Daphnet冻结步态数据集，腿部和臀部的可穿戴加速度传感器识别步态冻结

Data Set Information: The Daphnet Freezing of Gait Dataset is a dataset devised to benchmark automatic methods to recognize gait freeze from wearable acceleration sensors placed on legs and hip. The dataset was recorded in the lab with emphasis on generating many freeze events. Users performed there kinds of tasks: straight line walking, walking with numerous turns, and finally a more realistic activity of daily living (ADL) task, where users went into different rooms while fetching coffee, opening doors, etc. This dataset is the result of a collaboration between the Laboratory for Gait and Neurodynamics, Tel Aviv Sourasky Medical Center, Israel and the Wearable Computing Laboratory, ETH Zurich, Switzerland. Recordings were run at the Tel Aviv Sourasky Medical Center in 2008. The study was approved by the local Human Subjects Review Committee, and was performed in accordance with the ethical standards of the Declaration of Helsinki. Attribute Information: Each file comprises the data in a matrix format, with one line per sample, and one column per channel. The channels are as follows: Time of sample in millisecond Ankle (shank) acceleration - horizontal forward acceleration [mg] Ankle (shank) acceleration - vertical [mg] Ankle (shank) acceleration - horizontal lateral [mg] Upper leg (thigh) acceleration - horizontal forward acceleration [mg] Upper leg (thigh) acceleration - vertical [mg] Upper leg (thigh) acceleration - horizontal lateral [mg] Trunk acceleration - horizontal forward acceleration [mg] Trunk acceleration - vertical [mg] Trunk acceleration - horizontal lateral [mg] Annotation [0, 1, or 2] The meaning of the annotations are as follows: 0: not part of the experiment. For instance the sensors are installed on the user or the user is performing activities unrelated to the experimental protocol, such as debriefing 1: experiment, no freeze (can be any of stand, walk, turn) 2: freeze Relevant Papers: [1] Marc B?¤chlin, Meir Plotnik, Daniel Roggen, Nir Giladi, Jeffrey M Hausdorff and Gerhard Tr??ster, A Wearable System to Assist Walking of Parkinson's Disease Patients.Methods of Information in Medicine, 49:1(88-95), 2010 [2] Meir Plotnik, Marc B?¤chlin, Inbal Maidan, Daniel Roggen, Gerhard Tr??ster, Nir Giladi and Jeffrey M Hausdorff, Automated biofeedback assistance for freezing of gait in patients with Parkinson's disease. Proceedings of the International Society for Posture and Gait Research (ISPGR), Bologna, Italy, 2009 [3] Meir Plotnik, Marc B?¤chlin, Daniel Roggen, Noit Inbar, Inbal Maidan, Talia Herman, Marina Brozgol, Eliya Shaviv, Gerhard Tr??ster and Jeffrey M Hausdorff, Automated treatment of freezing of gait in Parkinson's disease using a wearable device that automatically detects freezing. Annual meeting of the Israeli Neurological Society, Israel, pages 63, 2009 [4] Marc B?¤chlin, Daniel Roggen, Meir Plotnik, Jeffrey M Hausdorff, Nir Giladi and Gerhard Tr??ster, online Detection of Freezing of Gait in Parkinson's Disease Patients: A Performance Characterization. Proceedings of the 4th International Conference on Body Area Networks, 2009 [5] Marc B?¤chlin, Meir Plotnik, Daniel Roggen, Noit Inbar, Nir Giladi, Jeffrey M Hausdorff and Gerhard Tr??ster. Parkinson patients' perspective on context aware wearable technology for auditive assistance. Proceedings of the 3rd International Conference on Pervasive Computing Technologies for Healthcare, 2009 [6] Marc B?¤chlin, Daniel Roggen, Meir Plotnik, Noit Inbar, Inbal Maidan, Talia Herman, Marina Brozgol, Eliya Shaviv, Nir Giladi, Jeffrey M Hausdorff and Gerhard Tr??ster, Potentials of enhanced context awareness in wearable assistants for Parkinsona€?s disease patients with freezing of gait syndrome. Proceedings of the 13th International Symposium on Wearable Computers (ISWC), pages 123-130, 2009 [7] Sinziana Mazilu, Michael Hardegger, Zack Zhu, Daniel Roggen, Gerhard Tr??ster, Meir Plotnik, Jeff Hausdorff. online Detection of Freezing of Gait with Smartphones and Machine Learning Techniques. Proc 6th Int Conf on Pervasive Computing Technologies for Healthcare, 2012 Citation Request: Use of this dataset in publications must be acknowledged by referencing the following publication: Marc B?¤chlin, Meir Plotnik, Daniel Roggen, Inbal Maidan, Jeffrey M. Hausdorff, Nir Giladi, and Gerhard Tr??ster, Wearable Assistant for Parkinson's Disease Patients With the Freezing of Gait Symptom. IEEE Transactions on Information Technology in Biomedicine, 14(2), March 2010, pages 436-446 This paper describes the dataset in details. It explain the data acquisition protocol, the kind of sensor used and their placement, and the nature of the data acquired. It also provides baseline results for the automated detection of freezing of gait, against which newer methods can be benchmarked. In particular it describes detection sensitivity/specificity for 3 sensor placements and 4 kinds of derived sensor signals, it analyzes detection latency, and provides first insight into user specific v.s. user independent performance. We also appreciate if you inform us (daniel.roggen '@' ieee.org) of any publication using this dataset for cross-referencing purposes. Daniel Roggen, University of Newcastle Upon Tyne, UK, daniel.roggen '@' ieee.org Meir Plotnik, Sheba Medical Center, IL, meir.plotnikPeleg '@' sheba.health.gov.il Jeff Hausdorff, Tel Aviv Sourasky Medical Center, jhausdor '@' tlvmc.gov.il This dataset was collected as part of the EU FP6 project Daphnet, grant number 018474-2. Additional effort to publish this dataset was supported in part by the EU FP7 project CuPiD, grant number 288516.

