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- Alsolai, Hadeel, et al.
(författare)
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A Systematic Review of Literature on Automated Sleep Scoring
- 2022
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Ingår i: IEEE Access. - : Institute of Electrical and Electronics Engineers (IEEE). - 2169-3536. ; 10, s. 79419-79443
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Forskningsöversikt (refereegranskat)abstract
- Sleep is a period of rest that is essential for functional learning ability, mental health, and even the performance of normal activities. Insomnia, sleep apnea, and restless legs are all examples of sleep-related issues that are growing more widespread. When appropriately analyzed, the recording of bio-electric signals, such as the Electroencephalogram, can tell how well we sleep. Improved analyses are possible due to recent improvements in machine learning and feature extraction, and they are commonly referred to as automatic sleep analysis to distinguish them from sleep data analysis by a human sleep expert. This study outlines a Systematic Literature Review and the results it provided to assess the present state-of-the-art in automatic analysis of sleep data. A search string was organized according to the PICO (Population, Intervention, Comparison, and Outcome) strategy in order to determine what machine learning and feature extraction approaches are used to generate an Automatic Sleep Scoring System. The American Academy of Sleep Medicine and Rechtschaffen & Kales are the two main scoring standards used in contemporary research, according to the report. Other types of sensors, such as Electrooculography, are employed in addition to Electroencephalography to automatically score sleep. Furthermore, the existing research on parameter tuning for machine learning models that was examined proved to be incomplete. Based on our findings, different sleep scoring standards, as well as numerous feature extraction and machine learning algorithms with parameter tuning, have a high potential for developing a reliable and robust automatic sleep scoring system for supporting physicians. In the context of the sleep scoring problem, there are evident gaps that need to be investigated in terms of automatic feature engineering techniques and parameter tuning in machine learning algorithms.
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- Azhari, Faris, et al.
(författare)
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A Global Indexing System for Integrating Mining Data Sets for Machine Learning and Analytics
- 2023
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Konferensbidrag (refereegranskat)abstract
- Azhari, F., Sennersten, C., Lindley, C., and Sellers, E., A Global Indexing System for Integrating Mining Data Sets for Machine Learning and Analytics, World Mining Congress 2023, in progress.
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- Azhari, Faris, et al.
(författare)
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Deep Learning Implementations in Mining Applications : a compact critical review
- 2023
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Ingår i: Artificial Intelligence Review. - : Springer Netherlands. - 0269-2821 .- 1573-7462. ; 56:12, s. 36
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Tidskriftsartikel (refereegranskat)abstract
- Deep learning is a sub-field of artificial intelligence that combines feature engineering and classification in one method. It is a data-driven technique that optimises a predictive model via learning from a large dataset. Digitisation in industry has included acquisition and storage of a variety of large datasets for interpretation and decision making. This has led to the adoption of deep learning in different industries, such as transportation, manufacturing, medicine and agriculture. However, in the mining industry, the adoption and development of new technologies, including deep learning methods, has not progressed at the same rate as in other industries. Nevertheless, in the past 5 years, applications of deep learning have been increasing in the mining research space. Deep learning has been implemented to solve a variety of problems related to mine exploration, ore and metal extraction and reclamation processes. The increased automation adoption in mining provides an avenue for wider application of deep learning as an element within a mine automation framework. This work provides a compact, comprehensive review of deep learning implementations in mining-related applications. The trends of these implementations in terms of years, venues, deep learning network types, tasks and general implementation, categorised by the value chain operations of exploration, extraction and reclamation are outlined. The review enables shortcomings regarding progress within the research context to be highlighted such as the proprietary nature of data, small datasets (tens to thousands of data points) limited to single operations with unique geology, mine design and equipment, lack of large scale publicly available mining related datasets and limited sensor types leading to the majority of applications being image-based analysis. Gaps identified for future research and application includes the usage of a wider range of sensor data, improved understanding of the outputs by mining practitioners, adversarial testing of the deep learning models, development of public datasets covering the extensive range of conditions experienced in mines.
