Using Bluetooth proximity sensing to determine where office workers spend time at work

AirPen: A Wearable Monitor for Characterizing Exposures to Particulate Matter and Volatile Organic Compounds

Exposure to air pollution is a leading risk factor for disease and premature death, but technologies for assessing personal exposure to particulate and gaseous air pollutants, including the timing and location of such exposures, are limited. We developed a small, quiet, wearable monitor, called the AirPen, to quantify personal exposures to fine particulate matter (PM_2.5) and volatile organic compounds (VOCs). The AirPen combines physical sample collection (PM onto a filter and VOCs onto a sorbent tube) with a suite of low-cost sensors (for PM, VOCs, temperature, pressure, humidity, light intensity, location, and motion). We validated the AirPen against conventional personal sampling equipment in the laboratory and then conducted a field study to measure at-work and away-from-work exposures to PM_2.5 and VOCs among employees at an agricultural facility in Colorado, USA. The resultant sampling and sensor data indicated that personal exposures to benzene, toluene, ethylbenzene, and xylenes were dominated by a specific workplace location. These results illustrate how the AirPen can be used to advance our understanding of personal exposure to air pollution as a function of time, location, source, and activity, even in the absence of detailed activity diary data.

How many days of indoor positioning system data are required to characterise typical movement behaviours of office workers?

Indoor Positioning Systems (IPS) appear to offer great potential to study the movement and interaction of people and their working environment, including office workplaces. But little is known about appropriate durations for data collection. In this study, location observations collected from 24 office workers on a 1220 m² office floor over a 3-month period, were analysed to determine how many days are required to estimate their typical movement and spatial behaviours. The analysis showed that up to 8 days of data was sufficient to characterise participants' typical daily movement behaviours and 10 days were required to estimate their typical spatial mobility. However, the results also indicate that 5 weeks of data collection are required to gather the necessary 10 days of data from each participant. These findings will help researchers and workplace professionals to understand the capabilities and requirements of IPS when considering their use in indoor work environments.

Association of wearable device-measured vigorous intermittent lifestyle physical activity with mortality

Wearable devices can capture unexplored movement patterns such as brief bursts of vigorous intermittent lifestyle physical activity (VILPA) that is embedded into everyday life, rather than being done as leisure time exercise. Here, we examined the association of VILPA with all-cause, cardiovascular disease (CVD) and cancer mortality in 25,241 nonexercisers (mean age 61.8 years, 14,178 women/11,063 men) in the UK Biobank. Over an average follow-up of 6.9 years, during which 852 deaths occurred, VILPA was inversely associated with all three of these outcomes in a near-linear fashion. Compared with participants who engaged in no VILPA, participants who engaged in VILPA at the sample median VILPA frequency of 3 length-standardized bouts per day (lasting 1 or 2 min each) showed a 38%-40% reduction in all-cause and cancer mortality risk and a 48%-49% reduction in CVD mortality risk. Moreover, the sample median VILPA duration of 4.4 min per day was associated with a 26%-30% reduction in all-cause and cancer mortality risk and a 32%-34% reduction in CVD mortality risk. We obtained similar results when repeating the above analyses for vigorous physical activity (VPA) in 62,344 UK Biobank participants who exercised (1,552 deaths, 35,290 women/27,054 men). These results indicate that small amounts of vigorous nonexercise physical activity are associated with substantially lower mortality. VILPA in nonexercisers appears to elicit similar effects to VPA in exercisers, suggesting that VILPA may be a suitable physical activity target, especially in people not able or willing to exercise.

The Importance of Research on Occupational Sedentary Behaviour and Activity Right Now

The workplace has been identified as a key setting for public health interventions to 'promote and maintain the highest degree of physical, mental and social well-being of workers in all occupations' by the World Health Organisation [...].

