Field Of Human-computer Interaction Research Articles

Navigating gender dynamics: a male researcher's experiences on conducting feminist HCI research

In this perspective article, I invite readers to accompany me on a personal journey of self-discovery and transformation as a male researcher within the field of feminist Human-Computer Interaction (HCI). I will delve into the specific topic of gender-opposite research in socio-technical environments and reflect on my experiences, especially the associated challenges and opportunities. My journey exposed me to critical literature and engaged me in long-term fieldwork simultaneously, expanding my understanding of the dynamics that shape our society. My perspectives, sensitization, and awareness of gender-related issues evolved significantly over the course of more than 3 years of being dedicated to a feminist research project. By sharing this narrative, I hope to promote critical discussions about the significance of both, personal growth, and transformation as well as the need for reflexivity in the pursuit of feminist research (with a focus on the specific context of gender-opposite research in socio-technical environments). I will dive into the complexities that I encountered in the settings and compromises I felt obliged to make which were influenced by the embodied nature of my research. As I will furthermore show, there had been an impact on my research practices in terms of planning, observation, analysis, and writing.

Reimagining the cycle: interaction in self-tracking period apps and menstrual empowerment

FemTech, technology often in the forms of apps developed to specifically target female health issues, have billions of users globally. Yet, despite the popularity of e.g., period trackers or pregnancy apps, we know little about the potential impact of these technologies, often developed outside controlled and regulated healthcare. How interactive technology is designed, and in our case the cycle represented through the design, has the potential to shape women's understanding of menstruation. In this study we analyse the interaction design of nine of the most downloaded menstruation apps, asking how commercial menstruation apps represent the cycle through their interaction design. While previous research has criticized these types of apps for lacking privacy and for enforcing normative ideals on women, they are often marketed in terms of female empowerment and users do indeed seem to find them very useful for period and fertility tracking. However, the term 'empowerment' is today used broadly and is known for having many potential meanings. Even within the field of Human Computer Interaction (HCI), 'empowerment' is used frequently but rarely explicitly defined. The question then becomes what empowerment could mean for menstruation tracking. In order to begin exploring the way menstruation and the period is represented in current apps and the way that future apps could design for empowerment, we engage in a comparative design investigation using what we call critical app-walkthrough methodology where an app's design is explored comprehensibly at one point in time and apps interacted with over a longer time period through researcher use-diares. Our results center around three ways in which these apps represent the cycle to users through design. We analyse; (1) interface metaphors used to represent the temporality of bleeding, (2) datafication of menstruation through input and output for intimate data tracking, and (3) the ways fertility predictions convey certainty over uncertainty. In a second step, we then explore what empowerment could mean for period trackers and how design could support empowering experiences. Finally, we present four design sensitivities meant to inspire designers to design for other types of period tracking experiences that might better empower bleeders. These are: support lived temporalities, embrace uncertainty, empower the self, and design less.

To think of interdisciplinarity as intercurrence: Or, working as an interdisciplinary team to develop an ML tool to tackle online gender-based violence and hate speech

The paper reflects on the working of an interdisciplinary team consisting of researchers and activists from the field of computer science and social sciences involved in developing a user-facing, browser plug-in to detect and moderate instances of online gender-based violence, hate speech and harassment in Hindi, Indian English, and Tamil. There have been multiple calls within the field of Human-Computer Interaction (HCI) to include qualitative methods in one’s research design. These calls, while attuned to the importance of qualitative methods for HCI, ignore the intercurrent nature of different research methods, disciplines and practices. The paper borrows the concept of intercurrence from Orren & Skowronek (1996) and reorients it to explicate the practice of interdisciplinary research. It argues that intercurrence i.e. (in between, an occurrence within an occurrence) is a useful image to perceive interdisciplinarity wherein we argue that at any given point, an interdisciplinary team navigates multiple, yet simultaneously occurring temporal dimensions of differently disciplined bodies. An awareness of these multiple temporalities adds another dimension to thinking about conflicts and possibilities emerging from interdisciplinary practices and reorients interdisciplinary research towards unexpected outcomes.

