  Classification methods based on sparse estimation have drawn much attention
recently, due to their effectiveness in processing high-dimensional data such
as images. In this paper, a method to improve the performance of a sparse
representation classification (SRC) approach is proposed; it is then applied to
the problem of online process monitoring of human workers, specifically manual
material handling (MMH) operations monitored using wearable sensors (involving
111 sensor channels). Our proposed method optimizes the design matrix (aka
dictionary) in the linear model used for SRC, minimizing its ill-posedness to
achieve a sparse solution. This procedure is based on the idea of dictionary
learning (DL): we optimize the design matrix formed by training datasets to
minimize both redundancy and coherency as well as reducing the size of these
datasets. Use of such optimized training data can subsequently improve
classification accuracy and help decrease the computational time needed for the
SRC; it is thus more applicable for online process monitoring. Performance of
the proposed methodology is demonstrated using wearable sensor data obtained
from manual material handling experiments, and is found to be superior to those
of benchmark methods in terms of accuracy, while also requiring computational
time appropriate for MMH online monitoring.

Machine Learning;Machine Learning;Methodology