| 1. |
- Aghanavesi, Somayeh, 1981-
(författare)
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Sensor-based knowledge- and data-driven methods : A case of Parkinson’s disease motor symptoms quantification
- 2020
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- The overall aim of this thesis was to develop and evaluate new knowledge- and data-driven methods for supporting treatment and providing information for better assessment of Parkinson’s disease (PD).PD is complex and progressive. There is a large amount of inter- and intravariability in motor symptoms of patients with PD (PwPD). The current evaluation of motor symptoms that are done at clinics by using clinical rating scales is limited and provides only part of the health status of PwPD. An accurate and clinically approved assessment of PD is required using frequent evaluation of symptoms.To investigate the problem areas, the thesis adopted the microdata analysis approach including the stages of data collection, data processing, data analysis, and data interpretation. Sensor systems including smartphone and tri-axial motion sensors were used to collect data from advanced PwPD experimenting with repeated tests during a day. The experiments were rated by clinical experts. The data from sensors and the clinical evaluations were processed and used in subsequent analysis.The first three papers in this thesis report the results from the investigation, verification, and development of knowledge- and data-driven methods for quantifying the dexterity in PD. The smartphone-based data collected from spiral drawing and alternate tapping tests were used for the analysis. The results from the development of a smartphone-based data-driven method can be used for measuring treatment-related changes in PwPD. Results from investigation and verification of an approximate entropy-based method showed good responsiveness and test-retest reliability indicating that this method is useful in measuring upper limb temporal irregularity.The next two papers, report the results from the investigation and development of motion sensor-based knowledge- and data-driven methods for quantification of the motor states in PD. The motion data were collected from experiments such as leg agility, walking, and rapid alternating movements of hands. High convergence validity resulted from using motion sensors during leg agility tests. The results of the fusion of sensor data gathered during multiple motor tests were promising and led to valid, reliable and responsive objective measures of PD motor symptoms.Results in the last paper investigating the feasibility of using the Dynamic Time-Warping method for assessment of PD motor states showed it is feasible to use this method for extracting features to be used in automatic scoring of PD motor states.The findings from the knowledge- and data-driven methodology in this thesis can be used in the development of systems for follow up of the effects of treatment and individualized treatments in PD.
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| 2. |
- Ahmed, Mobyen Uddin
(författare)
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A case-based multi-modal clinical system for stress management
- 2010
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- A difficult issue in stress management is to use biomedical sensor signal in the diagnosis and treatment of stress. Clinicians often make their diagnosis and decision based on manual inspection of physiological signals such as, ECG, heart rate, finger temperature etc. However, the complexity associated with manual analysis and interpretation of the signals makes it difficult even for experienced clinicians. Today the diagnosis and decision is largely dependent on how experienced the clinician is interpreting the measurements. A computer-aided decision support system for diagnosis and treatment of stress would enable a more objective and consistent diagnosis and decisions. A challenge in the field of medicine is the accuracy of the system, it is essential that the clinician is able to judge the accuracy of the suggested solutions. Case-based reasoning systems for medical applications are increasingly multi-purpose and multi-modal, using a variety of different methods and techniques to meet the challenges of the medical domain. This research work covers the development of an intelligent clinical decision support system for diagnosis, classification and treatment in stress management. The system uses a finger temperature sensor and the variation in the finger temperature is one of the key features in the system. Several artificial intelligence techniques have been investigated to enable a more reliable and efficient diagnosis and treatment of stress such as case-based reasoning, textual information retrieval, rule-based reasoning, and fuzzy logic. Functionalities and the performance of the system have been validated by implementing a research prototype based on close collaboration with an expert in stress. The case base of the implemented system has been initiated with 53 reference cases classified by an experienced clinician. A case study also shows that the system provides results close to a human expert. The experimental results suggest that such a system is valuable both for less experienced clinicians and for experts where the system may function as a second option.
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| 3. |
- Ahmed, Mobyen Uddin, et al.