Investigating user performance and preference for two magnitude levels of size and angle of bend on a smartphone-sized flexible device

ABSTRACT In the field of human-computer interaction, deformable user interfaces are gaining more attention. Gestures performed by bending the deformable device are commonly investigated in the literature. These bend gestures' inherent tactile and kinaesthetic feedback and their association with spatial directions benefit both blind and low vision (BLV) and sighted users. Bend gestures are naturally associated with two descriptors of bend, which are location and direction. One potential opportunity to extend this gesture space is to investigate additional descriptors of bend gestures without introducing new locations. In this context, size and angle are the two common additional descriptors of bend gestures. In this work, we conducted two (preliminary and main) studies to investigate BLV and sighted users' performance and preference for multiple magnitude levels of size and angle of bend on a smartphone-sized deformable prototype. Finally, we discuss the results and propose user group-specific design recommendations.

Ti3C2Tx MXene Paper-Based Wearable and Degradable Pressure Sensor for Human Motion Detection and Encrypted Information Transmission.

Paper-based flexible sensors are of great significance for promoting the development of green wearable electronic devices due to their good degradability and low cost. In this work, a paper-based wearable pressure sensor with a sandwich structure is proposed, which is assembled from a sensing layer printed with Ti3C2Tx MXene ink, an interdigitated electrode printed in the same simple and economical way, and two polyethylene terephthalate films. The demonstrated paper-based pressure sensor exhibits excellent sensitivity in a wide pressure sensing range, as well as cyclic stability at a certain pressure. The sensor can be attached to the human body's surface to monitor various pressure-related physical activities. Using a self-designed mobile phone APP, the special pressure signals collected from the sensor can be transmitted and translated, and an intelligent and encrypted information transmission system can be established. Since only ordinary printing paper and Ti3C2Tx MXene ink are used, the pressure sensor is easy to prepare, economical, and environmentally friendly, and it can be degraded by stirring in water without generating electronic waste. It can be foreseen that the proposed sensor shows bright application potential in the sustainable development of healthcare and human-computer interaction fields.

Glancing back at your hearing: Generating emotional calligraphy typography from musical rhythm

Typography design influences users’ reading experiences and emotional responses. Within the realm of Human–Computer Interaction (HCI), numerous researchers have focused on typography design by exploring the generation of parameterized typefaces, encompassing aspects such as character size, line spacing, and word spacing. However, scant attention has been given to non-parameterized typefaces, particularly calligraphy characters. Calligraphy, a profoundly expressive and quintessential art form in China, serves as a reflection of human emotions. Its association with music has garnered considerable attention within the field of HCI. In this paper, we introduce Inkphony, a system that leverages musical rhythm to generate calligraphy typography and its composition based on the conveyed emotion. Through empirical evaluations, we demonstrate the efficacy of our approach in accurately expressing varying levels of emotion, while also establishing a perceptible connection between typography and music. Our work aims to provide an intelligent cross-modal tool for music visualization and artistic creation, thereby contributing to the field of HCI.

Design of a miniaturized wearable EIT system for imaging and hand gesture recognition

Abstract Hand gesture recognition using data from electrical impedance tomography (EIT) systems offers many promising applications, for example in the field of human-computer interaction. Due to its real-time capability and the use of harmless currents for humans, it can be used in medicine, robotics, or virtual environments. As already shown in similar works, for example by Zhang et al. finding a good compromise between accuracy, precision, framerate and the size of the system is a challenge [1], [2]. This work presents a truly wearable compact EIT system on a single 24.9 mm × 22.5 mm circuit board consisting of standard components without application-specific integrated circuit. A neural network (NN) classifies two gestures. It is able to distinguish two different gestures with an accuracy of 78,33 % and a precision of 76,56 %. This work has a strong focus on the size of the system and provides a starting point for further research in compact wearable gesture recognition systems. It shows the challenge of the compromise between size and quality of the signals.

