| 1. |
- Ahmed, Mobyen Uddin, 1976-, et al.
(författare)
-
A case-based patient identification system using pulseoximeter and a personalized health profile
- 2012
-
Ingår i: Proceedings of the ICCBR 2012 Workshops. - Lyon, France. ; , s. 117-128
-
Konferensbidrag (refereegranskat)abstract
- This paper proposes a case-based system framework in order to identify patient using their health parameters taken with physiological sensors. It combines a personalized health profiling protocol with a Case-Based Reasoning (CBR) approach. The personalized health profiling helps to determine a number of individual parameters which are important inputs for a clinician to make the final diagnosis and treatment plan. The proposed system uses a pulse oximeter that measures pulse rate and blood oxygen saturation. The measurements are taken through an android application in a smart phone which is connected with the pulseoximeter and bluetooth communication. The CBR approach helps clinicians to make a diagnosis, classification and treatment plan by retrieving the most similar previous case. The case may also be used to follow the treatment progress. Here, the cases are formulated with person’s contextual information and extracted features from sensor signal measurements. The features are extracted considering three domain analysis:1) time domain features using statistical measurement, 2) frequency domain features applying Fast Fourier Transform (FFT), and 3) time-frequency domain features applying Discrete Wavelet Transform (DWT). The initial result is acceptable that shows the advancement of the system while combining the personalized health profiling together with CBR.
|
|
| 2. |
- Ahmed, Mobyen Uddin, 1976-, et al.
(författare)
-
Intelligent Healthcare Services to Support Health Monitoring of Elderly
- 2015
-
Ingår i: INTERNET OF THINGS. - Cham : Springer. - 9783319196565 - 9783319196558 ; , s. 178-186
-
Konferensbidrag (refereegranskat)abstract
- This paper proposed an approach of intelligent healthcare services to support health monitoring of old people through the project named SAAPHO. Here, definition and architecture of the proposed healthcare services are presented considering six different health parameters such as: 1) physical activity, 2) blood pressure, 3) glucose, 4) medication compliance, 5) pulse monitoring and 6) weight monitoring. The outcome of the proposed services is evaluated in a case study where total 201 subjects from Spain and Slovenia are involved for user requirements analysis considering 1) end users, 2) clinicians, and 3) field study analysis perspectives. The result shows the potentiality and competence of the proposed healthcare services for the users.
|
|
| 3. |
- Ahmed, Mobyen Uddin, 1976-, et al.
(författare)
-
Physical Activity Classification for Elderly based on Pulse Rate
- 2013
-
Ingår i: Studies in Health Technology and Informatics, vol. 189. - : IOS Press. - 9781614992677 ; , s. 152-157
-
Konferensbidrag (refereegranskat)abstract
- Physical activity is one of the key components for elderly in order to be actively ageing. However, it is difficult to differentiate and identify the body movement and actual physical activity using only accelerometer measurement. Therefore, this paper presents an application of case-based retrieval classification scheme to classify the physical activity of elderly based on pulse rate measurements. Here, case-based retrieval approach used the features extracted from both time and frequency domain. The evaluation result shows the best accuracy performance while considering the combination of time and frequency domain features. According to the evaluation result while considering the control measurements, the sensitivity, specificity and overall accuracy are achieved as 95%, 96% and 96% respectively. Considering the test dataset, the system was succeeded to identify 13 physical activities out of 16 i.e. the percentage of the correctness was 81%.
|
|
| 4. |
- Banaee, Hadi, 1986-, et al.
(författare)
-
A framework for automatic text generation of trends in physiological time series data
- 2013
-
Ingår i: IEEE International Conference on Systems, Man, and Cybernetics, 13-16 Oct. 2013, Manchester. - : IEEE conference proceedings. - 9781479906529 - 9780769551548 ; , s. 3876-3881
-
Konferensbidrag (refereegranskat)abstract
- Health monitoring systems using wearable sensorshave rapidly grown in the biomedical community. The mainchallenges in physiological data monitoring are to analyse largevolumes of health measurements and to represent the acquiredinformation. Natural language generation is an effective methodto create summaries for both clinicians and patients as it candescribe useful information extracted from sensor data in textualformat. This paper presents a framework of a natural languagegeneration system that provides a text-based representation ofthe extracted numeric information from physiological sensorsignals. More specifically, a new partial trend detection algorithmis introduced to capture the particular changes and events ofhealth parameters. The extracted information is then representedconsidering linguistic characterisation of numeric features. Ex-perimental analysis was performed using a wearable sensor and demonstrates a possible output in natural language text.
|
|
| 5. |
- Banaee, Hadi, 1986-, et al.
