- Browse by Author
Browsing by Author "Wawira, Judy"
Now showing 1 - 5 of 5
Results Per Page
Sort Options
Item Comparative Performance Analysis of Different Fingerprint Biometric Scanners for Patient Matching(IOS Press, 2017) Kasiiti, Noah; Wawira, Judy; Purkayastha, Saptarshi; Were, Martin C.; BioHealth Informatics, School of Informatics and ComputingUnique patient identification within health services is an operational challenge in healthcare settings. Use of key identifiers, such as patient names, hospital identification numbers, national ID, and birth date are often inadequate for ensuring unique patient identification. In addition approximate string comparator algorithms, such as distance-based algorithms, have proven suboptimal for improving patient matching, especially in low-resource settings. Biometric approaches may improve unique patient identification. However, before implementing the technology in a given setting, such as health care, the right scanners should be rigorously tested to identify an optimal package for the implementation. This study aimed to investigate the effects of factors such as resolution, template size, and scan capture area on the matching performance of different fingerprint scanners for use within health care settings. Performance analysis of eight different scanners was tested using the demo application distributed as part of the Neurotech Verifinger SDK 6.0.Item Creation and Curation of the Society of Imaging Informatics in Medicine Hackathon Dataset(Springer Nature, 2018-02) Kohli, Marc; Morrison, James J.; Wawira, Judy; Morgan, Matthew B.; Hostetter, Jason; Genereaux, Brad; Hussain, Mohannad; Langer, Steve G.; Radiology and Imaging Sciences, School of MedicineIn order to support innovation, the Society of Imaging Informatics in Medicine (SIIM) elected to create a collaborative computing experience called a "hackathon." The SIIM Hackathon has always consisted of two components, the event itself and the infrastructure and resources provided to the participants. In 2014, SIIM provided a collection of servers to participants during the annual meeting. After initial server setup, it was clear that clinical and imaging "test" data were also needed in order to create useful applications. We outline the goals, thought process, and execution behind the creation and maintenance of the clinical and imaging data used to create DICOM and FHIR Hackathon resources.Item Examining the effect of peer helping in a coping skills intervention: a randomized controlled trial for advanced gastrointestinal cancer patients and their family caregivers(Springer Nature, 2018-02) Kohli, Marc; Morrison, James J.; Wawira, Judy; Morgan, Matthew B.; Hostetter, Jason; Genereaux, Brad; Hussain, Mohannad; Langer, Steve G.; Psychology, School of SciencePURPOSE: At the end of life, spiritual well-being is a central aspect of quality of life for many patients and their family caregivers. A prevalent spiritual value in advanced cancer patients is the need to actively give. To address this need, the current randomized trial examined whether adding a peer helping component to a coping skills intervention leads to improved meaning in life and peace for advanced gastrointestinal cancer patients and their caregivers. Feasibility and acceptability outcomes were also assessed. METHODS: Advanced gastrointestinal cancer patients and caregivers (n = 50 dyads) were randomly assigned to a 5-session, telephone-based coping skills intervention or a peer helping + coping skills intervention. One or both dyad members had moderate-severe distress. Peer helping involved contributing to handouts on coping skills for other families coping with cancer. Patients and caregivers completed measures of meaning in life/peace, fatigue, psychological symptoms, coping self-efficacy, and emotional support. Patient pain and caregiver burden were also assessed. RESULTS: Small effects in favor of the coping skills group were found regarding meaning in life/peace at 1 and 5 weeks post-intervention. Other outcomes did not vary as a function of group assignment, with both groups showing small decreases in patient and caregiver fatigue and caregiver distress and burden. High recruitment and retention rates supported feasibility, and high participant satisfaction ratings supported acceptability. CONCLUSIONS: Although a telephone-based intervention is feasible and acceptable for this population, peer helping in the context of a coping skills intervention does not enhance spiritual well-being relative to coping skills alone.Item Human Activity Recognition using Deep Learning Models on Smartphones and Smartwatches Sensor Data(BIOSTEC, 2021) Oluwalade, Bolu; Neela, Sunil; Wawira, Judy; Adejumo, Tobiloba; Purkayastha, Saptarshi; BioHealth Informatics, School of Informatics and ComputingIn recent years, human activity recognition has garnered considerable attention both in industrial and academic research because of the wide deployment of sensors, such as accelerometers and gyroscopes, in products such as smartphones and smartwatches. Activity recognition is currently applied in various fields where valuable information about an individual’s functional ability and lifestyle is needed. In this study, we used the popular WISDM dataset for activity recognition. Using multivariate analysis of covariance (MANCOVA), we established a statistically significant difference (p < 0.05) between the data generated from the sensors embedded in smartphones and smartwatches. By doing this, we show that smartphones and smartwatches don’t capture data in the same way due to the location where they are worn. We deployed several neural network architectures to classify 15 different hand and non-hand oriented activities. These models include Long short-term memory (LSTM), Bi-directional Long short-term memory (BiLSTM), Convolutional Neural Network (CNN), and Convolutional LSTM (ConvLSTM). The developed models performed best with watch accelerometer data. Also, we saw that the classification precision obtained with the convolutional input classifiers (CNN and ConvLSTM) was higher than the end-to-end LSTM classifier in 12 of the 15 activities. Additionally, the CNN model for the watch accelerometer was better able to classify non-hand oriented activities when compared to hand-oriented activities.Item Human Activity Recognition using Deep Learning Models on Smartphones and Smartwatches Sensor Data(Scitepress, 2021) Oluwalade, Bolu; Neela, Sunil; Wawira, Judy; Adejumo, Tobiloba; Purkayastha, Saptarshi; BioHealth Informatics, School of Informatics and ComputingIn recent years, human activity recognition has garnered considerable attention both in industrial and academic research because of the wide deployment of sensors, such as accelerometers and gyroscopes, in products such as smartphones and smartwatches. Activity recognition is currently applied in various fields where valuable information about an individual’s functional ability and lifestyle is needed. In this study, we used the popular WISDM dataset for activity recognition. Using multivariate analysis of covariance (MANCOVA), we established a statistically significant difference (p < 0.05) between the data generated from the sensors embedded in smartphones and smartwatches. By doing this, we show that smartphones and smartwatches don’t capture data in the same way due to the location where they are worn. We deployed several neural network architectures to classify 15 different hand and non-hand oriented activities. These models include Long short-term memory (LSTM), Bi-directional Long short-term memory (BiLSTM), Convolutional Neural Network (CNN), and Convolutional LSTM (ConvLSTM). The developed models performed best with watch accelerometer data. Also, we saw that the classification precision obtained with the convolutional input classifiers (CNN and ConvLSTM) was higher than the end-to-end LSTM classifier in 12 of the 15 activities. Additionally, the CNN model for the watch accelerometer was better able to classify non-hand oriented activities when compared to hand-oriented activities.