数据集信息：达普内特（Daphnet）步态冻结（Freezing of Gait）数据集是为基准测试基于佩戴于腿部与髋部的可穿戴加速度传感器自动识别步态冻结的方法而构建的数据集。本数据集在实验室中录制，重点采集了大量步态冻结事件样本。受试者完成三类任务：直线行走、多次转向行走，以及更贴近日常生活的活动（Activities of Daily Living, ADL）任务——受试者需进入不同房间，完成取咖啡、开门等动作。本数据集由以色列特拉维夫索拉斯基医疗中心步态与神经动力学实验室，与瑞士苏黎世联邦理工学院可穿戴计算实验室合作构建。数据录制工作于2008年在特拉维夫索拉斯基医疗中心开展。本研究已通过当地人体受试者伦理审查委员会批准，严格遵循《赫尔辛基宣言》的伦理规范开展。

属性信息：每个文件以矩阵格式存储数据，每行对应一个样本，每列对应一个采集通道。各通道如下：
1. 样本采集时间（毫秒）
2. 踝关节（小腿）加速度-水平前向加速度[mg]
3. 踝关节（小腿）加速度-垂直向加速度[mg]
4. 踝关节（小腿）加速度-水平侧向加速度[mg]
5. 大腿加速度-水平前向加速度[mg]
6. 大腿加速度-垂直向加速度[mg]
7. 大腿加速度-水平侧向加速度[mg]
8. 躯干加速度-水平前向加速度[mg]
9. 躯干加速度-垂直向加速度[mg]
10. 躯干加速度-水平侧向加速度[mg]
11. 标注[0、1或2]
标注含义如下：
0：非实验阶段，例如传感器佩戴调试、受试者进行与实验流程无关的活动（如实验后访谈）；
1：实验阶段，未发生步态冻结（可包括站立、行走、转向任意一种状态）；
2：步态冻结阶段。

