Humans-as-a-Sensor for Buildings—Intensive Longitudinal Indoor Comfort Models

Evaluating and optimising human comfort within the built environment is challenging due to the large number of physiological, psychological and environmental variables that affect occupant comfort preference. Human perception could be helpful to capture these disparate phenomena and interpreting their impact; the challenge is collecting spatially and temporally diverse subjective feedback in a scalable way. This paper presents a methodology to collect intensive longitudinal subjective feedback of comfort-based preference using micro ecological momentary assessments on a smartwatch platform. An experiment with 30 occupants over two weeks produced 4378 field-based surveys for thermal, noise, and acoustic preference. The occupants and the spaces in which they left feedback were then clustered according to these preference tendencies. These groups were used to create different feature sets with combinations of environmental and physiological variables, for use in a multi-class classification task. These classification models were trained on a feature set that was developed from time-series attributes, environmental and near-body sensors, heart rate, and the historical preferences of both the individual and the comfort group assigned. The most accurate model had multi-class classification F1 micro scores of 64%, 80% and 86% for thermal, light, and noise preference, respectively. The discussion outlines how these models can enhance comfort preference prediction when supplementing data from installed sensors. The approach presented prompts reflection on how the building analysis community evaluates, controls, and designs indoor environments through balancing the measurement of variables with occupant preferences in an intensive longitudinal way.

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Revisiting the Role of Place in Geographic Information Science

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi7090364 ◽

2018 ◽

Vol 7 (9) ◽

pp. 364 ◽

Cited By ~ 9

Author(s):

Helena Merschdorf ◽

Thomas Blaschke

Keyword(s):

Information Science ◽

Human Perception ◽

Geographic Information Science ◽

Geographic Information ◽

Human Environment ◽

Subjective Data ◽

Main Research ◽

The Social ◽

The Individual

Although place-based investigations into human phenomena have been widely conducted in the social sciences over the last decades, this notion has only recently transgressed into Geographic Information Science (GIScience). Such a place-based GIS comprises research from computational place modeling on one end of the spectrum, to purely theoretical discussions on the other end. Central to all research that is concerned with place-based GIS is the notion of placing the individual at the center of the investigation, in order to assess human-environment relationships. This requires the formalization of place, which poses a number of challenges. The first challenge is unambiguously defining place, to subsequently be able to translate it into binary code, which computers and geographic information systems can handle. This formalization poses the next challenge, due to the inherent vagueness and subjectivity of human data. The last challenge is ensuring the transferability of results, requiring large samples of subjective data. In this paper, we re-examine the meaning of place in GIScience from a 2018 perspective, determine what is special about place, and how place is handled both in GIScience and in neighboring disciplines. We, therefore, adopt the view that space is a purely geographic notion, reflecting the dimensions of height, depth, and width in which all things occur and move, while place reflects the subjective human perception of segments of space based on context and experience. Our main research questions are whether place is or should be a significant (sub)topic in GIScience, whether it can be adequately addressed and handled with established GIScience methods, and, if not, which other disciplines must be considered to sufficiently account for place-based analyses. Our aim is to conflate findings from a vast and dynamic field in an attempt to position place-based GIS within the broader framework of GIScience.

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Designing a Compact Wireless Network Based Device-Free Passive Localisation System for Indoor Environments

Intelligent Systems ◽

10.4018/978-1-5225-5643-5.ch061 ◽

2018 ◽

pp. 1424-1439

Author(s):

Philip Vance ◽

Girijesh Prasad ◽

Jim Harkin ◽

Kevin Curran

Keyword(s):

Assisted Living ◽

Ambient Assisted Living ◽

Design Procedure ◽

The Elderly ◽

Healthcare Services ◽

Indoor Environments ◽

Signal Processing Technique ◽

Minimum Number ◽

The Individual ◽

Device Free

Determining the location of individuals within indoor locations can be useful in various scenarios including security, gaming and ambient assisted living for the elderly. Healthcare services globally are seeking to allow people to stay in their familiar home environments longer due to the multitude of benefits associated with living in non-clinical environments and technologies to determine an individual's movements are key to ensuring that home emergencies are detected through lack of movement can be responded to promptly. This paper proposes a device-free localisation (DFL) system which would enable the individual to proceed with normal daily activities without the concern of having to wear a traceable device. The principle behind this is that the human body absorbs/reflects the radio signal being transmitted from a transmitter to one or more receiving stations. The proposed system design procedure facilitates the use of a minimum number of wireless nodes with the help of a principle component analysis (PCA) based intelligent signal processing technique. Results demonstrate that human detection and tracking are possible to within 1m resolution with a minimal hardware infrastructure.

