with their own activity prediction algorithms.\n\n**Happy Coding!** 🚀👩‍💻👨‍💻🎉\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Faditya-xq%2Fhar-test","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Faditya-xq%2Fhar-test","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Faditya-xq%2Fhar-test/lists"}