AHFE Open AccessHuman Factors and Wearable Technologies
Editors: Tareq Ahram, Christianne Falcão
Topics: Wearable Technologies
Publication Date: 2024
ISBN: 978-1-964867-17-5
DOI: 10.54941/ahfe1005048
Articles
Unveiling Decision-Making Dynamics through Wearable Sensors in Business Simulation Games: A survey
Stress is a psychophysiological reaction to events or demands within business simulation games, necessitating the use of sensors for measurement. This article is grounded in a literature analysis of stress measurement in on-field settings, aiming to extrapolate methodologies for application in business simulation games. Specifically, the study derives selection criteria for wearables in business simulation game scenarios from the limitations and challenges associated with reliable stress measurement. The findings contribute valuable insights into the adaptation of stress measurement methods to the unique context of business simulation games, discussing ethical considerations.
Christoph Szedlak, Bert Leyendecker, Ralf Woll
Open Access
Article
Conference Proceedings
Application of wearable technologies for the assessment of an ergonomic intervention in hairdressers: preliminary results
Several authors conducted ergonomic risk assessments through standardized protocols, like REBA, founding high-risk levels of hairdressing jobs. Others measured shoulder and wrist movement with IMU or inclinometer and found a high biomechanical risk. One study used electromyography (sEMG) to investigate flexors and extensors of upper limb to compare the activity of male and female hairdressers founding those women had considerably higher sEMG activity. In our previous study, we investigated the kinematic of the neck, trunk, and upper limb and sEMG bilaterally from Latissimuss Dorsi, Erector Spinae, Trapezius Superior, Deltoideus Anterior, Extensor Carpi Ulnaris, Flexor Carpi Ulnaris in hair drying in two different ways (horizontally – HOR and upwardly - UP). We found a high standard deviation for RoMs, indicating a high heterogeneity in performing the same task. Our sEMG results showed that, in both investigated tasks, the left side of the body was generally more involved than the right one. The right side, the one holding the phone, showed less %MVC mean values than the right side, the one holding the comb. Our sEMG results suggest that handling a 1 kg phone in a static position is less demanding for upper limbs and shoulders than using a light comb in continuous motion. In another paper, we investigated, through REBA and 3DSSPP, the static posture of workers after a corrective action consisting of a hairdryer holder. We found that the holder contributes to changing the posture in either positively and negatively. The positive effects seemed more than the negative ones. In this new paper, we investigated the effect of the hair dryer holder in dynamic situations founding that there are no significative improvements in the biomechanics of the workers. Moreover, the holder seemed to increase several investigated RoMs. The workers also complained of decreased flexibility of the wrist. Our results suggest that the holder system seems to have more negative than positive effects. To reduce the biomechanical overload in hairdryer, we suggest to improve several aspects, the training, the equipment (lighter hairdryer and adjustable seats), and increasing the breaks.
Alessio Silvetti, Ari Fiorelli, Antonella Tatarelli, Lorenzo Fiori, Giorgia Chini, Tiwana Varrecchia, Adriano Papale, Alberto Ranavolo, Francesco Draicchio
Open Access
Article
Conference Proceedings
Developing Smart Shorts for University Footballers for Self-training Purposes
This research evaluates the views of 15 female and 15 male footballers on a Taiwanese University team with regard to smart shorts for self-training use during the Covid-19 pandemic. All 30 athletes reported being able to adapt their play following visual self-training in the form of data received from the smart shorts. The study gathers feedback from the participants via two semi-structured interviews undertaken at the design preparation and garment fitting stages, and employs the Kawakita Jiro method to assess the findings.Firstly, the study investigates the footballer’s preferred smart wear design via interviews. Secondly, the smart shorts are developed. Thirdly, once the 30 participants have been fitted with, and have worn the shorts for self-training purposes, a second interview is conducted to collect the wearers’ feedback.The study identifies six elements of the participants’ smart short preferences: comfort, appearance, textile functionality, practicality, design, and electronic module and app functionality. In the pre-design interview, the majority of the users focus on the functionality of the textiles to be employed. Specifically, 16.7% express views regarding textile thickness. Meanwhile, in the garment evaluation step, all the participants highlight comfort as a requirement. In both interview stages, garment fit is the least important issue, and the functionality of the textile the most important. Additionally, the females are more concerned that the design is fashionable than the males. The participants highlighted the functionality of the electronic module and the app more in the pre-design stage than during the garment evaluation phase.