(författare)
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A Case-Based Reasoning System for Knowledge and Experience Reuse
- 2007
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Ingår i: Proceedings of the 24th annual workshop of the Swedish Artificial Intelligence Society. ; , s. 70-80
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Konferensbidrag (refereegranskat)abstract
- Experience is one of the most valuable assets technicians and engineer have and may have been collected during many years and both from successful solutions as well as from very costly mistakes. Unfortunately industry rarely uses a systematic approach for experience reuse. This may be caused by the lack of efficient tools facilitating experience distribution and reuse. We propose a case-based approach and tool to facilitate experience reuse more systematically in industry. It is important that such a tool allows the technicians to give the problem case in a flexible way to increase acceptance and use. The proposed tool enables more structured handling of experience and is flexible and can be adapted to different situations and problems. The user is able to input text in a structured way and possibly in combination with other numeric or symbolic features. The system is able to identify and retrieve relevant similar experiences for reuse.
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| 4. |
- Ahmed, Mobyen Uddin, 1976-, et al.
(författare)
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A Case-Based Retrieval System for Post-Operative Pain Treatment
- 2011
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Konferensbidrag (refereegranskat)abstract
- This paper presents a clinical decision support system based on case-basedretrieval approach to assist physicians in post-operative pain treatment. Here,the cases are formulated by combining regular features and features using anumerical visual analogue scale (NVAS) through a questionnaire. Featureabstraction is done both in problem and outcome description of a case in order toreduce the number of attributes. The system retrieves most similar cases with theiroutcomes. The outcome of each case brings benefits for physicians since it presentsboth severity and fast recovery by the applied treatment in post-operative patients.Therefore, we have introduced a two-layer case structure i.e., solution is the firstlayer and outcome is the second layer that better suits this medical application. Inthe system, the solution presents the treatment and the outcome contains recoveryinformation of a patient, something physicians are interested in, especially the riskof side effects and complications.
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| 5. |
- Ahmed, Mobyen Uddin, et al.
(författare)
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A Computer Aided System for Post-operative Pain Treatment Combining Knowledge Discovery and Case-Based Reasoning
- 2012
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Ingår i: Lecture Notes in Computer Science, vol. 7466. - Berlin, Heidelberg : Springer. - 9783642329852 ; , s. 3-16
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Bokkapitel (refereegranskat)abstract
- The quality improvement for individual postoperative-pain treatment is an important issue. This paper presents a computer aided system for physicians in their decision making tasks in post-operative pain treatment. Here, the system combines a Case-Based Reasoning (CBR) approach with knowledge discovery. Knowledge discovery is applied in terms of clustering in order to identify the unusual cases. We applied a two layered case structure for case solutions i.e. the treatment is in the first layer and outcome after treatment (i.e. recovery of the patient) is in the second layer. Moreover, a 2nd order retrieval approach is applied in the CBR retrieval step in order to retrieve the most similar cases. The system enables physicians to make more informed decisions since they are able to explore similar both regular and rare cases of post-operative patients. The two layered case structure is moving the focus from diagnosis to outcome i.e. the recovery of the patient, something a physician is especially interested in, including the risk of complications and side effects.
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| 6. |
- Ahmed, Mobyen Uddin, et al.
(författare)
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A fuzzy rule-based decision support system for Duodopa treatment in Parkinson
- 2006
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Ingår i: 23rd annual workshop of the Swedish Artificial Intelligence Society. - Umeå.
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Konferensbidrag (refereegranskat)abstract
- A decision support system (DSS) was implemented based on a fuzzy logic inference system (FIS) to provide assistance in dose alteration of Duodopa infusion in patients with advanced Parkinson’s disease, using data from motor state assessments and dosage. Three-tier architecture with an object oriented approach was used. The DSS has a web enabled graphical user interface that presents alerts indicating non optimal dosage and states, new recommendations, namely typical advice with typical dose and statistical measurements. One data set was used for design and tuning of the FIS and another data set was used for evaluating performance compared with actual given dose. Overall goodness-of-fit for the new patients (design data) was 0.65 and for the ongoing patients (evaluation data) 0.98. User evaluation is now ongoing. The system could work as an assistant to clinical staff for Duodopa treatment in advanced Parkinson’s disease.
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| 7. |
- Ahmed, Mobyen Uddin, et al.