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Impact of Fidelity in 3D Space Visualisation Across the Construction Asset Lifecycle

International Journal of 3-D Information Modeling ◽

10.4018/ij3dim.2016010102 ◽

2016 ◽

Vol 5 (1) ◽

pp. 16-28

Author(s):

Noha Saleeb

Keyword(s):

Human Perception ◽

Virtual Spaces ◽

3D Space ◽

Building Models ◽

Effective Decision ◽

Effective Decision Making ◽

The Individual ◽

Physical Spaces ◽

The Impact ◽

Published Research

3D virtual building models are used to help clients reach decisions during concept and detailed design phases. However, previously published research provides evidence for discrepancies between human perception of virtual and physical spaces; thus perceiving each virtual dimension (height, width, depth) differently from its physical counterpart, with varying percentages. This can affect clients' effective decision-making during coordination if 3D virtual representations are not perceived identical to their physical equivalent. This paper discusses the impact of these discrepancies beyond the design phases and into the whole lifecycle, construction and operations. Moreover, descriptive and inferential statistical analysis provides evidence of relationships between the physical and virtual perception differences in dimension, discussing possible factors contributing to perception discrepancies affecting the individual viewer, in 2 main areas 1) 3D authoring software 2) psychophysical factors. Possible solutions are also proposed to accommodate for the discrepancy between physical and virtual spaces.

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Enabling Noninvasive Physical Assault Monitoring in Smart School with Commercial Wi-Fi Devices

Wireless Communications and Mobile Computing ◽

10.1155/2019/8186573 ◽

2019 ◽

Vol 2019 ◽

pp. 1-14

Author(s):

Qizhen Zhou ◽

Chenshu Wu ◽

Jianchun Xing ◽

Shuo Zhao ◽

Qiliang Yang

Keyword(s):

School Violence ◽

Domain Knowledge ◽

Global Perspective ◽

Detection Methods ◽

Physical Assault ◽

Indoor Environments ◽

Time Frequency ◽

True Detection ◽

Body Sensors ◽

Signal Processing Methods

Monitoring physical assault is critical for the prevention of juvenile delinquency and promotion of school harmony. A large portion of assault events, particularly school violence among teenagers, usually happen at indoor secluded places. Pioneering approaches employ always-on-body sensors or cameras in the limited surveillance area, which are privacy-invasive and cannot provide ubiquitous assault monitoring. In this paper, we present Wi-Dog, a noninvasive physical assault monitoring scheme that enables privacy-preserving monitoring in ubiquitous circumstances. Wi-Dog is based on widely deployed commodity Wi-Fi infrastructures. The key intuition is that Wi-Fi signals are easily distorted by human motions, and motion-induced signals could convey informative characteristics, such as intensity, regularity, and continuity. Specifically, to explicitly reveal the substantive properties of physical assault, we innovatively propose a set of signal processing methods for informative components extraction by selecting sensitive antenna pairs and subcarriers. Then a novel signal-complexity-based segmentation method is developed as a location-independent indicator to monitor targeted movement transitions. Finally, holistic analysis is employed based on domain knowledge, and we distinguish the violence process from both local and global perspective using time-frequency features. We implement Wi-Dog on commercial Wi-Fi devices and evaluate it in real indoor environments. Experimental results demonstrate the effectiveness of Wi-Dog which consistently outperforms the advanced abnormal detection methods with a higher true detection rate of 94% and a lower false alarm rate of 8%.

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Efficacy, cost-utility and physiological effects of Acceptance and Commitment Therapy (ACT) and Behavioural Activation Treatment for Depression (BATD) in patients with chronic low back pain and depression: study protocol of a randomised, controlled trial including mobile-technology-based ecological momentary assessment (IMPACT study)

BMJ Open ◽

10.1136/bmjopen-2020-038107 ◽

2020 ◽

Vol 10 (7) ◽

pp. e038107

Author(s):

Juan P Sanabria-Mazo ◽

Carlos G Forero ◽

Paula Cristobal-Narváez ◽

Carlos Suso-Ribera ◽

Azucena García-Palacios ◽

...

