Learning occupants’ workplace interactions from wearable and stationary ambient sensing systems

Ghahramani, Ali; Pantelic, Jovan; Lindberg, Casey; Mehl, Matthias R.; Srinivasan, Karthik; Gilligan, Brian; Arens, Edward

doi:10.1016/j.apenergy.2018.08.096

Cited by 28 publications

(25 citation statements)

References 39 publications

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“…We explored the idea that exposure to different levels of RH could affect one's stress responses as well as physical activity at the office, which in turn could lead to differences in sleep quality as anticipated by the reciprocal relationship between these health-related metrics. 22,23,35 Therefore, for structural equation modeling and ease of interpretation of the association between sleep and average physical activity and RH mediated by stress, SDNN values were reversed and multiplied by a constant value of 0.01. Global estimation with maximum-likelihood approach 38 F I G U R E 1 An illustration of recorded relative humidity (RH) over three days of recording for an individual participant.…”

Section: Discussionmentioning

confidence: 99%

“…The 1 Hz time‐series RH data were transmitted via Wi‐Fi or Ethernet to cloud‐based servers where the data are processed, analyzed, and stored. To evaluate levels of RH exposure, we tracked participants’ proximity to ambient RH measures using calendar information, recorded logs, and floor plan coding . The RH sensor reports values that range from 10% to 90%, with a resolution of 0.3% and an accuracy of ±4%.…”

Section: Methodsmentioning

confidence: 99%

“…In these models, we selected SDNN for representation of the stress responses, the SB‐SQI score to represent sleep quality, and the general tri‐axial accelerometer data (mG) to represent average physical activity at the office. We explored the idea that exposure to different levels of RH could affect one's stress responses as well as physical activity at the office, which in turn could lead to differences in sleep quality as anticipated by the reciprocal relationship between these health‐related metrics . Therefore, for structural equation modeling and ease of interpretation of the association between sleep and average physical activity and RH mediated by stress, SDNN values were reversed and multiplied by a constant value of 0.01.…”

Section: Methodsmentioning

confidence: 99%

“…We took continuous measurements of ambient levels of several IEQ factors at the workplace including RH, 34 T, carbon dioxide (CO2), and particulate matter (PM) as previously reported 35 (Figures S15-S18).…”

Section: Relative Humidity (Rh) and Other Ieq Measurementmentioning

confidence: 99%

“…To evaluate levels of RH exposure, we tracked participants' proximity to ambient RH measures using calendar information, recorded logs, and floor plan coding. 35 The RH sensor reports values that range from 10% to 90%, with a resolution of 0.3% and an accuracy of ±4%. Additional descriptions of sensors including type, range, resolution, and accuracy are included in supplemental materials S15-S18.…”

Section: Relative Humidity (Rh) and Other Ieq Measurementmentioning

confidence: 99%

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Wellbuilt for wellbeing: Controlling relative humidity in the workplace matters for our health

et al. 2019

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This study offers a new perspective on the role of relative humidity in strategies to improve the health and wellbeing of office workers. A lack of studies of sufficient participant size and diversity relating relative humidity (RH) to measured health outcomes has been a driving factor in relaxing thermal comfort standards for RH and removing a lower limit for dry air. We examined the association between RH and objectively measured stress responses, physical activity (PA), and sleep quality. A diverse group of office workers (n = 134) from four well‐functioning federal buildings wore chest‐mounted heart rate variability monitors for three consecutive days, while at the same time, RH and temperature (T) were measured in their workplaces. Those who spent the majority of their time at the office in conditions of 30%‐60% RH experienced 25% less stress at the office than those who spent the majority of their time in drier conditions. Further, a correlational study of our stress response suggests optimal values for RH may exist within an even narrower range around 45%. Finally, we found an indirect effect of objectively measured poorer sleep quality, mediated by stress responses, for those outside this range.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Relative Humidity (Rh) and Other Ieq Measurementmentioning

confidence: 99%

Section: Relative Humidity (Rh) and Other Ieq Measurementmentioning

confidence: 99%

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Wellbuilt for wellbeing: Controlling relative humidity in the workplace matters for our health

et al. 2019

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Equilibria in Personality States: A Conceptual Primer for Dynamics in Personality States

2020

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We provide a basic, step-by-step introduction to the core concepts and mathematical fundamentals of dynamic systems modelling through applying the Change as Outcome model, a simple dynamical systems model, to personality state data. This model characterizes changes in personality states with respect to equilibrium points, estimating attractors and their strength in time series data. Using data from the Personality and Interpersonal Roles study, we find that mean state is highly correlated with attractor position but weakly correlated with attractor strength, suggesting strength provides added information not captured by summaries of the distribution. We then discuss how taking a dynamic systems approach to personality states also entails a theoretical shift. Instead of emphasizing partitioning trait and state variance, dynamic systems analyses of personality states emphasize characterizing patterns generated by mutual, ongoing interactions. Change as Outcome modelling also allows for estimating nuanced effects of personality development after significant life changes, separating effects on characteristic states after the significant change and how strongly she or he is drawn towards those states (an aspect of resiliency). Estimating this model demonstrates core dynamics principles and provides quantitative grounding for measures of 'repulsive' personality states and 'ambivert' personality structures.

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Sensing and Data Collection Methods for Occupant-Centric Building Control: A Critical Review of State of the Art

Karimian

Ouf

Cotrufo

et al. 2022

Lecture Notes in Civil Engineering

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Learning occupants’ workplace interactions from wearable and stationary ambient sensing systems

Cited by 28 publications

References 39 publications

Wellbuilt for wellbeing: Controlling relative humidity in the workplace matters for our health

Wellbuilt for wellbeing: Controlling relative humidity in the workplace matters for our health

Equilibria in Personality States: A Conceptual Primer for Dynamics in Personality States

Sensing and Data Collection Methods for Occupant-Centric Building Control: A Critical Review of State of the Art

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