Measuring lamb activity during lactation by actigraphy

Twenty-two lambs were used to determine their locomotor activity during lactation, and its circadian rhythm, based on data collected by actigraphy; the relationships among lambs were also measured calculating their proximity and distance. Lambs were fitted with Bluetooth-enabled (BT) accelerometers during weeks 1 and 4 of age, and data were downloaded as activity counts at 1-min intervals (Vector Magnitude, VM). Sensors were programmed as receivers and as beacons of the BT signal to calculate proximity (min/h) and distance (m) to another lamb. For each week, mean daily VM was calculated at hourly intervals. Circadian rhythms in VM were graphed by fitting the time-series measurements of each lamb to the cosine curve of a 24-h activity rhythm. Week of lactation and lamb sex had a significant (P < .05) effect on VM. Lambs had a higher (P < .05) VM (counts/min ±S.E.) in week 1 (154 ± 13) than in week 4 (117 ± 4), and female (113 ± 10) were more active (P < .01) than male lambs (113 ± 10). Twin lambs spent more time (P < .001) close to its sibling than to another lambs. Lamb activity followed a 24-h rhythm in week 4, only. In conclusion, actigraphy demonstrated that circadian rhythms in activity were not present in the first week of life, and twin-born lambs had stronger associations with their littermates than did singletons with other lambs, which reflected the strong bond that is established between twin lambs.

Stop motion: using high resolution spatiotemporal data to estimate and locate stationary and movement behaviour in an office workplace

Prolonged periods of stationary behaviour, a common occurrence in many office workplaces, are linked with a range of physical disorders. Investigating the physical context of this behaviour may be a key to developing effective interventions. This study aimed to estimate and locate the stationary and movement behaviours of office workers (n = 10) by segmenting spatiotemporal data collected over 5 days in an office work-based setting. The segmentation method achieved a balanced accuracy ≥85.5% for observation classification and ≥90% for bout classification when compared to reference data. The results show the workers spent the majority of their time stationary (Mean = 86.4%) and had on average, 28.4 stationary and 25.9 moving bouts per hour. While these findings accord with other studies, the segmented data was also visualised, revealing that the workers were stationary for periods ≥5 min at multiple locations and these locations changed across time. Practitioner Summary: This study applied a data segmentation method to classify stationary and moving behaviours from spatiotemporal data collected in an office workplace. The segmented data revealed not only what behaviours occurred but also their location, duration, and time. Segmenting spatiotemporal data may add valuable physical context to aid workplace research.

Activity space, office space: Measuring the spatial movement of office workers

A key to the development of more effective interventions to promote movement and reduce physical inactivity in office workplaces may be to measure and locate individual's spatial movement. Using an activity space estimation method, high resolution location data collected from 15 office workers over 12 days were used to estimate and analyse the location and extent of their daily spatial movement whilst in an office work-based setting. The results indicated that the method, kernel density estimation, combined with location data offers significant opportunities to not only measure and compare spatial movement behaviours but also simultaneously identify the locations where the behaviours occur. Combined with other data streams, this method will allow researchers to further investigate the influence of different environmental characteristics on these behaviours, potentially leading the development of more effective, longer lasting interventions to promote movement and reduce stationary behaviour, ultimately improving the health of office workers.

Predicting Depressive Symptom Severity Through Individuals' Nearby Bluetooth Device Count Data Collected by Mobile Phones: Preliminary Longitudinal Study

BACKGROUND

Research in mental health has found associations between depression and individuals' behaviors and statuses, such as social connections and interactions, working status, mobility, and social isolation and loneliness. These behaviors and statuses can be approximated by the nearby Bluetooth device count (NBDC) detected by Bluetooth sensors in mobile phones.

OBJECTIVE

This study aimed to explore the value of the NBDC data in predicting depressive symptom severity as measured via the 8-item Patient Health Questionnaire (PHQ-8).

METHODS

The data used in this paper included 2886 biweekly PHQ-8 records collected from 316 participants recruited from three study sites in the Netherlands, Spain, and the United Kingdom as part of the EU Remote Assessment of Disease and Relapse-Central Nervous System (RADAR-CNS) study. From the NBDC data 2 weeks prior to each PHQ-8 score, we extracted 49 Bluetooth features, including statistical features and nonlinear features for measuring the periodicity and regularity of individuals' life rhythms. Linear mixed-effect models were used to explore associations between Bluetooth features and the PHQ-8 score. We then applied hierarchical Bayesian linear regression models to predict the PHQ-8 score from the extracted Bluetooth features.

RESULTS

A number of significant associations were found between Bluetooth features and depressive symptom severity. Generally speaking, along with depressive symptom worsening, one or more of the following changes were found in the preceding 2 weeks of the NBDC data: (1) the amount decreased, (2) the variance decreased, (3) the periodicity (especially the circadian rhythm) decreased, and (4) the NBDC sequence became more irregular. Compared with commonly used machine learning models, the proposed hierarchical Bayesian linear regression model achieved the best prediction metrics (R2=0.526) and a root mean squared error (RMSE) of 3.891. Bluetooth features can explain an extra 18.8% of the variance in the PHQ-8 score relative to the baseline model without Bluetooth features (R2=0.338, RMSE=4.547).

