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Analysis of patient domestic activity in recovery from hip or knee replacement surgery: modelling wrist-worn wearable RSSI and accelerometer data in the wild

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Analysis of patient domestic activity in recovery from hip or knee replacement surgery : modelling wrist-worn wearable RSSI and accelerometer data in the wild. / Holmes, Mike; Song, Hao; Tonkin, Emma L.; Perello Nieto, Miquel; Grant, Sabrina; Flach, Peter.

Proceedings of the 3rd International Workshop on Knowledge Discovery in Healthcare Data: co-located with the 27th International Joint Conference on Artificial Intelligence and the 23rd European Conference on Artificial Intelligence (IJCAI-ECAI 2018). CEUR Workshop Proceedings, 2018. p. 13-20 (CEUR Workshop Proceedings; Vol. 2148).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Harvard

Holmes, M, Song, H, Tonkin, EL, Perello Nieto, M, Grant, S & Flach, P 2018, Analysis of patient domestic activity in recovery from hip or knee replacement surgery: modelling wrist-worn wearable RSSI and accelerometer data in the wild. in Proceedings of the 3rd International Workshop on Knowledge Discovery in Healthcare Data: co-located with the 27th International Joint Conference on Artificial Intelligence and the 23rd European Conference on Artificial Intelligence (IJCAI-ECAI 2018). CEUR Workshop Proceedings, vol. 2148, CEUR Workshop Proceedings, pp. 13-20.

APA

Holmes, M., Song, H., Tonkin, E. L., Perello Nieto, M., Grant, S., & Flach, P. (2018). Analysis of patient domestic activity in recovery from hip or knee replacement surgery: modelling wrist-worn wearable RSSI and accelerometer data in the wild. In Proceedings of the 3rd International Workshop on Knowledge Discovery in Healthcare Data: co-located with the 27th International Joint Conference on Artificial Intelligence and the 23rd European Conference on Artificial Intelligence (IJCAI-ECAI 2018) (pp. 13-20). (CEUR Workshop Proceedings; Vol. 2148). CEUR Workshop Proceedings.

Vancouver

Holmes M, Song H, Tonkin EL, Perello Nieto M, Grant S, Flach P. Analysis of patient domestic activity in recovery from hip or knee replacement surgery: modelling wrist-worn wearable RSSI and accelerometer data in the wild. In Proceedings of the 3rd International Workshop on Knowledge Discovery in Healthcare Data: co-located with the 27th International Joint Conference on Artificial Intelligence and the 23rd European Conference on Artificial Intelligence (IJCAI-ECAI 2018). CEUR Workshop Proceedings. 2018. p. 13-20. (CEUR Workshop Proceedings).

Author

Holmes, Mike ; Song, Hao ; Tonkin, Emma L. ; Perello Nieto, Miquel ; Grant, Sabrina ; Flach, Peter. / Analysis of patient domestic activity in recovery from hip or knee replacement surgery : modelling wrist-worn wearable RSSI and accelerometer data in the wild. Proceedings of the 3rd International Workshop on Knowledge Discovery in Healthcare Data: co-located with the 27th International Joint Conference on Artificial Intelligence and the 23rd European Conference on Artificial Intelligence (IJCAI-ECAI 2018). CEUR Workshop Proceedings, 2018. pp. 13-20 (CEUR Workshop Proceedings).

