| Popis: |
Human activity recognition (HAR) attempts to classify performed activities from data retrieved from different sensors attached to the body. Most publications pertaining to HAR based on deep neural networks (DNNs) report the development of a suitable architecture to improve recognition accuracy by increasing the parameters of the architecture. Our work follows a different approach by attempting to optimise DNN-based HAR by reducing the dimensions of acceleration data, by finding a suitable sample size for processing by the DNN and by reducing the parameters of the proposed architecture. The experiments rely on employing two previously presented DNN-based HAR architectures as the baselines and starting points to create our candidate architectures. The variations in the dimensions of acceleration data, i.e., {xy, yz, xz, x, y, z}, and the sample size, i.e. {4, 6, 8} s duration, to these candidate architectures are experimented to produce the winner architecture which takes the shortest sample size and the minimal dimensions of acceleration data while preserving the recognition precision. The results indicate that despite the number of parameters is approximately half of the baseline architecture with two dimensions of acceleration data and shorter sample size (i.e., using a sample of 4 s duration instead of 8 s and only the xy 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. |