CONCLUSIONS

Our statistical results indicate that the NBDC data have the potential to reflect changes in individuals' behaviors and statuses concurrent with the changes in the depressive state. The prediction results demonstrate that the NBDC data have a significant value in predicting depressive symptom severity. These findings may have utility for the mental health monitoring practice in real-world settings.

Movement at work: A comparison of real time location system, accelerometer and observational data from an office work environment

Office workers can spend significant periods of time being stationary whilst at work, with potentially serious health consequences. The development of effective health interventions could be aided by a greater understanding of the location and environmental context in which this stationary behaviour occurs. Real time location systems (RTLS) potentially offer the opportunity to gather this much needed information, but they have not been extensively trialled in office workplaces, nor rigorously compared against more familiar devices such as accelerometers. The aim of this paper was to determine whether an RTLS can measure and spatially locate the non-stationary and stationary behaviours of adults working in an office work environment. Data collected from a series of comparison studies undertaken in a commercial office building suggests that RTLS can measure the velocity at which people are moving and locate them, when stationary, with an accuracy of 0.668 m (SD 0.389). This opens up significant opportunities to further understand how people move within buildings, the indoor physical environmental influences on that movement, and the development of effective interventions to help people to move more whilst at work.

Associations of Device-Measured Sitting, Standing, and Stepping Time With Informal Face-to-Face Interactions at Work

OBJECTIVE

This cross-sectional study examined the interrelationships between workplace movement (sitting, standing, and stepping), availability of discussion space, and face-to-face (FTF) interactions between workers.

METHODS

Desk-based workers (n = 221) wore an activity monitor for 7 days and self-reported their weekly frequency of FTF interactions and discussion space availability. Negative binomial regression models examined behavioral and spatial factors associated with the frequency of FTF interactions.

RESULTS

Adjusted for potential confounders, each one standard deviation increment in time spent sitting, standing, stepping, and discussion space availability was associated with 20% lower (P = 0.004), 19% higher (P = 0.003), 6% higher (P = 0.16), and 11% higher (P = 0.26) frequency of FTF interactions, respectively CONCLUSIONS:: Lower workplace sitting was often linked to reduced risk of chronic diseases. Our findings suggest that less sitting at work may have additional benefits of increasing informal interactions between office workers.

Development of a real-time indoor location system using bluetooth low energy technology and deep learning to facilitate clinical applications

PURPOSE

An indoor, real-time location system (RTLS) can benefit both hospitals and patients by improving clinical efficiency through data-driven optimization of procedures. Bluetooth-based RTLS systems are cost-effective but lack accuracy because Bluetooth signal is subject to significant fluctuation. We aim to improve the accuracy of RTLS using the deep learning technique.

METHODS

We installed a Bluetooth sensor network in a three-floor clinic building to track patients, staff, and devices. The Bluetooth sensors measured the strength of the signal broadcasted from Bluetooth tags, which was fed into a deep neural network to calculate the location of the tags. The proposed deep neural network consists of a long short-term memory (LSTM) network and a deep classifier for tracking moving objects. Additionally, a spatial-temporal constraint algorithm was implemented to further increase the accuracy and stability of the results. To train the neural network, we divided the building into 115 zones and collected training data in each zone. We further augmented the training data to generate cross-zone trajectories, mimicking the real-world scenarios. We tuned the parameters for the proposed neural network to achieve relatively good accuracy.

RESULTS

The proposed deep neural network achieved an overall accuracy of about 97% for tracking objects in each individual zone in the whole three-floor building, 1.5% higher than the baseline neural network that was proposed in an earlier paper, when using 10 s of signals. The accuracy increased with the density of Bluetooth sensors. For tracking moving objects, the proposed neural network achieved stable and accurate results. When latency is less of a concern, we eliminated the effect of latency from the accuracy and gained an accuracy of 100% for our testing trajectories, significantly improved from the baseline method.

CONCLUSIONS

The proposed deep neural network composed of a LSTM, a deep classifier and a posterior constraint algorithm significantly improved the accuracy and stability of RTLS for tracking moving objects.

