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Classification methods based on sparse estimation have drawn much attentionrecently, due to their effectiveness in processing high-dimensional data suchas images. In this paper, a method to improve the performance of a sparserepresentation classification (SRC) approach is proposed; it is then applied tothe problem of online process monitoring of human workers, specifically manualmaterial handling (MMH) operations monitored using wearable sensors (involving111 sensor channels). Our proposed method optimizes the design matrix (akadictionary) in the linear model used for SRC, minimizing its ill-posedness toachieve a sparse solution. This procedure is based on the idea of dictionarylearning (DL): we optimize the design matrix formed by training datasets tominimize both redundancy and coherency as well as reducing the size of thesedatasets. Use of such optimized training data can subsequently improveclassification accuracy and help decrease the computational time needed for theSRC; it is thus more applicable for online process monitoring. Performance ofthe proposed methodology is demonstrated using wearable sensor data obtainedfrom manual material handling experiments, and is found to be superior to thoseof benchmark methods in terms of accuracy, while also requiring computationaltime appropriate for MMH online monitoring.

A Method for Robust Online Classification using Dictionary Learning:  Development and Assessment for Monitoring Manual Material Handling Activities  Using Wearable Sensors