Miracle-dz/UniFAR-FTUs huggingface dataset:
TIARA: technology integrated apnea respiration analyser

Obstructive sleep apnea (OSA) is a serious disorder in which people repeatedly stop breathing during sleep. This paper presents the Technology Integrated Apnea Respiration Analyser (TIARA), an innovative wearable device as an alternative to polysomnography (PSG) that has the potential to enable cost-effective and repeated overnight tests of OSA even in a home environment. PSG normally collects bio-signals such as brain waves, oxygen saturation, heart rate in a controlled environment (e.g. sleep lab) to diagnose sleep disorders. Here we present the design and development of the TIARA device and demonstrate its potential to perform as well as PSG at differentiating sleep and wake states and different phases of sleep, based on machine learning models.
eTextiles: reviewing a practice through its tool/kits

Electronic textiles, or eTextiles, and connected research and practice communities grew within and across diverse disciplines over the past 20 years. Initially evolving from academic investigations, eTextiles now play a growing role in both industry and education alike. While we are increasingly confronted with resulting eTextile artefacts, we lack a thorough understanding of the underlying making practices, and in particular what role toolkits play in framing, promoting and supporting creation practices. It is timely then to undertake a review of currently available eTextile tools and kits, discussing the technical, cultural and social expectations inscribed in these settings and how their design and technology impacts the emerging field of eTextiles. Here we compile the first overview of both academic research and popular available toolkits, as a basis for an analysis of potential strategies for future directions: how to diversify and professionalise the field and its community of practice.
"Hear me out": smart speaker based conversational agent to monitor symptoms in mental health

Difference in features of voice such as tone, volume, intonation, and rate of speech have been suggested as sensitive and valid measures of mental illness. Researchers have used analysis of voice recordings during phone calls, response to the IVR systems and smartphone based conversational agents as a marker in continuous monitoring of symptoms and effect of treatment in patients with mental illness. While these methods of recording the patient's voice have been considered efficient, they come with a number of issues in terms of adoption, privacy, security, data storage etc. To address these issues we propose a smart speaker based conversational agent - "Hear me out". In this paper, we describe the proposed system, rationale behind using smart speakers, and the challenges we are facing in the design of the system.
Neural network-based indoor tag-less localization using capacitive sensors

Many applications aim to make smarter the indoor environments where most people spend much of their time (home, office, transportation, public spaces), but they need long-term low-cost human sensing and monitoring capabilities. Small capacitive sensors match well most requirements, like privacy, power, cost, and unobtrusiveness, and, importantly, they do not rely on wearables or specific human interactions. However, long-range capacitive sensors often need advanced data processing to increase their performance. Our ongoing research experimental results show that four 16 cm X 16 cm capacitive sensors deployed in a 3 m X 3 m room can taglessly track the movement of a person with a root mean square error as low as 26 cm. Our system uses a median and low-pass filter for sensor signal conditioning before an autoregressive neural network that we trained to infer the location of the person in the room.
Carrot & Stick: Electrical Muscle Stimulation output generated through incentivized/de-incentivized exergames

Exertion based video games have sought to encourage physical activity. In this paper we explore whether haptic intervention by the exertion game can encourage physical performance. Our system asks users to perform a series of repetitive strenuous tasks comparable to everyday exercise in a gym. Using Electrical Muscle Stimulation (EMS), we introduce two further conditions to the exercise. ‘Carrot’ condition applies EMS to enhance muscle contraction in compliance with the exercise motion, in our case exaggerating bicep contraction during a bicep flex. ‘Stick’ condition applies EMS to counteract muscle contraction in opposition to the exercise motion, in our case activating triceps contraction during a bicep flex. Our aim was to explore whether physical exertion in a virtual environment could be increased through these techniques, either by explicitly supporting or further challenging the movement with EMS. This paper discusses the design, implementation and results of this project.
CellTrans

Understanding citizens' main transportation modes at urban scale is beneficial to a range of applications, such as urban planning, user profiling, transportation management, and precision marketing. Previous methods on mode inference are mostly focused on utilizing GPS data with high spatiotemporal granularity. However, due to high costs of GPS data collection, the previous work typically is in small scales. In contrast, the cellular data logging interactions between cellphone users and cell towers cover much higher population given the ubiquity of cellphones. Nevertheless, utilizing cellular data introduces new challenges given their low spatiotemporal granularity compared to GPS data. In this paper, we design CellTrans, a novel framework to survey users' main transportation modes (public transportation or private car) at urban scale with cellular data. CellTrans extracts various mobility features that are pertinent to users' main transportation modes and presents solutions for different application scenarios including when there are no labeled users in the studied cities. We evaluate CellTrans on two real-world large-scale cellular datasets covering 3 million users, among which 2,589 users are with labels. We assess our method not only quantitatively with labeled users, but also qualitatively with the whole population. The experiments show that CellTrans infers users' main transportation modes with accuracy over 80% (with a performance gain of 20% compared to state-of-the-art), and CellTrans remains effective when applied at urban scale to the whole population.
Embodiment or Manipulation?: Understanding Users' Strategies for Free-Hand Character Control