Bibtex

@inproceedings{450d0fdb7a6c4b36b66b284d79ea126a,
title = "Analysis of patient domestic activity in recovery from hip or knee replacement surgery: modelling wrist-worn wearable RSSI and accelerometer data in the wild",
abstract = "The UK health service sees around 160,000 total hip or knee replacements every year and this number is expected to rise. Expectations of surgical outcome are changing alongside demographic trends, whilst aftercare may be fractured as a result of resource limitation or other factors. Conventional assessments of health outcomes must evolve to keep up with these changing trends. In practice, patients may visit a health care professional to discuss recovery and will provide survey feedback to clinicians using standardised instruments, such as the Oxford Hip & Knee score, in the months following surgery. To aid clinicians in providing accurate assessment of patient recovery a continuous home health care monitoring system would be beneficial. In this paper the authors explore how the SPHERE sensor network can be used to automatically generate measures of recovery from arthroplasty to facilitate continuous monitoring of behaviour, including location, room transitions, movement and activity; in terms of frequency and duration; in a domestic environment. The authors present a case study of data collected from a home equipped with the SPHERE sensor network. Machine learning algorithms are applied to a week of continuous observational data to generate insights into the domestic routine of the occupant. Testing of models shows that location and activity are classified with 86{\%} and 63{\%} precision, respectively.",
keywords = "Predictive analyses of home healthcare data, Internet of Things, Domestic Sensor Networks, Machine Learning, Indoor Localisation, Movement Classification, Activity Classification, Wearable Technology",
author = "Mike Holmes and Hao Song and Tonkin, {Emma L.} and {Perello Nieto}, Miquel and Sabrina Grant and Peter Flach",
year = "2018",
month = "7",
day = "13",
language = "English",
series = "CEUR Workshop Proceedings",
publisher = "CEUR Workshop Proceedings",
pages = "13--20",
booktitle = "Proceedings of the 3rd International Workshop on Knowledge Discovery in Healthcare Data",

}

RIS - suitable for import to EndNote

TY - GEN

T1 - Analysis of patient domestic activity in recovery from hip or knee replacement surgery

T2 - modelling wrist-worn wearable RSSI and accelerometer data in the wild

AU - Holmes, Mike

AU - Song, Hao

AU - Tonkin, Emma L.

AU - Perello Nieto, Miquel

AU - Grant, Sabrina

AU - Flach, Peter

PY - 2018/7/13

Y1 - 2018/7/13

N2 - The UK health service sees around 160,000 total hip or knee replacements every year and this number is expected to rise. Expectations of surgical outcome are changing alongside demographic trends, whilst aftercare may be fractured as a result of resource limitation or other factors. Conventional assessments of health outcomes must evolve to keep up with these changing trends. In practice, patients may visit a health care professional to discuss recovery and will provide survey feedback to clinicians using standardised instruments, such as the Oxford Hip & Knee score, in the months following surgery. To aid clinicians in providing accurate assessment of patient recovery a continuous home health care monitoring system would be beneficial. In this paper the authors explore how the SPHERE sensor network can be used to automatically generate measures of recovery from arthroplasty to facilitate continuous monitoring of behaviour, including location, room transitions, movement and activity; in terms of frequency and duration; in a domestic environment. The authors present a case study of data collected from a home equipped with the SPHERE sensor network. Machine learning algorithms are applied to a week of continuous observational data to generate insights into the domestic routine of the occupant. Testing of models shows that location and activity are classified with 86% and 63% precision, respectively.

AB - The UK health service sees around 160,000 total hip or knee replacements every year and this number is expected to rise. Expectations of surgical outcome are changing alongside demographic trends, whilst aftercare may be fractured as a result of resource limitation or other factors. Conventional assessments of health outcomes must evolve to keep up with these changing trends. In practice, patients may visit a health care professional to discuss recovery and will provide survey feedback to clinicians using standardised instruments, such as the Oxford Hip & Knee score, in the months following surgery. To aid clinicians in providing accurate assessment of patient recovery a continuous home health care monitoring system would be beneficial. In this paper the authors explore how the SPHERE sensor network can be used to automatically generate measures of recovery from arthroplasty to facilitate continuous monitoring of behaviour, including location, room transitions, movement and activity; in terms of frequency and duration; in a domestic environment. The authors present a case study of data collected from a home equipped with the SPHERE sensor network. Machine learning algorithms are applied to a week of continuous observational data to generate insights into the domestic routine of the occupant. Testing of models shows that location and activity are classified with 86% and 63% precision, respectively.

KW - Predictive analyses of home healthcare data

KW - Internet of Things

KW - Domestic Sensor Networks

KW - Machine Learning

KW - Indoor Localisation

KW - Movement Classification

KW - Activity Classification

KW - Wearable Technology

UR - http://ceur-ws.org/Vol-2148/

M3 - Conference contribution

T3 - CEUR Workshop Proceedings

SP - 13

EP - 20

BT - Proceedings of the 3rd International Workshop on Knowledge Discovery in Healthcare Data

PB - CEUR Workshop Proceedings

ER -