Unobtrusive Approach Research Articles

Atmospheric CO2 monitoring to identify zones of increased airborne pathogen transmission risk in hospital settings

Measures to reduce airborne pathogen transmission in health care settings, such as increased air exchange, air decontamination, and reductions in peak occupancy, can be expensive and disruptive, particularly when employed in an untargeted manner. We report the empirical identification of high transmission risk zones in a tertiary hospital, using carbon dioxide-based assessments of air exchange. This rapid, cost-effective, and unobtrusive approach led to the targeted remediation of a high transmission risk zone.

How Design Is Appropriated: Understanding Habitation through the Observation of Situated Behavior

The relationship between people and designed space is often approached exclusively with text-based research techniques (e.g., interviews and questionnaires). After proposing a theoretical framework for design from a sociological perspective, this paper first illustrates the limitations of those techniques when investigating appropriation of designed space, i.e., situated behaviors and perceptions. Then, the observation of socio-environmental indicators is proposed as a research technique, illustrating how this unobtrusive approach overcomes some of those limitations, allowing the study of habitation through the observation of effects of behaviors rather than the behavior itself. A taxonomy of socio-environmental indicators is introduced, showing how five categories can be construed (i.e., traces, alterations, adaptations, signs, and routines) for the analysis of habitation of designed spaces at various level of scales, from the proxemic space around the body to the larger designed environment. Certain specific examples are discussed, showing how indicators can be analyzed, generating insights and the formulation of hypotheses that may be further investigated triangulating with other research techniques. After discussing the limitations of this technique, the paper reflects on how its application contributes to a dialogical appreciation of design.

Crowdtesting Practices and Models: An Empirical Approach

Crowdsourced software testing (CST) has received significant attention. After these years, CST has made new progress and changes. While current literature lists many CST challenges, this paper analyzes industrial CST practices, finds that many challenges already have practical solutions, summarizes their commonalities, and comes up with new CST models and processes. We look for well-known CST websites to participate in and take a secret and unobtrusive approach where customers, platform managers, and fellow workers do not know that we are mainly interested in CST research. We then register at selected CST websites, collect any public documents such as whitepapers, open rules, and public training materials, and join as many test tasks as possible. We analyze the confrontation and collaboration among clients, platforms, and workers in the CST sessions. Clients want to get as much bug information as possible for a small amount of pay, but workers want to get paid as much as possible for a small amount of bug information. We also study the process and method of selecting suitable CST workers. Based on these, this paper proposes three future research directions. Data security and privacy at CST are paramount. If this problem can be overcome, CST will have wider applications. Additionally, the integration of workers, internal workers, software automation, and artificial intelligence will be major drivers for CST. It is also critical to develop a standardized CST structure and processes, and this will push the field to grow significantly.

Examining Chinese Users' Feedback Comments on HIV Self-testing Kits From e-Commerce Platforms: Thematic and Content Analysis.

HIV self-testing is preferred by many Chinese people for its convenience and confidentiality. However, most studies on HIV self-testing (HIVST) uptake in China overfocused on men who have sex with men and overrelied on obtrusive methods such as surveys and interviews to collect data. We aimed to explore Chinese HIVST kit users' authentic experience via their feedback comments posted on e-commerce platforms using an unobtrusive approach. In total, 21,018 feedback comments about buying and using HIVST kits posted on Chinese e-commerce platforms (Tmall and Pinduoduo) were collected. An inductive thematic analysis based on a random sample of 367 comments yielded several thematic features. These thematic features were developed into coding categories for a quantitative content analysis of another random sample of 1857 comments. Four themes were identified in the first study, including the expression of positive and negative emotions after and before getting the test, respectively, calling for living a clean and moral life in the future, comments on the sellers and HIVST kits, and the reasons for buying HIVST kits. The results from the second study suggested that there were significant associations between different platforms and several thematic features. Nearly 50% of the comments were related to the product itself and the disclosures of HIV-negative test results. More than 25% of the comments showed users' feelings of gratefulness after receiving negative test results such as "thank heavens for sparing my life." The results suggested that Chinese users relied on HIVST kits to reduce and prevent HIV infection, while they also considered HIV infection a punishment related to moral violation such as being sexually promiscuous. The traditional Chinese health belief that health is influenced by one's morality still persists among some Chinese users. Many users also lacked appropriate knowledge about HIV transmission and self-testing kits.

