Non-intrusive Data Collection Research Articles

Virtual design hackathons: a data collection framework

AbstractDesign hackathons offer a unique research opportunity to study time-pressured collaborative design. At the same time, research on design hackathons faces unique methodological challenges, prompting the exploration of new research approaches. This paper proposes a new data-collection framework that leverages a virtual format of hackathon events and enables a deeper insight into hackathon dynamics. The framework applicability is presented through a case study of the IDEA challenge hackathon, in which different intrusive and non-intrusive data collection approaches were used.

FaceTell: A novel solution to diagnosing Parkinsons disease with facial expressions and deep learning

Parkinsons disease (PD) is a neurodegenerative disease afflicting over 10 million patients worldwide, most commonly the elderly, that causes tremors, stiffness, movement loss, and other symptoms. Since symptoms are often mild and difficult to notice in the early stages of the condition, it can be hard to notice and diagnose until the condition has already become more severe. An earlier diagnosis of PD will allow treatment to begin earlier and lessen the impact of the disease. The goal of this work is to develop an affordable, non-intrusive, and accessible way of diagnosing PD. This neurodegenerative disorder leads to loss of movement control and other symptoms. Since there is no known cure for PD yet, early diagnosis would allow timely treatment and prevent the symptoms from worsening too quickly. Doing so in an affordable and non-intrusive way will minimize costs and maximize efficiency removing the need for lengthy consulting with doctors and possibly expensive testing and medical equipment. This work presents the FaceTell system, which combines and optimizes traditional machine learning and deep learning to make predictions on the patients PD status based on video data of their faces. By analyzing a variety of attributes such as facial expressions and emotion prevalence/intensity, the model was able to achieve a more thorough examination of the patients condition and make predictions of similar accuracy compared to prior results. One main innovation was collecting data affordably: sampling publicly available videos from platforms like YouTube. This serves as a proof-of-concept to show that simple, affordable, and non-intrusive data collection methods can still produce viable results. Using methods and tools such as hyperparameter tuning, data cleaning, and Face++, the performance of the system readily improved. The ultimate results obtained include an F1 score of 0.86 and an accuracy of 89%, compared to prior results which reached up to 95% accuracy.

Artificial intelligence-enabled non-intrusive vigilance assessment approach to reducing traffic controller’s human errors

To be vigilant is highly required for traffic controllers in transportation fields, such as air traffic management, vessel traffic service, and railway management, as they need to monitor traffic conditions and notice any potential hazards. Hence, emerging studies have been conducted to develop an objective and non-intrusive approach to assessing vigilance levels and generate warnings if needed. This study aims to investigate the effects of impaired vigilance on human performance via non-intrusive data analysis, namely spatial and temporal gaze pattern analytics, and develop an objective model for vigilance assessment accordingly. A novel four-phase framework, including vigilance test design, non-intrusive data collection, spatial and temporal gaze pattern analytics, and a shallow neural network-based model was proposed to achieve this aim. Meanwhile, an illustrative experiment in the maritime industry was conducted to verify the proposed method. The spatial and temporal gaze patterns analytics revealed that low vigilance levels impacted comprehension time but not perception time, with longer fixations duration but stable time-to-the-nearest-fixation under a low vigilance level. It is found that even a person with impaired vigilance can quickly notice abnormal events. The effectiveness and empirical implications of this model can help traffic controllers avoid fatigue-induced vigilance reduction. In addition, it provides evidence, references, and solutions for designing human–computer interfaces to reduce human errors caused by low vigilance.

"Care is not care if it isn't person-centred": A content analysis of how Person-Centred Care is expressed on Twitter.

BackgroundPerson‐Centred Care (PCC) has been the subject of growing interest in recent decades. Even though there is no conceptual consensus regarding PCC, many health‐care institutions have implemented elements into their care.ObjectiveThis study aimed to investigate the PCC topics presented by different stakeholder groups on Twitter and to explore the perceptions of PCC within the content of the tweets.MethodTweets with mentions of PCC in various translations were collected through a Twitter Application Programming Interface in October 2019. The tweets were analysed using quantitative and qualitative content analysis.ResultsFive stakeholder groups and ten topics were identified within 1540 tweets. The results showed that the PCC content focused on providing information and opinions rather than expressing experiences of PCC in practice. Qualitative content analysis of 428 selected tweets revealed content on a vision that all care should be person‐centred but that the realization of that vision was more complicated.ConclusionsTwitter has shown to be a quick and non‐intrusive data collection tool for uncovering stakeholders' expressions concerning PCC. The PCC content revealed that stakeholders feel a need to 'educate' others about their perception of PCC when experiences and real‐life applications are missing. More action should be taken for the implementation of PCC rather than circulating PCC vision without operationalization in care.Public ContributionThe public provided the data through their posts on Twitter, and it is their perception of PCC that is studied here.

Non-Intrusive Data Monitoring and Analysis of Occupant Energy-Use Behaviors in Shared Office Spaces

A non-intrusive data collection framework is developed to analyze the desk-level occupancy and energy use patterns of occupants in shared office spaces. The framework addresses the limitations of previous studies in the literature, which either lacked the granularity to study individual occupants’ behaviors or relied on data from complex Building Management Systems (BMS). The framework is applied to a shared office space of an academic institution in the United Arab Emirates (UAE), where occupancy, lighting, and plug-load data were collected from individual desks for 6 months. The results highlight weak relationships between the occupancy status and the total electric loads, with 35% of the total electric loads consumed when the area is completely vacant, and 64% of the plug-load energy consumed when the desks were reported as unoccupied. While specific to the studied building, the results highlight the role that a high-resolution data monitoring framework plays in capturing inefficient consumption patterns. The findings also confirm the contribution of occupant behavior (OB) to the energy performance gap commonly observed between predicted and actual energy levels.

