Kd
K. den Hertog
info
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Parkinson’s Disease is a neurodegenerative disease that has a decline in motor behaviour as one of its main symptoms. This decline is currently monitored using subjective measures, such as questionnaires and clinical observations. More detailed and objective tracking of this decline can improve treatment of the disease and allow for earlier identification. Earlier work in this area has lead to the development of a proof-of-concept for detecting behavioural changes in motor performance, but the linear regression model used was limited in its accuracy and had a high latency. This paper uses a non-linear Least Squares Support Vector Regression (LS-SVR) model to increase performance in those areas. LS-SVR was chosen for its ability to regress complex non-linear relationships and because it is highly adaptable and scalable, yet easy to understand and work with. Test data were simulated based on earlier measured data which resulted in a complete and varied data set that allows for exploring many different situations.
Results from the machine learning model when applied to the test data are promising. With the right combination of hyperparameter settings an improvement of 80% in accuracy was reached, and latency could be reduced by 30%. Additionally, a sensitivity analysis of the hyperparameters revealed further room for improvement with more careful tuning. Finally, it was shown that by using LS-SVR to make predictions on data from future trials a further reduction in latency can be achieved. Overall, the new model shows definite improvement over the earlier model and can be developed further in subsequent steps towards a clinical applicable method. ...
Results from the machine learning model when applied to the test data are promising. With the right combination of hyperparameter settings an improvement of 80% in accuracy was reached, and latency could be reduced by 30%. Additionally, a sensitivity analysis of the hyperparameters revealed further room for improvement with more careful tuning. Finally, it was shown that by using LS-SVR to make predictions on data from future trials a further reduction in latency can be achieved. Overall, the new model shows definite improvement over the earlier model and can be developed further in subsequent steps towards a clinical applicable method. ...
Parkinson’s Disease is a neurodegenerative disease that has a decline in motor behaviour as one of its main symptoms. This decline is currently monitored using subjective measures, such as questionnaires and clinical observations. More detailed and objective tracking of this decline can improve treatment of the disease and allow for earlier identification. Earlier work in this area has lead to the development of a proof-of-concept for detecting behavioural changes in motor performance, but the linear regression model used was limited in its accuracy and had a high latency. This paper uses a non-linear Least Squares Support Vector Regression (LS-SVR) model to increase performance in those areas. LS-SVR was chosen for its ability to regress complex non-linear relationships and because it is highly adaptable and scalable, yet easy to understand and work with. Test data were simulated based on earlier measured data which resulted in a complete and varied data set that allows for exploring many different situations.
Results from the machine learning model when applied to the test data are promising. With the right combination of hyperparameter settings an improvement of 80% in accuracy was reached, and latency could be reduced by 30%. Additionally, a sensitivity analysis of the hyperparameters revealed further room for improvement with more careful tuning. Finally, it was shown that by using LS-SVR to make predictions on data from future trials a further reduction in latency can be achieved. Overall, the new model shows definite improvement over the earlier model and can be developed further in subsequent steps towards a clinical applicable method.
Results from the machine learning model when applied to the test data are promising. With the right combination of hyperparameter settings an improvement of 80% in accuracy was reached, and latency could be reduced by 30%. Additionally, a sensitivity analysis of the hyperparameters revealed further room for improvement with more careful tuning. Finally, it was shown that by using LS-SVR to make predictions on data from future trials a further reduction in latency can be achieved. Overall, the new model shows definite improvement over the earlier model and can be developed further in subsequent steps towards a clinical applicable method.
Bachelor thesis
(2018)
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Z.T. Angyal, K. den Hertog, A. Kasteel, S.J.F. Knoops, L. Losch, V.A. Lucas, B.K.W. Martens, A.B. de Roos, B.H.L. van den Abbeele, V. Pallichadath, H. Lan, E.J.O. Schrama
Piazzi is a mission to the 1989 UQ asteroid, with the goal of collecting a sample and returning it to Earth. This will be accomplished by sending a spacecraft to the asteroid, which will consist of three separate spacecraft. Firstly the orbiter, which houses the instruments needed to observe and map the asteroid, and the primary propulsion and communications systems. Then two spacecraft will detach from the orbiter, and each collect a sample in a distinct manner. One, ACSAL, will land on the asteroid, and collect a core sample using a drill. The other, SASH, will hover above the surface, and collect a regolith sample. Both ACSAL and SASH will return to Earth individually, shoot off a reentry capsule with the sample and burn up in the atmosphere themselves.
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Piazzi is a mission to the 1989 UQ asteroid, with the goal of collecting a sample and returning it to Earth. This will be accomplished by sending a spacecraft to the asteroid, which will consist of three separate spacecraft. Firstly the orbiter, which houses the instruments needed to observe and map the asteroid, and the primary propulsion and communications systems. Then two spacecraft will detach from the orbiter, and each collect a sample in a distinct manner. One, ACSAL, will land on the asteroid, and collect a core sample using a drill. The other, SASH, will hover above the surface, and collect a regolith sample. Both ACSAL and SASH will return to Earth individually, shoot off a reentry capsule with the sample and burn up in the atmosphere themselves.