Identification of Human Activity and Associated Context Using Smartphone Inertial Sensors in Unrestricted Environment

Noorani, Sadam Hussain; Raheel, Aasim; Khan, Sheharyar; Arsalan, Aamir; Ehatisham-ul-Haq, Muhammad

doi:10.1109/c-code58145.2023.10139909

Cited by 4 publications

(1 citation statement)

References 18 publications

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“…The RFC model exhibited superior performance, achieving an accuracy score of 97.67% [51]. A proposed framework for HAR using smartphone sensors employed random forest, decision tree, and k-nearest neighbour classifiers, achieving an accuracy of 93.10% [52].…”

Section: Classification and Shallow Algorithms For Harmentioning

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: Classification and Shallow Algorithms For Harmentioning

confidence: 99%