Towards a methodology for acceptance testing and validation of monitoring bodyworn devices

Emma L. Tonkin; Miquel Perello Nieto; Haixia Bi; Antonis T Vafeas

doi:10.1109/PerComWorkshops48775.2020.9156257

Towards a methodology for acceptance testing and validation of monitoring bodyworn devices

Emma L. Tonkin, Miquel Perello Nieto, Haixia Bi, Antonis T Vafeas

Research output: Chapter in Book/Report/Conference proceeding › Conference Contribution (Conference Proceeding)

3 Citations (Scopus)

127 Downloads (Pure)

Abstract

In hardware deployments, it is often necessary to test platforms for suitability for particular purposes. As lengthy data collection processes often outlive specific iterations of hardware and firmware, it is likely that migration between platforms may become necessary. In this short paper we describe a practical approach employed for acceptance testing, comparison and validation of two iterations of a wearable accelerometer and localisation platform, based on an annotated 15-task activity script. We present an analysis on the data generated for the different activities, and compare device performance using common machine learning algorithms for activity recognition.

Original language	English
Title of host publication	2020 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops)
Publisher	IEEE Computer Society
Number of pages	6
ISBN (Electronic)	978-1-7281-4716-1
ISBN (Print)	978-1-7281-4717-8
DOIs	https://doi.org/10.1109/PerComWorkshops48775.2020.9156257
Publication status	Published - 4 Aug 2020
Event	2020 IEEE International Conference on Pervasive Computing and Communications Workshops - Austin, TX, United States Duration: 23 Mar 2020 → 27 Mar 2020

Conference

Conference	2020 IEEE International Conference on Pervasive Computing and Communications Workshops
Country/Territory	United States
City	Austin, TX
Period	23/03/20 → 27/03/20

Research Groups and Themes

Digital Health
SPHERE

Access to Document

10.1109/PerComWorkshops48775.2020.9156257

Full-text PDF (author’s accepted manuscript)
This is the author accepted manuscript (AAM). The final published version (version of record) is available online via IEEE at https://ieeexplore.ieee.org/document/9156257 . Please refer to any applicable terms of use of the publisher.
Accepted author manuscript, 953 KB

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SPHERE2
Craddock, I. J. (Principal Investigator), Mirmehdi, M. (Co-Investigator), Piechocki, R. J. (Co-Investigator), Flach, P. A. (Co-Investigator), Oikonomou, G. (Co-Investigator), Burghardt, T. (Co-Investigator), Damen, D. (Co-Investigator), Santos-Rodriguez, R. (Co-Investigator), O'Kane, A. A. (Co-Investigator), McConville, R. (Co-Investigator), Masullo, A. (Co-Investigator) & Gooberman-Hill, R. (Co-Investigator)
1/10/18 → 31/01/23
Project: Research, Parent

End to End System Optimisation of Wireless Interconnected Sensors for Healthcare Monitoring at Homes: Advancements in multimodal sensing technology for healthcare in residential environments
Vafeas, A. T. (Author), Oikonomou, G. (Supervisor), Piechocki, R. J. (Supervisor) & Craddock, I. J. (Supervisor), 21 Jan 2021
Student thesis: Doctoral Thesis › Doctor of Philosophy (PhD)

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Cite this

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title = "Towards a methodology for acceptance testing and validation of monitoring bodyworn devices",

abstract = "In hardware deployments, it is often necessary to test platforms for suitability for particular purposes. As lengthy data collection processes often outlive specific iterations of hardware and firmware, it is likely that migration between platforms may become necessary. In this short paper we describe a practical approach employed for acceptance testing, comparison and validation of two iterations of a wearable accelerometer and localisation platform, based on an annotated 15-task activity script. We present an analysis on the data generated for the different activities, and compare device performance using common machine learning algorithms for activity recognition.",

author = "Tonkin, {Emma L.} and {Perello Nieto}, Miquel and Haixia Bi and Vafeas, {Antonis T}",

year = "2020",

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doi = "10.1109/PerComWorkshops48775.2020.9156257",

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booktitle = "2020 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops)",

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address = "United States",

note = "2020 IEEE International Conference on Pervasive Computing and Communications Workshops ; Conference date: 23-03-2020 Through 27-03-2020",

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Tonkin, EL , Perello Nieto, M, Bi, H & Vafeas, AT 2020, Towards a methodology for acceptance testing and validation of monitoring bodyworn devices. in 2020 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops). IEEE Computer Society, 2020 IEEE International Conference on Pervasive Computing and Communications Workshops , Austin, TX, United States, 23/03/20. https://doi.org/10.1109/PerComWorkshops48775.2020.9156257

Towards a methodology for acceptance testing and validation of monitoring bodyworn devices. / Tonkin, Emma L.; Perello Nieto, Miquel; Bi, Haixia et al.
2020 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops). IEEE Computer Society, 2020.

Research output: Chapter in Book/Report/Conference proceeding › Conference Contribution (Conference Proceeding)

TY - GEN

T1 - Towards a methodology for acceptance testing and validation of monitoring bodyworn devices

AU - Tonkin, Emma L.

AU - Perello Nieto, Miquel

AU - Bi, Haixia

AU - Vafeas, Antonis T

PY - 2020/8/4

Y1 - 2020/8/4

N2 - In hardware deployments, it is often necessary to test platforms for suitability for particular purposes. As lengthy data collection processes often outlive specific iterations of hardware and firmware, it is likely that migration between platforms may become necessary. In this short paper we describe a practical approach employed for acceptance testing, comparison and validation of two iterations of a wearable accelerometer and localisation platform, based on an annotated 15-task activity script. We present an analysis on the data generated for the different activities, and compare device performance using common machine learning algorithms for activity recognition.

AB - In hardware deployments, it is often necessary to test platforms for suitability for particular purposes. As lengthy data collection processes often outlive specific iterations of hardware and firmware, it is likely that migration between platforms may become necessary. In this short paper we describe a practical approach employed for acceptance testing, comparison and validation of two iterations of a wearable accelerometer and localisation platform, based on an annotated 15-task activity script. We present an analysis on the data generated for the different activities, and compare device performance using common machine learning algorithms for activity recognition.

U2 - 10.1109/PerComWorkshops48775.2020.9156257

DO - 10.1109/PerComWorkshops48775.2020.9156257

M3 - Conference Contribution (Conference Proceeding)

SN - 978-1-7281-4717-8

BT - 2020 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops)

PB - IEEE Computer Society

T2 - 2020 IEEE International Conference on Pervasive Computing and Communications Workshops

Y2 - 23 March 2020 through 27 March 2020

ER -

Towards a methodology for acceptance testing and validation of monitoring bodyworn devices

Abstract

Conference

Research Groups and Themes

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Projects

SPHERE2

Student theses

End to End System Optimisation of Wireless Interconnected Sensors for Healthcare Monitoring at Homes: Advancements in multimodal sensing technology for healthcare in residential environments

Cite this