Toward Concurrent Identification of Human Activities with a Single Unifying Neural Network Classification: First Step
Andrew Smith,
Musa Azeem,
Chrisogonas O. Odhiambo,
Pamela J. Wright,
Hanim E. Diktas,
Spencer Upton,
Corby K. Martin,
Brett Froeliger,
Cynthia F. Corbett,
Homayoun Valafar
Affiliations
Andrew Smith
Department of Computer Science and Engineering, University of South Carolina, Columbia, SC 29208, USA
Musa Azeem
Department of Computer Science and Engineering, University of South Carolina, Columbia, SC 29208, USA
Chrisogonas O. Odhiambo
Department of Computer Science and Engineering, University of South Carolina, Columbia, SC 29208, USA
Pamela J. Wright
Advancing Chronic Care Outcomes through Research and iNnovation (ACORN) Center, Department of Biobehavioral Health & Nursing Science, College of Nursing, University of South Carolina, Columbia, SC 29208, USA
Hanim E. Diktas
Pennington Biomedical Research Center, Louisiana State University System, Baton Rouge, LA 70808, USA
Spencer Upton
Department of Psychiatry, Psychological Sciences, and Cognitive Neuroscience Systems Core Facility, University of Missouri, Columbia, MO 65211, USA
Corby K. Martin
Pennington Biomedical Research Center, Louisiana State University System, Baton Rouge, LA 70808, USA
Brett Froeliger
Department of Psychiatry, Psychological Sciences, and Cognitive Neuroscience Systems Core Facility, University of Missouri, Columbia, MO 65211, USA
Cynthia F. Corbett
Advancing Chronic Care Outcomes through Research and iNnovation (ACORN) Center, Department of Biobehavioral Health & Nursing Science, College of Nursing, University of South Carolina, Columbia, SC 29208, USA
Homayoun Valafar
Department of Computer Science and Engineering, University of South Carolina, Columbia, SC 29208, USA
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-F1 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.