A Multi-Sensor Dataset for Human Activity Recognition Using Inertial and Orientation Data

Jhonathan L. Rivas-Caicedo (), Laura Saldaña-Aristizabal, Kevin Niño-Tejada and Juan F. Patarroyo-Montenegro ()
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Jhonathan L. Rivas-Caicedo: Department of Electrical and Computer Engineering, University of Puerto Rico, Mayaguez, PR 00680, USA
Laura Saldaña-Aristizabal: Department of Electrical and Computer Engineering, University of Puerto Rico, Mayaguez, PR 00680, USA
Kevin Niño-Tejada: Department of Electrical and Computer Engineering, University of Puerto Rico, Mayaguez, PR 00680, USA
Juan F. Patarroyo-Montenegro: Department of Computer Science and Engineering, University of Puerto Rico, Mayaguez, PR 00680, USA

Data, 2025, vol. 10, issue 8, 1-11

Abstract: Human Activity Recognition (HAR) using wearable sensors is an increasingly relevant area for applications in healthcare, rehabilitation, and human–computer interaction. However, publicly available datasets that provide multi-sensor, synchronized data combining inertial and orientation measurements are still limited. This work introduces a publicly available dataset for Human Activity Recognition, captured using wearable sensors placed on the chest, hands, and knees. Each device recorded inertial and orientation data during controlled activity sessions involving participants aged 20 to 70. A standardized acquisition protocol ensured consistent temporal alignment across all signals. The dataset was preprocessed and segmented using a sliding window approach. An initial baseline classification experiment, employing a Convolutional Neural Network (CNN) and Long-Short Term Memory (LSTM) model, demonstrated an average accuracy of 93.5% in classifying activities. The dataset is publicly available in CSV format and includes raw sensor signals, activity labels, and metadata. This dataset offers a valuable resource for evaluating machine learning models, studying distributed HAR approaches, and developing robust activity recognition pipelines utilizing wearable technologies.

Keywords: activity classification; distributed neural networks; human activity recognition; IMU dataset; inertial data; multi-sensor systems; quaternions; wearable sensors (search for similar items in EconPapers)
JEL-codes: C8 C80 C81 C82 C83 (search for similar items in EconPapers)
Date: 2025
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