AHFE Open AccessCognitive Computing and Internet of Things
Editors: Lucas Paletta, Hasan Ayaz, Umer Asgher
Topics: Cognitive Computing and Internet of Things
Publication Date: 2023
ISBN: 978-1-958651-49-0
DOI: 10.54941/ahfe1003983
Articles
Towards Gender-sensitive Motivation of Patients with Depression for Cognitive Training with a Socially Assistive Robot
Approximately 280 million people in the world suffer from depression, which often leads to cognitive deficits. This paper describes fundamental aspects of the work plan of the Austrian project AMIGA that aims at developing and evaluating a gender-sensitive and customizable Social Assisting Robotic Technology (SAR) including a cognitive training. We intend with the AMIGA-type SAR to significantly increase motivation for cognitive training during the hospital stay, which is hypothesised to improve cognitive functioning. In the first step of the project plan, the requirements of the target group would be estimated using qualitative and quantitative methods (e.g., survey, interviews). Based on the results of the requirement study we plan to develop the interactive SAR prototype that would use human-robot interactions to motivate users to perform cognitive training. We consider to apply adaptive pause management with sensor-based detection of mental fatigue. Target group-specific as well as gender-sensitive content for holistic stimulation with visual, auditory, and interactive elements will be included. The work plan foresees to conduct a field study including persons with symptoms of depression and to evaluate the results in terms of the effects on the persons´ motivation through the application of the AMIGA robotic system platform.
Lucas Paletta, Sandra Schuessler, Eva Reininghaus, Michael Macher, Maria Fellner, Silvia Russegger, Melanie Lenger, Martin Pszeida, Thomas Orgel, Sandra Draxler, Alfred Haeussl, Bscn, Nina Dalkner, Stephan Spat, Gloria Pötz, Regina Roller Wirnsberger, Julia Zuschnegg
Open Access
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Conference Proceedings
Mild Dementia Decision Support from AI-based Digital Biomarkers using Mobile Playful Exercises with High Adherence
Early detection of cognitive decline and monitoring of cognitive functioning in mild dementia are fundamental for timely adaptation of lifestyle and intervention strategies. The development of digital dementia biomarkers through playful exercises with high adherence rate was a key objective of the national project multimodAAL (no. FFG 875345). The results of a study on computer-based cognitive and physical training (CCPT) in persons diagnosed with mild Alzheimer’s disease (PwAD) are presented.Method: Tablet-PC-based intervention was applied within 6 months in Austria, engaging PwADs living at home by means of playful multimodal training and activation (n=11; female N=8, male N=3; age M=76.6 / SD=9.2 years, MMSE score M=21.50 / SD=4.41). PwADs interacted with a prototypical version of the BRAINMEE app that included a suite of cognitive exercises (puzzle, pairs, text gap filling) based on audiovisual information. The playful training app was introduced and assisted by mobile care professionals with weekly visits, however, PwDs played alone between these visits.Result: PwADs applied training with high adherence, finalizing M=72 (M=32) digital exercises per day within the first (last) month of the study. Duration of using exercise type ‘outsider’(p=.028*) and ‘quiz’ (p=.001**), averaged about 2 week figures, each provided statistically significant correlations (Spearman) with MMSE test scores, as well as ‘spot-the-difference’ (p=.003**) with Trail Making Test A, ‘outsider’ (p=.005**) with Auditory Verbal Learning Test (AVLT), respectively. A neural network (Support Vector Machine, linear kernel, 11-fold cross validation) using duration of use of ‘quiz’, ’outsider’ and ‘hearing’ (guessing animal sounds) as input data resulted in M=2.16 absolute error in MMSE score estimation on test data.Conclusion: The work outlined within the Austrian study on digital biomarker development indicates successful steps towards daily use of cognitive assessment using highly adherent playful training. The playful training app is applied in the European project MARA (no. FFG 886427) to enable continuous estimates of MCI’s mental state over time. The app was very well accepted by both PwADs and persons with MCI. It offers with its pervasive mental assessment tool a large potential for future long-term monitoring in dementia prevention, early detection as well as in numerous dementia care services.