Ying-Chia Huang
Open Access
Article
Conference Proceedings
Wearable Technology and Machine Learning for Assessing Physical Fatigue in Industry 4.0
Industry 4.0 is a shift towards automation and data integration in manufacturing and process sectors. However, manual material handling and repetitive operations still cause significant physical strain on operators, leading to fatigue and exhaustion. This fatigue not only hampers performance but also compromises production quality and efficiency, potentially leading to human errors and accidents. Prolonged exposure to physical fatigue can lead to conditions like chronic fatigue syndrome (CFS) and work-related musculoskeletal disorders (WMSDs). Given these implications, safeguarding occupational health and safety necessitates a proactive approach to managing operator physical fatigue. This study uses wearable devices and health information to propose a real-time measurement and monitoring solution for operator physical fatigue in operational environments. The Empatica EmbracePlus smartwatch was used to quantify fatigue during simulated industrial tasks. Participants engaged in repetitive tasks, while the device monitored vital indicators like heart rate, electrodermal activity, and skin temperature. Self-reported fatigue levels were assessed using the Borg scale to provide ground truth labels for the collected data. The acquired dataset served as input for machine learning models to classify physical fatigue into discrete levels, ranging from 2 to 5 distinct categories. The results highlight the efficacy of the XGBoost algorithm in accurately classifying physical fatigue, demonstrating a classification accuracy of 94.1% for five levels and 99.4% for three levels and the pulse rate as the more reliable indicator of fatigue levels. Additionally, a Bayesian Neural Network model, while producing similar results to the XGBoost algorithm, offers the added advantage of providing credible intervals for its predictions. This research lays the foundation for future deployments of the developed human performance model in real-world industrial environments.
Carlos Albarrán Morillo, Micaela Demichela, Devesh Jawla, John Kelleher
Open Access
Article
Conference Proceedings
Advancing the Development of Intelligent Wearable Robots for Elderly Assistance: An Innovative User-Centric Co-Creation (UC3) Framework
Sarcopenia is an involuntary loss of muscle mass along with age, bringing pressure and challenges to older adults who demand independence. Wearable robots are promising solutions to address the adversities brought by sarcopenia. Using an innovative User-Centric Co-Creation (UC3) Framework, we conducted an iterative, transdisciplinary, multistage study in Hong Kong, to develop intelligent wearable robots to assist older adults in performing daily living tasks. A multimethod approach combining participatory workshops, quantitative assessments, and laboratory experiments was adopted. A total of 16 participants joined six sessions of participatory workshops to provide user requirements. 91 healthy older adults joined an experiment to provide reference data, and 55 older adults with sarcopenia potential joined an experiment to provide user data. User requirements provide insights for robotic design, reference group data informed the robotic team on system design, and user experiments in turn provided evidence for the robotic group to further improve robotic systems and identify outcome indicators at three levels, including physiological level, functional level, and behavioral level. Following the UC3 Framework, we involved users as equal partners in the development process and collected insightful data to develop wearable robots in iterative cycles.
Vivian Lou, Yuen Man Cheng, Ning Xi
Open Access
Article
Conference Proceedings
Digital Health Applications to Establish a Remote Pre-diagnosis through Intelligent Wearable Devices: Enhancing Healthcare Accessibility in China
The study explores the applications of intelligent wearable devices in digital health, particularly focusing on remote pre-diagnosis in the context of medical tourism. By thoroughly analyzing current practices and applications of wearable devices and identifying existing research gaps, the study aims to provide valuable insights into their potential in the field of medical care. While there is existing literature that covers the technical aspects of wearable devices and their general applications in chronic disease management, there is still a noticeable gap in terms of in-depth analysis of the medical care-seeking process and comprehensive exploration of their potential in remote diagnosis, especially in the pre-diagnosis phase.This research gap highlights the need for further exploration of the role of intelligent wearable devices in facilitating remote diagnosis. Such devices help bridge healthcare professionals and patients. Through in-depth case studies that identify their values and limitations, this study can provide possible applications of intelligent wearable devices for remote pre-diagnosis. The significance of the research lies in its potential to address urgent social issues, such as the strain on healthcare resources due to medical tourism, and to underscore the relevance of technology and design practice in creating a medical care experience that is both effective and meaningful. By exploring the new applications of intelligent wearable devices, this study contributes to optimizing traditional medical treatment processes and enhancing access to care through the integration of technology-driven solutions and future mobility.
