Is Human Walking a Network Medicine Problem? An Analysis Using Symbolic Regression Models with Genetic Programming. (July 2021)

• Record Type: Journal Article
• Authors: Dasgupta, Pritika; Hughes, James Alexander; Daley, Mark; Sejdić, Ervin
• Is Part Of: Computer methods and programs in biomedicine. Volume 206(2021)
• Publication Date: 2021-07
• Subjects: walking -- genetic programming -- mathematical model -- symbolic regression -- wearables -- acceleration gait measures; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2021.106104
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: Four computational experiments using genetic programming to perform symbolic regression producing a collection of non-linear symbolic models of human locomotion from raw accelerometer data in order to present a body sensor network relationship between these six areas of the body. The network models fit to the lower back's accelerometer were able to describe subject-specific data the best when compared to all other models by a significant amount, suggesting the relationship between different body sites is essential in describing the system. Characterizing changes to a person's locomotion when a cognitive load is present during walking can help researchers create a baseline for healthy walking. Our gait analyses can be used for clinical applications such as the early identification of at-risk gait for falls. ABSTRACT: Background and Objective: Human walking is typically assessed using a sensor placed on the lower back or the hip. Such analyses often ignore that the arms, legs, and body trunk movements all have significant roles during walking; in other words, these body nodes with accelerometers form a body sensor network (BSN). BSN refers to a network of wearable sensors or devices on the human body that collects physiological signals. Our study proposes that human locomotion could be considered as a network of well-connected nodes. Methods: While hypothesizing that accelerometer data can model this BSN, we collected accelerometer signals from six body areas from ten healthy participants performing a cognitive task. Machine learning based on genetic programming was used to produce a collection of non-linear symbolic models of human locomotion. Results: With implications in precision medicine, our primary finding was that our BSN models fit the data from the lower back's accelerometer and describe subject-specific data the best compared to all other models. Across subjects, models were less effective due to the diversity of human sizes. Conclusions: A BSN relationship between all six body nodes has been shown to describe the subject-specific data, which indicates that the network-medicine relationship between these nodes is essential in adequately describing human walking. Our gait analyses can be used for several clinical applications such as medical diagnostics as well as creating a baseline for healthy walking with and without a cognitive load.