{"id":20162651,"url":"https://github.com/codeamt/human-activity-recognition","last_synced_at":"2025-08-26T12:20:56.704Z","repository":{"id":201923635,"uuid":"322541402","full_name":"codeamt/Human-Activity-Recognition","owner":"codeamt","description":"Human Activity Recognition (HAR) with Samsung Mobile Sensor Data and Fast v2","archived":false,"fork":false,"pushed_at":"2022-07-10T00:08:06.000Z","size":101,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":2,"default_branch":"master","last_synced_at":"2025-03-03T02:45:27.354Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"language":"Jupyter Notebook","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/codeamt.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":"2020-12-18T08:56:57.000Z","updated_at":"2022-07-09T23:52:29.000Z","dependencies_parsed_at":null,"dependency_job_id":"ed0d4843-db5a-4a2e-8b62-8d838c459497","html_url":"https://github.com/codeamt/Human-Activity-Recognition","commit_stats":null,"previous_names":["codeamt/human-activity-recognition"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/codeamt/Human-Activity-Recognition","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/codeamt%2FHuman-Activity-Recognition","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/codeamt%2FHuman-Activity-Recognition/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/codeamt%2FHuman-Activity-Recognition/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/codeamt%2FHuman-Activity-Recognition/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/codeamt","download_url":"https://codeload.github.com/codeamt/Human-Activity-Recognition/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/codeamt%2FHuman-Activity-Recognition/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":272219746,"owners_count":24894475,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","status":"online","status_checked_at":"2025-08-26T02:00:07.904Z","response_time":60,"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":[],"created_at":"2024-11-14T00:26:07.388Z","updated_at":"2025-08-26T12:20:56.651Z","avatar_url":"https://github.com/codeamt.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Human Activity Recognition\n Human Activity Recognition (HAR) with Samsung Mobile Sensor Data and Fast v2\n\n \n## Dataset\n\n[1] Records 30 volunteer subjects, ages 19-48 years, performed six activities with mobile embedded accelerometer and gyroscope around the waist. captured 3-axial linear acceleration and 3-axial angular velocity at a constant rate of 50Hz. The obtained dataset has been randomly partitioned into two sets, where 70% of the volunteers were selected for generating the training data and 30% for the test data.\n\nClasses:\n\n\n0: Walking\n\n1: Walking Upstairs\n\n2: Walking Downstairs\n\n3: Sitting\n\n4: Standing\n\n5: Laying\n\n\nTraining Examples: 7352\n\nTest Examples: 2947\n\n[1] Davide Anguita, Alessandro Ghio, Luca Oneto, Xavier Parra and Jorge L. Reyes-Ortiz. \nA Public Domain Dataset for Human Activity Recognition Using Smartphones. \n21th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2013. Bruges, Belgium 24-26 April 2013.\n\n## Training\n\nHAR Model Architecture developed with PyTorch, with \na series on convolutional, dropout, and adaptive pooling layers and trained with the Fast.ai (v2) library:\n\n\u003cimg src=\"9DAF9945-70FE-424D-8D2E-86F8E2C8C1BF.jpeg\" /\u003e\n\n## Inference\n\n88% Accuracy\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fcodeamt%2Fhuman-activity-recognition","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fcodeamt%2Fhuman-activity-recognition","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fcodeamt%2Fhuman-activity-recognition/lists"}