An Unobtrusive Approach to Emotion Detection in E-Learning Systems

Abstract Researchers have proved that emotions play vital role in a human’s life. They affect our way of living, making decisions and also our way of learning. There are many methods for emotion detection in e-learning. However, each of them comes with its own set of disadvantages discussed in the literature review. In this paper, the attributes that have been identified are purely unobtrusive in nature; attributes that do not interfere with the learner’s activity and less is known to them that their emotions are being monitored. A methodology is presented to detect the emotions of the learner using keystrokes, mouse clicks, forum discussions and the results of assessments. Machine learning models have been trained and tested to predict the learner’s emotions. The logistic regression performed fairly well in comparison to the other algorithms with an accuracy of about 85% and cross-validation score of 86%. During this study, interesting patterns are observed in learner’s emotions that are discussed. Future directions include collecting diverse data to understand emotions of learners from various age groups and observing patterns in their emotional changes.

A reliable and unobtrusive approach to display area detection for imperceptible display camera communication

Object framework detection has been extensively studied in computer vision for applications such as document digitization and whiteboard scanning. Similarly, it is essential for display-camera communication systems, particularly when imperceptible data modulation is employed to enable simultaneous video playback and data transmission. Reliable and accurate localization of the encoded display area is critical for data demodulation and decoding. However, existing systems typically adapt established methods developed for other applications that do not meet the system requirements for high-rate data transmission.In this article, we propose a novel method for display area detection in the camera images by embedding a new localization marker into the display corners. While the localization marker is less obtrusive than conventional fiducial markers, our detection algorithm demonstrated excellent reliability regardless of the display content and background, according to simulation and experimental results. In addition, the detector achieved subpixel accuracy and real-time performance with modern smartphones.

Facing it: assessing the immediate emotional impacts of calorie labelling using automatic facial coding

AbstractAlthough there has been a proliferation of research and policy work into how nudges shape people's behaviour, most studies stop far short of consumer welfare analysis. In the current work, we critically reflect on recent efforts to provide insights into the consumer welfare impact of nudges using willingness to pay and subjective well-being reports and explore an unobtrusive approach that can speak to the immediate emotional impacts of a nudge: automatic facial expression coding. In an exploratory lab study, we use facial expression coding to assess the short-run emotional impact of being presented with calorie information about a popcorn snack in the context of a stylised ‘Cinema experience’. The results of the study indicate that calorie information has heterogeneous impacts on people's likelihood of choosing the snack and on the emotions they experience during the moment of choice which varies based on their level of health-consciousness. The information does not, however, affect the emotions people go on to experience while viewing movie clips, suggesting that the emotional effects of the information are short-lived. We conclude by emphasising the potential of automatic facial expression coding to provide new insights into the immediate emotional impacts of nudges and calling for further research into this promising technique.

Seasonal and diel activity patterns of the endangered taiga bean goose (Anser fabalis fabalis) during the breeding season, monitored with camera traps.

Taiga bean goose (Anser fabalis fabalis) is an endangered subspecies that breeds sporadically in remote habitats in the arctic and boreal zones. Due to its elusive behaviour, there is a paucity of knowledge on the behaviour of taiga bean goose during the breeding season, and survey methods for monitoring numbers in the breeding areas are lacking. Camera traps are a useful tool for wildlife monitoring, particularly when there is a need for non-invasive methods due to the shy nature of the species. In this study, we tested the use of camera traps to investigate seasonal and diel activity patterns of taiga bean goose in Finland over two successive breeding seasons, 2018 and 2019. We did this by modelling counts of geese from images with generalized linear and additive mixed models. The camera type (cameras placed by experts specialized in bean goose ecology vs randomly placed cameras) did not influence the count of taiga bean goose (p = 0.386). However, the activity varied significantly by region, Julian day, time of day and temperature, with the study site (individual peatland) and year adding substantial random variation and uncertainty in the counts. Altogether, the best fitting model explained nearly 70% of the variation in taiga bean goose activity. The peak in activity occurred about a month later in the southernmost region compared to the more northern regions, which may indicate behaviours related to migration rather than breeding and moulting. Our results show that long-term monitoring with game camera traps provide a potential unobtrusive approach for studying the behavioural patterns of taiga bean goose and can increase our ecological knowledge of this little-known subspecies. The results can be applied to planning of the annual censuses and finding the optimal time frame for their execution.