Keyword(s):

Controlled Trial ◽

Cost Utility ◽

Depression Symptoms ◽

Low Back ◽

Impact Study ◽

Physiological Variables ◽

Acceptance And Commitment ◽

Randomised Controlled ◽

Ecological Momentary ◽

Momentary Assessment

IntroductionThe IMPACT study focuses on chronic low back pain (CLBP) and depression symptoms, a prevalent and complex problem that represents a challenge for health professionals. Acceptance and Commitment Therapy (ACT) and Brief Behavioural Activation Treatment for Depression (BATD) are effective treatments for patients with persistent pain and depression, respectively. The objectives of this 12 month, multicentre, randomised, controlled trial (RCT) are (i) to examine the efficacy and cost-utility of adding a group-based form of ACT or BATD to treatment-as-usual (TAU) for patients with CLBP and moderate to severe levels of depressive symptoms; (ii) identify pre–post differences in levels of some physiological variables and (iii) analyse the role of polymorphisms in the FKBP5 gene, psychological process measures and physiological variables as mediators or moderators of long-term clinical changes.Methods and analysisParticipants will be 225 patients with CLBP and moderate to severe depression symptoms recruited at Parc Sanitari Sant Joan de Déu (St. Boi de Llobregat, Spain) and Hospital del Mar (Barcelona, Spain), randomly allocated to one of the three study arms: TAU vs TAU+ACT versus TAU+BATD. A comprehensive assessment to collect clinical variables and costs will be conducted pretreatment, post-treatment and at 12 months follow-up, being pain interference the primary outcome measure. The following physiological variables will be considered at pretreatment and post-treatment assessments in 50% of the sample: immune-inflammatory markers, hair cortisol and cortisone, serum cortisol, corticosteroid-binding globulin and vitamin D. Polymorphisms in the FKBP5 gene (rs3800373, rs9296158, rs1360780, rs9470080 and rs4713916) will be analysed at baseline assessment. Moreover, we will include mobile-technology-based ecological momentary assessment, through the Pain Monitor app, to track ongoing clinical status during ACT and BATD treatments. Linear mixed-effects models using restricted maximum likelihood, and a full economic evaluation applying bootstrapping techniques, acceptability curves and sensitivity analyses will be computed.Ethics and disseminationThis study has been approved by the Ethics Committee of the Fundació Sant Joan de Déu and Hospital del Mar. The results will be actively disseminated through peer-reviewed journals, conference presentations, social media and various community engagement activities.Trial registration numberNCT04140838

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Brief Acceptance and Commitment Therapy for Fibromyalgia: Feasibility and Effectiveness of a Replicated Single-Case Design

Pain Research and Management ◽

10.1155/2020/7897268 ◽

2020 ◽

Vol 2020 ◽

pp. 1-11

Author(s):

María Camino Gómez-Pérez ◽

Azucena García-Palacios ◽

Diana Castilla ◽

Irene Zaragozá ◽

Carlos Suso-Ribera

Keyword(s):

Ecological Momentary Assessment ◽

Large Scale ◽

Psychological Treatment ◽

Single Case ◽

Routine Care ◽

Single Case Design ◽

The Individual ◽

Ecological Momentary ◽

Momentary Assessment ◽

Case Design

Objective. Overall, the literature on the effectiveness of psychological treatments in general and those for fibromyalgia in particular has been dominated by research designs that focus on large groups and explore changes on average, so the treatment impact at the individual level remains unclear. In this quasi-experimental, replicated single-case design, we will test the feasibility and effectiveness of a brief acceptance and committed therapy intervention using ecological momentary assessment supported by technology. Methods. The sample comprised 7 patients (3 in the individual condition and 4 in the group condition) who received a brief, 5-week psychological treatment. Patient evolution was assessed one week prior to treatment onset and during the whole study with a smartphone app. Because ecological momentary assessment and the use of an app are not frequent practices in routine care, we also evaluated the feasibility of this assessment methodology (i.e., compliance with the app). Change was investigated with a nonoverlap of all pairs index. Outcomes were pain interference with sleep and social activities, fatigue, sadness, and pain intensity. Results. Patient change was not uniform across outcomes. Four patients (two in each condition) showed relatively moderate levels of change (approximately 60% nonoverlap in several outcomes). The remaining patients showed more modest improvements which affected a reduced number of outcomes. Based on nonoverlapping indices, there was no clear evidence in favor of any treatment format. Conclusions. An alternative design to large-scale trials, one that focuses on the individual change, exists and it can be implemented in pain research. The use of technology (e.g., smartphones) simplifies such designs by facilitating ecological momentary assessment. Based on our findings showing that changes were not homogeneous across patients or outcomes, more single-case designs and patient-centered analyses (e.g., responder and moderation analyses) are required.