Effects of Proximity between Companion Dogs and Their Caregivers on Heart Rate Variability Measures in Older Adults: A Pilot Study

Heart rate variability (HRV) is a noninvasive tool used to evaluate autonomic nervous system function and is affected by age, stress, postural changes, and physical activity. Dog ownership has been associated with higher 24-hr HRV and increased physical activity compared to nonowners. The current pilot study was designed to evaluate the effects of proximity to a dog in real time (minute-by-minute) on older dog caregivers’ HRV measures and stress index during normal daily life over a 24-hr period. Eleven caregivers (56–83 years of age) wore ActiGraph GT9X Link accelerometers and camntech electrocardiogram monitors, and 11 dogs wore PetPace Collars and ActiGraph monitors to determine (a) proximity (absence or presence of Received Signal Strength Indicator, RSSI), (b) heart rate and HRV measures, (c) position (lying vs. sitting vs. standing), and (d) physical activity in the 11 dyads. Twenty-four hour HRV (SDNN index) and physical activity in the caregivers and dogs were related. Stress index was lower, and HRV parameters (SDNN, rMSDD, high frequency power (HF)) were higher when an RSSI signal was detected (presence of dog) compared to no RSSI signal (absence of dog) in the caregivers while inactive (lying + sitting + standing combined). HRV parameters (rMSDD and HF) were lower in the caregivers while standing and sitting compared to lying. The results from this pilot study support the hypothesis that spending time in the presence of a companion dog increases caregivers’ HRV throughout the day and suggest that proximity to a dog may contribute to overall improvements in 24-hr HRV and cardiac health in dog caregivers.

Comparative analysis of computer-vision and BLE technology based indoor navigation systems for people with visual impairments

BACKGROUND

Considerable number of indoor navigation systems has been proposed to augment people with visual impairments (VI) about their surroundings. These systems leverage several technologies, such as computer-vision, Bluetooth low energy (BLE), and other techniques to estimate the position of a user in indoor areas. Computer-vision based systems use several techniques including matching pictures, classifying captured images, recognizing visual objects or visual markers. BLE based system utilizes BLE beacons attached in the indoor areas as the source of the radio frequency signal to localize the position of the user.

METHODS

In this paper, we examine the performance and usability of two computer-vision based systems and BLE-based system. The first system is computer-vision based system, called CamNav that uses a trained deep learning model to recognize locations, and the second system, called QRNav, that utilizes visual markers (QR codes) to determine locations. A field test with 10 blindfolded users has been conducted while using the three navigation systems.

RESULTS

The obtained results from navigation experiment and feedback from blindfolded users show that QRNav and CamNav system is more efficient than BLE based system in terms of accuracy and usability. The error occurred in BLE based application is more than 30% compared to computer vision based systems including CamNav and QRNav.

CONCLUSIONS

The developed navigation systems are able to provide reliable assistance for the participants during real time experiments. Some of the participants took minimal external assistance while moving through the junctions in the corridor areas. Computer vision technology demonstrated its superiority over BLE technology in assistive systems for people with visual impairments.

Associations between the Home Physical Environment and Children's Home-Based Physical Activity and Sitting

It is important to understand the correlates of children's physical activity (PA) and sitting at home, where children spend significant time. The home social environment has an important influence; however, much less is known about the home physical environment. Therefore, the study aimed to assess relationships between the physical environment and children's sitting and PA at home. In total, 235 child-parent dyads were included in the analyses. Children spent 67% of their time at home sitting. Linear regression analyses examined associations between physical home environmental factors obtained via an audit and children's (55% girl, 10.2 ± 0.7) objective PA and sitting at home. Following adjustment for socio-demographics and social environmental factors, an open plan living area (OPLA), musical instrument accessibility and availability, and perceived house size were negatively and positively associated, whereas media equipment accessibility and availability was positively and negatively associated with sitting and standing, respectively. Additionally, an OPLA was positively associated with total and moderate-to-vigorous PA. Furthermore, sitting breaks were positively associated with objective garden size and negatively associated with digital TV. The physical home environment may have an important influence on children's sitting, standing and PA at home; therefore, interventions that target this environment are needed.

Combining Actigraph Link and PetPace Collar Data to Measure Activity, Proximity, and Physiological Responses in Freely Moving Dogs in a Natural Environment

Simple Summary

The Actigraph accelerometry monitors, the most widely used and extensively validated devices for measuring physical activity in humans, have also been validated for use in dogs. The ActiGraph GT9X Link monitor has Bluetooth Smart technology and a proximity-tagging feature that potentially allows for the measurement of distance between subjects, e.g., between human caretakers and their dog(s). The PetPace Smart-collar is a non-invasive wireless collar that collects important health markers, including heart beats, variation in the intervals between heartbeats, breaths per minute, and position data (lying, sitting, standing), in addition to activity. The purpose of this study was to determine whether combining data from the Actigraph monitor and PetPace collar would provide reliable pulse, respiration, and heart rate variability results during various activity levels in dogs, and whether these variables were affected by the absence or presence of their caretakers.