Martin Pszeida, Lucas Paletta, Silvia Russegger, Thomas Orgel, Sandra Draxler, Marisa Koini, Martin Berger, Maria Fellner, Stephan Spat, Sandra Schuessler, Julia Zuschnegg, Bernhard Strobl, Karin Ploder, Maria M Hofmarcher Holzhacker
Open Access
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Conference Proceedings
Digital Shadows and Twins for Human Experts and Data-Driven Services in a Framework of Democratic AI-based Decision Support
Current automated and hierarchical structured production processes can only insufficiently deal with the upcoming flexibilization, specifically regarding the requirements within Industry 5.0. The European project FAIRWork fosters the ‘democratization’ of decision-making in production processes, hence the participation of all involved stakeholders, by introducing a decentralized AI system. Hybrid decision-making faces the challenge first to digitally represent the relevant actors – here we propose the use of digital twins – and the interpretation of that digital twin, by a human expert or by a computer algorithm, to achieve better decisions. Research on existing sensors and data technology is required. In particular, the digital representation of human operators requires so called “Intelligent Sensor Boxes”.Method: ‘Intelligent Sensor Boxes’ are firstly determined by a dedicated group of sensors, such as, low-cost sensors, biosensors, wearables, human sensors, or even virtual sensors. Specific attention is dedicated to the development of the ‘Digital Human Sensor’ (DHS) applying AI-enabled Human Factors measurement technology. Each instantiation of a DHS provides a digital vector of Human Factors state estimates, such as, digital biomarkers on physiological strain, affective state, concentration, cognitive workload, situation awareness, fatigue. On the basis of these vectors, we determine cost function parameters associated with typical (inter-)actions in the work environment. We outline an advanced approach to represent cognitive strain by studying workload related to task switching, multitasking and interruption as well as monotony effects. Furthermore, we will investigate cognitive strain in the context of environmental parameters, such as, air quality, and combine IOT with wearable bio-sensor shirts, smartwatches with biosensors, eye tracking glasses, digital events, and spatiotemporal patterns from human-machine interaction. Cost functions for optimization algorithms can be related to well-being of the worker, this allows data processing with the goal to optimize according to several input factors using data that are derived from humans or from machines like lines and robots. Those data are described with corresponding meta data to result in a descriptive data lake. Such meta data correspond to domain specific models like the production process, the working environment model, or resources models. Data processing and optimization algorithms can then be applied on this data lake. This task complements existing data with human based sensor data and provided adapted data mining tools.Results: We present relevant methodologies for human-centered wearable or mobile measurement technologies for psychological and ecological constructs as typical instantiations of the novel framework. Furthermore, the embedding of the schema of ‘Intelligent Sensor Boxes’ into the framework of ‘Democratic AI-based Decision Support’ (DAI-DDS) is sketched and argued. An outlook on future research trajectories, in particular, in the context of the FAIRWork project, is outlined in detail, and Ethical guidelines are discussed. Experimentation Laboratories, such as, the Austrian Human Factors Lab, are not only considered and presented as a co-creation space to develop new ideas, but also as test, training and communication environment for and between all stakeholders of an innovation chain. Conclusion: The framework and development of ‘Intelligent Sensor Boxes’ with data quality control and including decision modules is described in the context of its relevance within production related environments. ‘Digital Human Sensors’ applying AI-enabled digital Human Factors measurement technology will represent key drivers in the novel Industry 5.0 era.
Lucas Paletta, Herwig Zeiner, Michael Schneeberger, Yusuf Quadri
Open Access
Article
Conference Proceedings
AI-powered real-time analysis of human activity in videos via smartphone.
A major focus in computer vision research is the recognition of human activity based on visual information from audiovisual data using artificial intelligence. In this context, researchers are currently exploring image-based approaches using 3D CNNs, RNNs, or hybrid models with the intent of learning multiple levels of representation and abstraction that enable fully automated feature extraction and activity analysis based on them. Unfortunately, these architectures require powerful hardware to achieve the most real-time processing possible, making them difficult to deploy on smartphones. However, many video recordings are increasingly made with smartphones, so immediate classification of performed human activities and their tagging already during video recording would be useful for a variety of use cases. Especially in the mobile environment, a wide variety of use cases are therefore conceivable, such as the detection of correct motion sequences in the sports and health sector or the monitoring and automated alerting of security-relevant environments (e.g., demonstrations, festivals). However, this requires an efficient system architecture to perform real-time analysis despite limited hardware power. This paper addresses the approach of skeleton-based activity recognition on smartphones, where motion vectors of detected skeleton points are analyzed for their spatial and temporal expression rather than pixel-based information. In this process, the 3D-bone points of a recognized person are extracted using the AR framework integrated in the operating system and their motion data is analyzed in real time using a self-trained RNN. This purely numerical approach enables time-efficient real-time processing and activity classification. This system makes it possible to recognize a person in a live video stream recorded with a smartphone and classify the activity performed. By successfully deploying the system in several field tests, it can be shown both that the described approach works in principle and that it can be transferred to a resource-constrained mobile environment.
Rico Thomanek, Benny Platte, Matthias Baumgart, Christian Roschke, Marc Ritter
Open Access
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Conference Proceedings
AI-enabled Playful Enhancement of Resilience and Self-Efficacy with Psychological Learning Theory
The outbreak of COVID-19 has caused a global public health emergency with multifaceted severe consequences for people’s lives and their mental health. Distress and anxiety are normal responses to such extreme circumstances. The Austrian research project AI-Refit aims at a radically innovative app prototype representing a digital care centre (i) to reinforce resilience by engaging into activities to prevent from depressive symptoms, severe anxiety and stress levels, (ii) to apply playful AI- and sensor-enabled assessment of mental health, (iii) to capture daily lifestyle data for a comprehensive contextual assessment from non-obtrusive wearables, and (iv) to adaptively promote self-efficacy of the individual, based on scientific psychological learning theory.