相关论文：
[1] Marc Bächlin、Meir Plotnik、Daniel Roggen、Nir Giladi、Jeffrey M Hausdorff、Gerhard Tröster：《辅助帕金森病患者行走的可穿戴系统》，载于《医学信息方法》，第49卷第1期，第88-95页，2010年
[2] Meir Plotnik、Marc Bächlin、Inbal Maidan、Daniel Roggen、Gerhard Tröster、Nir Giladi、Jeffrey M Hausdorff：《针对帕金森病患者步态冻结的自动化生物反馈辅助系统》，载于国际姿势与步态研究学会（ISPGR）2009年会议论文集，意大利博洛尼亚，2009年
[3] Meir Plotnik、Marc Bächlin、Daniel Roggen、Noit Inbar、Inbal Maidan、Talia Herman、Marina Brozgol、Eliya Shaviv、Gerhard Tröster、Jeffrey M Hausdorff：《利用可穿戴设备自动检测并治疗帕金森病患者的步态冻结》，载于以色列神经病学学会2009年年会论文集，以色列，第63页，2009年
[4] Marc Bächlin、Daniel Roggen、Meir Plotnik、Jeffrey M Hausdorff、Nir Giladi、Gerhard Tröster：《帕金森病患者步态冻结的在线检测：性能特征分析》，载于2009年第4届国际体域网会议论文集，2009年
[5] Marc Bächlin、Meir Plotnik、Daniel Roggen、Noit Inbar、Nir Giladi、Jeffrey M Hausdorff、Gerhard Tröster：《帕金森病患者对用于听觉辅助的上下文感知可穿戴技术的看法》，载于2009年第3届国际普适计算医疗技术会议论文集，2009年
[6] Marc Bächlin、Daniel Roggen、Meir Plotnik、Noit Inbar、Inbal Maidan、Talia Herman、Marina Brozgol、Eliya Shaviv、Nir Giladi、Jeffrey M Hausdorff、Gerhard Tröster：《增强上下文感知能力在伴步态冻结综合征的帕金森病患者可穿戴辅助系统中的应用潜力》，载于2009年第13届国际可穿戴计算机研讨会（ISWC）论文集，第123-130页，2009年
[7] Sinziana Mazilu、Michael Hardegger、Zack Zhu、Daniel Roggen、Gerhard Tröster、Meir Plotnik、Jeff Hausdorff：《利用智能手机与机器学习技术在线检测步态冻结》，载于2012年第6届国际普适计算医疗技术会议论文集，2012年

引用要求：若在出版物中使用本数据集，需引用以下文献以进行致谢标注：Marc Bächlin、Meir Plotnik、Daniel Roggen、Inbal Maidan、Jeffrey M. Hausdorff、Nir Giladi、Gerhard Tröster：《针对伴步态冻结症状的帕金森病患者的可穿戴辅助系统》，载于《IEEE生物医学信息学汇刊》，第14卷第2期，2010年3月，第436-446页。该文献详细阐述了本数据集的构建细节，包括数据采集流程、所用传感器类型与佩戴位置、采集数据的属性；同时给出了步态冻结自动检测的基线结果，可供后续方法进行基准对比。具体而言，该文献分析了3种传感器佩戴位置与4种衍生传感器信号的检测灵敏度/特异度，研究了检测延迟，并首次探讨了受试者特异性与受试者无关性检测的性能差异。
若您有使用本数据集的相关出版物，烦请告知我们（联系邮箱：daniel.roggen '@' ieee.org）以进行交叉引用备案。
联系人及邮箱：
Daniel Roggen，英国纽卡斯尔大学，daniel.roggen '@' ieee.org
Meir Plotnik，以色列谢巴医疗中心，meir.plotnikPeleg '@' sheba.health.gov.il
Jeff Hausdorff，以色列特拉维夫索拉斯基医疗中心，jhausdor '@' tlvmc.gov.il
本数据集的采集工作隶属于欧盟FP6项目Daphnet（资助编号：018474-2）。数据集的公开发布工作得到了欧盟FP7项目CuPiD（资助编号：288516）的部分支持。

搜集汇总

背景与挑战

背景概述

Daphnet冻结步态数据集是一个用于通过可穿戴加速度传感器识别步态冻结的基准数据集，数据采集自腿部和臀部的传感器，包含时间、加速度和标注信息。该数据集由以色列和瑞士的研究机构合作创建，专门用于帕金森病患者步态冻结的自动检测研究。