Yangyang Pan, Yanchi Liu, Dai Pan, Yuting Chen
Open Access
Article
Conference Proceedings
WeMoveVirtual: Results from a Brief Virtual Movement Intervention for Musculoskeletal Pain and Well-being in Knowledge Workers
In 2022, the on-site multi-component intervention of the project “Neck Exercise for Productivity (NEXpro)” demonstrated effectiveness in reducing pain and enhancing well-being among office workers. However, the shift towards a virtual and remote work setting necessitates the adaption of interventions like NEXpro for independent use, irrespective of time and location. Thus, we developed a virtual version of the NEXpro intervention.Purpose: Our aim was to implement and pilot a virtual version of the NEXpro intervention – specifically, a virtual brief movement intervention designed to reduce musculoskeletal pain and improve well-being.Methods: This observational study was conducted from October to December 2022. We recruited 22 employees from the University of Bern, Switzerland, without severe neck pain. The intervention consisted of a 6-week smartphone application-based movement program with 10 exercises designed to strengthen neck and back muscles. Throughout the intervention period, participants completed daily electronic diary forms. These forms assessed self-reported neck and back pain (each on a Visual Analogue Scale VAS from 0=no pain to 10=maximum pain), muscle and joint flexibility (VAS from 0=bad flexibility to 10=good flexibility), and physical and mental well-being (each on a VAS from 0=bad well-being to 10=good well-being). Additionally, participants documented the number of training sessions (i.e., training adherence). We conducted multilevel regression analyses for all outcomes of interest, including neck pain, back pain, flexibility of muscles and joints, physical well-being, and mental well-being.Results: Data from 22 participants (mean age: 33.36 years, 90.90% female) resulted in 392 daily electronic diary reports. The most frequent reported areas of pain were the neck (90.90%), shoulders (81.80%), upper back (72.70%), and lower back (68.20%). Participants demonstrated an average training adherence of 1.45 training days per week. The correlation between the presence of back and neck pain was high (r=0.69, p<.001). Multilevel regression analyses indicated a positive linear trend, with significant improvements in neck pain (B=-0.02), back pain (B=-0.03), muscle flexibility (B=0.02), physical well-being (B=0.04), and mental well-being (B=0.03, all p-values<.01). The individual number of training sessions during the intervention period showed a significant positive association with back pain (B=0.11, p<.05). Regarding the implementation process, it is noteworthy that the reminder function for training and questionnaires did not function properly.Conclusion: Overall, the implementation of the smartphone application was successful, with minor technical issues. The study demonstrated that the smartphone application can be used as a brief movement intervention to reduce musculoskeletal pain and increase well-being in knowledge workers. Importantly, the intervention effect in reducing neck pain was comparable to the on-site multi-component NEXpro intervention. However, it's important to acknowledge that training adherence was nearly half as much as observed in the NEXpro study. This insight underscores the need for continued development and refinement of the brief virtual movement intervention. The study's findings serve as a foundation for future developments aimed at optimizing training adherence and maximizing the effectiveness of the smartphone application in reducing musculoskeletal pain and enhancing well-being among knowledge workers.
Andrea Martina Aegerter, Corina Schneider, Markus Melloh, Achim Elfering
Open Access
Article
Conference Proceedings
Analysis of Personal Safety Walking Alone at Night and an Innovative Wearable Solution
This study explores the acute problem of personal safety, particularly when walking alone at night, a concern that resonates globally across various demographics. The core of this issue lies in the alarming statistics indicating that in the UK, every second woman and every seventh man do not feel safe in such circumstances, with two out of three women experiencing public sexual harassment annually (Office for National Statistics, 2022). This widespread fear not only impacts mental and physical well-being but also the fundamental freedom of movement, contributing to broader societal and gender inequalities. In this paper, a smart wearable badge is presented to be worn by users walking at night, but more specifically women, children, students, and late-night workers. Unlike conventional safety gadgets, the badge operates on the principles of 'Prevent, Protect, and Provide' with a particular emphasis on prevention.The technology is not merely a reactive tool but a proactive deterrent, visibly indicating protection and thus potentially preventing incidents. Furthermore, its integration with cloud technology for evidence storage and its capacity to trigger an immediate response in crisis situations set it apart from existing solutions.This paper aims to dissect the effectiveness of such a badge in mitigating the fear and reality of walking alone at night. By examining its technological framework, user needs, and real-world applicability, the badge stands as a significant advancement in personal safety technology and clearly shows a positive potential impact on societal norms and individual well-being.