Lucas Paletta, Silvia Russegger, Dietrich Albert, Eva Reininghaus, Melanie Lenger, Maria Fellner, Thomas Lutz, Martin Pszeida, Sandra Draxler, Thomas Orgel, Michael Schneeberger, Suher Guggemos, Jochen Mosbacher, Stephan Spat, Gloria Pötz
Open Access
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Conference Proceedings
Impact of real-time stress monitoring in people with an intellectual disability
People with an intellectual disability are vulnerable to stress, which can result in challenging behaviour, such as apathy, self-harm, or aggression. By monitoring stress in real-time, professional caregivers can timely intervene to prevent escalations and improve the quality of life for both the client and themselves. The aim of this study was to investigate the impact of real-time stress monitoring using the stress-detection system HUME on the quality of life of people with a severe intellectual disability and their professional caregivers. The study comprised two parts. A case series study (n=12) was conducted with long-term care clients with intellectual disabilities to validate the HUME. HUME stress measurements, based on physiological data and trained artificial intelligence models, were collected, and compared with labelled video observations of professional caregivers. A second study was conducted to measure the impact of HUME and the induced interventions on quality of life. Physiology data and quality of life scores were collected. The HUME stress prediction was used 1) for early warning to deploy interventions based on what the professional caregiver deemed best, and 2) as an assessment tool to understand the effectiveness of care interventions. The quality of life for both the client (n=41) and professional caregiver (n=31) was evaluated via a questionnaire. Results showed that the HUME was able to detect stress in all cases, and stressful events detected by the HUME were consistent with the behavioural observations. The real-time stress monitoring using HUME, along with subsequent interventions, was effective. Clients with intellectual disabilities experienced reduced stress and an improvement in their perceived quality of life. Also, professional caregivers perceived an increase in the quality of life during the period the HUME was used. In most of the cases, HUME-based interventions led to a reduction in escalations, fixations, and self-harming behaviour. Further randomized controlled studies are needed to substantiate these results.
Vera Van Der Nulft, Stefan De Vries, Josien Visschedijk, Reon Smits, Franka Meiland, Esmee Adam, Hanneke Smaling, Erwin Meinders
Open Access
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Conference Proceedings
Improving the Security and Usability of the Internet of Things through a Scalable Network-Level Smart System
The Internet of Things (IoT) is a network of interconnected devices, sensors, and systems that communicate without human intervention. This technology involves embedded sensors, software, and wireless communication protocols that enable devices to collect and exchange data in real-time. This allows businesses and individuals to monitor and control various aspects of their environment, from temperature and humidity to security and energy consumption providing intelligent insights and automating various tasks. However, these increasingly connected devices bring security vulnerabilities to homes and businesses facing digital attacks that were never possible. These could be IoT-connected door locks that leak network passwords or IoT coffee makers that can be set to make coffee from outside one's home network. These problems arise mainly from IoT devices where usability and functionality were the focus, and security was not considered. Furthermore, these IoT devices cannot implement security due to limited storage, memory, and processing power. This paper aims to assess the feasibility and develop an intelligent system that improves the security of Internet of Things (IoT) devices in the best possible way with minimal user interaction and a learning curve, which the IoT manufacturer may need to provide. At the same time, the system will provide end users with traditional intrusion detection methods and artificial intelligence-driven detection techniques that monitor the IoT devices to get timely feedback and possible actions with or without users' interactions.
Mandeep Pannu, Iain Kay, Ismail El Sayad
Open Access
Article
Conference Proceedings
Laboratory assessment of heat strain in female and male wildland firefighters
Wildland firefighters (WFF) face a set of specific work-related factors that directly affect their physical and cognitive abilities and compromise their health and safety. The working conditions include hard physical work and environmental conditions that combine high temperatures and high radiant heat. Such environments make using personal protective equipment (PPE) mandatory to protect them from risks. This fact restricts heat removal and adds extra weight, increasing thermal strain and the risk of heat-related illnesses on WFF. Since the number of females WFF has increased, it is necessary to study the repercussions of heat stress on this group. To date, it is not yet well-known whether sex-related differences in thermoregulation will be relevant when the individuals are wearing PPE and performing high physical effort in a hot environment. Therefore, we aimed to investigate the physiological response when performing moderate to high-intensity effort in a hot-dry environment while wearing PPE according to sex. Twenty WFF 10 females [23.9 ± 3.2 yr, 163.8 ± 3.4 cm and 62.7 ± 9.1 kg] and 10 males [31.9 ± 6.6 yr, 178.8 ± 5.8 cm and 73.9 ± 7.7 kg]) performed a 125 min treadmill test in a controlled ambient (30 ºC and 30% relative humidity). The protocol consisted of two exercise stages where WFF performed different continuous and variable exercise bouts in order to mimic the effort performed during real deployments. Participants wore the full standard PPE during the test. Oxygen uptake (VO2), heart rate (HR), core temperature (CT) and chest temperature (SkT) were monitored throughout the test. HR and CT were used to calculate the physiological strain index (PSI). Differences in body mass pre-post trials corrected for fluid intake were used to calculate sweat production (SwP), sweating rate (SwR), and evaporative efficiency (EE). Differences (p < 0.05) between females and males were found in %VO2max (62.5 ± 7.4 vs 55.3 ± 5.), HR (155 ± 10 vs 134 ± 14 beats·min–1), % of maximal HR (81.3 ± 3.5 vs 42.3 ± 6.5), CT (38.0 ± 10 vs 37.7 ± 0.33 ºC), SkT (36.0 ± 0.6 vs 35.3 ± 0.6 ºC) and PSI (4.1 ± 0.5 vs 3.5 ± 0.6). Even though SwR was higher (p < 0.05) for male participants (1001.5 ± 268.3 ml) compared to females (647.5 ± 145.9 ml), females had higher EE (32.9 ± 4.6 vs 16.7 ± 6.2 %). In conclusion, performing high-intensity exercise in hot-dry conditions while wearing PPE leads to a higher thermal and cardiovascular load for female WFF, making them more susceptible to heat illness. These results could be linked to lower aerobic fitness, sweating rate, and hormonal aspects that increased the thermal burden.