Controlling a virtual character with your free hands is a useful task for many 3D applications such as games, computer puppetry, or emerging virtual reality applications. So far, only specialist controls have been established in the animation industry. Yet little is known about novices mental models for character control, a key to designing widely usable natural and expressive interfaces. To this end we conducted a gesture elicitation study with twelve participants performing mid-air gestures for thirteen given character motions. The mental models observed fall into two distinct categories: 1) external manipulation of an imagined physical puppet and 2) the gesturing hands embodying the motion of the virtual body part being "controlled". The employed mental model determined hand posture and the mental transformation from gesture to character motion. We present and discuss a gesture set that can inform virtual puppetry interfaces for various application domains.
Prediction of sleep efficiency from big physical exercise data

Physical exercise can improve sleep quality. However, how to perform physical exercise to achieve the best possible improvements is not clear. In this article, we build predictive models based on volume real data collected from wearable devices to predict the sleep efficiency related to users' daily exercise information. As far as we know, this is the first study to investigate insights of prediction of sleep efficiency from volume physical exercise data collected from real world.
Understanding drivers wellbeing: quantitative study analysis and wearable experiment

Driver's wellbeing has a positive impact on driving behavior and experience. Another way, driver's wellbeing depends on their daily lifestyle, demography, traffic and road conditions. Poor conditions in such factors are responsible for low wellbeing. These factors also initiate driving stress. A simple technology approach can play an important role to monitor driver's wellbeing, and help to provide better ways of increasing self-awareness regarding wellbeing. Here, we conducted a quantitative study on 88 drivers, and finally present a low cost wearable approach to support the drivers in the context of Bangladesh for better wellbeing.
Energy efficient Multi-hops Routing protocol based on Clusters Reorganization for wireless sensor networks

A crucial issue to be addressed in large-scale Wireless Sensor Networks composed of thousands of sensors, is the optimal management of the energy consumption in data routing. In this paper an Energy efficient Multi-hops Routing protocol based on Clusters Reorganization (EMRCR) is proposed to overcome the limitations of clustering-based routing protocols such as the random election of cluster-heads (CHs), the unequal distribution of clusters, and the one-hop communication between CHs and the base station. In EMRCR protocol, temporary CHs are first elected and each node is attached to the most appropriate CH. To ensure a good balance among nodes in terms of energy consumption, each formed cluster is then divided into zones to migrate the CH role toward the node having the highest energy and number of neighbors. Besides, the zones of each cluster are divided into sub-zones to reduce the energy consumption of the nodes located far from the CH. Finally, to decrease the energy consumption of the CHs located far from the base station, the collected data are transmitted through a multi-hop inter-clusters routing process. The obtained results show that the proposed protocol outperforms others clustering-based routing protocols in terms of energy consumption and network lifetime.
Semantic human activity annotation tool using skeletonized surveillance videos

Human activity data sets are fundamental for intelligent activity recognition in context-aware computing and intelligent video analysis. Surveillance videos include rich human activity data that are more realistic compared to data collected from a controlled environment. However, there are several challenges in annotating large data sets: 1) inappropriateness for crowd-sourcing because of public privacy, and 2) tediousness to manually select activities of people from busy scenes. We present Skeletonotator, a web-based annotation tool that creates human activity data sets using anonymous skeletonized poses. The tool generates 2D skeletons from surveillance videos using computer vision techniques, and visualizes and plays back the skeletonized poses. Skeletons are tracked between frames, and a unique id is automatically assigned to each skeleton. For the annotation process, users can add annotations by selecting the target skeleton and applying activity labels to a particular time period, while only watching skeletonized poses. The tool outputs human activity data sets which include the type of activity, relevant skeletons, and timestamps. We plan to open source Skeletonotator together with our data sets for future researchers.
Not Just About the User: Acceptance of Speech Interaction in Public Spaces