Using smart technologies to identify occupancy and plug-in appliance interaction patterns in an office environment

There has been a widely documented phenomenon identifying an energy performance gap between the building’s design and operational phases. This result has been attributed to the stochastic behaviours exhibited by the occupants, who are assumed to follow deterministic and routine schedules. With the recent advancements in smart monitoring technologies, the increasing affordability of wireless sensors has allowed researchers to collect detailed information on the occupants’ dynamic behaviours. However, past applications of such technologies have been highly intrusive and limit the validity of the data collected due to the Hawthorne effect. Therefore, this paper proposes a non-intrusive data collection methodology using a comprehensive range of wireless smart meters, Bluetooth beacons, and questionnaires to capture the occupants’ movement and appliance interaction patterns. The feasibility of the approach is demonstrated during a two-week data collection effort in a university office. By combining the occupants’ presence with appliance energy consumption data, the authors were able to identify the occupants’ appliance interaction patterns. An extension of this work includes the use of the data collected to identify different occupancy and appliance interaction profiles, which contributes to the development of an appliance interaction model that addresses the energy performance gap caused by occupants’ appliance interaction.

Robust non-intrusive interpretation of occupant thermal comfort in built environments with low-cost networked thermal cameras

About 40% of the energy produced globally is consumed within buildings, primarily for providing occupants with comfortable work and living spaces. However, despite the significant impacts of such energy consumption on the environment, the lack of thermal comfort among occupants is a common problem that can lead to health complications and reduced productivity. To address this problem, it is particularly important to understand occupants’ thermal comfort in real-time to dynamically control the environment. This study investigates an infrared thermal camera network to extract skin temperature features and predict occupants’ thermal preferences at flexible distances and angles. This study distinguishes from existing methods in two ways: (1) the proposed method is a non-intrusive data collection approach which does not require human participation or personal devices; (2) it uses low-cost thermal cameras and RGB-D sensors which can be rapidly reconfigured to adapt to various settings and has little or no hardware infrastructure dependency. The proposed camera network is verified using the facial skin temperature collected from 16 subjects in a multi-occupancy experiment. The results show that all 16 subjects observed a statistically higher skin temperature as the room temperature increases. The variations in skin temperature also correspond to the distinct comfort states reported by the subjects. The post-experiment evaluation suggests that the networked thermal cameras have a minimal interruption of building occupants. The proposed approach demonstrates the potential to transition the human physiological data collection from an intrusive and wearable device-based approach to a truly non-intrusive and scalable approach.

Identifying Time-of-Day Breakpoints Based on Nonintrusive Data Collection Platforms

To ensure the effective operation of traffic signal systems, different signal timings should be designed to accommodate traffic pattern variations. One of the greatest challenges is the identification of appropriate time-of-day (TOD) breakpoints, where different signal timings could be implemented during the time periods between two consecutive breakpoints. This research presents an advanced cluster analysis aimed at identifying TOD breakpoints for coordinated, semiactuated modes when it is necessary for multiple intersection operations to be considered simultaneously. Different from previous studies, this proposed methodology considers the time of traffic occurring as one dimension in clustering and uses continuous traffic data obtained through innovative, nonintrusive data collection techniques, which significantly improve this method's performance. The operability of this proposed method is demonstrated in a case study of a corridor located in Tampa, FL. The traffic simulation results reported in this article reveal that this novel procedure performs better than existing TOD signal timing plans.

Automated Diagnostics and Visualization of Potential Energy Performance Problems in Existing Buildings Using Energy Performance Augmented Reality Models

AbstractQuick and reliable identification of energy performance problems in buildings is a critical step in improving their efficiency. The current practice of building diagnostics typically involves nonintrusive data collection using thermal cameras. This requires large amounts of unordered and nongeo-tagged two-dimensional (2D) imagery to be manually analyzed at a later stage, which makes the analysis time-consuming and labor-intensive. Because of the absence of a benchmark for energy performance, identification of performance problems also heavily relies on the auditor’s knowledge, and consequently may lead to subjective and error-prone inspections. As a step towards rapid and objective identification of performance problems, this paper presents a new method for automated analysis and visualization of deviations between buildings’ actual and simulated energy performances. The proposed method is based on the recently developed energy performance augmented reality (EPAR) environments. In the EPAR modelin...

A server-based non-intrusive measurement infrastructure for enterprise networks

In this paper we propose a server-based measurement approach that can be deployed in replicated servers within a wide-area enterprise network to provide distributed service to a large number of clients across the Internet. In our approach, each server performs traffic measurement and exchanges the collected metrics with peer servers. This server-based measurement approach consists of three steps: non-intrusive data collection, data analysis and performance metrics generation, and exchange of performance metrics among peer servers. As part of an experimental system we have built, we describe a performance metrics generation tool called Woodpecker, which is designed based on a non-intrusive passive packet capturing mechanism. Experimental results obtained using Woodpecker demonstrate that it is indeed feasible to employ non-intrusive measurement tools to generate the majority of desired performance metrics for dynamic server selection.