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A dataset build using wearable inertial measurement and ECG sensors for activity recognition, fall detection and basic heart anomaly detection system

This paper defines two major data sets 1) from wearable inertial measurement sensors and 2) wearable ECG SHIMMER™ sensors. The first dataset is devised to benchmark techniques dealing with human behavior analysis based on multimodal inertial measurement wearable SHIMMER™ sensors unit during research...

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Autores principales: Nadeem, Adnan, Mehmood, Amir, Rizwan, Kashif
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6920471/
https://www.ncbi.nlm.nih.gov/pubmed/31886332
http://dx.doi.org/10.1016/j.dib.2019.104717
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author Nadeem, Adnan
Mehmood, Amir
Rizwan, Kashif
author_facet Nadeem, Adnan
Mehmood, Amir
Rizwan, Kashif
author_sort Nadeem, Adnan
collection PubMed
description This paper defines two major data sets 1) from wearable inertial measurement sensors and 2) wearable ECG SHIMMER™ sensors. The first dataset is devised to benchmark techniques dealing with human behavior analysis based on multimodal inertial measurement wearable SHIMMER™ sensors unit during research studies “Fall Detection System for the Elderly Based on the Classification of Shimmer Sensor Prototype Data” [2] and “A novel fall detection algorithm for elderly using SHIMMER wearable sensors” [3]. The SHIMMER inertial sensor is a lightweight sensing device, incorporated with tri-axial accelerometer, a tri-axial gyroscope and tri-axial magnetometer, mounted on the waist of the subjects. The second dataset is developed to assess the feasibility of using SHIMMER™ wearable third generation ECG sensors for identification of basic heart anomalies by remote ECG analysis. The experimental protocol was carried out according to the Timed Up and Go (TUG) test [1], which is mainly used in fall detection and fall risk assessment systems specially designed for elderly. Three daily life activities such as standing still, walking and sitting on chair and getup were performed along with fall activity in controlled environment. This dataset is available on Data in Brief Dataverse [4] and a data repository [5].
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spelling pubmed-69204712019-12-27 A dataset build using wearable inertial measurement and ECG sensors for activity recognition, fall detection and basic heart anomaly detection system Nadeem, Adnan Mehmood, Amir Rizwan, Kashif Data Brief Computer Science This paper defines two major data sets 1) from wearable inertial measurement sensors and 2) wearable ECG SHIMMER™ sensors. The first dataset is devised to benchmark techniques dealing with human behavior analysis based on multimodal inertial measurement wearable SHIMMER™ sensors unit during research studies “Fall Detection System for the Elderly Based on the Classification of Shimmer Sensor Prototype Data” [2] and “A novel fall detection algorithm for elderly using SHIMMER wearable sensors” [3]. The SHIMMER inertial sensor is a lightweight sensing device, incorporated with tri-axial accelerometer, a tri-axial gyroscope and tri-axial magnetometer, mounted on the waist of the subjects. The second dataset is developed to assess the feasibility of using SHIMMER™ wearable third generation ECG sensors for identification of basic heart anomalies by remote ECG analysis. The experimental protocol was carried out according to the Timed Up and Go (TUG) test [1], which is mainly used in fall detection and fall risk assessment systems specially designed for elderly. Three daily life activities such as standing still, walking and sitting on chair and getup were performed along with fall activity in controlled environment. This dataset is available on Data in Brief Dataverse [4] and a data repository [5]. Elsevier 2019-10-24 /pmc/articles/PMC6920471/ /pubmed/31886332 http://dx.doi.org/10.1016/j.dib.2019.104717 Text en © 2019 The Author(s) http://creativecommons.org/licenses/by/4.0/ This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Computer Science
Nadeem, Adnan
Mehmood, Amir
Rizwan, Kashif
A dataset build using wearable inertial measurement and ECG sensors for activity recognition, fall detection and basic heart anomaly detection system
title A dataset build using wearable inertial measurement and ECG sensors for activity recognition, fall detection and basic heart anomaly detection system
title_full A dataset build using wearable inertial measurement and ECG sensors for activity recognition, fall detection and basic heart anomaly detection system
title_fullStr A dataset build using wearable inertial measurement and ECG sensors for activity recognition, fall detection and basic heart anomaly detection system
title_full_unstemmed A dataset build using wearable inertial measurement and ECG sensors for activity recognition, fall detection and basic heart anomaly detection system
title_short A dataset build using wearable inertial measurement and ECG sensors for activity recognition, fall detection and basic heart anomaly detection system
title_sort dataset build using wearable inertial measurement and ecg sensors for activity recognition, fall detection and basic heart anomaly detection system
topic Computer Science
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6920471/
https://www.ncbi.nlm.nih.gov/pubmed/31886332
http://dx.doi.org/10.1016/j.dib.2019.104717
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