Heart rate estimation from ballistocardiographic signals using deep learning

Objective. Ballistocardiography (BCG) is an unobtrusive approach for cost-effective and patient-friendly health monitoring. In this work, deep learning methods are used for heart rate estimation from BCG signals and are compared against five digital signal processing methods found in literature. Approach. The models are evaluated on a dataset featuring BCG recordings from 42 patients, acquired with a pneumatic system. Several different deep learning architectures, including convolutional, recurrent and a combination of both are investigated. Besides model performance, we are also concerned about model size and specifically investigate less complex and smaller networks. Main results. Deep learning models outperform traditional methods by a large margin. Across 14 patients in a held-out testing set, an architecture with stacked convolutional and recurrent layers achieves an average mean absolute error (MAE) of 2.07 beat min−1, whereas the best-performing traditional method reaches 4.24 beat min−1. Besides smaller errors, deep learning models show more consistent performance across different patients, indicating the ability to better deal with inter-patient variability, a prevalent issue in BCG analysis. In addition, we develop a smaller version of the best-performing architecture, that only features 8283 parameters, yet still achieves an average MAE of 2.32 beat min−1 on the testing set. Significance. This is the first study that applies and compares different deep learning architectures to heart rate estimation from bed-based BCG signals. Compared to signal processing algorithms, deep learning models show dramatically smaller errors and more consistent results across different individuals. The results show that using smaller models instead of excessively large ones can lead to sufficient performance for specific biosignal processing applications. Additionally, we investigate the use of fully convolutional networks for 1D signal processing, which is rarely applied in literature.

RF-RVM: Continuous Respiratory Volume Monitoring With COTS RFID Tags

Continuous and accurate respiratory volume monitoring is crucial in many healthcare-related applications. Traditional respiratory volume monitoring approaches involve obtrusive devices that are uncomfortable for long-term monitoring, while unobtrusive approaches mainly focus on sensing the respiratory rate, which is insufficient for many healthcare-related applications. In this article, we present radio-frequency respiratory volume monitoring (RF-RVM), an unobtrusive system to sense the respiratory volume based on commercial off-the-shelf (COTS) RFID devices. Specifically, RF-RVM continuously collects the temporal phase information from tags attached to the chest and abdomen to extract the chest displacement and abdomen displacement caused by respiration. Then, we assess the respiratory volume by training a backpropagation neural network model to correlate chest and abdomen displacements and respiratory volume. We use a reference tag attached under the user's neck to eliminate the noise caused by slight movements of the upper body during respiration. We implement and evaluate RF-RVM based on COTS RFID devices. The experimental results show that RF-RVM can continuously monitor user's respiratory volume with an average accuracy of 94.52% for leave-one-session-out cross-validation and 91.96% for leave-one-record-out cross-validation based on a data set sampled from 20 volunteers.

Distinguishing Between Parkinson’s Disease and Essential Tremor Through Video Analytics Using Machine Learning: A Pilot Study

Parkinson's Disease (PD) is currently the fastest growing neurodegenerative disease. It decreases the quality of life for patients, especially when not diagnosed properly and timely. Accurate diagnostic of PD is complicated by the fact that there exist several neurodegenerative diseases with similar motor symptoms, e.g. essential tremor. In this work, we report on a second opinion system based on the video analysis and classification of subjects using machine learning methods including feature extraction, dimensionality reduction and classification. Our approach serves for avoiding a typical misdiagnosis of PD by essential tremor. Consequently, we designed 15 common tasks and recorded the movement video. Video data was collected from 89 subjects at a medical center and labeled by doctors. We first demonstrate classification between the healthy subjects and subjects with PD suspected case followed by the classification between the subjects with true PD and the subjects with essential tremor. We achieved f1 score 0.90 for the first classification and f1 score 0.84 for the second classification. The proposed unobtrusive approach demonstrated its feasibility through a pilot study. It opens up wide vista for differentiating PD patients against other patients and not against a cohort of healthy subjects.

