Document Type
Article
Abstract
The characterization of human behavior in real-world contexts is critical for developing a comprehensive model of human health. Recent technological advancements have enabled wearables and sensors to passively and unobtrusively record and presumably quantify human behavior. Better understanding human activities in unobtrusive and passive ways is an indispensable tool in understanding the relationship between behavioral determinants of health and diseases. Adult individuals (N = 60) emulated the behaviors of smoking, exercising, eating, and medication (pill) taking in a laboratory setting while equipped with smartwatches that captured accelerometer data. The collected data underwent expert annotation and was used to train a deep neural network integrating convolutional and long short-term memory architectures to effectively segment time series into discrete activities. An average macro-F score of at least 85.1 resulted from a rigorous leave-one-subject-out cross-validation procedure conducted across participants. The score indicates the method's high performance and potential for real-world applications, such as identifying health behaviors and informing strategies to influence health. Collectively, we demonstrated the potential of AI and its contributing role to healthcare during the early phases of diagnosis, prognosis, and/or intervention. From predictive analytics to personalized treatment plans, AI has the potential to assist healthcare professionals in making informed decisions, leading to more efficient and tailored patient care.
Digital Object Identifier (DOI)
Publication Info
Published in Sensors (Basel, Switzerland), Volume 24, Issue 14, 2024.
APA Citation
Smith, A., Azeem, M., Odhiambo, C. O., Wright, P. J., Diktas, H. E., Upton, S., Martin, C. K., Froeliger, B., Corbett, C. F., & Valafar, H. (2024). Toward Concurrent Identification of Human Activities with a Single Unifying Neural Network Classification: First Step. Sensors, 24(14), 4542. https://doi.org/10.3390/s24144542
Rights
© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license(https://creativecommons.org/licenses/by/4.0/).