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- Ackeby, Susanne, et al.
(författare)
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The role and interaction of microgrids and centralized grids in developing modern power systems
- 2017
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Konferensbidrag (refereegranskat)abstract
- An extension of microgrids is now underway, primarily to allow increased electrification in growing economies but also to meet the need to reduce global CO2 emissions and to provide ancillary services to centralized grids. Energy access constitutes one of the fundamental building blocks for economic growth as well as social equity in the modern world. Access to sustainable energy is needed to achieve sustainable development. A microgrid should not be seen as a competitor to the centralized grid but as a complement.Through examination of several implemented cases from different parts of the world the following topics are considered: Analysis of the interaction between centralized grids and microgrids Analysis of stakeholder decision parameters for electrification Analysis of design differences and requirements for microgrids, depending on the intended purpose and the need of the end customer.It is determined that good planning, suitable requirements and clear regulations for microgrids (in relation to centralized grids) limits the risk of stranded assets and enables better business cases for the involved stakeholders.The paper is based on the discussion paper The role and interaction of microgrids and centralized grids in developing modern power systems – A case review publiced by ISGAN (International Smart Grid Action Network) Annex 6: Power T&D Systems.The discussion paper and further information about ISGAN is available at http://www.iea-isgan.org/.
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- Aghanavesi, Somayeh, 1981-, et al.
(författare)
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A review of Parkinson’s disease cardinal and dyskinetic motor symptoms assessment methods using sensor systems
- 2016
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Konferensbidrag (refereegranskat)abstract
- This paper is reviewing objective assessments of Parkinson’s disease(PD) motor symptoms, cardinal, and dyskinesia, using sensor systems. It surveys the manifestation of PD symptoms, sensors that were used for their detection, types of signals (measures) as well as their signal processing (data analysis) methods. A summary of this review’s finding is represented in a table including devices (sensors), measures and methods that were used in each reviewed motor symptom assessment study. In the gathered studies among sensors, accelerometers and touch screen devices are the most widely used to detect PD symptoms and among symptoms, bradykinesia and tremor were found to be mostly evaluated. In general, machine learning methods are potentially promising for this. PD is a complex disease that requires continuous monitoring and multidimensional symptom analysis. Combining existing technologies to develop new sensor platforms may assist in assessing the overall symptom profile more accurately to develop useful tools towards supporting better treatment process.
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- Aghanavesi, Somayeh, 1981-, et al.
(författare)
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A smartphone-based system to quantify dexterity in Parkinson's disease patients
- 2017
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Ingår i: Informatics in Medicine Unlocked. - : Elsevier BV. - 2352-9148. ; 9, s. 11-17
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Tidskriftsartikel (refereegranskat)abstract
- Objectives The aim of this paper is to investigate whether a smartphone-based system can be used to quantify dexterity in Parkinson's disease (PD). More specifically, the aim was to develop data-driven methods to quantify and characterize dexterity in PD. Methods Nineteen advanced PD patients and 22 healthy controls participated in a clinical trial in Uppsala, Sweden. The subjects were asked to perform tapping and spiral drawing tests using a smartphone. Patients performed the tests before, and at pre-specified time points after they received 150% of their usual levodopa morning dose. Patients were video recorded and their motor symptoms were assessed by three movement disorder specialists using three Unified PD Rating Scale (UPDRS) motor items from part III, the dyskinesia scoring and the treatment response scale (TRS). The raw tapping and spiral data were processed and analyzed with time series analysis techniques to extract 37 spatiotemporal features. For each of the five scales, separate machine learning models were built and tested by using principal components of the features as predictors and mean ratings of the three specialists as target variables. Results There were weak to moderate correlations between smartphone-based scores and mean ratings of UPDRS item #23 (0.52; finger tapping), UPDRS #25 (0.47; rapid alternating movements of hands), UPDRS #31 (0.57; body bradykinesia and hypokinesia), sum of the three UPDRS items (0.46), dyskinesia (0.64), and TRS (0.59). When assessing the test-retest reliability of the scores it was found that, in general, the clinical scores had better test-retest reliability than the smartphone-based scores. Only the smartphone-based predicted scores on the TRS and dyskinesia scales had good repeatability with intra-class correlation coefficients of 0.51 and 0.84, respectively. Clinician-based scores had higher effect sizes than smartphone-based scores indicating a better responsiveness in detecting changes in relation to treatment interventions. However, the first principal component of the 37 features was able to capture changes throughout the levodopa cycle and had trends similar to the clinical TRS and dyskinesia scales. Smartphone-based scores differed significantly between patients and healthy controls. Conclusions Quantifying PD motor symptoms via instrumented, dexterity tests employed in a smartphone is feasible and data from such tests can also be used for measuring treatment-related changes in patients. © 2017
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- Aghanavesi, Somayeh, 1981-, et al.