An Eye Tracking Approach to Understanding Misinformation and Correction Strategies on Social Media: The Mediating Role of Attention and Credibility to Reduce HPV Vaccine Misperceptions

ABSTRACT This study uses an unobtrusive eye tracking approach to examine understudied psychological mechanisms – message attention and credibility – when people are exposed to misinformation and correction on social media. We contrast humor versus non-humor correction strategies that point out rhetorical flaws in misinformation regarding the HPV vaccine, which was selected for its relevance and impact on public health. We randomly assigned participants to one of two experimental conditions: humor correction versus non-humor correction. Our analyses revealed that the humor correction increased attention to the image portion of the correction tweet, and this attention indirectly lowered HPV misperceptions by reducing the credibility of the misinformation tweet. The study also found that the non-humor correction outperformed the humor correction in reducing misperceptions via its higher credibility ratings. Practical implications for correcting misinformation on social media are discussed.

A perinatal stress calculator for the neonatal intensive care unit: an unobtrusive approach

Objective: Early experience of pain and stress in the neonatal intensive care unit is known to have an effect on the neurodevelopment of the infant. However, an automated method to quantify the procedural pain or perinatal stress in premature patients does not exist. Approach: In the current study, EEG and ECG data were collected for more than 3 hours from 136 patients in order to quantify stress exposure. Specifically, features extracted from the EEG and heart-rate variability in both quiet and non-quiet sleep segments were used to develop a subspace linear-discriminant analysis stress classifier. Main results: The main novelty of the study lies in the absence of intrusive methods or pain elicitation protocols to develop the stress classifier. Three main findings can be reported. First, we developed different stress classifiers for the different age groups and stress intensities, obtaining an area under the curve in the range [0.78-0.93] for non-quiet sleep and [0.77-0.96] for quiet sleep. Second, a dysmature EEG was found in patients under stress. Third, an enhanced cortical connectivity and increased brain–heart communication was correlated with a higher stress load, while the autonomic activity did not seem to be associated to stress exposure. Significance: The results shed a light on the pain and stress processing in preterm neonates, suggesting that software tools to investigate dysmature EEG might be helpful to assess stress load in premature patients. These results could be the foundation to assess the impact of stress on infants’ development and to tune preventive care.

Computer aided detection of breathing disorder from ballistocardiography signal using convolutional neural network

Sleep-related breathing disorders are diseases related to pharyngeal airway collapse. It can lead to several health problems such as somnolence, poorer daytime cognitive performance, and cardiovascular morbidity and mortality. However, computer-aided diagnostic (CAD) tools play a very important role in the detection of breathing disorders. It is possible to measure breathing activity, but most approaches require some type of device placed on the human body. This paper proposes a novel methodology of an unobtrusive CAD system to the breathing disorder detection. Unobtrusive approach is ensured by ballistocardiography (BCG) sensors located on the measured bed. The significant pieces of information from the signals are extracted by Cartan curvatures. Thereafter, important features are separated from individual samples as an input to our 9-layer deep convolutional neural network. We achieved an average accuracy of 98.00%, sensitivity of 94.26%, and specificity of 99.22% on 4009 regular and 1307 disordered breathing samples.

Smart sensors for real-time monitoring of patients on dialysis.

Peritoneal dialysis and haemodialysis are lifesaving but intrusive treatments that are associated with high morbidity and mortality. Despite the considerable time patients with kidney failure spend tied to their life-saving therapies, relatively few vital signs are monitored. Smart, unobtrusive approaches to track clinical parameters could help to individualize treatments and improve patient outcomes.

Targeted awareness: promoting niche resources using confirmation emails

PurposeThe purpose of this paper is to examine the effectiveness of a targeted email advertising method that informs university students about library resources relevant to their major.Design/methodology/approachOver the course of one semester, students with business and communications majors who reserved group study rooms received customized confirmation emails that included targeted advertisements for library databases. These advertisements invited students to click a link that led to a database related to a student's major. Near the end of the semester, students were invited to complete a short survey about the advertisements. Survey questions were designed to discover whether the advertisements were seen and to measure database relevancy. The survey also gathered participant perceptions about the value of this targeted awareness advertising approach.FindingsOverall, the technique of appending database advertisements to reservation emails proved ineffective; very few students recalled seeing the advertisements or clicking the links. On the positive side, this study showed that very few of the survey respondents disapproved of receiving advertisements. Another positive result was that a majority indicated the selected library databases were relevant to their needs. Respondents also offered suggestions for improving this marketing technique.Originality/valueThis research introduces a novel, unobtrusive approach to advertising library resources to a targeted audience. The results can be used by library administrators as a starting point for experimenting with similar (yet improved) methods of sharing information about relevant library resources with specific user populations.

