Browsing by Subject "sensors"

Now showing 1 - 4 of 4
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    Development of model-based and sensor-based programming techniques for optimizing directional deep brain stimulation therapy for movement disorders
    (2022-01) Brinda, AnneMarie
    Deep brain stimulation (DBS) therapy is a programmable neurosurgical intervention that can significantly improve quality of life for individuals with medication-refractory movement disorders, such as Essential Tremor and Parkinson’s Disease. However, clinical outcomes with DBS therapy still vary across patients, and the clinical time and effort necessary to program the stimulation settings to each patient’s symptoms presents practical challenges in the clinic. With the advent of directional lead technology and independent multi-channel current-controlled stimulation, the scope of possible DBS configurations is now substantially larger than it was even five years ago. This has greatly increased the time to determine the most effective electrode configuration, and in reality, much of the stimulation parameter space is left unexplored during a clinical visit. This thesis addressed the gap between the directional lead technology and its clinical implementation by developing three promising techniques to program directional DBS lead systems. The first programming technique involved developing subject-specific computational models of DBS based on individual MRI/CT scans. Comparing model predictions to clinical outcomes from patients with Essential Tremor revealed that lateral and medial parcellations of the motor-thalamic afferents of the cerebellothalamic tract were differentially associated with stimulation-induced therapy and side effects, respectively. Second, sensor-based evaluation of DBS in Essential Tremor patients revealed that directional contacts were superior to ring-mode contacts in providing optimized tremor reduction with reduced dysarthria. The third programming technique involved using neurophysiological feedback to guide the selection of which electrode(s) to use during DBS. In Parkinson’s disease, for example, stimulation through electrodes with higher resting-state beta-band oscillatory power in the subthalamic nucleus generally results in better clinical outcomes. Using a non-human primate model, we tracked how beta-band power changed spatially and temporally between intraoperative and chronic time points and showed that the strongest variability occurred within the first two weeks after lead implantation. This suggested that neurofeedback-based programming may be most consistent after the immune tissue response settles. Together, these results showed how model- and sensor-based programming techniques can limit the parameter space for programming directional DBS enabling more efficient and effective clinical outcomes in the future.
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    Implementation and Evaluation of a Low-Cost Weigh-In- Motion System
    (Center for Transportation Studies, University of Minnesota, 2016-03) Kwon, Taek M
    Building a WIM system around polymer piezoelectric film sensors, called BL sensors, costs only a fraction of the traditional WIM system built around crystalline-quartz piezoelectric sensors called Lineas sensors. However, BL sensors are highly sensitive to temperature, which limits the accuracy of weight measurements. The objective of this research was to investigate the performance of BL sensors head-to- head with Lineas sensors by installing a BL WIM system and collecting data from the same highway. After the test site installation, pavement temperatures were recoded as part of each vehicle record from both Lineas and BL sensor-based WIM stations. The analysis of data collected over 10 months showed that temperature dependency of BL sensors can be removed in terms of average but not variance. More specifically, the average of axle weights after temperature-based calibration was about the same for both BL and Lineas sensors, but the variance was much higher for BL sensors. In conclusion, if BL sensors are used, pavement temperatures must be recorded as part of vehicle records. Then, the weights calibrated based temperature would be as accurate as Lineas sensors in terms of the average but not variance.
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    In-Situ Vehicle Classification Using an ILD and a Magnetoresistive Sensor Array
    (University of Minnesota Center for Transportation Studies, 2009-02) Burns, Stanley G.
    This report provides a summary of results from a multi-year study that includes both the use of inductive loop detectors (ILDs) and magnetoresistive sensors for in-situ vehicle classification. There were strengths and weaknesses noted in both type of sensor systems. Although the magnetoresistive array provides the best vehicle profile resolution, the standard inductive loop detector provides a significant cost, hardware and software complexity, and reliability advantage. The ILD installed base far exceeds the number of magnetoresistive sensors. Several electrical and computer engineering students participated in the study and their contributions are included in the individual chapter headings. Under my direction, these students also presented project work and Research Day conferences at MN/DOT District 1 Headquarters.
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    Precision Irrigation for Golf Courses Using Sensor and Mapping Technologies
    (2019-11) Straw, Chase; Friell, Joshua; Horgan, Brian
    The golf course industry is under increasing public pressure to improve environmental impacts by reducing management inputs, particularly irrigation. Precision irrigation is a viable strategy; however, in practice, adoption of soil moisture sensors (SMS) and mapping technologies necessary for implementation has been slow. The purpose of this research is to demonstrate that adoption of currently available SMS and mapping technologies can provide golf course superintendents with appropriate, actionable information that can result in significant water and cost savings relative to evapotranspiration (ET)-based and traditional irrigation scheduling methods.