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（Inertial Measurement Unit，IMU）跌倒检测数据集（以下简称IMU数据集）是一款用于开展跌倒检测与预测算法基准测评的数据集，其数据来源于佩戴于7个身体部位（右踝、左踝、右大腿、左大腿、头部、胸骨与腰部）的APDM Opal IMU传感器采集的加速度、角速度与磁场数据，采样频率为128赫兹。本数据集的详细说明与字段名称均收录于"README.txt"文件中。若在学术出版物中使用本数据集，必须通过引用下述文献予以致谢：Omar Aziz、Magnus Musngi、Edward J. Park、Greg Mori、Stephen N. Robinovitch. 《基于腰部佩戴式三轴加速度计信号的跌倒检测算法精度对比——阈值法与机器学习法》，发表于SpringerLink旗下《Medical & Biological Engineering & Computing》2017年第55卷第45页（原文献著录格式：SpringerLink Med Biol Eng Comput (2017) 55: 45）。此外，若您的学术成果使用了本数据集，欢迎通过邮件（[e-mail]与[e-mail]）告知我们，以便我们在官方网页中收录您的研究成果。本数据集采用表格格式，内容类型为传感器数据，数据处理所用软件为Excel。保密声明：本数据集未包含个人可识别信息，所有人类受试者均在数据采集前签署了书面知情同意书。本数据集最初提交至西蒙弗雷泽大学（Simon Fraser University）机构知识库。

搜集汇总

背景与挑战

背景概述

该数据集是一个基于惯性测量单元（IMU）的跌倒检测数据集，旨在为跌倒检测和预测算法提供基准测试。它包含从身体7个位置采集的加速度、角速度和磁场数据，采样频率为128 Hz，数据格式为表格型传感器数据，无个人身份信息，适用于健康科学和康复医学研究。