Space-time analytics of human physiology for urban planning

Assessing the Current Integration of Multiple Personalised Wearable Sensors for Environment and Health Monitoring

“…Cost remains a significant factor in the success of a campaign. To improve the costeffectiveness, issues related to the weight of the sensor packages [42], cost and accessibility [37], operability [41], accuracy [54], comfort or ease of use [49] and the accessibility of raw data [62] should be taken into considerations. That may explain why studies that employ various kinds of environmental sensors and health trackers only recruit a small number of volunteers, since the cost, weight and number of sensors can skew the small sample size.…”

“…To extract the data features, professional software is useful, but often needs a paid license, which increase the cost. Further, advanced methodological and analytical techniques, such as machine learning [43], can be utilized to deal with the ever-growing data from multiple-sources, but it might demand high computing power [54], which also requires further financial support.…”

“…Time interpolation, to increase the sampling rate of low-resolution data to the same level as high-resolution data, preserves the higher frequency. It can obtain higher resolution datasets [40] and enhance the precision in the analysis [54], but it may increase the computation as well. Otherwise, frequency reduction, to decrease the sampling rate of high-resolution data to the same level with low-resolution data, is often used to merge health data [29,62], accelerometer data [36] with GPS points, since it can save the calculation time of spatial analysis, but may lead to the loss of resolution to some degree.…”

Advances in portable sensing for urban environments: Understanding cities from a mobility perspective

“…In the field of environmental health, it has been suggested that dynamic approaches focusing on the duration, sequence, and accumulation of exposures reduce exposure misclassification problems (Helbich, 2018), while allowing longer periods of exposure monitoring (Loh et al, 2017). Furthermore, it allows researchers to investigate the effect of the urban environment at the microlevel (i.e., the effect of the immediate environment) (Birenboim, 2018;Millar et al, 2021 in this special issue; Su, Zhou, Kwan, Chai, & Zhang, 2021).…”

“…Most studies that utilize this data collection paradigm are focused on presenting a proof of concept or discussing methodological issues, and they tend to rely on limited samples. Prominent research fields that have already started to utilize this approach more substantially in an urban context include environmental health and health geography research in which the level of individual exposure to physical and social environmental factors and their impact on people's physical and mental health and wellbeing is measured at high resolutions (Kou et al, 2020;Kwan et al, 2019;Roberts & Helbich, 2021;Zhang, Zhou, et al, 2020); urban and transportation management and planning (Long & Reuschke, 2021;and Millar et al, 2021 in this special issue); and health monitoring including measuring mobility, physical activity, and physiological status (Li et al, 2017). The study of urban subjective experiences and emotions using portable sensors is another field that has emerged in recent years (Birenboim, 2018;Osborne & Jones, 2017;Shoval, Schvimer, & Tamir, 2018).…”