Image was made by Zhaoxi Zhang ( Image source )
“What is lifelogging?”
As early as 1945, Vannevar Bush proposed the concept of ‘lifelogging’- the use of intelligent devices to capture the full and continuous characteristics of an individual’s life, creating a multitude of individual databases that digitally capture their activities. With the advent of the Fourth Industrial Revolution, people have begun to explore the potential of new technologies and new devices to study the relationship between human behaviour and urban design. Common wearable devices, such as smart wristbands and watches, can record the user’s physical condition and activities through human-computer interactions.
Today, small and lightweight wearable cameras that can take photos periodically, passively and automatically offer outstanding features for the visual representation of personal behavioural data.The emergence of wearable cameras offers more possibilities for monitoring individual behaviour in the built environment as a kind of ‘lifelogging’.
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In my PhD research, I believe that new emerging wearable sensors provide an opportunity to objectively monitor human exposure to urban features at street level, which can help us combat environment-related mental disorders.Therefore, I have proposed a novel approach using a FrontRow wearable lifestyle camera, a GPS tracker and an Empatica 4 wristband as a sensor package to track individuals in urban public open spaces and assess physiological stress response at an aggregate level. Afterwards, I link stress response to urban features to measure the health impact of urban design and suggest health-promoting urban design strategies.
Citation:
Zhang, Z., & Long, Y. (2019). Application of wearable cameras in studying individual behaviors in built environments. Landscape Architecture Frontiers, 7(2), 22-37. doi:10.15302/j-laf-20190203
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Contact:
Zhaoxi Zhang
Application of wearable cameras in studying individual behaviors in built environments
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Abstract:
With the advent of the Fourth Industrial Revolution, people have begun to explore the potential for new technologies and new devices in studying the relationship between human behavior and urban design. The emergence of wearable cameras offers more possibilities for monitoring individual behavior in built environments as a kind of “lifelog.” This article explores the applications of wearable cameras in studying the relationship between individual behavior and built environments. Using manual image identification, image recognition with Computer Vision Application Programming Interface (API), and color calculation in Matlab, this study analyzed 8,598 photos recording the volunteer’s behaviors and activities during a week. Based on high-accuracy manual image identification results, the research analyzed the volunteer’s behavior, time use, movement path, and experiencing scenes. The study showed that the big data base of images collected by the wearable cameras contained rich individual activities and spatiotemporal information that could be used to effectively describe the individual behavior in space and further contribute to the study of the relationship between individual behaviors and built environments.
Citation:
Zhang, Z., Long, Y., Chen, L., & Chen, C. (2021). Assessing personal exposure to urban greenery using wearable cameras and machine learning. Cities, 109. doi:10.1016/j.cities.2020.103006
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Contact:
Zhaoxi Zhang
Assessing personal exposure to urban greenery using wearable cameras and machine learning
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Abstract:
Urban greenery is closely related to people's behaviour. With the advancement of science and technology in Artificial Intelligence, wearable sensors and cloud computing, the potential for studying the relationship between people and urban greenery through new data and technology is constantly being explored, such as assessing population exposure to urban greenery using multi-source big data. Taking one individual participant as a case study, this paper proposes and validates the effectiveness of using wearable camera (Narrative Clip 2) and machine learning (Applications Programming Interface of Microsoft Cognitive Service) to assess personal exposure to urban greenery. Microsoft API is used to identify urban greenery tags, including “flower”, “forest”, “garden”, “grass”, “green”, “plant”, “scene” and “tree”, in personal images taken by the wearable camera. Personal exposure to urban greenery is assessed by calculating the frequency of the urban greenery tags in all the images taken. Furthermore, the overall evaluation and regularity of personal exposure to urban greenery (including “static exposure” and “dynamic exposure”) are explored to identify the characteristics of individual's greenery lifelogging. This study makes a brave attempt that may contribute a new perspective in applying personal big data in studying individual behaviour.
Citation:
Zhang, Z., Amegbor, P. M., & Sabel, C. E. (2021). Assessing the Current Integration of Multiple Personalised Wearable Sensors for Environment and Health Monitoring. Sensors (Basel), 21(22). doi:10.3390/s21227693
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Contact:
Zhaoxi Zhang
Assessing the Current Integration of Multiple Personalised Wearable Sensors for Environment and Health Monitoring
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Abstract:
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.
Citation:
Zhang, Z., Amegbor, P. M., & Sabel, C. E. (2022). The feasibility of integrating wearable cameras and health trackers for measuring personal exposure to urban features: a pilot study in Roskilde, Denmark. International Journal of E-Planning Research (IJEPR), 11(1), 1-21. http://doi.org/10.4018/IJEPR.313181
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Contact:
Zhaoxi Zhang
The feasibility of integrating wearable cameras and health trackers for measuring personal exposure to urban features: a pilot study in Roskilde, Denmark
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Abstract:
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.
Citation:
Zhang, Z., Amegbor, P. M., Sigsgaard, T., & Sabel, C. E. (2022). Assessing the association between urban features and human physiological stress response using wearable sensors in different urban contexts. Health & Place, 78, 102924. doi:https://doi.org/10.1016/j.healthplace.2022.102924
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Contact:
Zhaoxi Zhang
Assessing the association between urban features and human physiological stress response using wearable sensors in different urban contexts
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Abstract:
Public open space (POS) plays a significant role in fostering human health and wellbeing in cities. A major limitation of current research on POS and health is that there is little attention on the role of various urban features on people's mental health, in different urban context. This study employed wearable sensors (a wearable camera, Empatica 4 wristband and a GPS device) to measure human physiological responses to urban indicators, objectively. To do this, we selected six kinds of public open space (water area, transit area, green area, commercial area, motor traffic area and mixed office and residential area) and recruited 86 participants for an experimental study. Next, we detected urban features by using Microsoft Cognitive Services (MCS) and calculated a change score to assess human physiological stress responses based on galvanic skin response (GSR) and skin temperature from the wristband. Lastly, we applied random effect model and geographically weighted regression analysis to examine the relationship between urban indicators and human physiological stress responses. The findings show that urban flow (vehicles, bikes and people), waterbodies, greenery and places to sit are associated with the changes of human physiological stress response. The findings indicate that the type of urban context may confound the effect of green and blue urban features; i.e., the effect on physiological stress response can be positive or negative depending on the context. The paper highlights the relevance of considering urban context in research on associations between urban features and stress response.