Gaelic Jara-reinhold, Ina Jovicic, Akash Nandi, Evangelos Markopoulos
Open Access
Article
Conference Proceedings
Enhancing Body Ownership of Non-Human Avatars in Virtual Reality through Multimodal Haptic Feedback
With the rise of virtual reality (VR) and an increase in avatar options, there is a need to enhance users’ sense of body ownership when embodying non-human avatars. This research develops an integrated system using multimodal haptic feedback to strengthen feelings of embodiment for avian avatars in VR. A user study evaluates the approach through retractable bands guiding limb movements and inflatable cushions simulating environmental flight conditions. Results demonstrate moderately positive overall usability, with variable individual responses. Spatial haptics augmented realistic wing simulations for over half of the participants. However, limitations exist regarding personalized interactions and simulating comprehensive tactile sensations. This pioneering work contributes an innovative methodology for prototyping and assessing bodily transformations in virtual environments. It advances avatar embodiment knowledge by focusing on replicating the motor experiences of avian flight. The findings underscore the nuanced interplay between multiple sensory stimuli in immersive environments. Further refinements to this system may build empathy and connections with the natural world.
Ziqi Wang, Ze Gao
Open Access
Article
Conference Proceedings
Occupational Exoskeletons as Symbionts: Defining Operator-Exoskeleton Interactions
The fourth industrial revolution heralds the emergence of the Operator 4.0, characterized by the augmentation of physical, sensory or cognitive capabilities of workers. This transformation involves a shift from collaborative activities between artificial and human agents toward a more radical coupling of these two entities (Romero et al., 2017). The novel forms of interactions resulting from these couplings no longer precisely align with taxonomies traditionally proposed in ergonomics for relationship between operators and artificial agents. In response, the concept of symbiosis has been introduced to characterize these new human cyber-physical systems (Gerber et al., 2020; Inga et al., 2023). Among the technologies presumed to enhance operators' physical capabilities, occupational exoskeletons are frequently cited. They contribute to the development of the "super strength-operator" aspect of Operator 4.0 (Ruppert et al., 2018). Defined as "wearables, external mechanical structures that enhance the power of a person" (de Looze et al., 2016, p.671), exoskeletons constitute a promising set of technologies in mitigating biomechanical risk factors associated with musculoskeletal disorders (MSDs) (Theurel & Desbrosses, 2019). Currently, attempts to implement exoskeletons in the workplace are accelerating, raising questions about their acceptance by operators (Elprama et al., 2022). Interactions with these devices, as they induce close and permanent contact with the user, are interesting to address from the perspective of symbiotic relationships. However, the qualification of interactions with such devices is not often addressed in the literature. In the multivariate perspective of the symbiosis concept by Inga et al. (2023), the human’s experience of symbiosis consists of four constructs: embodiment, flow state, sense of agency, and acceptance. Then, acceptance represents only one facet of the experience dimension of symbiotic relationships. To comprehensively grasp the challenges associated with the use of exoskeletons and the potential symbiotic relationship between operators and exoskeletons, it is essential to broaden the comprehension of operator-exoskeleton interactions beyond acceptance. An initial step towards achieving this understanding involves clarifying the terminology used to describe interactions between humans and exoskeletons. By positioning exoskeletons as potential symbionts and drawing insights from the ergonomics literature on symbiosis, this article aims to clarify the nature of operator-exoskeleton interactions.