(författare)
-
Data mining for wearable sensors in health monitoring systems : a review of recent trends and challenges
- 2013
-
Ingår i: Sensors. - Basel : MDPI. - 1424-8220. ; 13:12, s. 17472-17500
-
Forskningsöversikt (refereegranskat)abstract
- The past few years have witnessed an increase in the development of wearable sensors for health monitoring systems. This increase has been due to several factors such as development in sensor technology as well as directed efforts on political and stakeholder levels to promote projects which address the need for providing new methods for care given increasing challenges with an aging population. An important aspect of study in such system is how the data is treated and processed. This paper provides a recent review of the latest methods and algorithms used to analyze data from wearable sensors used for physiological monitoring of vital signs in healthcare services. In particular, the paper outlines the more common data mining tasks that have been applied such as anomaly detection, prediction and decision making when considering in particular continuous time series measurements. Moreover, the paper further details the suitability of particular data mining and machine learning methods used to process the physiological data and provides an overview of the properties of the data sets used in experimental validation. Finally, based on this literature review, a number of key challenges have been outlined for data mining methods in health monitoring systems
|
|
| 6. |
- Banaee, Hadi, 1986-, et al.
(författare)
-
Descriptive Modelling of Clinical Conditions with Data-driven Rule Mining in Physiological Data
- 2015
-
Ingår i: Proceedings of the 8th International conference of Health Informatics (HEALTHINF 2015). - Lisbon, Portugal : SciTePress. - 9789897580680
-
Konferensbidrag (refereegranskat)abstract
- This paper presents an approach to automatically mine rules in time series data representing physiologicalparameters in clinical conditions. The approach is fully data driven, where prototypical patterns are mined foreach physiological time series data. The generated rules based on the prototypical patterns are then describedin a textual representation which captures trends in each physiological parameter and their relation to the otherphysiological data. In this paper, a method for measuring similarity of rule sets is introduced in order tovalidate the uniqueness of rule sets. This method is evaluated on physiological records from clinical classesin the MIMIC online database such as angina, sepsis, respiratory failure, etc.. The results show that the rulemining technique is able to acquire a distinctive model for each clinical condition, and represent the generatedrules in a human understandable textual representation
|
|
| 7. |
- Banaee, Hadi, 1986-, et al.
(författare)
-
Towards NLG for Physiological Data Monitoring with Body Area Networks
- 2013
-
Ingår i: 14th European Workshop on Natural Language Generation. ; , s. 193-197
-
Konferensbidrag (refereegranskat)abstract
- This position paper presents an on-goingwork on a natural language generationframework that is particularly tailored fornatural language generation from bodyarea networks. We present an overview ofthe main challenges when considering thistype of sensor devices used for at homemonitoring of health parameters. The paperpresents the first steps towards the implementationof a system which collectsinformation from heart rate and respirationusing a wearable sensor.
|
|
| 8. |
- Köckemann, Uwe, 1983-, et al.
(författare)
-
Open-source data collection and data sets for activity recognition in smart homes
- 2020
-
Ingår i: Sensors. - : MDPI AG. - 1424-8220. ; 20:3
-
Tidskriftsartikel (refereegranskat)abstract
- As research in smart homes and activity recognition is increasing, it is of ever increasing importance to have benchmarks systems and data upon which researchers can compare methods. While synthetic data can be useful for certain method developments, real data sets that are open and shared are equally as important. This paper presents the E-care@home system, its installation in a real home setting, and a series of data sets that were collected using the E-care@home system. Our first contribution, the E-care@home system, is a collection of software modules for data collection, labeling, and various reasoning tasks such as activity recognition, person counting, and configuration planning. It supports a heterogeneous set of sensors that can be extended easily and connects collected sensor data to higher-level Artificial Intelligence (AI) reasoning modules. Our second contribution is a series of open data sets which can be used to recognize activities of daily living. In addition to these data sets, we describe the technical infrastructure that we have developed to collect the data and the physical environment. Each data set is annotated with ground-truth information, making it relevant for researchers interested in benchmarking different algorithms for activity recognition.
|
|
