RecGym: Gym Workouts Recognition Dataset with IMU and Capacitive Sensor

Donated on 2/18/2025

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.

Dataset Characteristics

Time-Series

Subject Area

Health and Medicine

Associated Tasks

Classification

Feature Type

Real

# Instances

4432070

# Features

11

Dataset Information

Has Missing Values?

No

Introductory Paper

The Contribution of Human Body Capacitance/Body-Area Electric Field To Individual and Collaborative Activity Recognition

By Sizhen Bian, V. F. Rey, Siyu Yuan, P. Lukowicz. 2025

Published in 7th International Conference on Activity and Behavior Computing

Variables Table

Variable NameRoleTypeDescriptionUnitsMissing Values
ObjectFeatureBinaryno
WorkoutFeatureCategoricalno
PositionFeatureCategoricalno
A_xFeatureContinuousno
A_yFeatureContinuousno
A_zFeatureContinuousno
G_xFeatureContinuousno
G_yFeatureContinuousno
G_zFeatureContinuousno
C_1FeatureContinuousno

0 to 10 of 11

Dataset Files

FileSize
RecGym.csv453 MB

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Creators

Sizhen Bian

Paul Lukowicz

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