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Fuel consumption (FC) is one of the key factors indetermining expenses of operating a heavy-duty vehicle. A customermay therefore request an estimate of the FC of a desired vehicle.The modular design of heavy-duty vehicles allows their constructionby specifying the building blocks, such as gear box, engine andchassis type. If the combination of building blocks is unprecedented,it is unfeasible to measure the FC, since this would first r equire theconstruction of the vehicle. This paper proposes a machine learningapproach to predict FC. This study uses around 40,000 vehiclesspecific a nd o perational e nvironmental c onditions i nformation, suchas road slopes and driver profiles. A ll v ehicles h ave d iesel enginesand a mileage of more than 20,000 km. The data is used to investigatethe accuracy of machine learning algorithms Linear regression (LR),K-nearest neighbor (KNN) and Artificial n eural n etworks ( ANN) inpredicting fuel consumption for heavy-duty vehicles. Performance ofthe algorithms is evaluated by reporting the prediction error on bothsimulated data and operational measurements. The performance of thealgorithms is compared using nested cross-validation and statisticalhypothesis testing. The statistical evaluation procedure finds thatANNs have the lowest prediction error compared to LR and KNNin estimating fuel consumption on both simulated and operationaldata. The models have a mean relative prediction error of 0.3% onsimulated data, and 4.2% on operational data.

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.

This study proposes a platform to objectively assess motor states in Parkinson’s disease (PD) using sensor technology and machine learning. The platform uses sensor information gathered during standardized motor tasks and fuses them in a data-driven manner to produce an index representing motor states of the patients. After investigating clinimetric properties of the platform it was found that the platform had good validity and responsiveness to treatment, which are essential for developing systems to individualize treatments.

The overall aim of this thesis was to develop and evaluate new data-driven methods for supporting treatment and providing information for better management of Parkinson’s disease (PD). This disease is complex and progressive. There is a large amount of inter- and intra-variability in motor symptoms of patients with PD (PwPD). Current evaluation of motor symptoms which is done at clinics by using clinical rating scales provides limited and only part of the health status of PwPD. PD requires an accurate assessment that is approved by clinics. Therefore frequent evaluation of symptoms at micro-level is required. Sensor systems including smartphone and motion sensors were employed to collect data from PwPD and the recruited healthy controls. Repeated measures consisting of subjective assessment of symptoms and objective assessment of motor functions were collected. First, the smartphone-based data-driven methods were developed to quantify the dexterity presented in fine motor tests of spiral drawing and alternate tapping. The upper extremities temporal irregularity measure presented in spiral drawing tests of PwPD was further analyzed by the approximate entropy (ApEn) method. Second, tri-axial motion sensor data were collected from various tests like leg agility, walking, and rapid alternating movements of hands of PwPD during a full cycled levodopa challenge. Data driven methods for quantification of leg agility tests and a combination of multiple motor tests were developed. The clinimetric properties of the methods such as reliability, validity, and responsiveness were evaluated. In addition, the feasibility of using smartphone inertial measurement unit (IMU) sensors in comparison to motion sensors for quantifying the motor states in PD during rapid alternating movements of hands tests was investigated. Results of the developed methods for quantification of PD motor symptoms via dexterity tests in a smartphone can be used for measuring treatment related changes in PwPD. Investigation of the ApEn method showed good sensitivity and test-retest reliability indicating that this method is useful in measuring upper limb temporal irregularity at micro-level. High convergence validity resulted from using motion sensors during leg agility tests which led to valid and reliable objective measures of PD motor symptoms. The results of fusion of sensor data gathered during standardized motor tests were promising and led to highly valid, reliable and sensitive objective measures of PD motor symptoms. The results of the analyzing acceleration IMU data showed that smartphone IMU is capable of capturing symptom information from hand rotation tests. It can provide sufficient data for quantification of the motor states. The findings from the data-driven methodology in this thesis can be used in development of systems for follow up of the effects of treatment and individualizing treatments in PD.