Using ChatGPT for human-computer interaction research: a primer.

ChatGPT could serve as a tool for text analysis within the field of Human-Computer Interaction, though its validity requires investigation. This study applied ChatGPT to: (1) textbox questionnaire responses on nine augmented-reality interfaces, (2) interview data from participants who experienced these interfaces in a virtual simulator, and (3) transcribed think-aloud data of participants who viewed a real painting and its replica. Using a hierarchical approach, ChatGPT produced scores or summaries of text batches, which were then aggregated. Results showed that (1) ChatGPT generated sentiment scores of the interfaces that correlated extremely strongly (r > 0.99) with human rating scale outcomes and with a rule-based sentiment analysis method (criterion validity). Additionally, (2) by inputting automatically transcribed interviews to ChatGPT, it provided meaningful meta-summaries of the qualities of the interfaces (face validity). One meta-summary analysed in depth was found to have substantial but imperfect overlap with a content analysis conducted by an independent researcher (criterion validity). Finally, (3) ChatGPT's summary of the think-aloud data highlighted subtle differences between the real painting and the replica (face validity), a distinction corresponding with a keyword analysis (criterion validity). In conclusion, our research indicates that, with appropriate precautions, ChatGPT can be used as a valid tool for analysing text data.

MIUIC: A Human-Computer Collaborative Multimodal Intention-Understanding Algorithm Incorporating Comfort Analysis

The naturalness and safety of human-computer interaction have always been primary research focuses in the field of human-computer interaction. This paper proposes a multimodal intention understanding algorithm (MIUIC), which incorporates comfort analysis, as a solution to address the issues of low intention understanding rate, weak interaction, and weak collaboration that are often observed in most massage systems. The algorithm efficiently fuses multimodal data based on objective implicit information to address the challenge of low intention understanding rates caused by non-standard expression of natural behavior. Moreover, this algorithm incorporates comfort reasoning to detect and address intentions related to security threats while providing the ability for robots to make behavioral decisions through inverse active interaction, leading to more equitable human-robot interactions. To test the validity and safety of the MIUIC algorithm, we embedded the algorithm into a mechanical arm massage system. Subsequently, 45 elderly volunteers were invited to participate in experimental tests. Finally, to verify the validity and safety of the MIUIC algorithm, we assessed the algorithm in terms of four aspects, including multimodal intention recognition rate, the ability to reduce data dispersion, the intention enhancement rate under reverse human-machine interaction, and the rate of avoiding dangerous intentions. In conclusion, the MIUIC algorithm enhances the intention understanding rate and promotes.

Dual-Layer All-Textile Flexible Pressure Sensor Coupled by Silver Nanowires with Ti3C2-Mxene for Monitoring Athletic Motion during Sports and Transmitting Information.

At present, wearable flexible pressure sensors have broad application prospects in fields such as motion monitoring and information transmission. However, it is still a challenge to design flexible pressure sensors with high sensitivity over a large sensing range and simple fabrication. Here, we use a simple "dipping-drying" method to fabricate a fabric-based flexible pressure sensor by coupling silver nanowires (AgNWs) with Ti3C2-MXene. The interaction between MXene and AgNWs helps realize a dual-layer sensing network, achieving good synergistic effects between pressure sensitivity and sensing range. The effects of the material combination and dip-coating sequence on the sensor's performance are systematically studied. The results show that the sensor was impregnated sequentially with AgNWs solution, and the MXene solution has the highest sensitivity (0.168 kPa-1) over a wide range (190 kPa). Meanwhile, it has the advantages of low response hysteresis and detection limit, as well as good linearity and durability. We further demonstrate the application of this sensor in human physiological signal monitoring and motion pattern recognition. It can also encrypt and transmit information according to different pressing states. In addition, the proposed pressure sensor array exhibits spatial resolution detection capabilities, laying the foundation for applications in the fields of motion monitoring and human-computer interaction.

