HARTH — Free-Living Human Activity Recognition Dataset
6,461,328 annotated samples from 22 free-living participants wearing two accelerometers; CSVs contain eight core fields, with extra index columns in three files.
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6,461,328 annotated samples from 22 free-living participants wearing two accelerometers; CSVs contain eight core fields, with extra index columns in three files.
View DatasetiPhone 6s accelerometer and gyroscope dataset from 24 subjects performing 6 activities (walking, jogging, stairs up/down, sitting, standing) at 50 Hz. CSV. Kaggle and GitHub. Used for human activity recognition (HAR) and mobile sensor privacy research. Imperial College London, 2018.
View DatasetOpen-access wearable gait dataset with 400 CSV files from 20 healthy participants across 5 smartphone orientations and 2 gait trial types. 100 Hz IMU sampling. Used for smartphone-based gait metric validation and wearable sensor accuracy studies.
View DatasetGAITEX is a comprehensive multimodal human motion dataset capturing impaired gait and rehabilitation exercises using nine wearable IMUs and optical motion capture systems, designed for biomechanical analysis and rehabilitation monitoring.
View DatasetWearable IMU dataset from 22 Parkinson's disease patients performing standardized motor tasks, with four inertial sensors (ankles, wrist, lower back) capturing freezing of gait (FoG) episodes, designed for algorithm development and clinical gait analysis.
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