(författare)
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Feasibility of a multi-sensor data fusion method for assessment of Parkinson’s disease motor symptoms
- 2018
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- Title: Feasibility of a multi-sensor data fusion method for assessment of Parkinson’s disease motor symptomsObjective: To assess the feasibility of measuring Parkinson’s disease (PD) motor symptoms with a multi-sensor data fusion method. More specifically, the aim is to assess validity, reliability and sensitivity to treatment of the methods.Background: Data from 19 advanced PD patients (Gender: 14 males and 5 females, mean age: 71.4, mean years with PD: 9.7, mean years with levodopa: 9.5) were collected in a single center, open label, single dose clinical trial in Sweden [1].Methods: The patients performed leg agility and 2-5 meter straight walking tests while wearing motion sensors on their limbs. They performed the tests at baseline, at the time they received the morning dose, and at pre-specified time points until the medication wore off. While performing the tests the patients were video recorded. The videos were observed by three movement disorder specialists who rated the symptoms using a treatment response scale (TRS), ranging from -3 (very off) to 3 (very dyskinetic). The sensor data consisted of lower limb data during leg agility, upper limb data during walking, and lower limb data during walking. Time series analysis was performed on the raw sensor data extracted from 17 patients to derive a set of quantitative measures, which were then used during machine learning to be mapped to mean ratings of the three raters on the TRS scale. Combinations of data were tested during the machine learning procedure.Results: Using data from both tests, the Support Vector Machines (SVM) could predict the motor states of the patients on the TRS scale with a good agreement in relation to the mean ratings of the three raters (correlation coefficient = 0.92, root mean square error = 0.42, p<0.001). Additionally, there was good test-retest reliability of the SVM scores during baseline and second tests with intraclass-correlation coefficient of 0.84. Sensitivity to treatment for SVM was good (Figure 1), indicating its ability to detect changes in motor symptoms. The upper limb data during walking was more informative than lower limb data during walking since SVMs had higher correlation coefficient to mean ratings. Conclusions: The methodology demonstrates good validity, reliability, and sensitivity to treatment. This indicates that it could be useful for individualized optimization of treatments among PD patients, leading to an improvement in health-related quality of life.
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| 7. |
- Aghanavesi, Somayeh, 1981-, et al.
(författare)
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Feasibility of Using Dynamic Time Warping to Measure Motor States in Parkinson’s Disease
- 2020
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Ingår i: Journal of Sensors. - London : Hindawi Publishing Corporation. - 1687-725X .- 1687-7268. ; , s. 1-14
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Tidskriftsartikel (refereegranskat)abstract
- The aim of this paper is to investigate the feasibility of using the Dynamic Time Warping (DTW) method to measure motor states in advanced Parkinson's disease (PD). Data were collected from 19 PD patients who experimented leg agility motor tests with motion sensors on their ankles once before and multiple times after an administration of 150% of their normal daily dose of medication. Experiments of 22 healthy controls were included. Three movement disorder specialists rated the motor states of the patients according to Treatment Response Scale (TRS) using recorded videos of the experiments. A DTW-based motor state distance score (DDS) was constructed using the acceleration and gyroscope signals collected during leg agility motor tests. Mean DDS showed similar trends to mean TRS scores across the test occasions. Mean DDS was able to differentiate between PD patients at Off and On motor states. DDS was able to classify the motor state changes with good accuracy (82%). The PD patients who showed more response to medication were selected using the TRS scale, and the most related DTW-based features to their TRS scores were investigated. There were individual DTW-based features identified for each patient. In conclusion, the DTW method can provide information about motor states of advanced PD patients which can be used in the development of methods for automatic motor scoring of PD. © 2020 Somayeh Aghanavesi et al.
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| 9. |
- Aghanavesi, Somayeh, 1981-, et al.
