| 1. |
- Ahmed, Mobyen Uddin, 1976-, et al.
(författare)
-
Health monitoring for elderly : an application using case-based reasoning and cluster analysis
- 2013
-
Ingår i: ISRN Artificial Intelligence. - Sweden : Hindawi Limited. - 2090-7435 .- 2090-7443. ; 2013:2013, s. 1-11
-
Tidskriftsartikel (refereegranskat)abstract
- This paper presents a framework to process and analyze data from a pulse oximeter which measures pulse rate and blood oxygen saturation from a set of individuals remotely. Using case-based reasoning (CBR) as the backbone to the framework, records are analyzed and categorized according to how well they are similar. Record collection has been performed using a personalized health profiling approach where participants wore a pulse oximeter sensor for a fixed period of time and performed specific activities for pre-determined intervals. Using a variety of feature extraction in time, frequency and time-frequency domains, and data processing techniques, the data is fed into a CBR system which retrieves most similar cases and generates alarm and flag according to the case outcomes. The system has been compared with an expert's classification and 90% match is achieved between the expert's and CBR classification. Again, considering the clustered measurements the CBR approach classifies 93% correctly both for the pulse rate and oxygen saturation. Along with the proposed methodology, this paper provides a basis for which the system can be used in analysis of continuous health monitoring and be used as a suitable method as in home/remote monitoring systems.
|
|
| 2. |
- 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%.
|
|
| 3. |
- Ahmed, Mobyen Uddin, 1976-, et al.
(författare)
-
Physical activity identification using supervised machine learning and based on pulse rate
- 2013
-
Ingår i: International Journal of Advanced Computer Sciences and Applications. - : The Science and Information (SAI) Organization. - 2158-107X .- 2156-5570. ; 4:7, s. 210-217
-
Tidskriftsartikel (refereegranskat)abstract
- Physical activity is one of the key components for elderly in order to be actively ageing. Pulse rate is a convenient physiological parameter to identify elderly’s physical activity since it increases with activity and decreases with rest. However, analysis and classification of pulse rate is often difficult due to personal variation during activity. This paper proposed a Case-Based Reasoning (CBR) approach to identify physical activity of elderly based on pulse rate. The proposed CBR approach has been compared with the two popular classification techniques, i.e. Support Vector Machine (SVM) and Neural Network (NN). The comparison has been conducted through an empirical experimental study where three experiments with 192 pulse rate measurement data are used. The experiment result shows that the proposed CBR approach outperforms the other two methods. Finally, the CBR approach identifies physical activity of elderly 84% accurately based on pulse rate
|
|
| 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
-
Tidskriftsartikel (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, et al.
(författare)
-
Towards NLG for Physiological Data Monitoring with Body Area Networks
- 2013
-
Ingår i: 14th European Workshop on Natural Language Generation ENLG. - London, United Kingdom. - 9781937284565
-
Konferensbidrag (refereegranskat)abstract
- This position paper presents an on-going work on a natural language generation framework that is particularly tailored for summary text generation from body area networks. We present an overview of the main challenges when considering this type of sensor devices used for at home monitoring of health parameters. This pa- per describes the first steps towards the im- plementation of a system which collects information from heart rate and respira- tion rate using a wearable sensor. The pa- per further outlines the direction for future work and in particular the challenges for NLG in this application domain
|
|
| 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. |
- Begum, Shahina, et al.
(författare)
-
Physiological Sensor Signals Analysis to Represent Cases in a Case-based Diagnostic System
- 2013
-
Ingår i: Innovations in Knowledge-based Systems in Biomedicine, vol. 250. - Berlin, Heidelberg : Springer. - 9783642330148 ; , s. 1-25
-
Bokkapitel (övrigt vetenskapligt/konstnärligt)abstract
- Today, medical knowledge is expanding so rapidly that clinicians cannot follow all progress any more. This is one reason for making knowledge- based systems desirable in medicine. Such systems can give a clinician a second opinion and give them access to new experience and knowledge. Recent advances in Artificial Intelligence (AI) offers methods and techniques with the potential of solving tasks previously difficult to solve with computer-based systems in medical domains. This chapter is especially concerned with diagnosis of stress-related dysfunctions using AI methods and techniques. Since there are large individual variations between people when looking at biological sensor signals to diagnose stress, this is a worthy challenge. Stress is an inevitable part of our human life. No one can live without stress. However, long-term exposure to stress may in the worst case cause severe mental and/or physical problems that are often related to different kind of psychosomatic disorders, coronary heart disease etc. So, diagnosis of stress is an important issue for health and well-being. Diagnosis of stress often involves acquisition of biological signals for example finger temperature, electrocardiogram (ECG), electromyography (EMG) signal, skin conductance (SC) signals etc. and is followed by a careful analysis by an expert. However, the number of experts to diagnose stress in psycho-physiological domain is limited. Again, responses to stress are different for different persons. So, interpreting a particular curve and diagnosing stress levels is difficult even for experts in the domain due to large individual variations. It is a highly complex and partly intuitive process which experienced clinicians use when manually inspecting biological sensor signals and classifying a patient. Clinical studies show that the pattern of variation within heart rate i.e., HRV signal and finger temperature can help to determine stress-related disorders. This chapter presents a signal pre-processing and feature extraction approach based on electrocardiogram (ECG) and finger temperature sensor signals. The extracted features are used to formulate cases in a case-based reasoning system to develop a personalized stress diagnosis system. The results obtained from the evaluation show a performance close to an expert in the domain in diagnosing stress.
|
|