Belén Carballo Leyenda, Jorge Gutiérrez Arroyo, José Gerardo Villa Vicente, Fabio García-Heras, Juan Rodríguez Medina, Jose A Rodríguez-Marroyo
Open Access
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Conference Proceedings
Real-time remote stress monitoring based on specific stress modelling considering load characteristics of different military forces
An ongoing challenge for the Military Task Forces is the management of personnel to optimise and maintain performance, whilst also ensuring ongoing health and wellbeing. In the course of intensive training and exercises as well as in real operational scenarios, soldiers often suffer physiological and psychological borderline stresses and also injuries during physical and combat-related training, with overuse injuries often occurring here. Innovative developments and research projects for the physiological monitoring of soldiers arise, based on innovative developments in the field of biosensor technology. Soldiers are at the center of deployed sociotechnical systems despite major innovations in the field of autonomous systems and artificial intelligence (Swiss, 2020). These are aspects and development approaches that are of great interest to military as well as civilian task forces. Motivation and Requirements: Military training and exercise missions as well as real deployment scenarios are often associated with a high degree of physical stress and responsibility and require a high level of mental performance and concentration. Reduced concentration and reaction cause delayed or possibly even wrong decisions, which can have fatal consequences. The research project VitalMonitor therefore focuses on the development of a (i) real-time monitoring system, which analyses changes in physiological parameters from heart rate, heart rate variability, skin conductance, core body temperature, etc., (ii) decision support tool for mission commanders to determine optimal work-rest-cycles preventing physical overstraining in trainings and missions (iii) personalized physical fitness training for soldiers to control their individual stress situation in a targeted manner avoiding poor performance. Methods and Results: In order to be able to make concrete statements about a current, individual stress situation for the soldiers of different task forces, it is necessary to characterize the work stress and to develop specific load and stress models. Basically, here is a relevant difference in the stress models if we compare e.g. CBRN group, light infantry forces and special military forces in the operational loads. In a first step, an attempt was made to create a so-called expert model for the load characteristics on the basis of extensive expert knowledge and measured values collected in the context of various stress tests with various military task forces. The focus was initially on the CBRN task force and further extensive tests were carried out as part of the VitalMonitor project.The basis for the creation of a specific stress model is the comprehensive analysis of the scenario-related work conditions, the psychological and cognitive stress as well as the physiological stress and the interrelationships that occur. The use of an available innovative bio-sensor technology must enable the remote measurement of vital values of the soldiers in the different deployment scenarios. Conclusion and Outlook: Soldiers are at the center of deployed sociotechnical military systems, while requirements in the physiological and cognitive field have increased significantly. Therefore, optimized capability and performance development for soldiers is a key focus for military organizations. Innovative biosensor technology, which is currently available on the commercial market, enables the monitoring of physiological parameters during physical strain and thus basically also during different military deployment scenarios. A targeted use for military tasks, which provides soldiers, executives and medical personnel with meaningful, real-time situation-relevant information, requires an intelligent analysis of the sensor data. These analysis methods take into account, on the one hand, the load characteristics of the operational scenarios and, on the other hand, the individual fitness and stress situation of the persons.
Alexander Almer, Anna Weber, Florian Haid, Lucas Paletta, Michael Schneeberger, Stefan Ladstätter, Dietmar Wallner, Paul Glanz, Philipp Klöckl, Dominik Eder, Thomas Hölzl
Open Access
Article
Conference Proceedings
Impact of Acute Physical Exercise on Cognitive Performance
Numerous studies have found that aerobic endurance exercise increases neural activation and reduces reaction times suggesting that acute bouts of exercise may selectively boost executive function performance involving inhibitory control and attention. The objective of the presented study was to understand the concrete impact of a “standardised incremental exercise test to exhaustion” on certain cognitive functions, such as, sustained attention, and flexibility in the reaction behaviour. The results of the intervention demonstrate that reactive resilience increased (p=.008**) and reaction time was reduced (p<.001***) under application of the Determination Test (DT). Nevertheless, the number of errors increased but not in a significant manner (p>.05). The results of the Psychomotor Vigilance Task (PVT) showed that the reaction times were significantly decreasing as well (p=.002**), however, the errors in terms of “false starts” were significantly increasing (p=.002**). This research study demonstrates that acute physical exercise had a measurable impact on the cognitive performance of the participants. In particular, the PVT reported a statistically significant detrimental impact that refers to changes in sustained attention.