This thesis consists of four research papers presenting a microdata analysis approach to assess and evaluate the Parkinson’s disease (PD) motor symptoms using smartphone-based systems. PD is a progressive neurological disorder that is characterized by motor symptoms. It is a complex disease that requires continuous monitoring and multidimensional symptom analysis. Both patients’ perception regarding common symptom and their motor function need to be related to the repeated and time-stamped assessment; with this, the full extent of patient’s condition could be revealed. The smartphone enables and facilitates the remote, long-term and repeated assessment of PD symptoms. Two types of collected data from smartphone were used, one during a three year, and another during one-day clinical study. The data were collected from series of tests consisting of tapping and spiral motor tests. During the second time scale data collection, along smartphone-based measurements patients were video recorded while performing standardized motor tasks according to Unified Parkinson’s disease rating scales (UPDRS). At first, the objective of this thesis was to elaborate the state of the art, sensor systems, and measures that were used to detect, assess and quantify the four cardinal and dyskinetic motor symptoms. This was done through a review study. The review showed that smartphones as the new generation of sensing devices are preferred since they are considered as part of patients’ daily accessories, they are available and they include high-resolution activity data. Smartphones can capture important measures such as forces, acceleration and radial displacements that are useful for assessing PD motor symptoms. Through the obtained insights from the review study, the second objective of this thesis was to investigate whether a combination of tapping and spiral drawing tests could be useful to quantify dexterity in PD. More specifically, the aim was to develop data-driven methods to quantify and characterize dexterity in PD. The results from this study showed that tapping and spiral drawing tests that were collected by smartphone can detect movements reasonably well related to under- and over-medication. The thesis continued by developing an Approximate Entropy (ApEn)-based method, which aimed to measure the amount of temporal irregularity during spiral drawing tests. One of the disabilities associated with PD is the impaired ability to accurately time movements. The increase in timing variability among patients when compared to healthy subjects, suggests that the Basal Ganglia (BG) has a role in interval timing. ApEn method was used to measure temporal irregularity score (TIS) which could significantly differentiate the healthy subjects and patients at different stages of the disease. This method was compared to two other methods which were used to measure the overall drawing impairment and shakiness. TIS had better reliability and responsiveness compared to the other methods. However, in contrast to other methods, the mean scores of the ApEn-based method improved significantly during a 3-year clinical study, indicating a possible impact of pathological BG oscillations in temporal control during spiral drawing tasks. In addition, due to the data collection scheme, the study was limited to have no gold standard for validating the TIS. However, the study continued to further investigate the findings using another screen resolution, new dataset, new patient groups, and for shorter term measurements. The new dataset included the clinical assessments of patients while they performed tests according to UPDRS. The results of this study confirmed the findings in the previous study. Further investigation when assessing the correlation of TIS to clinical ratings showed the amount of temporal irregularity present in the spiral drawing cannot be detected during clinical assessment since TIS is an upper limb high frequency-based measure.

Parkinson’s disease (PD) is a progressive movement disorder caused by the death of dopamine-producing cells in the midbrain. There is a need for frequent symptom assessment, since the treatment needs to be individualized as the disease progresses. The aim of this paper was to verify and further investigate the clinimetric properties of an entropy-based method for measuring PD-related upper limb temporal irregularities during spiral drawing tasks. More specifically, properties of a temporal irregularity score (TIS) for patients at different stages of PD, and medication time points were investigated. Nineteen PD patients and 22 healthy controls performed repeated spiral drawing tasks on a smartphone. Patients performed the tests before a single levodopa dose and at specific time intervals after the dose was given. Three movement disorder specialists rated videos of the patients based on the unified PD rating scale (UPDRS) and the Dyskinesia scale. Differences in mean TIS between the groups of patients and healthy subjects were assessed. Test-retest reliability of the TIS was measured. The ability of TIS to detect changes from baseline (before medication) to later time points was investigated. Correlations between TIS and clinical rating scores were assessed. The mean TIS was significantly different between healthy subjects and patients in advanced groups (p-value = 0.02). Test-retest reliability of TIS was good with Intra-class Correlation Coefficient of 0.81. When assessing changes in relation to treatment, TIS contained some information to capture changes from Off to On and wearing off effects. However, the correlations between TIS and clinical scores (UPDRS and Dyskinesia) were weak. TIS was able to differentiate spiral drawings drawn by patients in an advanced stage from those drawn by healthy subjects, and TIS had good test-retest reliability. TIS was somewhat responsive to single-dose levodopa treatment. Since TIS is an upper limb high-frequency-based measure, it cannot be detected during clinical assessment., Funding Agencies:Cenvigo AB Sensidose AB Swedish ICT Acreo Uppsala University Sahlgrenska University Dalarna University

