Name
Title | ||
|---|---|---|
Is Human Walking A Network Medicine Problem? An Analysis Using Symbolic Regression Models With Genetic Programming |
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. Published by Elsevier B.V. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1016/j.cmpb.2021.106104 | COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE |
Keywords | DocType | Volume |
walking, genetic programming, mathematical model, symbolic regression, wearables, acceleration gait measures | Journal | 206 |
ISSN | Citations | PageRank |
0169-2607 | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Pritika Dasgupta | 1 | 0 | 0.34 |
| James Alexander Hughes | 2 | 0 | 0.34 |
| Mark Daley | 3 | 0 | 0.34 |
| Ervin Sejdic | 4 | 146 | 25.55 |