Marc Dufraisse, Lien Wioland, Jean-Jacques Atain-Kouadio, Julien Cegarra
Open Access
Article
Conference Proceedings
Monitoring Rehabilitation of Stroke Patients Using Automated Fugl-Meyer Assessment
The Fugl-Meyer Assessment (FMA) is a widely used method for evaluating the motor function of stroke patients. During the assessment, patients are instructed to perform a series of predefined motions outlined in the FMA manual, while an evaluator scores each motion using a 3-point Likert scale. A score of 2 is given for flawless execution, a score of 1 for partial completion, and a score of 0 for no execution or lack of motion. Traditional FMA assessments rely on manual visual inspection, but recent studies have explored automating the process using motion capture technology or IMU and EMG sensors. However, motion capture technology is limited in terms of portability due to its complex setup, making IMU and EMG sensors the preferred choice. Previous studies utilizing these sensors have collected data to train machine learning algorithms for predicting FMA scores. Although this approach eliminates manual inspection, it still yields a 3-point score based on the Likert scale, which can be ambiguous and fails to capture subtle improvements or differences in motor function during motion execution. To address this issue, the current study aims to implement a Modified Automated FMA that employs a percentage-based scoring system to overcome the ambiguity of the 3-point Likert scale. Scoring will be based on data collected from IMU and EMG sensors while participants perform various upper limb motions. A maximum threshold will be established as the baseline, representing the normal range of motion for individuals without mobility impairment. The assumption is that stroke patients with mobility impairments will struggle to achieve the normal range of motion indicated in the FMA manual, resulting in sensor data falling below the maximum threshold. The dataset will be obtained by instructing participants to perform a series of upper limb motions. Two scenarios will be simulated to train and test the algorithm. The first scenario involves full execution of the upper limb motions, serving as baseline data for the normal range of motion. The second scenario entails partial execution of the motions, representing data from individuals with mobility impairment. By training the algorithm on this dataset using Support Vector Machine (SVM) and Dynamic Time Warping (DTW), it will be capable of detecting whether a participant's motion falls within the normal range and providing immediate feedback in the form of percentage scores through, indicating the deviation from normal execution. In conclusion, the Modified Automated FMA, utilizing a percentage-based scoring system based on IMU and EMG sensors, offers a promising solution for assessing motor function in stroke patients. The percentage-based scoring system provides a precise assessment of motor function, capturing even subtle improvements which contribute to improved treatment planning and better tracking of rehabilitation outcomes. Additionally, the integration of digital twin technology with wearable devices allows for remote rehabilitation and personalized care. Patients can now engage in rehabilitation exercises from the comfort of their homes while healthcare professionals remotely monitor their progress. This innovative approach enhances the quality of life for stroke patients by providing convenient access to rehabilitation services and personalized feedback.
Lucky John Tutor, Yi Cai
Open Access
Article
Conference Proceedings
Exploring Simultaneous Localization and Mapping(SLAM) Technology for Complex Equipment Maintenance with the Perspective of Human-Machine Collaboration
The rapid development of Augmented Reality (AR) and Mixed Reality (MR) technologies across various industries has intensified the need for advanced maintenance solutions in the context of the digital transformation of Industry 4.0. Taking the forming machine equipment in Taiwan's metal centre as an example. The declining birth rates and labor shortages makes it challenging for experts to be present on-site for timely repairs, leading to prolonged downtime and additional costs due to production line halts. This study aims to propose an MR-based maintenance guide, enabling on-site technicians to conduct repairs using digital technology even in the absence of equipment experts. By using head-mounted MR devices, technicians can instantly see system alerts and access 3D visualized step-by-step guidance, significantly improving repair speed and accuracy. This approach also reduces the need for expert travel, thus saving time and resources. More importantly, integrating Simultaneous Localization and Mapping (SLAM) technology ensures precise alignment between virtual and actual machine imagery, enhancing the accuracy of maintenance instructions and focusing on a human-centered experience. This technological innovation is not only applicable to forming machine equipment but can also be extended to other complex equipment maintenance fields requiring specialized knowledge. With the maturation of MR technology, this maintenance strategy is expected to become more prevalent in the future, promoting Industry 4.0's digital transformation in manufacturing.To achieve these goals, a systematic methodology was developed. Initially, the scope was defined, and the overall research process was planned during the preparation phase. The literature review phase involved an in-depth investigation into the development of head-mounted MR devices in various settings and the exploration of their application possibilities. Literature about remote collaboration was also reviewed to understand the technical requirements and limitations of this mode. Therefore, SLAM testing with Microsoft HoloLens2 in an indoor factory setting was conducted to evaluate its effectiveness in aligning virtual and physical machinery models. Finally, system development and testing were undertaken using Unity3D and Microsoft's Mixed Reality Toolkit (MRTK) to implement the MR device functionalities on-site. Overall, this methodology aims to comprehensively consider the application of digital technology in industrial maintenance to enhance efficiency and accuracy.This research has yielded several significant findings, including valuable perspectives on SLAM technology. It also sheds light on the performance capabilities of HoloLens 2 in industrial environments, particularly regarding system operation processes.
