Measuring Success, One Sensor at a Time: A Sensing Infrastructure for Longitudinal Workspace Behavior Monitoring

2.1.1 Smart Wearables. For this pilot study we used Fitbit Sense and the associated API, which allows the automated extraction of unit steps, heart rate (HR), heart rate variability (HRV), skin temperature, and estimated oxygen variation. Most of the other metrics, such as sleep stages and exercise minutes, are derivatives of these metrics. The sampling frequency varied as skin temperature and respiratory rate were only recorded during detected sleep events. Heart rate was also measured at a higher frequency during detected exercise, as described in Table 1.

Sleep is recorded with a timestamp, sleep stage, and time spent in the sleep stage. Raw data must be processed to retrieve bedtime and wake-up time, the total duration of time in each stage of sleep, and the overall duration of sleep. Participants were excluded if they did not have enough observations, as underlined in Table 3. Data processing must account for moments of stillness mistaken for sleep, napping, and waking up at night before falling asleep again. To help clean the data, an arbitrary value of 100 minutes was used to group sleep cycles such that if the time difference between rows was greater than 100 minutes, then the next row was the start of the next sleep cycle. Self-reported sleep times were also used to help filter out detected sleep not within range.

Additionally, we want to use the survey responses as validation for the smart wearable observations, thus we only use sleep observations that have corresponding surveys for the day after. This validation step helps us to clean the Fitbit data prior to analysis. As a result, the original 731 sleep observations were reduced to 524 observations of sleep and daily surveys across twelve participants. Furthermore, Fitbit sleep observations can be used to validate self-reported bedtimes and wake-up times, as the error in selecting AM and PM is common. The comparison can be used to remove data considered outside the scope and to evaluate the discrepancy of self-reported sleep times.

Sleep stages include light, deep, REM, asleep, wake and restless. Light sleep, the initial stage of sleep, helps restore both mental and physical restoration; it is marked by a readiness to wake up and a slight decrease in breathing and heart rate. Deep sleep occurs in the early hours of sleep with a reduced response to external stimuli and slower breathing; it aids physical recovery, memory storage, and immune system support [14]. REM sleep, which occurs later in the sleep cycle, is characterized by active brain activity, dreaming, increased heart rate, and irregular breathing and is essential for mood regulation, learning, and memory storage [14]. While the asleep stage is where the smartwatch detects sleep but is unable to categorize the specific stage. Each stage of sleep has its own benefits and has a specific optimal range. Two of the major measures of proper sleep quality in the literature are described in Eqns. 1, 2, from Fritz et al. [15]. The first is Total Sleep Time (TST), which has an optimal range of seven to nine hours and REM to non-REM ratio (REM:nREM) with an optimal range between 0.25 and 0.33 [17, 27].

2.1.2 Smartphones. Smartphone data is collected through AWARE, an open source framework application designed to help conduct user studies in the mobile context [3]. The application can collect data from various smartphone sensors, including WiFi networks, location, applications, and communication. We observed varying degrees of data collection in iPhone and Android users, where Androids were able to collect more detailed data as described in 1. Smartphone data can provide details on the daily behaviors of participants and help understand events of interest found in the other data modalities. The amount of data collected depends not only on the type of operating software, but also on the sensors that are selected when the application is downloaded on the phone. For the pilot study, we did not utilize all available sensors. To have central storage for all the smartphone data, the AWARE application allows the syncing of data to a database, which also records the timestamp of the last sync and allows for compliance tracking.

Processing of smartphone data requires reference to the AWARE documentation as some fields are nominal data (i.e., screen status: 0 = off, 1 = on, 2 = locked, 3 = unlocked) and also depend on the type of phone that the participant has. The data processing for iPhones will not necessarily apply to that of Androids due to the available data. Even when the same type of sensor data is available for both types of smartphones, such as screen status, the detail of the data is higher on Android than on the iPhone.

2.1.3 Environmental Sensors. For the collection of environmental data, we deployed Awair Omni sensors as described in Table 1. An Awair Omni sensor was installed at the desk or location closest to the participant in the workspace. Figure 1 shows how Awair Omni sensors were installed for remote and in-person participants. The sampling frequency for the two different work contexts is also different; sensors in the office had a sampling frequency of ten seconds, while those in the home workspaces have a sampling frequency of five minutes. The environmental sensors in the office space are connected to the cloud and, through the Awair API, more granular data can be extracted. On the contrary, the sensors in homes could not be accessed by the API, so less granular data was extracted from the Awair dashboard. Each IEQ metric has associated recommended threshold values that can be used to analyze the collected data. As a way to limit and refine the data that is analyzed, location data from smartphones can be utilized to define the times that the participant is at their workspace (office or home) as opposed to being outside.