An Epileptic Seizure Detection Technique Using EEG Signals with Mobile Application Development

Epileptic seizure detection classification distinguishes between epileptic and non-epileptic signals and is an important step that can aid doctors in diagnosing and treating epileptic seizures. In this paper, we studied the existing epileptic seizure detection methods in terms of challenges and processes developed based on electroencephalograph (EEG) signals. To identify the research deficiencies and provide a feasible solution, we surveyed the existing techniques at each phase, including signal acquisition, pre-processing, feature extraction, and classification. Most previous and current research efforts have used traditional features and decomposing techniques. Therefore, in this paper, we introduced an enhanced and efficient epileptic seizure technique using EEG signals, for which we also developed a mobile application for monitoring the classification of EEG signals. The application triggers notifications to all associated users and sends a visual notification should an EEG signal be classified as epileptic. In this research, we have used publicly available EEG data from the University of Bonn. Our proposed method achieved an average accuracy of 98% by utilizing different machine-learning algorithms for classification, and it has outperformed recently published studies. Though there have been other mobile applications for epileptic seizure detection, they have been based on motion and falling detection, as opposed to ours, which was developed based on EEG classification. Our proposed method will have an impact in the medical field, particularly for epilepsy seizure monitoring as well as in the Human–Computer Interaction fields, majorly in the Brain–Computer Interaction (BCI) applications.

Deep Neural Network-based Mixed Speech Recognition Technology for Chinese and English

In the field of human-computer interaction, the current more advanced speech recognition systems are all single speech recognition, and it is urgent to adopt new in-depth learning technology to improve the existing speech recognition system. In this context, this research is based on DNN and investigates mixed speech recognition techniques for both Chinese and English. A single speech recognition algorithm based on DNN is first investigated, and then a new hybrid Chinese and English speech recognition model is constructed by fusing the attention mechanism and CTC loss function. In the construction of the hybrid speech recognition model, the end-to-end model and Transformer framework are used to combine the monotonic alignment property of the CTC loss function, which allows complex sound units to be transformed into characters for easy extraction and recognition. The performance of the constructed models was tested on Chinese speech dataset, English speech dataset and mixed Chinese and English speech dataset to determine the recognition accuracy and speed of the models. The results show that the proposed recognition model achieves 81.2% recognition accuracy and 100 recognition speed/minute on the Chinese-English mixed speech dataset, which is much better than the other three models. This study successfully addresses the need for improved speech recognition systems by introducing a novel hybrid model for mixed Chinese-English speech recognition. The experimental results confirm the superiority of the proposed model, achieving high accuracy and rapid recognition speed. The developed model holds promising potential for enhancing human-computer interaction and enabling efficient communication between Chinese and English speakers.

Machine Learning-Based Human Posture Identification from Point Cloud Data Acquisitioned by FMCW Millimetre-Wave Radar.

Human posture recognition technology is widely used in the fields of healthcare, human-computer interaction, and sports. The use of a Frequency-Modulated Continuous Wave (FMCW) millimetre-wave (MMW) radar sensor in measuring human posture characteristics data is of great significance because of its robust and strong recognition capabilities. This paper demonstrates how human posture characteristics data are measured, classified, and identified using FMCW techniques. First of all, the characteristics data of human posture is measured with the MMW radar sensors. Secondly, the point cloud data for human posture is generated, considering both the dynamic and static features of the reflected signal from the human body, which not only greatly reduces the environmental noise but also strengthens the reflection of the detected target. Lastly, six different machine learning models are applied for posture classification based on the generated point cloud data. To comparatively evaluate the proper model for point cloud data classification procedure-in addition to using the traditional index-the Kappa index was introduced to eliminate the effect due to the uncontrollable imbalance of the sampling data. These results support our conclusion that among the six machine learning algorithms implemented in this paper, the multi-layer perceptron (MLP) method is regarded as the most promising classifier.