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Do Daily Fluctuations in Psychological and App-Related Variables Predict Engagement With an Alcohol Reduction App? A Series of N-Of-1 Studies (Preprint)

10.2196/preprints.14098 ◽

2019 ◽

Cited By ~ 1

Author(s):

Olga Perski ◽

Felix Naughton ◽

Claire Garnett ◽

Ann Blandford ◽

Emma Beard ◽

...

Keyword(s):

Mixed Models ◽

Perceived Usefulness ◽

Group Level ◽

Individual Level ◽

Generalized Additive Mixed Models ◽

Additive Mixed Models ◽

The Individual ◽

Rate Ratios ◽

Ecological Momentary ◽

Alcohol Reduction

BACKGROUND Previous studies have identified psychological and smartphone app–related predictors of engagement with alcohol reduction apps at a group level. However, strategies to promote engagement need to be effective at the individual level. Evidence as to whether group-level predictors of engagement are also predictive for individuals is lacking. OBJECTIVE The aim of this study was to examine whether daily fluctuations in (1) the receipt of a reminder, (2) motivation to reduce alcohol, (3) perceived usefulness of the app, (4) alcohol consumption, and (5) perceived lack of time predicted within-person variability in the frequency and amount of engagement with an alcohol reduction app<italic>.</italic> METHODS We conducted a series of observational <italic>N</italic>-of-1 studies. The predictor variables were measured twice daily for 28 days via ecological momentary assessments. The outcome variables were measured through automated recordings of the participants’ app screen views. A total of nine London-based adults who drank alcohol excessively and were willing to set a reduction goal took part. Each participant’s dataset was analyzed separately using generalized additive mixed models to derive incidence rate ratios (IRRs) for the within-person associations of the predictor and outcome variables. Debriefing interviews, analyzed using thematic analysis, were used to contextualize the findings. RESULTS Predictors of the frequency and amount of engagement differed between individuals, and for the variables 'perceived usefulness of the app' and 'perceived lack of time', the direction of associations also differed between individuals. The most consistent predictors of within-person variability in the frequency of engagement were the receipt of a daily reminder (IRR=1.80-3.88; <italic>P</italic><.05) and perceived usefulness of the app (IRR=0.82-1.42; <italic>P</italic><.05). The most consistent predictors of within-person variability in the amount of engagement were motivation to reduce alcohol (IRR=1.67-3.45; <italic>P</italic><.05) and perceived usefulness of the app (IRR=0.52-137.32; <italic>P</italic><.05). CONCLUSIONS The utility of the selected psychological and app-related variables in predicting the frequency and amount of engagement with an alcohol reduction app differed at the individual level. This highlights that key within-person associations may be masked in group-level designs and suggests that different strategies to promote engagement may be required for different individuals.

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A Real-Time Eating Detection System for Capturing Eating Moments and Triggering Ecological Momentary Assessments to Obtain Further Context: System Development and Validation Study

JMIR mhealth and uhealth ◽

10.2196/20625 ◽

2020 ◽

Vol 8 (12) ◽

pp. e20625

Author(s):

Mehrab Bin Morshed ◽

Samruddhi Shreeram Kulkarni ◽

Richard Li ◽

Koustuv Saha ◽

Leah Galante Roper ◽

...

Keyword(s):

