Optimization of deep neural network-based human activity recognition for a wearable device

Abstract

axes of acceleration data), the resulting DNN-based HAR classifiers can produce comparable or better recognition precision than the baseline classifiers. The experimental results were obtained using three different popular datasets: the WISDM, the UCI HAR, and the Real World 2016. The proposed classifiers with optimised settings are useful as they require less processing time and reduce power consumption, both in terms of retrieving acceleration data from the sensor and the CPU processing time. Furthermore, they reduce the memory requirements for parameter storing and are suitable for incorporation in a wearable device.

Topics & Concepts