Internet of things based multi-sensor patient fall detection system
Accidental falls of patients cannot be completely prevented. However, timely fall detection can help prevent further complications such as blood loss and unconsciousness. In this study, the authors present a cost-effective integrated system designed to remotely detect patient falls in hospitals in a...
محفوظ في:
| المؤلف الرئيسي: | |
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| مؤلفون آخرون: | , , , , |
| التنسيق: | article |
| منشور في: |
2019
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| الموضوعات: | |
| الوصول للمادة أونلاين: | http://hdl.handle.net/11073/16598 |
| الوسوم: |
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| _version_ | 1864513434527203328 |
|---|---|
| author | Khan, Sarah |
| author2 | Qamar, Ramsha Zaheen, Rahma Al-Ali, Abdul-Rahman Al Nabulsi, Ahmad Al-Nashash, Hasan |
| author2_role | author author author author author |
| author_facet | Khan, Sarah Qamar, Ramsha Zaheen, Rahma Al-Ali, Abdul-Rahman Al Nabulsi, Ahmad Al-Nashash, Hasan |
| author_role | author |
| dc.creator.none.fl_str_mv | Khan, Sarah Qamar, Ramsha Zaheen, Rahma Al-Ali, Abdul-Rahman Al Nabulsi, Ahmad Al-Nashash, Hasan |
| dc.date.none.fl_str_mv | 2019 2020-02-16T06:41:05Z 2020-02-16T06:41:05Z |
| dc.format.none.fl_str_mv | application/pdf |
| dc.identifier.none.fl_str_mv | Khan, S., Qamar, R., Zaheen, R., Al-Ali, A. R., Al Nabulsi, A., & Al-Nashash, H. (2019). Internet of things based multi-sensor patient fall detection system. Healthcare technology letters, 6(5), 132–137. https://doi.org/10.1049/htl.2018.5121 2053-3713 http://hdl.handle.net/11073/16598 10.1049%2Fhtl.2018.5121 |
| dc.language.none.fl_str_mv | en_US |
| dc.publisher.none.fl_str_mv | National Center for Biotechnology Information |
| dc.relation.none.fl_str_mv | https://dx.doi.org/10.1049%2Fhtl.2018.5121 |
| dc.subject.none.fl_str_mv | Pattern classification Body sensor networks Biomedical equipment Gyroscopes Geriatrics Bayes methods Medical signal processing Microcomputers Accelerometers Patient monitoring Internet of Things Nearest neighbour methods Cost-effective integrated system Credit card-sized single board microcomputer Visual-based classifier Sensor data Naive Bayes' classifiers Internet of things based multisensor patient fall detection system Nonfall motions classification k-nearest neighbour |
| dc.title.none.fl_str_mv | Internet of things based multi-sensor patient fall detection system |
| dc.type.none.fl_str_mv | Peer-Reviewed Published version info:eu-repo/semantics/publishedVersion info:eu-repo/semantics/article |
| description | Accidental falls of patients cannot be completely prevented. However, timely fall detection can help prevent further complications such as blood loss and unconsciousness. In this study, the authors present a cost-effective integrated system designed to remotely detect patient falls in hospitals in addition to classifying non-fall motions into activities of daily living. The proposed system is a wearable device that consists of a camera, gyroscope, and accelerometer that is interfaced with a credit card-sized single board microcomputer. The information received from the camera is used in a visual-based classifier and the sensor data is analysed using the k-Nearest Neighbour and Naïve Bayes’ classifiers. Once a fall is detected, an attendant at the hospital is informed. Experimental results showed that the accuracy of the device in classifying fall versus non-fall activity is 95%. Other requirements and specifications are discussed in greater detail. |
| format | article |
| id | aus_0f367c2d624766b26cbcbd1bd6f50a2b |
| identifier_str_mv | Khan, S., Qamar, R., Zaheen, R., Al-Ali, A. R., Al Nabulsi, A., & Al-Nashash, H. (2019). Internet of things based multi-sensor patient fall detection system. Healthcare technology letters, 6(5), 132–137. https://doi.org/10.1049/htl.2018.5121 2053-3713 10.1049%2Fhtl.2018.5121 |
| language_invalid_str_mv | en_US |
| network_acronym_str | aus |
| network_name_str | aus |