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- Ekanayake, Hiran B., 1978-
(författare)
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Validating User Engagement and Effectiveness of Training Simulations : A mixed-methods approach informed by embodied cognition and psychophysiological measures
- 2015
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Simulation-based training has gained widespread attention recently as a response to drawbacks associated with traditional training approaches, such as high training costs (instructors, equipment, etc.), high risks (e.g. pilot training), and ethical issues (e.g. medical training), as well as a lack of availability of certain training environments (e.g. space exploration). Apart from their target training domains, many of aspects of simulations differ, such as their degree of physical realism (fidelity), scenarios (e.g. story), and pedagogical aspects (e.g. after-action reviews and collaborative learning). Among those aspects, designers have mostly focused on developing high-fidelity simulations with the expectation of increasing the effectiveness of training. However, some authors suggest that the above belief is a myth as researchers have failed to identify a linear relationship between the (physical) fidelity and training effectiveness of simulations. Most researchers have therefore evaluated the correspondence between the behaviours of trainees in both real world and simulated contexts, however, the existing methods of simulation validation using behavioural measures have a number of drawbacks, such as the fact that they do not address certain complex phenomena of skills acquisition.Bridging the above knowledge gap, this research reports on empirical investigations using an improved methodology for validating training simulations. This research includes an investigation of the user experience of trainees, with respect to the acceptance of virtual scenarios provoking a similar psychophysiological response as in real world scenarios, and the training potential of simulations with respect to the positive transfer of training from a simulator to real world operational contexts. The most prominent features of the proposed methodology include the use of psychophysiological measures in addition to traditional behavioural measures and the use of natural (quasi-) experiments. Moreover, its conceptual framework was influenced by contemporary theories in cognitive science (e.g. constructivism and embodied cognition). The results of this research have several important theoretical and methodological implications, involving, for example, the dependency of the effectiveness of simulations on the perceived realism of trainees, which is more embodied than has been predicted by previous researchers, and the requirement of several different types/levels of adaptive training experience, depending on the type of trainee.
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- Grimshaw, Mark, et al.
(författare)
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Sound and Immersion in the First-Person Shooter : Mixed Measurement of the Player's Sonic Experience
- 2008
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Konferensbidrag (refereegranskat)abstract
- Player immersion is the holy grail of computer game designers particularly in environments such as those found in first-person shooters. However, little is understood about the processes of immersion and much is assumed. This is certainly the case with sound and its immersive potential. Some theoretical work explores this sonic relationship but little experimental data exist to either confirm or invalidate existing theories and assumptions. This paper summarizes and reports on the results of a preliminary psychophysiological experiment to measure human arousal and valence in the context of sound and immersion in first-person shooter computer games. It is conducted in the context of a larger set of psychophysiological investigations assessing the nature of the player experience and is the first in a series of systematic experiments investigating the player's relationship to sound in the genre. In addition to answering questionnaires, participants were required to play a bespoke Half-Life 2 level whilst being measured with electroencephalography, electrocardiography, electromyography, galvanic skin response and eye tracking equipment. We hypothesize that subjective responses correlated with objective measurements provide a more accurate assessment of the player's physical arousal and emotional valence and that changes in these factors may be mapped to subjective states of immersion in first-person shooter computer games.
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- Hagelbäck, Johan, et al.
(författare)
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Psychophysiological Interaction and Empathic Cognition for Human-Robot Cooperative Work (PsyIntEC)
- 2014
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Ingår i: Gearing Up and Accelerating Cross-Fertilization between Academic and Industrial Robotics Research in Europe. - Cham : Springer. - 9783319029337 ; , s. 283-299
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Bokkapitel (refereegranskat)abstract
- The aim of the PsyIntEC project is to explore affective and cognitive modeling of humans in human-robot interaction (HRI) as a basis for behavioral adaptation. To achieve this we have explored human affective perception of relevant modalities in human-human and human-robot interaction on a collaborative problem-solving task using psychophysiological measurements. The experiments conducted have given us valuable insight into the communicational and affective queues interplaying in such interactions from the human perspective. The results indicate that there is an increase in both positive and negative emotions when interacting with robots compared to interacting with another human or solving the task alone, but detailed analysis on shorter time segments is required for the results from all sensors to be conclusive and significant.
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