The goal of this study was to investigate the feasibility of wrist worn motion sensors to objectively measure motor functions in Parkinson’s disease (PD). More specifically, the aim was to construct a sensor-based levodopa-response index (SBLRI) and evaluate its clinimetric properties (convergent validity and internal consistency). Nineteen advanced PD patients and 22 healthy controls were recruited in a single center, open label, single dose clinical trial in Sweden. The subjects performed standardized motor tasks while wearing one sensor on each wrist and one on each ankle. Each sensor unit consisted of three-dimensional accelerometer and gyroscope. The patients were video recorded and the videos were blindly rated by three independent movement disorder specialists. The clinical scores were given using the Treatment Response Scale (TRS) on a scale from -3 = ‘Very Off’ to 0 = ‘On’ to +3 = ‘Very dyskinetic’. The clinical assessments were based on the overall motor function of the patients. A mean TRS was defined as the mean of the three specialists’ assessments per time point. The measurements were repeated over several time points following a single levodopa/carbidopa morning dose (50% over normal to induce dyskinesias). Sensor measurements during rapid alternating movements of hands were processed with time series analysis methods to calculate spatiotemporal parameters designed to measure bradykinesia and dyskinesia. For each hand, 96 spatiotemporal parameters were calculated and their average scores were then used in a principal component analysis to reduce the dimensionality by retaining 6 principal components. These components were then used as predictors to support vector machines and to be mapped to the mean TRS ratings of the three specialists and to calculate the SBLRI. For this analysis, a 10-fold stratified cross-validation was performed. The SBLRI was strongly correlated to mean TRS with a Pearson correlation coefficient of 0.79 (CI: 0.74-0.83, p

Panda

OBJECTIVES: To develop a method for objective assessment of fine motor timing variability in Parkinson’s disease (PD) patients, using digital spiral data gathered by a touch screen device. BACKGROUND: A retrospective analysis was conducted on data from 105 subjects including65 patients with advanced PD (group A), 15 intermediate patients experiencing motor fluctuations (group I), 15 early stage patients (group S), and 10 healthy elderly subjects (HE) were examined. The subjects were asked to perform repeated upper limb motor tasks by tracing a pre-drawn Archimedes spiral as shown on the screen of the device. The spiral tracing test was performed using an ergonomic pen stylus, using dominant hand. The test was repeated three times per test occasion and the subjects were instructed to complete it within 10 seconds. Digital spiral data including stylus position (x-ycoordinates) and timestamps (milliseconds) were collected and used in subsequent analysis. The total number of observations with the test battery were as follows: Swedish group (n=10079), Italian I group (n=822), Italian S group (n = 811), and HE (n=299). METHODS: The raw spiral data were processed with three data processing methods. To quantify motor timing variability during spiral drawing tasks Approximate Entropy (APEN) method was applied on digitized spiral data. APEN is designed to capture the amount of irregularity or complexity in time series. APEN requires determination of two parameters, namely, the window size and similarity measure. In our work and after experimentation, window size was set to 4 and similarity measure to 0.2 (20% of the standard deviation of the time series). The final score obtained by APEN was normalized by total drawing completion time and used in subsequent analysis. The score generated by this method is hence on denoted APEN. In addition, two more methods were applied on digital spiral data and their scores were used in subsequent analysis. The first method was based on Digital Wavelet Transform and Principal Component Analysis and generated a score representing spiral drawing impairment. The score generated by this method is hence on denoted WAV. The second method was based on standard deviation of frequency filtered drawing velocity. The score generated by this method is hence on denoted SDDV. Linear mixed-effects (LME) models were used to evaluate mean differences of the spiral scores of the three methods across the four subject groups. Test-retest reliability of the three scores was assessed after taking mean of the three possible correlations (Spearman’s rank coefficients) between the three test trials. Internal consistency of the methods was assessed by calculating correlations between their scores. RESULTS: When comparing mean spiral scores between the four subject groups, the APEN scores were different between HE subjects and three patient groups (P=0.626 for S group with 9.9% mean value difference, P=0.089 for I group with 30.2%, and P=0.0019 for A group with 44.1%). However, there were no significant differences in mean scores of the other two methods, except for the WAV between the HE and A groups (P