Machine Learning Methods for functional Near Infrared Spectroscopy

Identification of user state is of interest in a wide range of disciplines that fall under the umbrella of human machine interaction. Functional Near Infra-Red Spectroscopy (fNIRS) device is a relatively new device that enables inference of brain activity through non-invasively pulsing infra-red light into the brain. The fNIRS device is particularly useful as it has a better spatial resolution than the Electroencephalograph (EEG) device that is most commonly used in Human Computer Interaction studies under ecologically valid settings. But this key advantage of fNIRS device is underutilized in current literature in the fNIRS domain. We propose machine learning methods that capture this spatial nature of the human brain activity using a novel preprocessing method that uses `Region of Interest\u27 based feature extraction. Experiments show that this method outperforms the F1 score achieved previously in classifying `low\u27 vs `high\u27 valence state of a user. We further our analysis by applying a Convolutional Neural Network (CNN) to the fNIRS data, thus preserving the spatial structure of the data and treating the data similar to a series of images to be classified. Going further, we use a combination of CNN and Long Short-Term Memory (LSTM) to capture the spatial and temporal behavior of the fNIRS data, thus treating it similar to a video classification problem. We show that this method improves upon the accuracy previously obtained by valence classification methods using EEG or fNIRS devices. Finally, we apply the above model to a problem in classifying combined task-load and performance in an across-subject, across-task scenario of a Human Machine Teaming environment in order to achieve optimal productivity of the system

Research Data Management Practices And Impacts on Long-term Data Sustainability: An Institutional Exploration

With the \u27data deluge\u27 leading to an institutionalized research environment for data management, U.S. academic faculty have increasingly faced pressure to deposit research data into open online data repositories, which, in turn, is engendering a new set of practices to adapt formal mandates to local circumstances. When these practices involve reorganizing workflows to align the goals of local and institutional stakeholders, we might call them \u27data articulations.\u27 This dissertation uses interviews to establish a grounded understanding of the data articulations behind deposit in 3 studies: (1) a phenomenological study of genomics faculty data management practices; (2) a grounded theory study developing a theory of data deposit as articulation work in genomics; and (3) a comparative case study of genomics and social science researchers to identify factors associated with the institutionalization of research data management (RDM). The findings of this research offer an in-depth understanding of the data management and deposit practices of academic research faculty, and surfaced institutional factors associated with data deposit. Additionally, the studies led to a theoretical framework of data deposit to open research data repositories. The empirical insights into the impacts of institutionalization of RDM and data deposit on long-term data sustainability update our knowledge of the impacts of increasing guidelines for RDM. The work also contributes to the body of data management literature through the development of the data articulation framework which can be applied and further validated by future work. In terms of practice, the studies offer recommendations for data policymakers, data repositories, and researchers on defining strategies and initiatives to leverage data reuse and employ computational approaches to support data management and deposit

Towards multimodal driver state monitoring systems for highly automated driving

Real-time monitoring of drivers’ functional states will soon become a required safety feature for commercially available vehicles with automated driving capability. Automated driving technology aims to mitigate human error from road transport with the progressive automatisation of specific driving tasks. However, while control of the driving task remains shared between humans and automated systems, the inclusion of this new technology is not exempt from other human factors-related challenges. Drivers’ functional states are essentially a combination of psychological, emotional, and cognitive states, and they generate a constant activity footprint available for measurement through neural and peripheral physiology, among other measures. These factors can determine drivers’ functional states and, thus, drivers’ availability to safely perform control transitions between human and vehicle. This doctoral project aims at investigating the potential of electrocardiogram (ECG), electrodermal activity (EDA) and functional near-infrared spectroscopy (fNIRS) as measures for a multimodal driver state monitoring (DSM) system for highly automated driving (i.e., SAE levels 3 and 4). While current DSM systems relying on gaze behaviour measures have proven valid and effective, several limitations and challenges could only be overcome using eye-tracking in tandem with physiological parameters. This thesis investigates whether ECG, EDA and fNIRS would be good candidates for such a purpose. Two driving simulator studies were performed to measure mental workload, trust in automation, stress and perceived risk, all identified as modulators of drivers’ functional states and that could eventually determine drivers’ availability to take-over manual control. The main findings demonstrate that DSM systems should adopt multiple physiological measures to capture changes in functional states relevant for driver readiness. Future DSM systems will benefit from the knowledge generated by this research by applying machine learning methods to these measures for determining drivers’ availability for optimal take-over performance

Internet and Biometric Web Based Business Management Decision Support

Internet and Biometric Web Based Business Management Decision Support MICROBE MOOC material prepared under IO1/A5 Development of the MICROBE personalized MOOCs content and teaching materials Prepared by: A. Kaklauskas, A. Banaitis, I. Ubarte Vilnius Gediminas Technical University, Lithuania Project No: 2020-1-LT01-KA203-07810