| oai_identifier_str | oai:repository.aus.edu:11073/16598 |
| publishDate | 2019 |
| publisher.none.fl_str_mv | National Center for Biotechnology Information |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| spelling | Internet of things based multi-sensor patient fall detection systemKhan, SarahQamar, RamshaZaheen, RahmaAl-Ali, Abdul-RahmanAl Nabulsi, AhmadAl-Nashash, HasanPattern classificationBody sensor networksBiomedical equipmentGyroscopesGeriatricsBayes methodsMedical signal processingMicrocomputersAccelerometersPatient monitoringInternet of ThingsNearest neighbour methodsCost-effective integrated systemCredit card-sized single board microcomputerVisual-based classifierSensor dataNaive Bayes' classifiersInternet of things based multisensor patient fall detection systemNonfall motions classificationk-nearest neighbourAccidental falls of patients cannot be completely prevented. However, timely fall detection can help prevent further complications such as blood loss and unconsciousness. In this study, the authors present a cost-effective integrated system designed to remotely detect patient falls in hospitals in addition to classifying non-fall motions into activities of daily living. The proposed system is a wearable device that consists of a camera, gyroscope, and accelerometer that is interfaced with a credit card-sized single board microcomputer. The information received from the camera is used in a visual-based classifier and the sensor data is analysed using the k-Nearest Neighbour and Naïve Bayes’ classifiers. Once a fall is detected, an attendant at the hospital is informed. Experimental results showed that the accuracy of the device in classifying fall versus non-fall activity is 95%. Other requirements and specifications are discussed in greater detail.National Center for Biotechnology Information2020-02-16T06:41:05Z2020-02-16T06:41:05Z2019Peer-ReviewedPublished versioninfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfKhan, S., Qamar, R., Zaheen, R., Al-Ali, A. R., Al Nabulsi, A., & Al-Nashash, H. (2019). Internet of things based multi-sensor patient fall detection system. Healthcare technology letters, 6(5), 132–137. https://doi.org/10.1049/htl.2018.51212053-3713http://hdl.handle.net/11073/1659810.1049%2Fhtl.2018.5121en_UShttps://dx.doi.org/10.1049%2Fhtl.2018.5121oai:repository.aus.edu:11073/165982024-08-22T12:08:43Z |
| spellingShingle | Internet of things based multi-sensor patient fall detection system Khan, Sarah Pattern classification Body sensor networks Biomedical equipment Gyroscopes Geriatrics Bayes methods Medical signal processing Microcomputers Accelerometers Patient monitoring Internet of Things Nearest neighbour methods Cost-effective integrated system Credit card-sized single board microcomputer Visual-based classifier Sensor data Naive Bayes' classifiers Internet of things based multisensor patient fall detection system Nonfall motions classification k-nearest neighbour |
| status_str | publishedVersion |
| title | Internet of things based multi-sensor patient fall detection system |
| title_full | Internet of things based multi-sensor patient fall detection system |
| title_fullStr | Internet of things based multi-sensor patient fall detection system |
| title_full_unstemmed | Internet of things based multi-sensor patient fall detection system |
| title_short | Internet of things based multi-sensor patient fall detection system |
| title_sort | Internet of things based multi-sensor patient fall detection system |
| topic | Pattern classification Body sensor networks Biomedical equipment Gyroscopes Geriatrics Bayes methods Medical signal processing Microcomputers Accelerometers Patient monitoring Internet of Things Nearest neighbour methods Cost-effective integrated system Credit card-sized single board microcomputer Visual-based classifier Sensor data Naive Bayes' classifiers Internet of things based multisensor patient fall detection system Nonfall motions classification k-nearest neighbour |
| url | http://hdl.handle.net/11073/16598 |