Deep Recurrent Neural Networks for Human Activity Recognition

By Abdulmajid Murad, Jae-Young Pyun. Italian National Conference on Sensors, 2017

449 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/899db02ba28ef2479b5bea3e51627685be5b3865

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Computer Vision and Pattern Recognition

Citations in the corpus

Human Activity Recognition using Inertial, Physiological and Environmental Sensors: A Comprehensive Survey (2020, background): “[89], [112], [129], [213], [216], [217], over an average number of 14 obtaining an average accuracy of 95%.”
Deep ConvLSTM With Self-Attention for Human Activity Decoding Using Wearable Sensors (2020, background): “Therefore, recurrent neural networks (RNNs) that can also give importance to temporal information from time series data are used in activity recognition from wearable sensors [26].”
Deep Learning for Sensor-based Activity Recognition: A Survey (2017, unknown): “14) Body-worn SAE ADL D03 (Liu et al., 2016) Body-worn CNN, RBM ADL Self (Mohammed and Tashev, 2017) Body-worn CNN ADL, gesture Self (Morales and Roggen, 2016) Body-worn CNN ADL, smart home D01, D02 (Murad and Pyun, 2017) Body-worn RNN ADL, smart home D01, D02, D05, D14 (Ordo´nez and Roggen, 2016)˜ ”
Deep Learning in Human Activity Recognition with Wearable Sensors: A Review on Advances (2021, background): “Previous studies have shown that LSTMs have high performance in wearable HAR [199, 202, 203].”