A Novel Multi-Stage Training Approach for Human Activity Recognition from Multimodal Wearable Sensor Data

The prevalence of a variety of wearable gadgets enables performing human activity recognition. Yet, selecting productive capabilities is continue to complicated when applying quite a few sensors. A the latest paper on arXiv.org proposes a novel multi-stage schooling methodology to overcome recent difficulties.

Fitness tracker. Image credit: StockSnap via Pixabay, CC0 Public Domain

Physical fitness tracker. Impression credit: StockSnap by using Pixabay, CC0 Public Area

A novel deep convolutional neural community architecture enables characteristic extraction from a lot of transformed areas alternatively of relying on a single area. The different networks are then combined applying multi-stage sequential schooling to attain the most robust and correct characteristic illustration.

The method achieves optimization with a smaller sized total of schooling facts and avoids noise or other perturbations. It outperforms state-of-the-artwork ways with an eleven.forty nine% common improvement. The scheme can also be utilized in other fields that call for to teach neural networks deploying transformed representations of facts.

Deep neural community is an productive choice to instantly recognize human steps utilizing facts from a variety of wearable sensors. These networks automate the process of characteristic extraction relying wholly on facts. Nonetheless, a variety of noises in time sequence facts with complex inter-modal interactions between sensors make this process far more complicated. In this paper, we have proposed a novel multi-stage schooling strategy that will increase variety in this characteristic extraction process to make correct recognition of steps by combining varieties of capabilities extracted from varied views. At first, alternatively of applying single sort of transformation, a lot of transformations are utilized on time sequence facts to attain variegated representations of the capabilities encoded in uncooked facts. An productive deep CNN architecture is proposed that can be separately educated to extract capabilities from different transformed areas. Afterwards, these CNN characteristic extractors are merged into an optimal architecture finely tuned for optimizing diversified extracted capabilities through a combined schooling stage or numerous sequential schooling levels. This strategy delivers the possibility to investigate the encoded capabilities in uncooked sensor facts utilizing multifarious observation home windows with immense scope for productive variety of capabilities for last convergence. Comprehensive experimentations have been carried out in 3 publicly accessible datasets that deliver exceptional performance continually with common 5-fold cross-validation precision of 99.29% on UCI HAR databases, 99.02% on USC HAR databases, and 97.21% on SKODA databases outperforming other state-of-the-artwork ways.

Connection: https://arxiv.org/abs/2101.00702

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