The use of speech interaction in public spaces doesn't only affect the user experience, but also people being present in the same situation. Whether they like it or not, they are confronted and influenced by the interaction of the user. This study examined the subjective perception of speech interaction in public spaces from two different perspectives: user and bystanders. The scientific assumption is that speech interaction might be misunderstood and unpleasantly experienced by bystanders. Therefore, we further assume that users are aware of this effect and feeling uncomfortable in causing such perceptions to others. The expected consequence for the users is that they are inhibited in their interaction. An online survey has been conducted to examine subjective perceptions of the experience with speech interaction in public spaces from the perspective of bystanders and users. Three factors have been identified, which include 17 aspects that may influence the social acceptance of speech interaction. Furthermore, the location has a significant influence on the willingness to use speech interaction in public spaces.
Point-and-Lift: 3DoF Travel in Virtual Environments

Travel techniques enabling 3 degrees of freedom (DoF) locomotion allow the user to obtain an overview of the virtual environment (VU). As a result spatial information gathering as well as the user's orientation skills in the VU are supported. Existing 3DoF travel techniques are, however, rather inefficient and enhance simulator sickness symptoms which can lead to a reduced sense of presence. We propose a novel method called Point-and-Lift to address these problems. Furthermore, we present a user study concept comparing our method to state-of-the-art travel techniques regarding simulator sickness, presence, orientation skills and performance.
AttriNet: learning mid-level features for human activity recognition with deep belief networks

Human activity recognition (HAR) is essential to many context-aware applications in mobile and ubiquitous computing. A human's physical activity can be decomposed into a sequence of simple actions or body movements, corresponding to what we denote as mid-level features. Such mid-level features ("leg up," 'leg down," "leg still,".), which we contrast to high-level activities ("walking," "sitting,".) and low-level features (raw sensor readings), can be developed manually. While proven to be effective, this manual approach is not scalable and relies heavily on human domain expertise. In this paper, we address this limitation by proposing a machine learning method, AttriNet, based on deep belief networks. Our AttriNet method automatically constructs mid-level features and outperforms baseline approaches. Interestingly, we show in experiments that some of the features learned by AttriNet highly correlate with manually defined features. This result demonstrates the potential of using deep learning techniques for learning mid-level features that are semantically meaningful, as a replacement to handcrafted features. Generally, this empirical finding provides an improved understanding of deep learning methods for HAR.
Inclusive Education Course Design for Informatics Teachers

This paper proposes an application model of the "Flipped classroom" approach in the undergraduate course "Inclusive Education". The course is for students from the Bachelor's programme "Mathematics and Informatics" of the Faculty of Mathematics and Informatics at Sofia University, who are trained for teachers of mathematics, informatics and information technologies following a new national curriculum. The paper reviews the current educational trends leading to the need of "Inclusive Education". It takes into account the constraints of the traditional lecture approach and the duration limits set in the curriculum. The paper proposes a solution to these problems by applying the flipped classroom approach. A specific model that includes active use of e-learning environment is presented as the focus is on the application of inclusive education knowledge in mathematics, informatics and information technologies. The advantages of this model application are also shown.
Forgetfulness as a feature: Imitation of Human Weaknesses for Realizing Privacy Requirements

The increasing use of robots, especially social robots, at home constitute severe risks for privacy. Equipped with a broad variety of sensors, social robots are collecting a large amount of data about their environment, which reveal personal information about the inhabitants, their family and friends, including communication and habits. But on the other hand, this data is important for providing comfort and natural interaction between humans and robots. Previous research has shown that imitating human weaknesses such as forgetfulness can have a positive impact on the human-robot communication. Inspired by these studies, we consider whether imitating human weaknesses in robotics can help to realize privacy requirements. To this aim, concepts of forgetfulness in social robotics are investigated in the context of privacy and security patterns.
Smile to Me: Investigating Emotions and their Representation in Text-based Messaging in the Wild

Emotions are part of human communication shaping mimics and revealing feelings. Therefore, conveying emotions has been integrated in text-based messaging applications using emojis. While visualizing emotions in text messages has been investigated in prior work, we studied the effects of emotion sharing by augmenting the WhatsApp Web user interface. For this, we designed and developed four different visualizations to represent emotions detected through facial expression recognition of chat partners using a web cam. Investigating emotion representation and its effects, we conducted a four weeks longitudinal study with 28 participants being inquired via 48 semi-structured interviews and 64 questionnaires. Our findings revealed that users want to maintain control over their emotions, particularly regarding sharing, and their preference to view positive emotions avoiding unpleasant social situations. Based on that, we phrased four design recommendations stimulating novel approaches for augmenting chats.
A multicenter effort to identify driver mutations and employ targeted therapy in patients with lung adenocarcinomas: The Lung Cancer Mutation Consortium (LCMC).

