HHAR: Heterogeneity Human Activity Recognition Dataset
Accelerometer and gyroscope data from nine users carrying eight phones and four watches at once: 33.7 million rows built to study device heterogeneity.
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Accelerometer and gyroscope data from nine users carrying eight phones and four watches at once: 33.7 million rows built to study device heterogeneity.
View Dataset396,602 rows and 33 columns of smartphone motion signals and labels; the publisher describes 20 participants with and without mobility disabilities.
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 DatasetTime-series accelerometer and gyroscope data from smartphones and smartwatches carried by 51 subjects performing 18 activities, suitable for human activity recognition and motion-based biometrics.[web:119][web:123][web:137]
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