Human activity recognition in cognitive environments using sequential ELM

Kumar, Ranjeet; Bharadwaj, Skanda; Sumukha, B. N.; George, Koshy

doi:10.1109/ccip.2016.7802880

Cited by 6 publications

(1 citation statement)

References 16 publications

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“…For the UCI-HAD dataset, we found that the unidirectional DRNN model with four layers yields best performance results in terms of per-class precision and recall, as shown in Figure 8 a. The overall classification accuracy is 96.7%, outperforming other methods, such as CNNs [ 28 ], support vector machine (SVM) [ 19 ], and sequential extreme learning machine (ELM) [ 29 ]. Figure 8 b presents a chart of the observed accuracy from our model in comparison with the accuracies achieved by other methods.…”

Section: Resultsmentioning

confidence: 97%

Deep Recurrent Neural Networks for Human Activity Recognition

Murad

Pyun

2017

Sensors

393

216

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Adopting deep learning methods for human activity recognition has been effective in extracting discriminative features from raw input sequences acquired from body-worn sensors. Although human movements are encoded in a sequence of successive samples in time, typical machine learning methods perform recognition tasks without exploiting the temporal correlations between input data samples. Convolutional neural networks (CNNs) address this issue by using convolutions across a one-dimensional temporal sequence to capture dependencies among input data. However, the size of convolutional kernels restricts the captured range of dependencies between data samples. As a result, typical models are unadaptable to a wide range of activity-recognition configurations and require fixed-length input windows. In this paper, we propose the use of deep recurrent neural networks (DRNNs) for building recognition models that are capable of capturing long-range dependencies in variable-length input sequences. We present unidirectional, bidirectional, and cascaded architectures based on long short-term memory (LSTM) DRNNs and evaluate their effectiveness on miscellaneous benchmark datasets. Experimental results show that our proposed models outperform methods employing conventional machine learning, such as support vector machine (SVM) and k-nearest neighbors (KNN). Additionally, the proposed models yield better performance than other deep learning techniques, such as deep believe networks (DBNs) and CNNs.

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Section: Resultsmentioning

confidence: 97%