Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
The Performance of an Algorithm for Classifying Gym-based Tasks across Individuals with Different Body Mass Index
Measurement in Physical Education and Exercise Science ( IF 2.1 ) Pub Date : 2020-09-04 , DOI: 10.1080/1091367x.2020.1815749
Simon Gerrard-Longworth 1 , Stephen J Preece 1 , Alexandra M Clarke-Cornwell 1 , Yannis Goulermas 1
Affiliation  

ABSTRACT

Previous activity classification studies have typically been performed on normal weight individuals. Therefore, it is unclear whether a generic classification algorithm could be developed that would perform consistently across individuals who fall within different BMI categories. Acceleration data were collected from the hip and ankle joints of 50 individuals: 17 normal weight, 14 overweight, and 19 obese. Each participant performed a set of 10 dynamic tasks, which included activities of daily living and gym-based exercises. The performance of a generic classification algorithm, developed using linear discriminant analysis, was compared across the three separate BMI groups for each sensor. Higher classification accuracies (92–95%) were observed for the ankle sensor; however, both sensors demonstrated consistent performance across the three groups. This is the first study to demonstrate the effectiveness of a generic classification algorithm across individuals with different BMI and may be a first step toward automated activity profiling in weight-loss programs.



中文翻译:

具有不同体重指数的个人对基于健身房的任务进行分类的算法的性能

摘要

先前的活动分类研究通常是针对体重正常的个体进行的。因此,目前尚不清楚是否可以开发出一种通用分类算法,该算法将在属于不同BMI类别的个人之间一致地执行。从50个人的髋和踝关节收集加速数据:正常体重17,超重14和肥胖19。每个参与者执行一组10项动态任务,其中包括日常生活活动和体育锻炼。使用线性判别分析开发的通用分类算法的性能在每个传感器的三个单独的BMI组之间进行了比较。脚踝传感器的分类精度更高(92-95%);但是,这两个传感器在三组中均表现出一致的性能。

更新日期:2020-09-04
down
wechat
bug