Abstract

Although several studies have examined the effects of an owner’s absence and presence on a dog’s physiological responses under experimental conditions over short periods of time (minutes), little is known about the effects of proximity between humans and freely moving dogs under natural conditions over longer periods of time (days). The first aim of our study was to determine whether the combined data generated from the PetPace Collar and Actigraph Link accelerometer provide reliable pulse, respiration, and heart rate variability results during sedentary, light-moderate, and vigorous bouts in 11 freely moving dogs in a foster caretaker environment over 10–15 days. The second aim was to determine the effects of proximity (absence and presence of caretaker) and distance (caretaker and dog within 0–2 m) on the dogs’ physiological responses. Aim 1 results: Pulse and respiration were higher during light-moderate bouts compared to sedentary bouts, and higher at rest while the dogs were standing and sitting vs. lying. Heart rate variability (HRV) was not different between activity levels or position. Aim 2 results: During sedentary bouts, pulse and respiration were higher, and HRV lower, when there was a proximity signal (caretaker present) compared to no proximity signal (caretaker absent). Using multiple regression models, we found that activity, position, distance, and signal presence were predictors of physiological response in individual dogs during sedentary bouts. Our results suggest that combining data collected from Actigraph GT9X and PetPace monitors will provide useful information, both collectively and individually, on dogs’ physiological responses during activity, in various positions, and in proximity to their human caretaker.

Associations of context-specific sitting time with markers of cardiometabolic risk in Australian adults

Background

High volumes of sitting time are associated with an elevated risk of type 2 diabetes and cardiovascular disease, and with adverse cardiometabolic risk profiles. However, previous studies have predominately evaluated only total sitting or television (TV) viewing time, limiting inferences about the specific cardiometabolic health impacts of sitting accumulated in different contexts. We examined associations of sitting time in four contexts with cardiometabolic risk biomarkers in Australian adults.

Methods

Participants (n = 3429; mean ± SD age 58 ± 10 years) were adults without clinically diagnosed diabetes or cardiovascular disease from the 2011–2012 Australian Diabetes, Obesity and Lifestyle (AusDiab) study. Multiple linear regressions examined associations of self-reported context-specific sitting time (occupational, transportation, TV-viewing and leisure-time computer use) with a clustered cardiometabolic risk score (CMR) and with individual cardiometabolic risk biomarkers (waist circumference, BMI, resting blood pressure, triglycerides, HDL- and LDL-cholesterol, and fasting and 2-h post-load plasma glucose).

Results

Higher CMR was significantly associated with greater TV-viewing and computer sitting time (b [95%CI] = 0.07 [0.04, 0.09] and 0.06 [0.03, 0.09]), and tended to be associated with higher occupational and transport sitting time (0.01 [− 0.01, 0.03] and 0.03 [− 0.00, 0.06]), after adjustment for potential confounders. Furthermore, keeping total sitting time constant, accruing sitting via TV-viewing and computer use was associated with significantly higher CMR (0.05 [0.02, 0.08] and 0.04 [0.01, 0.06]), accruing sitting in an occupational context was associated with significantly lower CMR (− 0.03 [− 0.05, − 0.01]), while no significant association was seen for transport sitting (0.00 [− 0.03, 0.04]). Results varied somewhat between the respective biomarkers; however, higher sitting time in each domain tended to be associated detrimentally with individual biomarkers except for fasting glucose (non-significant associations) and systolic blood pressure (a beneficial association was observed). Overall, associations were stronger for TV-viewing and computer use, and weaker for occupational sitting.

Conclusions

Higher context-specific sitting times tended to be detrimentally associated, albeit modestly, with CMR and several cardiometabolic risk biomarkers. There was some evidence suggesting that the context in which people sit is relevant above and beyond total sitting time. Methodological issues notwithstanding, these findings may assist in identifying priorities for sitting-reduction initiatives, in order to achieve optimal cardiometabolic health benefits.

Electronic supplementary material

The online version of this article (10.1186/s12966-018-0748-3) contains supplementary material, which is available to authorized users.