Inertial Measurement Unit Fall Detection Dataset

Inertial Measurement Unit Fall Detection Dataset (IMU Dataset) is a dataset devised to benchmark fall detection and prediction algorithms based on acceleration, angular velocity and magnetic fields of body-worn APDM Opal IMU sensors recording at 128 Hz at 7 body locations (right ankle, left ankle, right thigh, left thigh, head, sternum, and waist). Detailed description of the dataset and column names are in README.txt file.

Use of this dataset in publications must be acknowledged by referencing the following publication: - Omar Aziz, Magnus Musngi, Edward J. Park, Greg Mori, Stephen N. Robinovitch. "A comparison of accuracy of fall detection algorithms (threshold-based vs. machine learning) using waist-mounted tri-axial accelerometer signals from a comprehensive set of falls and non-fall trials". SpringerLink Med Biol Eng Comput (2017) 55: 45.

We also appreciate if you drop us an email ([e-mail] and [e-mail]) to inform us of any publication using this dataset, so we can point to your publication on our webpage.

Format of data is tabular and content type is sensor data. Software used was Excel.

Confidentiality declaration: The dataset does not contain personal identifiable information. All human subjects provided written consent prior to data collection.

This dataset was originally deposited in the Simon Fraser University institutional repository.

惯性测量单元跌倒检测数据集（IMU 数据集）是一项旨在评估基于穿戴式APDM Opal IMU传感器记录的加速度、角速度和磁场数据，以衡量跌倒检测与预测算法性能的数据集。该数据集在7个身体部位（右踝、左踝、右大腿、左大腿、头部、胸骨和腰部）以128 Hz的频率进行记录。数据集的详细描述及列名均在 README.txt 文件中给出。

在出版物中使用本数据集时，必须通过引用以下出版物来表明其使用：- Omar Aziz, Magnus Musngi, Edward J. Park, Greg Mori, Stephen N. Robinovitch. “基于全面跌倒与非跌倒试验的腰部三轴加速度计信号，比较跌倒检测算法（阈值法与机器学习）的准确性”。SpringerLink Med Biol Eng Comput (2017) 55: 45。

我们亦欢迎您通过电子邮件（[e-mail] 和 [e-mail]）告知我们任何使用本数据集的出版物，以便我们能在网页上指向您的出版物。

数据格式为表格形式，内容类型为传感器数据。所使用的软件为 Excel。

保密声明：该数据集不包含可识别个人身份的信息。所有受试者在数据收集前均提供了书面同意。

本数据集最初存放在西蒙弗雷泽大学机构存储库。