(författare)
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Motion sensor-based assessment of Parkinson's disease motor symptoms during leg agility tests : results from levodopa challenge
- 2020
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Ingår i: IEEE journal of biomedical and health informatics. - : IEEE Computer Society. - 2168-2194 .- 2168-2208. ; 24:1, s. 111-118
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Tidskriftsartikel (refereegranskat)abstract
- Parkinson's disease (PD) is a degenerative, progressive disorder of the central nervous system that mainly affects motor control. The aim of this study was to develop data-driven methods and test their clinimetric properties to detect and quantify PD motor states using motion sensor data from leg agility tests. Nineteen PD patients were recruited in a levodopa single dose challenge study. PD patients performed leg agility tasks while wearing motion sensors on their lower extremities. Clinical evaluation of video recordings was performed by three movement disorder specialists who used four items from the motor section of the Unified PD Rating Scale (UPDRS), the treatment response scale (TRS) and a dyskinesia score. Using the sensor data, spatiotemporal features were calculated and relevant features were selected by feature selection. Machine learning methods like support vector machines (SVM), decision trees and linear regression, using 10-fold cross validation were trained to predict motor states of the patients. SVM showed the best convergence validity with correlation coefficients of 0.81 to TRS, 0.83 to UPDRS #31 (body bradykinesia and hypokinesia), 0.78 to SUMUPDRS (the sum of the UPDRS items: #26-leg agility, #27-arising from chair and #29-gait), and 0.67 to dyskinesia. Additionally, the SVM-based scores had similar test-retest reliability in relation to clinical ratings. The SVM-based scores were less responsive to treatment effects than the clinical scores, particularly with regards to dyskinesia. In conclusion, the results from this study indicate that using motion sensors during leg agility tests may lead to valid and reliable objective measures of PD motor symptoms.
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| 10. |
- Aghanavesi, Somayeh, 1981-, et al.
(författare)
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Objective assessment of Parkinson’s disease motor symptoms during leg agility test using motion sensors
- 2018
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Konferensbidrag (refereegranskat)abstract
- Title: Objective assessment of Parkinson’s disease motor symptoms during leg agility test using motion sensorsObjective: To develop and evaluate machine learning methods for assessment of Parkinson’s disease (PD) motor symptoms using leg agility (LA) data collected with motion sensors during a single dose experiment.Background: Nineteen advanced PD patients (Gender: 14 males and 5 females, mean age: 71.4, mean years with PD: 9.7, mean years with levodopa: 9.5) were recruited in a single center, open label, single dose clinical trial in Sweden [1].Methods: The patients performed up to 15 LA tasks while wearing motions sensors on their foot ankle. They performed tests at pre-defined time points starting from baseline, at the time they received a morning dose (150% of their levodopa equivalent morning dose), and at follow-up time points until the medication wore off. The patients were video recorded while performing the motor tasks. and three movement disorder experts rated the observed motor symptoms using 4 items from the Unified PD Rating Scale (UPDRS) motor section including UPDRS #26 (leg agility), UPDRS #27 (Arising from chair), UPDRS #29 (Gait), UPDRS #31 (Body Bradykinesia and Hypokinesia), and dyskinesia scale. In addition, they rated the overall mobility of the patients using Treatment Response Scale (TRS), ranging from -3 (very off) to 3 (very dyskinetic). Sensors data were processed and their quantitative measures were used to develop machine learning methods, which mapped them to the mean ratings of the three raters. The quality of measurements of the machine learning methods was assessed by convergence validity, test-retest reliability and sensitivity to treatment.Results: Results from the 10-fold cross validation showed good convergent validity of the machine learning methods (Support Vector Machines, SVM) with correlation coefficients of 0.81 for TRS, 0.78 for UPDRS #26, 0.69 for UPDRS #27, 0.78 for UPDRS #29, 0.83 for UPDRS #31, and 0.67 for dyskinesia scale (P<0.001). There were good correlations between scores produced by the methods during the first (baseline) and second tests with coefficients ranging from 0.58 to 0.96, indicating good test-retest reliability. The machine learning methods had lower sensitivity than mean clinical ratings (Figure. 1).Conclusions: The presented methodology was able to assess motor symptoms in PD well, comparable to movement disorder experts. The leg agility test did not reflect treatment related changes.
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