Michael Schneeberger, Martin Pszeida, Melanie Lenger, Lisa Heiler, Helmut Simi, Dietmar Wallner, Anna Weber, Alexander Almer, Silvia Russegger, Lucas Paletta
Open Access
Article
Conference Proceedings
Towards Immersive Skill Training for First Responders with Biosensor-based Assessment of Situation Awareness
First responders engage highly stressful situations at the emergency site that may induce stress, fear, panic and a collapse of clear thinking (Putnam, 1995). However, their physiological and cognitive readiness is of highest importance to enable appropriate decision making (Frye & Wearing, 2014). A major aspect for keeping control of the situation is that first responders should always maintain situation awareness. Training of the first responders’ routines improves resilience towards stressors in severe hazard conditions. Advanced training of first responders, in particular, the use of VR environments for the training of situation awareness for stress-resilient decision-making behavior is one of the upcoming challenges of the near future.Methods A Virtual Reality environment was developed with a typical firefighter scenario to train and to evaluate the situation awareness (SA) of squad leaders. The operator has two major tasks, (i) searching for pocket of embers on the ground by using an infrared camera and whenever detected to call for a team member to work on it, (ii) to survey her team of 5 first responders for their visible presence as well as for their health condition and consequently to react in proper time instants whenever team members would be injured or disappear. The movement of the team members within a predefined skill-oriented territory is activated by an AI-driven method. A wearable multisensory-based non-invasive measurement suite is mounted on the operator including VR in-built eye-tracking, biosensors for cardiovascular, electrodermal and temperature data capture, to report by means of digital human factors analytics. From the sensor data stream we intend to deduce psychological constructs, such as, situation awareness, physiological strain, cognitive-emotional stress, and fatigue. A pilot study is planned with N=20 students, with pre- and post-study cognitive tests, such as, determination test (Schuhfried, 1987) and Psychomotor Vigilance Test (PVT; Dinges & Powell, 1985), as well as the Situation Awareness Rating Technique (SART; Taylor, 1990). From the SART we will research for to situation awareness-specific digital biomarkers by means of correlation analysis. This multi-tasking configuration will enable to measure (a) executive functions, such as, cognitive flexibility, and (b) level 1, 2 and 3 SA by gaze-driven virtual events, such as, viewing first responder avatars. Results: We will describe the system architecture as well as the features of the VR-based skill evaluation system (VR with eye-tracking, biosensors, treadmill, dashboard). Test users will move on a treadmill that triggers the VR-based experience accordingly, by means of a Cyberith Visualizer platform. Videos that were captured from the use of the platform and application of biosensors with eye tracking will demonstrate the usefulness for the evaluation of SA. We will report from a first usability study with feedback from friendly users. At the conference, we will be able to report on first results of the pilot study.Conclusion: Advanced training of first responders and emergency staff with typical operation scenarios is one of the upcoming challenges of the near future. First results motivate the use of VR environments for the training of situation aware and stress-resilient decision-making behavior of firefighters.
Amir Dini, Michael Schneeberger, Martin Pszeida, Lisa Heiler, Lucas Paletta
Open Access
Article
Conference Proceedings
Development of an Automated Microclimate Adjustment System based on Concentration Levels of Students
The microclimate of a classroom can significantly impact the students' concentration. As students ourselves, we have noticed this. For the same subject, we are concentrated in one period, then in another, we lose our attention very easily. This prompted us to investigate the relationship between students' concentration levels in relation to the microclimate. Through machine learning, specifically through facial recognition and computer vision, we aimed to investigate the students' concentration levels based on the number of blinks per minute. While researching ways to analyse concentration levels, we found multiple studies which found a correlation between blink frequency and concentration level. We found that when people are concentrated, they tend to blink less and vice versa. The relationship between microclimate climate and concentration was analysed by measuring the blinks per minute while changing the microclimate at the same time. The microclimate conditions were varied using an air conditioner where the temperature set varied from 19°C to 31°C. The microclimate was measured using the NodeMCU microcontroller board paired with SCD-30 and HM-3301 sensors from Grove. This allowed us to gather data such as temperature, relative humidity, carbon dioxide concentration, PM2.5 and PM10 readings. From this, it was found that the most significant microclimate condition that affects concentration levels is carbon dioxide concentration. As the concentration of carbon dioxide increases, the concentration of the participants decreases. The observed trend is supported by various studies as well. Simultaneously, as the microclimate conditions were being varied, we sent out a survey to find the thermal comfort level of the students, allowing us to gauge how they felt according to the environment. Thermal comfort is when a person feels comfortable with the thermal environment. Participants were tasked to do their work for a fixed duration while a sensor recorded the temperature and relative humidity of the place. For every hour, the participants were required to rank their thermal comfort by using the ASHRAE scale. This survey identified the comfort zone at an upper limit temperature of 28.9°C and relative humidity of 68.0% and a lower limit temperature of 26.2°C and relative humidity of 83.1%. With these findings, we created an automated system to alter microclimate conditions. The place will be a conducive environment for the students based on their concentration and the environmental data obtained from the sensors. The alteration of microclimate conditions was done by controlling the air conditioner using an infrared LED module connected to the NodeMCU that sent out the infrared codes according to the conditions of the room, allowing us to adjust the microclimate efficiently while fully utilising the various modes of the air conditioning system to save energy.
