Now showing items 21-40 of 52

    • Deep Learning for Blind Calibration of Wireless Sensor Networks 

      Ljunggren, Erling (Master thesis, 2020)
      Temporal drift of low-cost sensors is a crucial problem when considering the applicability of wireless sensor networks (WSN). Since they provide highly local measurements, which is key to combat the ever increasing problem ...
    • Design of a clinician dashboard to facilitate co-decision making in the management of non-specific low back pain 

      Bach, Kerstin; Marling, Cindy; Mork, Paul Jarle; Aamodt, Agnar; Mair, Frances; Nicholl, Barbara I (Journal article; Peer reviewed, 2018)
      This paper presents the design of a Clinician Dashboard to promote co-decision making between patients and clinicians. Targeted patients are those with non-specific low back pain, a leading cause of discomfort, disability ...
    • Detecting and Localizing Cell Nuclei in Medical Images. 

      Loudon, Johan Scott (Master thesis, 2018)
      In this master thesis we have adapted and implemented Mask R-CNN to the task of detecting and localizing nuclei in medical imaging. Mask R-CNN, which does instance segmentation, was chosen as the architecture to implement, ...
    • A digital decision support system (selfBACK) for improved self-management of low back pain: a pilot study with 6-week follow-up 

      Sandal, Louise Fleng; Øverås, Cecilie K.; Nordstoga, Anne Lovise; Wood, Karen; Bach, Kerstin; Hartvigsen, Jan; Søgaard, Karen; Mork, Paul Jarle (Peer reviewed; Journal article, 2020)
      Background Very few of the publicly available apps directed towards self-management of low back pain (LBP) have been rigorously tested and their theoretical underpinnings seldom described. The selfBACK app was developed ...
    • Effectiveness of App-Delivered, Tailored Self-management Support for Adults With Lower Back Pain–Related Disability A selfBACK Randomized Clinical Trial 

      Fleng Sandal, Louise; Bach, Kerstin; Øverås, Cecilie K.; Jagd Svendsen, Malene; Dalager, Tina; Stejnicher Drongstrup Jensen, Jesper; Kongsvold, Atle Austnes; Nordstoga, Anne Lovise; Bardal, Ellen Marie; Ashikhmin, Ilya; Wood, Karen; Rasmussen, Charlotte Diana Nørregaard; Stochkendahl, Mette J; Nicholl, Barbara I; Wiratunga, Nirmalie; Cooper, Kay; Hartvigsen, Jan; Kjaer, Per; Sjøgaard, Gisela; Nilsen, Tom Ivar Lund; Mair, Frances S; Søgaard, Karen; Mork, Paul Jarle (Journal article; Peer reviewed, 2021)
    • Ensemble Classifier Managing Uncertainty in Accelerometer Data within Human Activity Recognition Systems 

      Wold, Thomas; Skaugvoll, Sigve André Evensen (Master thesis, 2019)
      Human activity recognition (HAR) er et forskningsområde med mål om å klassifisere aktiviteter utført av personer ved hjelp av data hentet inn av video eller sensorer festet på kroppen. HUNT er den største helseundersøkelsen ...
    • Ensemble Classifier Managing Uncertainty in Accelerometer Data within Human Activity Recognition Systems 

      Wold, Thomas; Skaugvoll, Sigve André Evensen (Master thesis, 2019)
      Human activity recognition (HAR) er et forskningsområde med mål om å klassifisere aktiviteter utført av personer ved hjelp av data hentet inn av video eller sensorer festet på kroppen. HUNT er den største helseundersøkelsen ...
    • FishNet: A Unified Embedding For Salmon Recognition 

      Meidell, Espen; Sjøblom, Edvard Schreiner (Master thesis, 2019)
      Dagens metoder for markering og sporing av oppdrettslaks baserer seg p˚a fysisk kontakt med fisken. Denne prosessen er b˚ade ineffektiv, stressende og potensielt skadelig for laksen. Bruken av dyp læring har de siste ˚arene ...
    • FishNet: A Unified Embedding for Salmon Recognition 

      Meidell, Espen; Sjøblom, Edvard Schreiner (Master thesis, 2019)
      Dagens metoder for markering og sporing av oppdrettslaks baserer seg på fysisk kontakt med fisken. Denne prosessen er både ineffektiv, stressende og potensielt skadelig for laksen. Bruken av dyp læring har de siste årene ...
    • FishNet: A Unified Embedding for Salmon Recognition 