Lightweight and Unobtrusive Data Obfuscation at IoT Edge for Remote Inference

Executing deep neural networks for inference on the server-class or cloud backend based on data generated at the edge of Internet of Things is desirable due primarily to the limited compute power of edge devices and the need to protect the confidentiality of the inference neural networks. However, such a remote inference scheme incurs concerns regarding the privacy of the inference data transmitted by the edge devices to the curious backend. This paper presents a lightweight and unobtrusive approach to obfuscate the inference data at the edge devices. It is lightweight in that the edge device only needs to execute a small-scale neural network; it is unobtrusive in that the edge device does not need to indicate whether obfuscation is applied. Extensive evaluation by three case studies of free spoken digit recognition, handwritten digit recognition, and American sign language recognition shows that our approach effectively protects the confidentiality of the raw forms of the inference data while effectively preserving the backend's inference accuracy.

Unobtrusive Sleep Monitoring Using Movement Activity by Video Analysis

Sleep healthcare at home is a new research topic that needs to develop new sensors, hardware and algorithms with the consideration of convenience, portability and accuracy. Monitoring sleep behaviors by visual sensors represents one new unobtrusive approach to facilitating sleep monitoring and benefits sleep quality. The challenge of video surveillance for sleep behavior analysis is that we have to tackle bad image illumination issue and large pose variations during sleeping. This paper proposes a robust method for sleep pose analysis with human joints model. The method first tackles the illumination variation issue of infrared videos to improve the image quality and help better feature extraction. Image matching by keypoint features is proposed to detect and track the positions of human joints and build a human model robust to occlusion. Sleep poses are then inferred from joint positions by probabilistic reasoning in order to tolerate occluded joints. Experiments are conducted on the video polysomnography data recorded in sleep laboratory. Sleep pose experiments are given to examine the accuracy of joint detection and tacking, and the accuracy of sleep poses. High accuracy of the experiments demonstrates the validity of the proposed method.

Operational and Experimental Tasks, Performance, and Voice in Space.

BACKGROUND: Voice analysis offers an unobtrusive approach for psychological monitoring. We demonstrate the relationship between voice parameters and cognitive performance in: 1) a task with psychological test character, and 2) performance in an operational, mission-relevant task. The central methodological aim was to verify the usefulness of voice commands and counting in providing anchor values for the step-function model of voice pitch.METHODS: During a 22-yr period, 42 cosmonauts participated in the Russian space experiment "Pilot", which was a hand-controlled docking maneuver. As reference the experiment included the cognitive task "Manometer." This task was controlled through voice commands. These voice commands were stored and are the basis for the present analysis.RESULTS: Cosmonauts differed in their working style and respective performance during the Manometer task. Clustered groups can be assumed to represent different effort. Importantly, these groups differed in the changes of voice pitch among mission phases and among task repetitions. However, there were no differences between these motivation groups and performance in the professional task.DISCUSSION: The differing effort is the effect of different motivation of cosmonauts for experimental test tasks vs. mission-relevant professional tasks. Latter ones provide a more reliable chance to assess the real actual state and skills of a cosmonaut. Voice pitch measurement seems to be reliable and useful under space conditions for monitoring this volitional effort.Johannes B, Bronnikov SV, Bubeev JA, Kotrovskaya TI, Shastlivtseva DV, Piechowski S, Hoermann H-J, Rittweger J, Jordan J. Operational and experimental tasks, performance, and voice in space. Aerosp Med Hum Perform. 2019; 90(7):624-631.

Writing Process Differences in Subgroups Reflected in Keystroke Logs

We used an unobtrusive approach, keystroke logging, to examine students’ cognitive states during essay writing. Based on data contained in the logs, we classified writing process data into three states: text production, long pause, and editing. We used semi-Markov processes to model the sequences of writing states and compared the state transition time and probability for demographic subgroups that were matched on writing proficiency. Results suggested that the subgroups employed different processes in essay writing.