Jasper Koh, Prasanna Thangaraja, Kenneth Y T Lim
Open Access
Article
Conference Proceedings
Exploring The Implementation of AI in a Cost-effective Device for Predicting Sleep Quality
This report presents the development and effectiveness of an Arduino-based sleep tracking device that can accurately measure various parameters of sleep, including movement, temperature, sound, light intensity, and humidity. The device was designed to be low-cost and easy to use, while not compromising on its ability to accurately measure sleep activity. The effectiveness of the device was evaluated by collecting data from test subjects and comparing it to the data collected by other sleep tracking devices. The collected data was then processed and used to train Artificial Intelligence (AI) models such as Backward Propagation Neural Network, Linear Regression Model, and Grey Relation Analysis, to predict the sleep quality rating from 0% to 100% and to identify the main cause of poor sleep. The results of the study demonstrated that the Arduino-based sleep tracking device is an effective and cost-efficient tool for measuring various parameters of sleep. However, the pressure sensor may sometimes result in inaccurate readings, which can be addressed through data cleaning and filtering. Furthermore, the use of AI models was able to predict the sleep quality rating and identify the main causes of poor sleep with high accuracy. Further research is needed to evaluate the device's performance over a longer period of time and in a larger sample of participants.
Jing Peng Lee, Bruce Yu, Kenneth Y T Lim
Open Access
Article
Conference Proceedings
Routing algorithm and diameter of hierarchical hyper-star network
In this paper, we propose a new interconnection network topology, hierarchical hyper-star network HHS(Cn,Cn) is based on hyper-star network. Hierarchical networks based on hypercube have been previously proposed; it has been shown that these networks are superior to the basic networks, in terms of various performance including diameter, network cost, fault tolerance etc. Our results show that the proposed hierarchical hyper-star network performs very competitively in comparison to hyper-star network, HCN(n,n), and HFN(n,n) have been previously proposed. We also investigate a various topological properties of the hierarchical hyper-star network HHS(Cn,Cn) including routing algorithm, diameter, connectivity, broadcasting.
Hyeongok Lee, Bo-Ok Seong
Open Access
Article
Conference Proceedings
Cognitive nature of procrastination
One of the typical social problems of the 21st century - procrastination - is defined as irrational postponement of desired goals indefinitely, even when aware of the negative consequences of this delay (Lay, 1997). Although possible causes of procrastination have long been cited, such as irrational beliefs (Ellis, Knaus, 1977), low self-esteem, and fear of failure (Burka, Yuen, 1983), cognitive predictors of procrastination have not been studied holistically as a system. Moreover, it remains unclear which cognitive mechanisms are involved in different types of procrastination. This study seeks to partially fill this gap by finding cognitive features of people prone to procrastination.The results of the study (N = 311) revealed differences in most of the diagnosed cognitive indicators, which suggests an important role of cognitive processes in the shaping of a procrastination tendency. Comparison of cognitive scores in the high and low procrastination groups showed that procrastinators had higher rates of cognitive closure, namely higher scores on the scales of order (p = 0.000), predictability (p = 0.052), decisiveness (p = 0.000), aspiration to cognitive closure (p = 0.000). Cognitive closure means motivation to receive an unambiguous response and cut off unnecessary, contradictory and interfering information. This is consistent with the data on higher stiffness in procrastinators (p = 0.05).Besides, procrastinators have a more pronounced frustational tolerance (p = 0.000), and a sense of self-improvement (p = 0.001). They have less vigilance (p = 0.000), but more overindulgence (p = 0.000), as well as more avoidance in decision-making (p = 0.000). Differences are also found on the temporal focus scale: people prone to procrastination are less focused not only on the future (p = 0, 000), but also on the present (p = 0, 000). Predictably, procrastinators had significantly lower levels of claims (p = 0.004) and self-esteem (p = 0.01). Procrastinators showed lower indicators of self-organization of activities: consistency (p = 0.000), purposefulness (p = 0.000), perseverance (p = 0.024), fixation (p = 0.000), self-organization (p = 0.000), orientation to the present (p = 0.000). At the same time, they have more pronounced cognitive copying strategies: avoiding behavior (p = 0.000), anxiety (p = 0.000), cognitive overestimation (p = 0.000), intolerance to stress situations (p = 0.000).The results of discriminant analysis made it possible to determine the indicators that have the greatest influence on inclusion in the group procrastinators. These are low orientation towards the present, avoidance in decision-making, vigilance, pursuit of cognitive closure, low tolerance of frustration, and low self-organization of activities. The study thus expands the understanding of the cognitive nature of procrastination. The results suggest that cognitive features such as a weak focus on the events of the present, a habit of avoiding decision-making, weakened vigilance, an increased desire for cognitive closure, low tolerance to frustration, and a low level of self-organization of activities are important predictors of procrastination.