Hsiao-fan Lin, Yong-siang Su, Chao-tse Cheng, Chao-hung Wang, Kuo-Wei Su
Open Access
Article
Conference Proceedings
An Intelligent Monitoring Method of Pilot's Operating State Based on PCA and WOA-KELM
In this paper, a pilot’s operating state monitoring method based on Whale Optimization Algorithm (WOA) and Kernel-based Extreme Learning Machine (KELM), is introduced to improve the monitoring accuracy of pilot’s operating state.In the first place, collect the peripheral physiological signal data via portable and wearable devices to construct a feature set containing 89 features. Secondly, the Principal Component Analysis (PCA) is employed to reduce the feature dimensionality, and iterative optimization is performed to the key parameters in KELM with Whale Optimization Algorithm. Finally, establish the WOA-KELM recognition model based on these optimized parameters to monitor the pilot’s operating state. The method also overcomes the challenges of poor robustness of single physiological signal, insufficient reliability of the selected features according to previous experience, as well as low recognition accuracy of the classification models. By comparison with the performance verification data of the typical recognition model, the proposed method presents a higher recognition accuracy in monitoring pilot’s operating state.The study firstly creates corresponding operating states and collects physiological data by carrying out flight mission experiment. Flight mission simulation platform is used to accomplish the flight test. This platform is composed of curved screen display, touch screen display, control components, simulator host and other hardware components. Depending on Falcon BMS flight simulation software, various actual combating missions by fighters can be simulated with high fidelity. In this study, the multi-modal human factor perception terminal PTES100 from PsychTech for the gathering of GSR and PPG signals is selected. The sampling frequency of the GSR sensor is 4 Hz, and the sampling frequency of the PPG sensor is 100 Hz. The bracelet is worn on the left wrist of the subject, and the data is transmitted to the computing terminal for processing through blue-tooth.14 subjects were recruited for this study, all of whom were practitioners with aviation knowledge background. After operation trainings, they were relatively familiar with the flight driving operation and had certain basis of using Falcon BMS flight simulation software.The study set the number of features after PCA dimensionality reduction to 10, reducing 89 features to 10 dimensions while preserving the original feature information as much as possible. Secondly, the data set was divided at a ratio of training set accounting for 80 percent, verification set for 10 percent and testing set for 10 percent, namely, 650 pieces of training data, 81 pieces of testing data, and 81 pieces of verification data. WOA algorithm was used to optimize the regularization parameter C and kernel parameter γ of KELM. The population quantity of the whale swarm was set to 100 and the number of iterations was set to 30 to find out the optimal model with the prediction accuracy of the verification set as the fitness function.In terms of model performance verification results, the prediction accuracy of this model in the test set is 96.3%, indicating that it has high recognition performance.
Wanchen Jia, Xu Wu, Xiang Xu, Lin Ding, Chongchong Miao
Open Access
Article
Conference Proceedings
Evaluation of Stress Intervention Performance and Usability of Smartwatch Guided Breathing Practice
Breathing practice is an effective method for alleviating mental stress. With the advancements in science and technology and the increasing demand for self-stress management, many mobile device based breathing practice applications have been developed. Based on the advantage of enabling multimodal breathing guidance, most commercial smartwatches are configured with breathing practice. However, current studies lack evaluation of stress intervention performance and usability for smartwatch guided breathing practice. In this study, we aim to explore which breathing guidance patterns and guided breathing frequencies for smartwatch-based breathing practice can effectively intervene with stress while ensuring a positive usability, and to make design recommendations for smartwatch-based breathing practice.Methods:Based on the investigation of the commonalities and characteristics of the breathing practice function settings of commercial smartwatches, we categorized the settings into one visual guidance pattern, two haptic guidance patterns, and three commonly used breath-guided frequencies. According to these guidance patterns, visual animation and haptic guidance prototypes were developed based on After Effects and Arduino. We used the Wizard of Oz method to implement visual and haptic guidance based on Apple Watch and Arduino, which can run independently or in concert.In the specific experimental phase, each participants completed a total of eight sets of experiments, including five guidance patterns and three breathing guidance frequencies. In each set of experiments, participants were required to complete a 1-minute mental arithmetic task as a stress input, a 1-minute breathing practice as a stress intervention, and to complete the Spielberger State-Trait Anxiety Inventory (STAI) and USE questionnaire to report subjective stress levels before and after the stress intervention and the subjective experience of using the stress intervention. Throughout the experiment, participants' heart rate variability (HRV) and respiratory rate (RR) were recorded using BIOPAC.Results: There was a significant difference between the stress state after the calculation task and the breathing practice. Regarding the breathing guidance patterns, the two haptic-only breathing guidance showed significantly lower usability, suggesting that haptics are more suitable for a supporting role in synergizing other patterns to guide the breathing practice. The breathing guidance that operates synergistically by visual and breath-like haptic guidance received the highest overall rating, possibly because this haptic feedback reduces cognitive workload by creating a natural mapping to the target action. In addition, acoustic feedback is considered as a developable smartwatch-based breathing guidance pattern. In terms of breathing guidance frequencies, the three commonly used deep breathing frequencies did not differ significantly in stress intervention performance and usability assessments, and individuals had different preferences for deep breathing frequencies. Therefore, personalizable and dynamic guidance frequencies might be a better solution.Conclusions:This paper analyzes the stress intervention performance and usability of smartwatch-guided breathing practice based on self-reported and objective physiological data, and proposes corresponding design recommendations for the breath-guided pattern and the guided breathing frequency. The findings of this paper could provide a reference for the design of the breathing practice function of smartwatches.
