Enhanced configurable snapshot

Lee, Kyungsik; Lee, Gun-Ho; Song, Tao

doi:10.1145/3477314.3507061

Cited by 2 publications

(2 citation statements)

References 17 publications

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“…Deep learning models, such as those based on the self-supervised learning framework SimCLR, have showcased competitive performance in HAR using ambient sensor data [ 34 ]. In smart homes, the use of ambient sensors has become crucial due to the increasing demand for applications that can recognise activities in real-time [ 35 ]. Transformer-based filtering networks combined with LSTM-based early classifiers have been proposed to address the challenges posed by unrefined data in real-time HAR [ 35 ].…”

Section: Introductionmentioning

confidence: 99%

“…In smart homes, the use of ambient sensors has become crucial due to the increasing demand for applications that can recognise activities in real-time [ 35 ]. Transformer-based filtering networks combined with LSTM-based early classifiers have been proposed to address the challenges posed by unrefined data in real-time HAR [ 35 ]. Cross-house human activity recognition is another area of interest, aiming to use labelled data from available houses (source domains) to train recognition models for unlabelled houses (target domains) [ 36 ].…”

Section: Introductionmentioning

confidence: 99%

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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%

Section: Introductionmentioning

confidence: 99%