MmSignature: Semi-supervised human identification system based on millimeter wave radar

Human identification is vital in health monitoring, human-computer interaction, safety detection, and other fields. Compared with traditional vision-based methods, millimeter wave radar sensors can protect users' privacy and work in dark environments, which has a wide range of application prospects in iot fields such as smart homes and smart medical care. Previous studies need to manually collect labeled data, which makes the data collection work need substantial human resources and is unsuitable for popularization and application. We automatically collect multi-modal radar signals in users ' daily lives without requiring researchers to label data manually. Based on the proposed data collection method, we established the first semi-supervised data set for human identification, which includes synchronous radar point cloud data and range-velocity map data. The dataset contains four experiments, including ten monitoring users and ten other users. We propose a semi-supervised co-training framework based on multi-modal data fusion for human identification. The framework guides the models to learn from unlabeled data using the complementary characteristics of point cloud data and range-velocity map data. In addition, we propose an information fusion method to fuse the radar data of two modes to further improve the model's performance. The experimental results show that the proposed method achieves 93.7% human identification accuracy, showing radar-based human identification technology's application and promotion potential.

Analysis of Convolutional Neural Networks for Facial Expression Recognition on GPU, TPU and CPU

In light of the increasing computational capacity provided by Central Processing Units (CPUs), Graphics Processing Units (GPUs), and Tensor Processing Units (TPUs), all of these were designed to speed up deep learning workloads, and the fact that this iteration of human-computer interaction is becoming more natural and social, it is clear that the field of human-computer interaction is poised for significant growth. The scientific community has found emotion recognition to be of tremendous interest and significance. Despite these advances, it is still desired that research into computational methods for identifying and recognizing emotions at the same ease as humans. This study uses Convolutional Neural Networks (CNN) for human emotion identification from facial expressions to delve deeper into this topic. The results demonstrated that training an Artificial Neural Networks (ANN) on GPUs might cut computational time by as much as 90% while accuracy could be raised up to 65%.

Sturdies Advanced in chatbots based on Deep learning

Chat robots have always been a hot research topic in the field of natural language processing and human-computer interaction, aiming to build models to understand human input and provide logical answers. Early chat robots were mostly based on matching or rules, but due to the lack of manual feature representation capabilities and the complexity of rules, they could not meet practical application requirements. Thanks to the rapid development of deep learning, chat robots based on convolutional neural networks have made breakthroughs in accuracy and speed in recent years. According to the different design ideas of chat robots, the existing work mainly includes two categories: retrieval type chat robots and generation type robots. Focusing on the two main frameworks mentioned above, this article introduces the latest research progress of chat robots in detail, including the basic steps, design ideas, advantages and disadvantages of representative algorithms. We also introduced common chat robot modeling data sets and evaluation indicators. Finally, we discussed the existing problems and future development directions in the field of chat robots.

Estimation on Human Motion Posture using Improved Deep Reinforcement Learning

<p>Estimating human motion posture can provide important data for intelligent monitoring systems, human-computer interaction, motion capture, and other fields. However, the traditional human motion posture estimation algorithm is difficult to achieve the goal of fast estimation of human motion posture. To address the problems of traditional algorithms, in the paper, we propose an estimation algorithm for human motion posture using improved deep reinforcement learning. First, the double deep Q network is constructed to improve the deep reinforcement learning algorithm. The improved deep reinforcement learning algorithm is used to locate the human motion posture coordinates and improve the effectiveness of bone point calibration. Second, the human motion posture analysis generative adversarial networks are constructed to realize the automatic recognition and analysis of human motion posture. Finally, using the preset human motion posture label, combined with the undirected graph model of the human, the human motion posture estimation is completed, and the precise estimation algorithm of the human motion posture is realized. Experiments are performed based on MPII Human Pose data set and HiEve data set. The results show that the proposed algorithm has higher positioning accuracy of joint nodes. The recognition effect of bone joint points is better, and the average is about 1.45%. The average posture accuracy is up to 98.2%, and the average joint point similarity is high. Therefore, it is proved that the proposed method has high application value in human-computer interaction, human motion capture and other fields.</p> <p> </p>