      Mathisen, Bjørn Magnus; Bach, Kerstin; Meidell, Espen; Måløy, Håkon; Sjøblom, Edvard Schreiner (Chapter, 2020)
      Identifying individual salmon can be very beneficial for the aquaculture industry as it enables monitoring and analyzing fish behavior and welfare. For aquaculture researchers identifying indi- vidual salmon is imperative ...
    • From a Low-Cost Air Quality Sensor Network to Decision Support Services: Steps towards Data Calibration and Service Development 

      Veiga, Tiago Santos; Munch-Ellingsen, Arne; Papastergiopoulos, Christoforos; Tzovaras, Dimitrios; Kalamaras, Ilias; Bach, Kerstin; Votis, Konstantinos; Akselsen, Sigmund (Peer reviewed; Journal article, 2021)
      Air pollution is a widespread problem due to its impact on both humans and the environment. Providing decision makers with artificial intelligence based solutions requires to monitor the ambient air quality accurately and ...
    • From data to context-aware decision making: Challenges and opportunities 

      Bach, Kerstin (Journal article; Peer reviewed, 2018)
      This extended abstract presents the research challenges for developing context-aware, explainable artificial intelligence challenges and briefly presents the opportunities ahead when utilizing the vast amount of data generated.
    • Harth: A human activity recognition dataset for machine learning 

      Logacjov, Aleksej; Bach, Kerstin; Kongsvold, Atle; Bårdstu, Hilde Bremseth; Mork, Paul Jarle (Peer reviewed; Journal article, 2021)
      Existing accelerometer-based human activity recognition (HAR) benchmark datasets that were recorded during free living suffer from non-fixed sensor placement, the usage of only one sensor, and unreliable annotations. We ...
    • Human Activity Recognition With Two Body-Worn Accelerometer Sensors 

      Hessen, Hans-Olav; Tessem, Astrid Johnsen (Master thesis, 2016)
      Data used in studies about physical activity is primarily collected from questionnaires and other subjective methods, which may lead to biased and inaccurate data. As subjective data collection methods have shown to be ...
    • Information-Driven Adaptive Sensing Based on Deep Reinforcement Learning 

      Murad, Abdulmajid; Kraemer, Frank Alexander; Bach, Kerstin; Taylor, Gavin (Chapter, 2020)
      In order to make better use of deep reinforcement learning in the creation of sensing policies for resource-constrained IoT devices, we present and study a novel reward function based on the Fisher information value. This ...
    • IoT Sensor Gym: Training Autonomous IoT Devices with Deep Reinforcement Learning 

      Murad, Abdulmajid Abdullah Yahya; Kraemer, Frank Alexander; Bach, Kerstin; Taylor, Gavin (Chapter, 2019)
      We describe IoT Sensor Gym, a framework to train the behavior of constrained IoT devices using deep reinforcement learning. We focus on the main architectural choices to align problems from the IoT domain with cutting-edge ...
    • Learning similarity measures from data 

      Mathisen, Bjørn Magnus; Aamodt, Agnar; Langseth, Helge; Bach, Kerstin (Journal article; Peer reviewed, 2019)
      Defining similarity measures is a requirement for some machine learning methods. One such method is case-based reasoning (CBR) where the similarity measure is used to retrieve the stored case or a set of cases most similar ...
    • A Machine Learning Classifier for Detection of Physical Activity Types and Postures During Free-Living 

      Bach, Kerstin; Kongsvold, Atle Austnes; Bårdstu, Hilde Bremseth; Bardal, Ellen Marie; Kjærnli, Håkon Slåtten; Herland, Sverre; Logacjov, Aleksej; Mork, Paul Jarle (Journal article; Peer reviewed, 2021)
      Accelerometer-based measurements of physical activity types are commonly used to replace self-reports. To advance the field, it is desirable that such measurements allow accurate detection of key daily physical activity ...
    • Machine learning methods for sleep-wake classification using two body-worn accelerometers 

      Hay, Aileen (Master thesis, 2019)
      Søvn er en viktig faktor for beskytte en persons fysiske og mentale trivsel. Derfor utføres mange studier som fokuserer på forebygge, diagnostisere og behandle søvnforstyrrelser. En avgjørende del av disse studiene er ...
    • Mobile User Interface for a patient-centered health care application 

      Stokvik, Linn Kristin (Master thesis, 2017)
      Low back pain is a prevalent issue in the developed world as people adopt a more sedentary lifestyle. This thesis gives insight to available interactive health communication applications for self-managing non-specific low ...