8019 Background: The detection of driver mutations in the EGFR and ALK genes and targeted therapy has transformed treatment of lung cancer. The LCMC was established in 2009 to assay lung adenocarcinomas for driver genomic alterations in 10 genes and to study and treat patients by their molecular subtypes. Methods: The 14-member LCMC enrolled patients with metastatic adenocarcinoma of the lung and tested their tumors in CLIA laboratories for KRAS, EGFR, HER2, BRAF, PIK3CA, AKT1, MEK1, and NRAS mutations using multiplexed assays, and for ALK rearrangements and MET amplifications using fluorescence in situ hybridization (FISH). Results: 1,102 eligible patients were enrolled; 1,007 underwent testing for at least one genomic alteration with 733 undergoing testing for all 10 genes. 600 patients were women (60%) with a median age of 63; 341 were never smokers (34%) and 589 former smokers (58%). A driver alteration was detected in 622 (62%) of the 1,007 with any genotyping, and in 465 (63%) of the 733 fully genot.
User preferences regarding smart assistant for private- and work-related availability: design science research

Communication technologies (CTs) have made it possible for employees to stay connected with their jobs beyond the traditional boundaries of the workplace and workday. Some researchers implied that CT may become the "electronic leash" [1], since employees may feel trapped or excessively tied to their CT device [2]. In contrast to these findings, we follow the design science research [3] aiming at the development of a smart assistant that will reflect the variety of individuals' availability preferences. To better understand the needs and demands of potential users, we conducted standardized qualitative open-ended interviews with 67 employees from different backgrounds (knowledge workers, social workers, and blue collar workers). The survey revealed many important features concerning this technological solution, like filtering by the priority of the person contacting, time, location and actual life domain, as well as location tracking different availability stages and feedback loops.
Readability and legibility of fonts considering shakiness of head mounted displays

In wearable computing environments, users acquire visual information in various scenes using a head mounted display (HMD). However, this induces problems due to their differences from conventional displays such as smartphone and e-books. In this research, we focused on the problem of vertical shock caused by walking. This problem interferes with seeing information on HMDs. In this paper, we discuss selection of font shapes to minimize the effect of this problem. If we can clarify the characteristics of the readability of fonts in wearable computing environments, the application designers can select fonts that strike a balance between intended design elements and readability. In this paper, we first investigated the characteristics of fonts used in Japan considering the HMD swing that results from walking, from the viewpoints of readability (text readability) and legibility (ease of letter recognition) using six different fonts. From this evaluation, we find that fonts with very thin horizontal lines and with very thin horizontal and vertical lines should not be presented on HMDs.
Towards automating smart homes: contextual and temporal dynamics of activity prediction

The existing smart-home ecosystem has the capability to perceiving the ambient environment by using cutting-edge sensing technologies but is limited to reacting autonomously and timely. Successfully predicting the subsequent human activity can effectively infer human intention and instruct the smart homes to react in a timely, customized and accurate way. However, predicting the next activity and its precise occurrence period are challenging due to the complexity of modelling human behaviour. In this paper, the Long Short-Term Memory (LSTM) network equipped with temporal information is investigated to understand whether integrated temporal information on the model has better prediction performance or not. Our results highlight that, accurately integrating the temporal information into the models bring better prediction accuracy. In terms of modelling and further predicting human activity, comprehending the contextual-temporal dynamics is highly significant.
Unpacking a model: An interactive visualization of a text similarity algorithm for legal documents

This paper presents a functional prototype for an interactive web-based interface i_sift developed to foreground the decision-making process of an algorithm that detects similarities in legal texts through word embeddings. Using this as a case study in computational social science, our goal is, first, to highlight the importance of making computational tools and methods transparent to social scientists. Secondly, we suggest an approach that accomplishes this using methods and principles from interactive machine learning and the algorithmic experience framework.
Attention Guidance in Second Screen Applications

The use of a secondary device alongside a first screen, such as a television, is referred to as second screening and is generally considered as a common and widespread behavior. With the advent of smart first screens (smart TVs, set-top boxes, streaming sticks, etc.), this type of activity gained a new perspective in the form of connected second screen applications such as YouTube, Netflix, or Amazon Prime Video which enable functionalities such as the display of additional information or remote control aspects on the second screen. The design of these distributed applications is difficult because the user's attention cannot be focused on both parts at the same time, which is why this paper presents two eye-tracking studies investigating this problem. The studies examined on the one hand the general viewing behavior with second screen applications and on the other hand the targeted directing of attention to the first and second screens. From these results, recommendations for the design of second screen applications are derived and presented, with the aim that the two application parts complement each other rather than competing for the user's attention.