UCI Human Activity Recognition (HAR) Dataset Dataset Description The UCI Human Activity Recognition dataset is a widely used benchmark for human activity recognition using smartphone sensors. This dataset contains sensor readings from accelerometers and gyroscopes of smartphones worn by volunteers performing six different activities. Activities The dataset includes the following 6 activities: 1 : WALKING 2 : WALKING UPSTAIRS 3 : WALKING DOWNSTAIRS 4 : SITTING 5 : STANDING 6 : LAYING Dataset Statistics Number of subjects : 30 Number of activities : 6 Number of features : 561 Training samples : 7,352 Test samples : 2,947 Total samples : 10,299 Dataset Structure Data Fields 0 to 560 : Individual sensor feature columns (561 float values total) target : Integer ID of the activity (1 6) activity label : String label of the activity subject id : Integer ID of the subject Data Splits Train : 7,352 samples Test : 2,947 samples Usage with Flower Datasets This dataset is optimized for federated learning scenarios. Here's how to use it with Flower: Federated Learning Scenarios This dataset supports several FL scenarios: 1. Subject based partitioning : Each client represents one subject (natura…
We use cookies for essential functionality and analytics. You can accept or reject analytics cookies.Cookie policy