OverviewThis dataset contains anonymized inertial sensor data collected from wearable devices during human activity recognition (HAR) validation studies. The data consists of raw 3-axis accelerometer and 3-axis gyroscope readings captured during various daily activities. Dataset Characteristics- Sensor Type: Inertial Measurement Unit (IMU) - 3-axis accelerometer and 3-axis gyroscope- Sampling Rate: Fixed-rate IMU sampling as defined by the commercial wearable device firmware- Activities: 12 activity classes including walking, stairs ascent, stairs descent, forward fall, backward fall, sitting, standing, and additional daily activities- Participants: Data were collected from multiple participants; the exact number is not disclosed as the dataset is fully anonymized and provided by a commercial partner- Total Windows: 1,674 time-series windows- Window Size: 5 seconds (fixed-length windows)- Device: Generic wrist-worn wearable device- Data Format: Excel (.xls) files, one per recording session Data StructureEach file contains raw sensor readings with the following columns:- accel-X: X-axis acceleration (raw sensor units)- accel-Y: Y-axis acceleration (raw sensor units)- accel-Z: Z-axis acceleration (raw sensor units)- Gyro-X: X-axis angular velocity (raw sensor units)- Gyro-Y: Y-axis angular velocity (raw sensor units)- Gyro-Z: Z-axis angular velocity (raw sensor units) File Naming: Files are labeled with random pseudonymous identifiers (e.g., "participant1.xls") for organizational purposes. These labels do not correspond to actual participant identities and no linking key exists. AnonymizationThis dataset is fully anonymized and contains NO personally identifiable information:- No names, contact information, or demographics- No device serial numbers or identifying metadata- No temporal or location markers- Random pseudonymous file labels with no linking key- Only raw sensor measurements included The data collection entity retained no linking information between file labels and participant identities. Use CasesThis dataset is suitable for:- Human activity recognition algorithm development- Coverage analysis and data blindness studies- Wearable sensor signal processing research- Edge AI and TinyML model validation- Generalization and robustness testing Related PublicationThis dataset supports the research presented in: Pal, B., Bhattacharya, S., & Singh, M. (2025). "Coverage blindness for reliable wearable human activity recognition." Scientific Reports. [DOI to be added upon publication] The publication introduces a mathematical framework for measuring coverage blindness in wearable HAR systems and uses this dataset to demonstrate coverage gaps under varying support thresholds. LicenseThis dataset is released under Creative Commons Attribution 4.0 International (CC-BY 4.0). You are free to share and adapt the data for any purpose, including commercial use, provided appropriate credit is given. CitationIf you use this dataset, please cite: Pal, B., Bhattacharya, S., & Singh, M. (2025). Wearable Inertial Sensor Dataset for Human Activity Recognition Coverage Analysis [Data set]. Zenodo. https://doi.org/10.5281/zenodo.18480659 ContactFor questions or additional information:- Biplab Pal - bpal1@umbc.edu- Santanu Bhattacharya - santanu2@media.mit.edu

Wearable HAR Dataset - README Dataset Structure----------------- Anonymized inertial sensor data for human activity recognition research.Supports the paper: "Coverage blindness for reliable wearable human activity recognition" File Structure--------------- Excel (.xls) files containing raw IMU readings- Each file represents one recording session- Files labeled with random pseudonyms (not real identities) Data Columns------------accel-X : X-axis acceleration (raw sensor units)accel-Y : Y-axis acceleration (raw sensor units)accel-Z : Z-axis acceleration (raw sensor units)Gyro-X : X-axis angular velocity (raw sensor units)Gyro-Y : Y-axis angular velocity (raw sensor units)Gyro-Z : Z-axis angular velocity (raw sensor units) Specifications--------------- Window size: 5 seconds (fixed-length)- Activities: 12 classes- Total windows: 1,674- Sampling rate: Fixed-rate IMU (device firmware defined) Anonymization-------------- No personally identifiable information- No demographics or linking metadata- File labels are random pseudonyms with no linking key Sample Python Code------------------import pandas as pd # Load a single filedata = pd.read_excel('participant1.xls')print(data.head())print(f"Shape: {data.shape}")print(f"Columns: {list(data.columns)}") License-------CC-BY 4.0 International Contact-------Biplab Pal: bpal1@umbc.eduSantanu Bhattacharya: santanu2@media.mit.edu