Gesture Recognition Based on EEMD and Cosine Laplacian Eigenmap

Gesture recognition has become a research hotspot in the fields of human-computer interaction, sign language recognition, rehabilitation training, sports medicine, etc. The surface electromyography (sEMG) signal contains abundant motion intention features which can be used for gesture recognition. Existing feature engineering methods are commonly used to extract linear features while ignoring the nonlinearity and nonstationarity of sEMG and its potential spatial representation. To solve this problem, we propose a gesture recognition method based on manifold learning, which extracts the features through time-frequency domain and dimension reduction. Firstly, the Ensemble Empirical Mode Decomposition (EEMD) is used to remove low-frequency noise in sEMG, followed by extraction of a total of four time-frequency features. Secondly, we adopt a cosine Laplacian Eigenmap (cos-LE) to extract the inherent manifold features of feature vector, which uses cosine similarity instead of Euclidean distance to evaluate the similarity between samples. Finally, SVM is used for classification. We conduct the experiments on the NinaPro DB5 to validate the effectiveness of the proposed method. The experimental results demonstrate that the cos-LE has a better clustering effect than other dimensionality reduction methods. In the subsets of NinaPro DB5 (Exercise A, Exercise B, Exercise B), the proposed method achieves 99.15%, 96.47%, and 94.26%, respectively, and the accuracies of 40 and 52 gestures are 92.18% and 90.82%. The successful application of cos-LE in gesture recognition based on sEMG proves the potential of manifold learning in biological signal processing.

The effect of prototypicality on webpage aesthetics, usability, and trustworthiness

The user expects webpages of specific categories to have a look-and-feel specific to that category. For example, unlike university homepages, online-shop webpages typically feature relatively little text, a long grid-like structure listing products, and numerous functional elements for product search, filtering, and recommendation. Ensuring that a webpage meets user expectations makes it highly prototypical and improves the user impression of the webpage. Despite the potential impact on users, the concept of webpage prototypicality has not been fully explored or extensively employed in Human-Computer Interaction (HCI). This paper addresses this gap by conducting a user study with 1530 participants to investigate webpage prototypicality. The study revealed a strong correlation between prototypicality and webpage visual aesthetics, perceived pre-use usability, and trustworthiness. Notably, the direct effect of prototypicality on trustworthiness outweighed the indirect effects through aesthetics and usability. Overall, prototypicality, aesthetics, and usability collectively accounted for 29% to 68% of the variance in trustworthiness, depending on a webpage category. These findings underscore the importance of embracing prototypicality within the field of HCI, encouraging its wider adoption.

AutoML-Emo: Automatic Knowledge Selection Using Congruent Effect for Emotion Identification in Conversations

Emotion recognition in conversations (ERC) has wide applications in medical care, human-computer interaction, and other fields. Unlike the general task of emotion analysis, humans usually rely on context and commonsense knowledge to convey emotions in conversations. Only when the model can connect and fully utilize a large-scale commonsense knowledge base, it can better understand latent contents in conversations. Unfortunately, there is no available knowledge selection mechanism to address such knowledge needs and to make sure the system is not flooded with irrelevant commonsense knowledge. Therefore, we propose an AutoML strategy based on emotion congruent effect to select suitable knowledge and models, called AutoML-Emo. Global exploration and local exploitation-based selection mechanisms (G&LESM) are used for automatic knowledge selection. The transformer-based architecture search (TAS) is applied to model selection, the selected transformer-based model is employed to incorporate knowledge and capture context information in conversations. The experimental results show that AutoML-Emo can effectively enhance external knowledge in different sizes and domain datasets. Moreover, the selected transformer-based model derived from TAS is superior to the most advanced models.