Land-use patterns and rapid urban sprawl greatly influence older adults’ mobility in China. Older pedestrians’ safety issues are crucial because these people are more frequently injured in traffic accidents. This research aims to investigate what street characteristics influence perceived safety among older pedestrians in Shanghai, China. A mix of research methods containing both quantitative and qualitative analyses were employed. The researchers recruited 68 elderly urban dwellers who each agreed to take a perceived safety survey using 39 simulated streetscape images that contained 12 street characteristics extracted from four sectional zones of streets. Ordinal logit regression was performed to investigate the impact of street characteristics on older pedestrians’ perceived safety. A semi-structured, in-depth interview was conducted with 8 out of the 68 participants. It was found that the street interface type, elevation differences, footpath width, paver directions, isolation facility type, and vehicle traffic all affected pedestrians’ perceived safety after adjusting for individual demographic attributes and the physical limitations of the participants. Understanding the street characteristics related to perceived safety will contribute to making more inclusive and walkable cities.

The ever-growing development of sensor technology brings new opportunities to investigate impacts of the outdoor environment on human health at the individual level. However, there is limited literature on the use of multiple personalized sensors in urban environments. This review paper focuses on examining how multiple personalized sensors have been integrated to enhance the monitoring of co-exposures and health effects in the city. Following PRISMA guidelines, two reviewers screened 4898 studies from Scopus, Web of Science, ProQuest, Embase, and PubMed databases published from January 2010 to April 2021. In this case, 39 articles met the eligibility criteria. The review begins by examining the characteristics of the reviewed papers to assess the current situation of integrating multiple sensors for health and environment monitoring. Two main challenges were identified from the quality assessment: choosing sensors and integrating data. Lastly, we propose a checklist with feasible measures to improve the integration of multiple sensors for future studies.

Built environment factors such as greenery, walkability, and crowd density are related to physical activity and mental health. New emerging wearable sensors provide an opportunity to objectively monitor human exposure to street-level urban features. However, very few studies have demonstrated how to objectively measure the association between the built environment, human emotions, and health. This pilot study proposes a new approach that employs a FrontRow wearable lifestyle camera, a GPS tracker, and an Empatica 4 wristband as a sensor package to track individuals during their everyday activities. Machine-learning methods are adopted to extract urban features. For this study, volunteers were asked to conduct a self-led city tour in Roskilde, Denmark, while using the wearable sensors. Study results demonstrate the feasibility of the proposed approach and the potential for using integrated, multi-sourced data in the study of urban health.