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HARTH
The Human Activity Recognition Trondheim (HARTH) dataset is a professionally-annotated dataset containing 22 subjects wearing two 3-axial accelerometers for around 2 hours in a free-living setting. The sensors were attached to the right thigh and lower back. The professional recordings and annotations provide a promising benchmark dataset for researchers to develop innovative machine learning approaches for precise HAR in free living.
HAR70+
The Human Activity Recognition 70+ (HAR70+) dataset is a professionally-annotated dataset containing 18 fit-to-frail older-adult subjects (70-95 years old) wearing two 3-axial accelerometers for around 40 minutes during a semi-structured free-living protocol. The sensors were attached to the right thigh and lower back.
RecGym: Gym Workouts Recognition Dataset with IMU and Capacitive Sensor
The RecGym dataset is a collection of gym workouts with IMU and Capacitive sensors, designed for research and development in recommendation systems and fitness applications. The data set records ten volunteers' gym sessions with a sensing unit composed of an IMU sensor (columns of A_x, A_y, A_z, G_x, G_y, G_z) and a Body Capacitance sensor (column of C_1). The sensing units were worn at three positions: on the wrist, in the pocket, and on the calf, with a sampling rate of 20 Hz. The data set contains the motion signals of twelve activities, including eleven workouts: Adductor, ArmCurl, BenchPress, LegCurl, LegPress, Riding, RopeSkipping, Running, Squat, StairsClimber, Walking, and a "Null" activity when the volunteer hangs around between different workouts session. Each participant performed the above-listed workouts for five sessions in five days (each session lasts around one hour). Altogether, fifty sessions of normalized gym workout data are presented in this data set.
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