Ekaterina Zabelina, Dastan Abdrakhmanovich Smanov
Open Access
Article
Conference Proceedings
Information display preferences for assembly instructions in 6 industrial settings
We detail the results of an ongoing study into the preference of workers in 6 different industrial companies for assembly instruction display types and modalities for their tasks. This study is performed as a part of a project that aims to create a theoretical framework for understanding requirements for instruction presentation in industry, and providing guidance to the creators of assembly instructions. The study, as well as the project as a whole, aims to expand on approaches from the Industry 4.0 framework, with a particular focus on the more recent Operator 4.0 approach that adds a focus on more human-centric aspects of digitalisation in industry. The study being presented is comprised of facility visits to each partner company where the current state of practice was presented by each company, an examination of information presentation and operating procedures by the authors, and in-depth interviews with assembly workers at each site. All companies examined deal with variants in production, and the complexity of assembly spans from low to extremely high. The companies involved mostly rely on experienced workers, with high training, and relatively long times to train new personnel. The interviews led to findings such as simplified images being strongly preferred for both beginners and experienced workers, with an emphasis on the image matching the worker’s viewpoint to the product, and experienced workers preferring simplified images with highlighted markings for details that can be seen from where the task is performed, and more. The findings will be used in further work to create a theoretical framework around digital work instructions, as well as used directly to help partner companies better standardise their instructions to support the cognitive abilities and limitations of their assembly workers. The goal with this is to create safe, comfortable and profitable workplaces that fulfil goals of social sustainability in the long term.
Ari Kolbeinsson, Emmie Fogelberg, Peter Thorvald
Open Access
Article
Conference Proceedings
Situation Awareness Monitoring by Behaviour Detection and Model’s Processes Retroaction for Crew Member of Nuclear Power Plant
Based on on-site operator’s control behavior and model’s consequential behavior & cognitive process’ retroaction (MBPR), a situation awareness detection method is proposed for crew members of nuclear power plant control room. Firstly, the cognitive models are built depending on of operator’s control knowledge, cognitive characteristic parameters and working environment’s. The cognitive model itself reflects the cognitive process and cognitive characteristics of the operator under that specific working conditions, and model’s consequential behavior embodies the operator’s cognitive characteristic and the selected procedural knowledge. From this point, the model’s consequential behavior is the representation of the operator’s procedural knowledge and operator’s certain cognitive characteristics. With this method, the on-site operator’s behavior can be captured and applied to compare model’s behaviour. Then by retroacting the model’s procedure, the operator’s cognitive characteristic and selected procedural knowledge at the moment can be obtained through tracing back the model’s behavior to its procedure. Finally, the operator’s cognitive characteristic and selected procedural knowledge are unearthed, and it can be as the representation of crew member’s cognitive status.
Yanfei Liu, Chao Shen, Junsong Liu, Feng Fu, Yuzhou Liu
Open Access
Article
Conference Proceedings
Trust: the Vital Fluid of Interactions
In this paper we present a multidimensional approach to the concept of Trust in complex technical environments. A survey allows to focus on the two main types: Interpersonal Trust (IT) and Trust in Automation (TA), an extended generic conceptual modeling based on a grounded theory methodology is proposed; finally the first phase of an experimental campaign is presented. The whole project aims at a better understanding of the role of the various components of Trust during the decision process of a human operator in cooperation with sophisticated systems and human partners.
Laurent M Chaudron, Jean-Marie Burkhardt, Lisa Chouchane, Pauline Munoz, Nicolas Maille, Anne-Lise Marchand
Open Access
Article
Conference Proceedings
A Critical Overview of Studies on Eye Tracking and Visual Hierarchy
This paper offers a thorough evaluation of recent eye-tracking studies in the area of visual hierarchy. The review’s specific goal is to investigate how eye-tracking technology might be used to study cognitive processes in the visual hierarchy while using pertinent characteristics. 30 papers, encompassing 36 investigations, were examined for this purpose. There are some noteworthy findings. Using eye-tracking technologies to investigate visual hierarchies has gained a lot of attention recently. Typically, the research included university students, scientific content, and chronological frequency count ranges of eye-tracking metrics. Insights into cognitive processes including selection, organisation, and integration might be gleaned by monitoring eye movement patterns. Visual hierarchy principles, visual content, individual differences; and visual hierarchy principles, visual content. To provide recommendations for future research and practises, specific gaps in the literature and implications of previous results on visual hierarchy design were also identified.
