Automated classification of time-activity-location patterns for improved estimation of personal exposure to air pollution

Chatzidiakou, Lia; Krause, Anika; Kellaway, Mike; Li, Yilin; Martin, Elizabeth A.; Kelly, Frank J.; Zhu, Tong; Barratt, Benjamin; Jones, R. L.

doi:10.21203/rs.3.rs-1407884/v1

Cited by 2 publications

(2 citation statements)

References 42 publications

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“…Strong evidence exists that different activities increase exposure to stressors, e.g., elevated levels of airborne particulate matter when dusting, folding clothes, making a bed [ 4 ], smoking cigarettes [ 5 ], vaping [ 6 ], or walking/vacuuming on carpeted flooring [ 7 , 8 ], and increased exposure to noise on public transport [ 9 ]. Manually recording activities by a large group of individuals can be imprecise or require more resources [ 10 ]. An important constraint is temporal resolution, which has to be suited to participants/subjects’ availability and responsiveness.…”

Section: Introductionmentioning

confidence: 99%

Empowering Participatory Research in Urban Health: Wearable Biometric and Environmental Sensors for Activity Recognition

Novak,

Robinson,

Kanduč

et al. 2023

Sensors

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Participatory exposure research, which tracks behaviour and assesses exposure to stressors like air pollution, traditionally relies on time-activity diaries. This study introduces a novel approach, employing machine learning (ML) to empower laypersons in human activity recognition (HAR), aiming to reduce dependence on manual recording by leveraging data from wearable sensors. Recognising complex activities such as smoking and cooking presents unique challenges due to specific environmental conditions. In this research, we combined wearable environment/ambient and wrist-worn activity/biometric sensors for complex activity recognition in an urban stressor exposure study, measuring parameters like particulate matter concentrations, temperature, and humidity. Two groups, Group H (88 individuals) and Group M (18 individuals), wore the devices and manually logged their activities hourly and minutely, respectively. Prioritising accessibility and inclusivity, we selected three classification algorithms: k-nearest neighbours (IBk), decision trees (J48), and random forests (RF), based on: (1) proven efficacy in existing literature, (2) understandability and transparency for laypersons, (3) availability on user-friendly platforms like WEKA, and (4) efficiency on basic devices such as office laptops or smartphones. Accuracy improved with finer temporal resolution and detailed activity categories. However, when compared to other published human activity recognition research, our accuracy rates, particularly for less complex activities, were not as competitive. Misclassifications were higher for vague activities (resting, playing), while well-defined activities (smoking, cooking, running) had few errors. Including environmental sensor data increased accuracy for all activities, especially playing, smoking, and running. Future work should consider exploring other explainable algorithms available on diverse tools and platforms. Our findings underscore ML’s potential in exposure studies, emphasising its adaptability and significance for laypersons while also highlighting areas for improvement.

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Section: Introductionmentioning

confidence: 99%

Empowering Participatory Research in Urban Health: Wearable Biometric and Environmental Sensors for Activity Recognition

Novak,

Robinson,

Kanduč

et al. 2023

Sensors

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“…One important element of AIRLESS is to automatically detect and classify major exposurerelated micro-environments (home, work, other static, in-transit) using GPS coordinates, accelerometry, and noise. The classification of micro-environment can remarkably improve exposure metrics since pollutant inhalation rates vary significantly by location and micro-environment [7].…”

Section: Introductionmentioning

confidence: 99%

Learning the micro-environment from rich trajectories in the context of mobile crowd sensing

Hafyani

Abboud²,

Zuo³

et al. 2022

Geoinformatica

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With the rapid advancements of sensor technologies and mobile computing, Mobile Crowd Sensing (MCS) has emerged as a new paradigm to collect massive-scale rich trajectory data. Nomadic sensors empower people and objects with the capability of reporting and sharing observations on their state, their behavior and/or their surrounding environments. Processing and mining multi-source sensor data in MCS raise several challenges due to their multi-dimensional nature where the measured parameters (i.e., dimensions) may differ in terms of quality, variability, and time scale. We consider the context of air quality MCS and focus on the task of mining the micro-environment from the MCS data. Relating the measures to their micro-environment is crucial to interpret them and analyse the participant's exposure properly. In this paper, we focus on the problem of investigating the feasibility of recognizing the human's micro-environment in an environmental MCS scenario. We propose a novel approach for learning and predicting the micro-environment of users from their trajectories enriched with environmental data represented as multidimensional time series plus GPS tracks. We put forward a multi-view learning approach that we adapt to our context, and implement it along with other time series classification approaches. We extend the proposed approach to a hybrid method that employs trajectory segmentation to bring the best of both methods. We optimise the proposed approaches either by analysing the

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Automated classification of time-activity-location patterns for improved estimation of personal exposure to air pollution

Cited by 2 publications

References 42 publications

Empowering Participatory Research in Urban Health: Wearable Biometric and Environmental Sensors for Activity Recognition

Empowering Participatory Research in Urban Health: Wearable Biometric and Environmental Sensors for Activity Recognition

Learning the micro-environment from rich trajectories in the context of mobile crowd sensing

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