2.1.4 Surveys. At the start of the study, a baseline survey was administered to participants to collect data on their sociodemographic status, personality, and psychological well-being through various standardized scales. Table 2 details the various subjective measures collected at different frequencies throughout the study. The baseline and pre-study was conducted once at the very start, which collected data that would not see much change during the course of the study. Five formal scales were used in the baseline and pre-study survey:

Weekly and daily surveys serve to collect more granular measures of sleep quality, stress, and productivity. More specifically, the weekly survey assesses the completion of tasks, the efficiency, satisfaction, and quality of the completed tasks, the time management, the workload, and the physical health. The daily check-in is a brief survey that is open from 9 am to 5 pm and evaluates overall sleep rating, frequency of trouble staying awake, and self-reports of when they went to bed and when they woke up that morning.

For the purposes of this paper, in proposing a sensing framework, most of the analysis is focused on responses to daily surveys. The daily surveys contain questions about the day before, which assess the frequency of difficulty staying awake, self-reported bedtime and wake-up time, sleep quality rating, physical activity, and social interactions. Additionally, it also assesses their current state with respect to a rating of their stress, productivity, and emotions.

As mentioned in 2.1.1, self-reported bedtime and wake-up time are very prone to errors, as participants overlook the option of AM and PM. Sleep observations from their Fitbit devices helped to correct the values. A histogram of responses can be plotted to assess the validity of this justification, which is as shown in our data. Therefore, missing data can be imputed with the most likely value, which is the default value of the slider. Daily surveys offer a subjective view of the data collected from the other sensors, which helps to understand the state and behavior of the occupants.

2.1.5 Pilot Study. A recruitment email was sent through the listserv to residents of the smart building space, which includes students, faculty, and staff. Fifteen participants were recruited between April and May 2022. Two participants dropped out. A majority of participants (n = 11) were graduate students, while the rest were faculty (n=2). There were four female and nine male participants, and the age distribution was as follows: 20-29 years old (n=8), 30-39 (n=4), and 50-59 (n=1). The countries of origin of the participants included the United States (n=5), China (n=2), Brazil, Iran, Sri Lanka, South Korea, Bangladesh and Egypt. Participants were compensated with Tango gift cards of up to $100 for two months, depending on compliance with data collection. The study was conducted from the end of April 2022 to July 2022. The study was approved by the Institutional Review Board for Social & Behavioral Sciences at the University of Virginia (IRB-SBS Protocol #2668). All individuals who participated in the study read and accepted the informed consent form. All procedures were carried out according to applicable guidelines and regulations.

For the purpose of this paper, we focus on the relationship between physical movement as a proxy for distraction and lack of productivity, and environmental factors, namely CO₂, TVOC, and noise. More specifically, we are interested to know whether abrupt changes in CO₂, TVOC, and noise are associated with increased physical activity measured through step count.

Table 3 shows the breakdown of each participant with respect to their primary place of work, role, and number of available observations for analysis. For two of the participants at home, their workspace is in their bedroom, so the environmental sensors are installed in their bedroom, as noted in Table 3. The merged sleep and survey observations were used for sleep validation, whereas the merged steps and IEQ data were used to perform the Bayesian change point analysis. The underlined values show which participants had less than 15 days of data and were subsequently excluded from the analysis.

Bayesian Change Points: To quantify the probability and presence of abrupt changes in the indoor environment measures and the physical activity of the occupant, we leverage Bayesian change points (BCP) detection analysis as described by Barry and Hartigan [4]. This model uses a probabilistic framework and product partition models to detect the point at which the mean changes. Using a change-point detector, we can detect the moments in the time series of an occupant's physical activity that can be associated with changes in the environment and vice versa. This has also been explored in previous studies using BCP for HR analysis [34]. We perform this process in R using the package bcp with default values [39]. Additionally, due to the presence of numerous change points, as evidenced by the posterior probability peaks, we utilized the scipy package in Python to attenuate noise and identify the local maxima of these peaks.

We applied Bayesian change point analysis to examine physical activity (steps), CO₂ levels, noise, and TVOC data across ten participants due to the availability of data. The ten participants are divided based on their working location for analysis purposes – in office (n = 7) and at home (n = 3). We investigated possible associations between workplace environmental changes and physical activity patterns, as indicated by step counts, of occupants. Also, note that Bayesian change point analysis was conducted separately for those who worked remote and in person due to the different contexts and sampling frequency. For participants who primarily work from home, it becomes uncertain whether they are in their workspace near the environmental sensor or not. In comparison, for those who work primarily in office, when the location data show that they are in the office, they are more likely to be at their desk.