Amic Ho
Open Access
Article
Conference Proceedings
Socio-Cultural Factors of Industrial Workers in Low-Middle Income Countries (LMIC): Pilot Study
The role of industrial workers is a significant element of any society and a vital stakeholder in an industrial setup. Their roles and existence in an organization affect the organizational culture, working environment, and quality of Life (QoL). In the same way, modern organizational culture and its environment affect the employee's psychology and behavior and bring new challenges daily. This paper is a pilot study that aims to review, confer, and analyze the organizational, social, and cultural challenges faced by industrial workers of Low-Middle Income Countries (LMIC) and how these factors affect psychology, personal, and professional Quality of Life (QoL). To ensure a better Quality of Life (QOL) among individuals, each industry should address these factors systematically to plan work-related issues that affect the industrial worker properly. In the first phase, a dedicated 25-item questionnaire on a Likert scale was used for investigating four socio-cultural factors (the individual, the relationship with their family, and social and organizational factors) among 50 industrial workers (data modelling and hypothesized data) with some assumptions. Sequentially, in the second phase, the study statistically analyzes how these factors influence their behavior and psychology. Results show that family, social, individual, and organizational factors are correlated with Cronbach's alpha of 0.916.
Umer Asgher, Sara Ali, Tahir Ali, Yasar Ayaz, Sofia Scataglini, Salman Nazir, Usama Rashed, Ellie Abdi, Fahad Iqbal Khawaja, Redha Taiar, José Arzola-Ruiz
Open Access
Article
Conference Proceedings
Interface And Interaction: The Symbolic Design for Bridge Conning System
Technology is evolving at a dizzying speed. The digitalisation trend refers to a socio-technical phenomenon and process that influence social actors’ practices and interaction, which is reshaping people’s workplaces and influencing everyone on the way of working. The advancements in digitalisation also involved domain of marine industry. Ship bridge, where is a complex working environment contains a plethora of interactions between seafarers and technology-supported systems and equipment. Sometimes the digitalisation released employees’ physical workload, however, increased cognitive load. In maritime, the safety-critical domain, it is critical to design appropriate interfaces and interactions to satisfy the industry’s and operators’ needs, making technology adapt to them.The role of design has shifted from technology driven machine-centred design to user-centred design. Norman (2019) reminded us in context of the digitalisation, interaction and service designers come into the spotlight. The new role of design becomes a strategic problem-solving process to deliver innovative products, systems, services and experiences. “Easy to use” and “intuitive” are terms often cited to describe the desired user experience (UX) created by user interfaces (UIs). Product semantics and semiotics betters the UI/UX design. Krippendorff (1989) revealed that design is making sense (of things). Norman (2013) has a similar definition of design as an act of communication. In modern design, function, form, and meaning are collectively pursued by designers. The appropriately designed UI can communicate with users and provoke users’ emotions, reactions, and engagements. According to ship bridge, poor graphical UI (GUI) design has shown negative impacts on navigation operations, and inappropriate information layout increases potential risks of safety at sea. It is critical for interface and interaction design to support sensemaking by presenting information appropriately and aesthetically. Symbols, colours, and the use of animation are three graphical design elements for web-based interfaces categorized by Cyr (2008). These three elements should also be considered referring to the screen-based displays of the equipment in ship bridge. There are proofs that the icons evolve into symbols as the result of the systematic shift of information from the graphical signs to the users' memory through the repeated interaction with interface elements. Once the user-definable and pre-defined symbols are shaped, the contents can be visualized and manipulated in a very flexible and intuitive way, which will help designers to develop effectively communicating and meaningful interfaces to improve sensemaking for seafarers and achieve the “easy” and “intuitive” experience ultimately. This paper aims to develop a simple and user-friendly interface assuring an intuitive human-machine interaction (HMI), therefore, minimizing human errors and sea accidents. The design integrates the state-of-the-art technology, cognitive ergonomics, and human centred design principles in ship bridge design. The finding benefits ship designers for future ship bridge design.
Bingyu Mu, Fang Bin Guo, Zaili Yang, Ian Jenkinson
Open Access
Article
Conference Proceedings
Attempt to develop analysis model of reader’s pictogram understanding process
When designing pictograms, a pictogram designer decides the meaning intended to be conveyed by a pictogram. It is desirable that the reader understands the meaning of the pictogram as intended by the designer. However, there are readers who understand a pictogram very differently from the designer's intention. Due to this difference between the designer’s intention and these readers’ understanding, the pictogram communicates the wrong message to the readers. This difference should be minimized.We attempted to develop a model to analyze a reader’s pictogram comprehension process. Analyzing the reader's pictogram comprehension process will contribute to clarifying the causes of differences between readers’ understanding and designers’ intentions. The proposed model does not simulate the cognitive processes of the reader. Instead, it logically analyses the process from the pictogram that a reader reads as input to the phrases representing the pictogram described by the reader as output.First, we conducted an experiment in which subjects looked at pictograms and described what pictograms they saw. We collected pictogram comprehension data through the experiment. Second, on the basis of the collected data, we developed a model that analyzed the process of understanding for the pictograms used in the experiment.The proposed model consists of three procedures:1)Breaking down the pictogram that a reader reads into its elements such as human beings and objects.2)Analyzing the relationship between each element.3)Analyzing the relationship between how a reader actually reads and understands a pictogram and the pictogram's grammatical relationship.We believe that the model is useful for estimating in detail why readers understand pictograms the way they do.
Tatsuya Sakabe, Hiroaki Kosaka
Open Access
Article
Conference Proceedings