(författare)
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A Generic System-level Framework for Self-Serve Health Monitoring System through Internet of Things(IoT)
- 2015
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Ingår i: Studies in Health Technology and Informatics, Volume 211. - 9781614995159 ; , s. 305-307
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Konferensbidrag (refereegranskat)abstract
- Sensor data are traveling from sensors to a remote server, data is analysed remotely in a distributed manner, and health status of a user is presented in real-time. This paper presents a generic system-level framework for a self-served health monitoring system through the Internet of Things (IoT) to facilities an efficient sensor data management.
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| 8. |
- Ahmed, Mobyen Uddin, 1976-, et al.
(författare)
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A Hybrid Case-Based System in Stress Diagnosis and Treatment
- 2012
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Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- Computer-aided decision support systems play anincreasingly important role in clinical diagnosis and treatment.However, they are difficult to build for domains where thedomain theory is weak and where different experts differ indiagnosis. Stress diagnosis and treatment is an example of such adomain. This paper explores several artificial intelligencemethods and techniques and in particular case-based reasoning,textual information retrieval, rule-based reasoning, and fuzzylogic to enable a more reliable diagnosis and treatment of stress.The proposed hybrid case-based approach has been validated byimplementing a prototype in close collaboration with leadingexperts in stress diagnosis. The obtained sensitivity, specificityand overall accuracy compared to an expert are 92%, 86% and88% respectively.
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| 9. |
- Ahmed, Mobyen Uddin, Dr, 1976-, et al.
(författare)
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A machine learning approach for biomass characterization
- 2019
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Ingår i: Energy Procedia. - : Elsevier Ltd. - 1876-6102. ; , s. 1279-1287
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Konferensbidrag (refereegranskat)abstract
- The aim of this work is to apply and evaluate different chemometric approaches employing several machine learning techniques in order to characterize the moisture content in biomass from data obtained by Near Infrared (NIR) spectroscopy. The approaches include three main parts: a) data pre-processing, b) wavelength selection and c) development of a regression model enabling moisture content measurement. Standard Normal Variate (SNV), Multiplicative Scatter Correction and Savitzky-Golay first (SG1) and second (SG2) derivatives and its combinations were applied for data pre-processing. Genetic algorithm (GA) and iterative PLS (iPLS) were used for wavelength selection. Artificial Neural Network (ANN), Gaussian Process Regression (GPR), Support Vector Regression (SVR) and traditional Partial Least Squares (PLS) regression, were employed as machine learning regression methods. Results shows that SNV combined with SG1 first derivative performs the best in data pre-processing. The GA is the most effective methods for variable selection and GPR achieved a high accuracy in regression modeling while having low demands on computation time. Overall, the machine learning techniques demonstrate a great potential to be used in future NIR spectroscopy applications. © 2019 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review under responsibility of the scientific committee of ICAE2018 - The 10th International Conference on Applied Energy.
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| 10. |
- Ahmed, Mobyen Uddin, Dr, 1976-, et al.
(författare)
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A Machine Learning Approach to Classify Pedestrians’ Event based on IMU and GPS
- 2019
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Ingår i: International Conference on Modern Intelligent Systems Concepts MISC'18. - : CESER Publications. ; 17:2, s. 154-167
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Konferensbidrag (refereegranskat)abstract
- This paper investigates and implements six Machine Learning (ML) algorithms, i.e. Artificial Neural Network (ANN), Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), Extra Tree (ET), and Gradient Boosted Trees (GBT) to classify different Pedestrians’ events based on Inertial Measurement Unit (IMU) and Global Positioning System (GPS) signals. Pedestrians’ events are pedestrian movements as the first step of H2020 project called SimuSafe1 with a goal to reduce traffic fatalities by doing risk assessments of the pedestrians. The movements the MLs’ models are attempting to classify are standing, walking, and running. Data, i.e. IMU, GPS sensor signals and other contextual information are collected by a smartphone through a controlled procedure. The smartphone is placed in five different positions onto the body of participants, i.e. arm, chest, ear, hand and pocket. The recordings are filtered, trimmed, and labeled. Next, samples are generated from small overlapping sections from which time and frequency domain features are extracted. Three different experiments are conducted to evaluate the performances in term of accuracy of the MLs’ models in different circumstances. The best performing MLs’ models determined by the average accuracy across all experiments is Extra Tree (ET) with a classification accuracy of 91%.
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