Accelerometer, gyroscope, PPG and multi-sensor wearable recordings for human activity recognition, stress detection and fitness analytics.
Accelerometer and gyroscope data from nine users carrying eight phones and four watches at once: 33.7 million rows built to study device heterogeneity.
Body-worn, object and ambient sensor recordings of daily activities from four participants: 24 runs, 869,387 samples, 242 sensor channels and seven annotation tracks.
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.
Wristwatch-derived measurements for researching aggressive tendencies: 40 participants, eight days each within a three-month collection period, with processed and augmented tables.
1,143,070 rows across 80 activity traces from 10 participants; each trace has 78 fields from five IMUs, a smartphone and activity/fatigue metadata.
396,602 rows and 33 columns of smartphone motion signals and labels; the publisher describes 20 participants with and without mobility disabilities.
Novel multimodal HAR dataset for basketball training with synchronized IMU (accelerometer, gyroscope, angle, magnetic field at 200 Hz, MPU9250/WT901) + heart rate and skin surface temperature (1 Hz, Si1141 wrist sensor) across 14 activity classes. ArXiv 2025. Used for sports performance analysis and LLM-based coaching reports.
Multi-site IMU dataset (chest, hands, knees) with heart rate and SpO2 recorded at 0.5 Hz during structured daily activities and a 3-minute step test. Raw + preprocessed IMU (accelerometer, gyroscope, quaternions) + demographic metadata. Zenodo, July 2025. Used for cardiorespiratory fitness (CRF) estimation and HAR.
Large-scale PPG-based heart rate dataset from 15 subjects wearing wrist (Empatica E4: PPG + 3-axis ACC) and chest (RespiBAN: ECG + respiration + 3-axis ACC) devices during 8 real-life activities. ECG provides HR ground truth. UCI ML Repository. Published in MDPI Sensors 2019 — DOI: 10.3390/s19143079.
iPhone 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.
Real-time posture monitoring dataset from the MPU6050 6-axis sensor (3D gyroscope + 3D accelerometer) attached to the upper body, streaming wirelessly via Phyphox. Angular orientation values: Angle_X, Angle_Y, Angle_Z. Mendeley Data, March 2025 — DOI: 10.17632/ftn4rjnd6x.1. Used for LSTM-based posture classification and ergonomics.
Wearable IoT dataset with 18 physical activities from 9 subjects wearing 3 IMUs and a heart rate monitor. 54 columns including temperature, acceleration, and gyroscope data. CSV format. Used for HAR, activity classification, and intensity estimation.
High-frequency wearable sensor data from 9 subjects performing 18 different daily and sports activities, recorded with three 100 Hz IMUs and a heart rate monitor.[web:125][web:128][web:138][web:142]