Eating Behavior ◽

Detection System ◽

System Development ◽

Contextual Factors ◽

Well Being ◽

Survey Study ◽

Dominant Hand ◽

Contextual Data ◽

The Individual ◽

Ecological Momentary

Background Eating behavior has a high impact on the well-being of an individual. Such behavior involves not only when an individual is eating, but also various contextual factors such as with whom and where an individual is eating and what kind of food the individual is eating. Despite the relevance of such factors, most automated eating detection systems are not designed to capture contextual factors. Objective The aims of this study were to (1) design and build a smartwatch-based eating detection system that can detect meal episodes based on dominant hand movements, (2) design ecological momentary assessment (EMA) questions to capture meal contexts upon detection of a meal by the eating detection system, and (3) validate the meal detection system that triggers EMA questions upon passive detection of meal episodes. Methods The meal detection system was deployed among 28 college students at a US institution over a period of 3 weeks. The participants reported various contextual data through EMAs triggered when the eating detection system correctly detected a meal episode. The EMA questions were designed after conducting a survey study with 162 students from the same campus. Responses from EMAs were used to define exclusion criteria. Results Among the total consumed meals, 89.8% (264/294) of breakfast, 99.0% (406/410) of lunch, and 98.0% (589/601) of dinner episodes were detected by our novel meal detection system. The eating detection system showed a high accuracy by capturing 96.48% (1259/1305) of the meals consumed by the participants. The meal detection classifier showed a precision of 80%, recall of 96%, and F1 of 87.3%. We found that over 99% (1248/1259) of the detected meals were consumed with distractions. Such eating behavior is considered “unhealthy” and can lead to overeating and uncontrolled weight gain. A high proportion of meals was consumed alone (680/1259, 54.01%). Our participants self-reported 62.98% (793/1259) of their meals as healthy. Together, these results have implications for designing technologies to encourage healthy eating behavior. Conclusions The presented eating detection system is the first of its kind to leverage EMAs to capture the eating context, which has strong implications for well-being research. We reflected on the contextual data gathered by our system and discussed how these insights can be used to design individual-specific interventions.

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Human perception of room temperature and intermittent air jet cooling in a classroom

Indoor and Built Environment ◽

10.1177/1420326x16628931 ◽

2016 ◽

Vol 26 (4) ◽

pp. 528-537 ◽

Cited By ~ 9

Author(s):

Alan Kabanshi ◽

Hans Wigö ◽

Robert Ljung ◽

Patrik Sörqvist

Keyword(s):

Energy Efficient ◽

Room Temperature ◽

Human Perception ◽

High Energy ◽

Indoor Environments ◽

Indoor Climate ◽

Convective Cooling ◽

Jet Cooling ◽

Air Jet ◽

Operative Temperatures

Steady indoor environments are perceived poor at high temperatures and require a high energy input to cool to comfortable operative temperatures. The introduction of airflow variations in such environments improves occupant perception and is shown to be far more energy efficient than cooling the entire space as only the occupants’ are cooled. However, with this method the risk of draft is high and use of velocity variations reduces the risk. This paper discusses and compares two occupant cooling methods in a classroom setup. Cooling by reducing the room temperature and enhanced convective cooling with intermittent air velocities. The experiments were performed in a full scale mockup classroom with a total of 85 student-participants. In study 1, participants sat in a classroom for about 60 min, in one of two temperature conditions: 20℃ and 25℃. In study 2, all participants sat in a room with a temperature of 25℃, but airflow variation in the sitting zone was manipulated. In both studies, the participants performed various tasks and answered questionnaires on their perception of the indoor climate. As shown here, higher classroom temperature deteriorates human perception of the indoor climate, and the use of intermittent air velocities improves the perception of indoor climate just like cooling by reducing the room air temperature. The results reveal that convective cooling can effectively be used as an energy efficient method of cooling in school environments.

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A hierarchical Bayesian analysis of multiple order constraints in behavioral science

10.32469/10355/67655 ◽

2018 ◽

Author(s):

◽

Julia M. Haaf

Keyword(s):

Model Comparison ◽

Behavioral Science ◽

Meta Analysis ◽

Human Perception ◽

Empirical Science ◽

Single Experiment ◽

Left Hand ◽

Hierarchical Bayesian Analysis ◽

Bayesian Model Comparison ◽

The Individual

[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT AUTHOR'S REQUEST.] Psychology is an empirical science, and oftentimes the main target of interest is an empirical effect. For example, we may be interested in human perception and ask participants to react to light spots flashing up on a screen as fast as they can. Psychologists typically ask whether, on average, participants respond faster to bright lights than to dim ones. In my dissertation, I attempt to extend this question on the individual participant's level: Does everyone react to bright lights faster than to dim ones? In case of perception, this seems reasonable: After accounting for sample noise, we probably would expect that indeed everyone is better at perceiving higher-signal visual stimuli. Yet, we may not expect that everyone throws a ball further with their right hand than their left hand. Clearly, left-handed people may not. And in other areas, we do not have any expectation of whether everyone truly shows an effect or not. In my dissertation, I provide the means of studying the "Does Everyone" question. I develop a set of statistical models including a model where some people show an effect while others show the opposite effect; a model where some people show an effect while others do not; and a model where all people show an effect. I provide a Bayesian model-comparison approach to quantify evidence for these theoretically motivated models. And, finally, I show how the modeling approach can be applied both in a single-experiment setting and in meta-analysis to quantify evidence across many studies.

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