Wearable Inertial Sensor Dataset for Human Activity Recognition Coverage Analysis

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 - [e-mail]- Santanu Bhattacharya - [e-mail]### 概览 本数据集包含经匿名化处理的惯性传感器数据，采集自可穿戴设备，用于人类活动识别（Human Activity Recognition, HAR）验证研究。数据涵盖了多种日常活动中采集的原始三轴加速度计与三轴陀螺仪读数。 ### 数据集特征 - 传感器类型：惯性测量单元（Inertial Measurement Unit, IMU），包含三轴加速度计与三轴陀螺仪 - 采样率：遵循商用可穿戴设备固件定义的固定速率IMU采样 - 活动类别：共12种活动类型，包括行走、上楼梯、下楼梯、前向跌倒、后向跌倒、坐姿、站姿以及其他日常活动 - 参与者：数据来自多名参与者；由于数据集已完全匿名且由商业合作伙伴提供，具体参与者数量未公开 - 总时间窗数：1674个时间序列窗口 - 窗口尺寸：5秒（固定长度窗口） - 设备类型：通用腕部可穿戴设备 - 数据格式：Excel（.xls）文件，每个录制会话对应一个文件 ### 数据结构 每个文件包含原始传感器读数，各列信息如下： - accel-X：X轴加速度（原始传感器单位） - accel-Y：Y轴加速度（原始传感器单位） - accel-Z：Z轴加速度（原始传感器单位） - Gyro-X：X轴角速度（原始传感器单位） - Gyro-Y：Y轴角速度（原始传感器单位） - Gyro-Z：Z轴角速度（原始传感器单位） ### 文件命名规则 文件采用随机匿名标识符进行标注（例如"participant1.xls"），仅用于组织管理。此类标注不对应真实参与者身份，且无关联密钥。 ### 匿名化处理 本数据集已完全匿名，不包含任何个人可识别信息： - 无姓名、联系方式或人口统计信息 - 无设备序列号或可识别元数据 - 无时间或位置标记 - 采用随机匿名文件标签，且无关联密钥 - 仅包含原始传感器测量数据 数据采集实体未留存文件标签与参与者身份之间的关联信息。 ### 应用场景 本数据集适用于： - 人类活动识别算法开发 - 覆盖性分析与数据盲区研究 - 可穿戴传感器信号处理研究 - 边缘AI与微型机器学习（TinyML）模型验证 - 泛化性与鲁棒性测试 ### 相关出版物 本数据集支持下述研究成果： Pal, B., Bhattacharya, S., & Singh, M. (2025). "Coverage blindness for reliable wearable human activity recognition." Scientific Reports. [DOI将在出版后补充] 该研究提出了一种用于衡量可穿戴HAR系统覆盖盲区的数学框架，并使用本数据集演示了不同支持阈值下的覆盖缺口。 ### 授权协议 本数据集采用知识共享署名4.0国际许可（Creative Commons Attribution 4.0 International, CC-BY 4.0）发布。您可自由共享、改编本数据用于任何用途，包括商业用途，但需提供适当的署名。 ### 引用方式 若您使用本数据集，请引用如下文献： 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 ### 联系方式 如有疑问或需获取更多信息，请联系： - Biplab Pal - [e-mail] - Santanu Bhattacharya - [e-mail]