Shuyi Liu, Rong Rong, Hanling Zhang
Open Access
Article
Conference Proceedings
Insights Through Gaze: Unraveling Visual Patterns in Domain-Specific Learning
Human mind weaves visual experiences and cognitive processes together in its learning journey to build specific knowledge domains. In this study, we explore how prior knowledge influences what we see and visualize, suggesting that our experience profoundly shapes our perceptions and eye movements. Further, we aim to discern the level of domain knowledge expertise present in participants from diverse engineering fields, utilizing eye-tracking technology. Studies in the past, highlight that eye markers such as: fixation count, total scanning duration, and saccadic duration are essential in understanding the presence of domain-specific knowledge of an individual, further giving us valuable insights into the cognitive processing underlying the information processing and comprehension during the visual task. 102 graduate students (59 male and 43 female) with age ranging between 21 to 34 years (mean age of 27.23 and a standard deviation of 2.98) from different domain knowledge backgrounds were considered in this study. In specific, 33 students from the architecture domain, 36 from the mechanical domain, and 33 from diverse domains of humanities, computer sciences, and biosciences participated in this experiment. Participants with a weighted gaze of 80% and/or above were further considered to continue with the experiments. Participants were initially presented with the Raven’s Advanced Progressive Matrix (RAPM) task set to ensure that there was homogeneity in intellectual ability within the representative sample, aiming to mitigate the influence of intelligence on problem-solving task performance. Thereafter, architecture and mechanical domain-specific tasks were presented in order of increasing task complexity to all the participants. Participants’ eye movements were analyzed to identify distinct eye patterns associated with varying expertise levels and prior domain knowledge. Our findings reveal that there are significant differences in the eye movements across participants from various domains, suggesting that different inherent visual strategies were adopted to meet the demands of the tasks under consideration. Participants with prior domain knowledge (experts in the task) exhibited more efficient information processing with fewer fixations and shorter scanning durations than novice performing the same task. Several key eye markers based on dwells, fixations, saccades, and pupils were investigated to understand the relationship between visual perception, prior domain knowledge acquisition, and learning. Significant eye markers are instrumental in discerning individuals with varying domain-specific knowledge. Notably, metrics such as Total Time Duration, Total Dwell Time, Number of Fixations, and Average Fixation Duration exhibit significance in distinguishing individuals across different domains, each manifesting at distinct time intervals. This research contributes to understanding how visual strategies evolve across diverse knowledge domains in response to varying task complexities. In addition, this research gives a systematic analysis of the visual scanning process during problem-solving by individuals in different domain specializations. The use of machine learning models such as decision trees, random forests, and support vector machines to classify novice participants from experts based on eye markers is reported. Our experiments show as high as 70% accuracy in classifying participants with domain knowledge against those who do not have domain knowledge in a domain specific task. Through this research, educators and technologists can design more effective learning environments and personalized training programs with an emphasis on the nuanced interplay between visual perception and cognitive processes. This study contributes to the ongoing dialogue surrounding integrating visual elements in educational practices and underscores the transformative potential of eye-tracking technology in enhancing learning outcomes across diverse domains.
Sonali Aatrai, Sandhya Gayatri Prabhala, Saurabh Sharma, Rajlakshmi Guha
Open Access
Article
Conference Proceedings