Comparative analysis and conversion between Actiwatch and ActiGraph open-source counts
Comparative analysis and conversion between Actiwatch and ActiGraph open-source counts
Body-worn sensors have contributed to a rich and growing body of literature in public health and clinical research in the last decades. A major challenge in sensor research is the lack of consistency and standardization of the collection and reporting of the sensor data. The algorithms used to derive these activity counts can be vastly different between manufactures and not always transparent to the researchers. With Philips, one of the major research-grade wearable device manufacturers, discontinuing this product line, many researchers are left in need of alternative solutions and at the risk of not being able to relate their historical data using the Philips Actiwatch 2 devices to future findings with other devices. We herein provide a comparison analysis and conversion method that can be used to convert activity counts from Philips to those from ActiGraph, another major manufacturer who provide both raw acceleration data and count data based on their open-source algorithm to the research community. This work provides an approach to maximize the scientific value of historical actigraphy data collected by the Actiwatch devices to support research continuity in this community. The conversion, however, is not perfect and only offers an approximation, due to the intrinsic difference in the count algorithms between the two accelerometers, and the permanent information loss during data reduction. We encourage future research using body-worn sensors to retain the raw sensor data to ensure data consistency, comparability, and the ability to leverage future algorithm improvement.
accelerometry, calibration, measurement, motion sensor, physical activity, sedentary behavior
Lee, Paul H.
02620eab-ae7f-4a1c-bad1-8a50e7e48951
Neishabouri, Ali
163f5a77-855e-4336-b698-2d1aa580379a
Tse, Andy C.Y.
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Guo, Christine C.
db3bad06-28a6-4701-a6e8-b63af92aae05
Lee, Paul H.
02620eab-ae7f-4a1c-bad1-8a50e7e48951
Neishabouri, Ali
163f5a77-855e-4336-b698-2d1aa580379a
Tse, Andy C.Y.
e0d6b2be-a736-43ac-b03e-d2d58a56e114
Guo, Christine C.
db3bad06-28a6-4701-a6e8-b63af92aae05
Lee, Paul H., Neishabouri, Ali, Tse, Andy C.Y. and Guo, Christine C.
(2023)
Comparative analysis and conversion between Actiwatch and ActiGraph open-source counts.
Journal for the Measurement of Physical Behaviour, 7 (1), [jmpb.2022-0054].
(doi:10.1123/jmpb.2022-0054).
Abstract
Body-worn sensors have contributed to a rich and growing body of literature in public health and clinical research in the last decades. A major challenge in sensor research is the lack of consistency and standardization of the collection and reporting of the sensor data. The algorithms used to derive these activity counts can be vastly different between manufactures and not always transparent to the researchers. With Philips, one of the major research-grade wearable device manufacturers, discontinuing this product line, many researchers are left in need of alternative solutions and at the risk of not being able to relate their historical data using the Philips Actiwatch 2 devices to future findings with other devices. We herein provide a comparison analysis and conversion method that can be used to convert activity counts from Philips to those from ActiGraph, another major manufacturer who provide both raw acceleration data and count data based on their open-source algorithm to the research community. This work provides an approach to maximize the scientific value of historical actigraphy data collected by the Actiwatch devices to support research continuity in this community. The conversion, however, is not perfect and only offers an approximation, due to the intrinsic difference in the count algorithms between the two accelerometers, and the permanent information loss during data reduction. We encourage future research using body-worn sensors to retain the raw sensor data to ensure data consistency, comparability, and the ability to leverage future algorithm improvement.
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e-pub ahead of print date: 21 August 2023
Keywords:
accelerometry, calibration, measurement, motion sensor, physical activity, sedentary behavior
Identifiers
Local EPrints ID: 488093
URI: http://eprints.soton.ac.uk/id/eprint/488093
ISSN: 2575-6605
PURE UUID: d90ac826-a5d8-483c-8c25-b08c30060460
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Date deposited: 15 Mar 2024 17:36
Last modified: 06 Jun 2024 02:15
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Author:
Paul H. Lee
Author:
Ali Neishabouri
Author:
Andy C.Y. Tse
Author:
Christine C. Guo
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