Empirical study of decision trees and ensemble classifiers for monitoring of diabetes patients in pervasive healthcare
- Authors: Kelarev, Andrei , Stranieri, Andrew , Yearwood, John , Jelinek, Herbert
- Date: 2012
- Type: Text , Conference proceedings
- Full Text: false
- Description: Diabetes is a condition requiring continuous everyday monitoring of health related tests. To monitor specific clinical complications one has to find a small set of features to be collected from the sensors and efficient resource-aware algorithms for their processing. This article is concerned with the detection and monitoring of cardiovascular autonomic neuropathy, CAN, in diabetes patients. Using a small set of features identified previously, we carry out an empirical investigation and comparison of several ensemble methods based on decision trees for a novel application of the processing of sensor data from diabetes patients for pervasive health monitoring of CAN. Our experiments relied on an extensive database collected by the Diabetes Complications Screening Research Initiative at Charles Sturt University and concentrated on the particular task of the detection and monitoring of cardiovascular autonomic neuropathy. Most of the features in the database can now be collected using wearable sensors. Our experiments included several essential ensemble methods, a few more advanced and recent techniques, and a novel consensus function. The results show that our novel application of the decision trees in ensemble classifiers for the detection and monitoring of CAN in diabetes patients achieved better performance parameters compared with the outcomes obtained previously in the literature. © 2012 IEEE.
- Description: 2003009675
An assessment of the utility and functionality of wearable head impact sensors in Australian Football
- Authors: McIntosh, Andrew , Willmott, Catherine , Patton, Declan , Mitra, Biswadev , Brennan, James , Dimech-Betancourt, Bleydy , Howard, Teresa , Rosenfeld, Jeffrey
- Date: 2019
- Type: Text , Journal article
- Relation: Journal of Science and Medicine in Sport Vol. 22, no. 7 (2019), p. 784-789
- Full Text: false
- Reviewed:
- Description: Objectives: To assess the utility and functionality of the X-Patch® as a measurement tool to study head impact exposure in Australian Football. Accuracy, precision, reliability and validity were examined. Designs: Laboratory tests and prospective observational study. Methods: Laboratory tests on X-Patch® were undertaken using an instrumented Hybrid III head and neck and linear impactor. Differences between X-Patch® and reference data were analysed. Australian Football players wore the X-Patch® devices and games were video-recorded. Video recordings were analysed qualitatively for head impact events and these were correlated with X-Patch® head acceleration events. Wearability of the X-Patch® was assessed using the Comfort Rating Scale for Wearable Computers. Results: Laboratory head impacts, performed at multiple impact sites and velocities, identified significant correlations between headform-measured and device-measured kinematic parameters (p < 0.05 for all). On average, the X-Patch®-recorded peak linear acceleration (PLA) was 17% greater than the reference PLA, 28% less for peak rotational acceleration (PRA) and 101% greater for the Head Injury Criterion (HIC). For video analysis, 118 head acceleration events (HAE) were included with PLA ≥30 g across 53 players. Video recordings of X-Patch®-measured HAEs (PLA ≥30 g) determined that 31.4% were direct head impacts, 9.3% were indirect impacts, 44.1% were unknown or unclear and 15.3% were neither direct nor indirect head impacts. The X-Patch® system was deemed wearable by 95–100% of respondents. Conclusions: This study reinforces evidence that use of the current X-Patch® devices should be limited to research only and in conjunction with video analysis.
An intelligent healthcare monitoring framework using wearable sensors and social networking data
- Authors: Ali, Farman , El-Sappagh, Shaker , Islam, S. , Ali, Amjad , Imran, Muhammad
- Date: 2020
- Type: Text , Journal article
- Relation: Future Generation Computer Systems Vol. 114, no. (2020), p. 23-43
- Full Text: false
- Reviewed:
- Description: Wearable sensors and social networking platforms play a key role in providing a new method to collect patient data for efficient healthcare monitoring. However, continuous patient monitoring using wearable sensors generates a large amount of healthcare data. In addition, the user-generated healthcare data on social networking sites come in large volumes and are unstructured. The existing healthcare monitoring systems are not efficient at extracting valuable information from sensors and social networking data, and they have difficulty analyzing it effectively. On top of that, the traditional machine learning approaches are not enough to process healthcare big data for abnormality prediction. Therefore, a novel healthcare monitoring framework based on the cloud environment and a big data analytics engine is proposed to precisely store and analyze healthcare data, and to improve the classification accuracy. The proposed big data analytics engine is based on data mining techniques, ontologies, and bidirectional long short-term memory (Bi-LSTM). Data mining techniques efficiently preprocess the healthcare data and reduce the dimensionality of the data. The proposed ontologies provide semantic knowledge about entities and aspects, and their relations in the domains of diabetes and blood pressure (BP). Bi-LSTM correctly classifies the healthcare data to predict drug side effects and abnormal conditions in patients. Also, the proposed system classifies the patients’ health condition using their healthcare data related to diabetes, BP, mental health, and drug reviews. This framework is developed employing the Protégé Web Ontology Language tool with Java. The results show that the proposed model precisely handles heterogeneous data and improves the accuracy of health condition classification and drug side effect predictions. © 2020 Elsevier B.V.