Low-back electromyography (EMG) data-driven load classification for dynamic lifting tasks.

ABSTRACT:

Objective

Numerous devices have been designed to support the back during lifting tasks. To improve the utility of such devices, this research explores the use of preparatory muscle activity to classify muscle loading and initiate appropriate device activation. The goal of this study was to determine the earliest time window that enabled accurate load classification during a dynamic lifting task.

Methods

Nine subjects performed thirty symmetrical lifts, split evenly across three weight conditions (no-weight, 10-lbs and 24-lbs), while low-back muscle activity data was collected. Seven descriptive statistics features were extracted from 100 ms windows of data. A multinomial logistic regression (MLR) classifier was trained and tested, employing leave-one subject out cross-valida

SUBMITTER: Totah D 

PROVIDER: S-EPMC5814006 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature