Time series activity classification using gated recurrent units

<span>The population of elderly is growing and is projected to outnumber the youth in the future. Many researches on elderly assisted living technology were carried out. One of the focus areas is activity monitoring of the elderly. AReM dataset is a time series activity recognition dataset for seven different types of activities, which are bending 1, bending 2, cycling, lying, sitting, standing and walking. In the original paper, the author used a many-to-many Recurrent Neural Network for activity recognition. Here, we introduced a time series classification method where Gated Recurrent Units with many-to-one architecture were used for activity classification. The experimental results obtained showed an excellent accuracy of 97.14%.</span>

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Spiking Echo State Convolutional Neural Network for Robust Time Series Classification

IEEE Access ◽

10.1109/access.2018.2887354 ◽

2019 ◽

Vol 7 ◽

pp. 4927-4935 ◽

Cited By ~ 2

Author(s):

Anguo Zhang ◽

Wei Zhu ◽

Juanyu Li

Keyword(s):

Neural Network ◽

Time Series ◽

Convolutional Neural Network ◽

Time Series Classification

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CLR-based deep convolutional spiking neural network with validation based stopping for time series classification

Applied Intelligence ◽

10.1007/s10489-019-01552-y ◽

2019 ◽

Vol 50 (3) ◽

pp. 830-848 ◽

Cited By ~ 1

Author(s):

Anjali Gautam ◽

Vrijendra Singh

Keyword(s):

Neural Network ◽

Time Series ◽

Spiking Neural Network ◽

Time Series Classification

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HARGRURNN: Human activity recognition using inertial body sensor gated recurrent units recurrent neural network

Journal of Discrete Mathematical Sciences and Cryptography ◽

10.1080/09720529.2019.1696552 ◽

2019 ◽

Vol 22 (8) ◽

pp. 1577-1587

Author(s):

Mahendra Kumar Jangir ◽

Karan Singh

Keyword(s):

Neural Network ◽

Activity Recognition ◽

Human Activity ◽

Recurrent Neural Network ◽

Human Activity Recognition ◽

Inertial Body ◽

Gated Recurrent Units

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Ambient Assisted Living: A Research on Human Activity Recognition and Vital Health Sign Monitoring using Deep Learning Approaches

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.f1111.0486s419 ◽

2019 ◽

Vol 8 (6S4) ◽

pp. 531-540

Keyword(s):

Deep Learning ◽

Activity Recognition ◽

Human Activity ◽

Assisted Living ◽

Independent Living ◽

Ambient Assisted Living ◽

The Elderly ◽

Human Activity Recognition ◽

Living Environment ◽

Learning Approaches

The rise in life expectancy rate and dwindled birth rate in new age society has led to the phenomenon of population ageing which is being witnessed across the world from past few decades. India is also a part of this demographic transition which will have the direct impact on the societal and economic conditions of the country. In order to effectively deal with the prevailing phenomenon, stakeholders involved are coming up with the Information and Communication Technology (ICT) based ecosystem to address the needs of elderly people such as independent living, activity recognition, vital health sign monitoring, prevention from social isolation etc. Ambient Assisted Living (AAL) is one such ecosystem which is capable of providing safe and secured living environment for the elderly and disabled people. In this paper we will focus on reviewing the sensor based Human Activity Recognition (HAR) and Vital Health Sign Monitoring (VHSM) which is applicable for AAL environments. At first we generally describe the AAL environment. Next we present brief insights into sensor modalities and different deep learning architectures. Later, we survey the existing literature for HAR and VHSM based on sensor modality and deep learning approach used.

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FilterNet: A Many-to-Many Deep Learning Architecture for Time Series Classification

10.20944/preprints202002.0318.v1 ◽

2020 ◽

Author(s):

Robert D. Chambers ◽

Nathanael C. Yoder

Keyword(s):

Time Series ◽

Deep Learning ◽

Activity Recognition ◽

Sensor Data ◽

Time Series Classification ◽

Discrete Events ◽

Segmentation Accuracy ◽

Model Size ◽

Classification Tasks

We present and benchmark FilterNet, a flexible deep learning architecture for time series classification tasks, such as activity recognition via multichannel sensor data. It adapts popular CNN and CNN-LSTM motifs which have excelled in activity recognition benchmarks, implementing them in a many-to-many architecture to markedly improve frame-by-frame accuracy, event segmentation accuracy, model size, and computational efficiency. We propose several model variants, evaluate them alongside other published models using the Opportunity benchmark dataset, demonstrate the effect of model ensembling and of altering key parameters, and quantify the quality of the models’ segmentation of discrete events. We also offer recommendations for use and suggest potential model extensions. FilterNet advances the state of the art in all measured accuracy and speed metrics on the benchmarked dataset, and it can be extensively customized for other applications.

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AttnSense: Multi-level Attention Mechanism For Multimodal Human Activity Recognition

Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence ◽

10.24963/ijcai.2019/431 ◽

2019 ◽

Cited By ~ 5

Author(s):

HaoJie Ma ◽

Wenzhong Li ◽

Xiao Zhang ◽

Songcheng Gao ◽

Sanglu Lu

Keyword(s):

Neural Network ◽

Activity Recognition ◽

Human Activity ◽

Domain Knowledge ◽

Research Problem ◽

Human Activity Recognition ◽

Attention Mechanism ◽

The Rich ◽

Public Datasets ◽

Gated Recurrent Units

Sensor-based human activity recognition is a fundamental research problem in ubiquitous computing, which uses the rich sensing data from multimodal embedded sensors such as accelerometer and gyroscope to infer human activities. The existing activity recognition approaches either rely on domain knowledge or fail to address the spatial-temporal dependencies of the sensing signals. In this paper, we propose a novel attention-based multimodal neural network model called AttnSense for multimodal human activity recognition. AttnSense introduce the framework of combining attention mechanism with a convolutional neural network (CNN) and a Gated Recurrent Units (GRU) network to capture the dependencies of sensing signals in both spatial and temporal domains, which shows advantages in prioritized sensor selection and improves the comprehensibility. Extensive experiments based on three public datasets show that AttnSense achieves a competitive performance in activity recognition compared with several state-of-the-art methods.

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Comparing End-to-End Machine Learning Methods for Spectra Classification

Applied Sciences ◽

10.3390/app112311520 ◽

2021 ◽

Vol 11 (23) ◽

pp. 11520

Author(s):

Yue Sun ◽

Sandor Brockhauser ◽

Péter Hegedűs

Keyword(s):

Neural Network ◽

Time Series ◽

Short Term Memory ◽

Classification Problem ◽

High Energy ◽

High Energy Density ◽

Time Series Classification ◽

Classification Models ◽

Attention Model ◽

End To End

In scientific research, spectroscopy and diffraction experimental techniques are widely used and produce huge amounts of spectral data. Learning patterns from spectra is critical during these experiments. This provides immediate feedback on the actual status of the experiment (e.g., time-resolved status of the sample), which helps guide the experiment. The two major spectral changes what we aim to capture are either the change in intensity distribution (e.g., drop or appearance) of peaks at certain locations, or the shift of those on the spectrum. This study aims to develop deep learning (DL) classification frameworks for one-dimensional (1D) spectral time series. In this work, we deal with the spectra classification problem from two different perspectives, one is a general two-dimensional (2D) space segmentation problem, and the other is a common 1D time series classification problem. We focused on the two proposed classification models under these two settings, the namely the end-to-end binned Fully Connected Neural Network (FCNN) with the automatically capturing weighting factors model and the convolutional SCT attention model. Under the setting of 1D time series classification, several other end-to-end structures based on FCNN, Convolutional Neural Network (CNN), ResNets, Long Short-Term Memory (LSTM), and Transformer were explored. Finally, we evaluated and compared the performance of these classification models based on the High Energy Density (HED) spectra dataset from multiple perspectives, and further performed the feature importance analysis to explore their interpretability. The results show that all the applied models can achieve 100% classification confidence, but the models applied under the 1D time series classification setting are superior. Among them, Transformer-based methods consume the least training time (0.449 s). Our proposed convolutional Spatial-Channel-Temporal (SCT) attention model uses 1.269 s, but its self-attention mechanism performed across spatial, channel, and temporal dimensions can suppress indistinguishable features better than others, and selectively focus on obvious features with high separability.

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ConvTimeNet: A Pre-trained Deep Convolutional Neural Network for Time Series Classification

2019 International Joint Conference on Neural Networks (IJCNN) ◽

10.1109/ijcnn.2019.8852105 ◽

2019 ◽

Cited By ~ 5

Author(s):

Kathan Kashiparekh ◽

Jyoti Narwariya ◽

Pankaj Malhotra ◽

Lovekesh Vig ◽

Gautam Shroff

Keyword(s):

Neural Network ◽

Time Series ◽

Convolutional Neural Network ◽

Deep Convolutional Neural Network ◽

Time Series Classification

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Performance Boosting of Scale and Rotation Invariant Human Activity Recognition (HAR) with LSTM Networks Using Low Dimensional 3D Posture Data in Egocentric Coordinates

Applied Sciences ◽

10.3390/app10238474 ◽

2020 ◽

Vol 10 (23) ◽

pp. 8474

Author(s):

Ibrahim Furkan Ince

Keyword(s):

Time Series ◽

Coordinate System ◽

Dimension Reduction ◽

Activity Recognition ◽

Human Activity ◽

Feature Space ◽

Human Activity Recognition ◽

Time Series Classification ◽

Rotation Invariant ◽

Low Dimensional

Human activity recognition (HAR) has been an active area in computer vision with a broad range of applications, such as education, security surveillance, and healthcare. HAR is a general time series classification problem. LSTMs are widely used for time series classification tasks. However, they work well with high-dimensional feature vectors, which reduce the processing speed of LSTM in real-time applications. Therefore, dimension reduction is required to create low-dimensional feature space. As it is experimented in previous study, LSTM with dimension reduction yielded the worst performance among other classifiers, which are not deep learning methods. Therefore, in this paper, a novel scale and rotation invariant human activity recognition system, which can also work in low dimensional feature space is presented. For this purpose, Kinect depth sensor is employed to obtain skeleton joints. Since angles are used, proposed system is already scale invariant. In order to provide rotation invariance, body relative direction in egocentric coordinates is calculated. The 3D vector between right hip and left hip is used to get the horizontal axis and its cross product with the vertical axis of global coordinate system assumed to be the depth axis of the proposed local coordinate system. Instead of using 3D joint angles, 8 number of limbs and their corresponding 3D angles with X, Y, and Z axes of the proposed coordinate system are compressed with several dimension reduction methods such as averaging filter, Haar wavelet transform (HWT), and discrete cosine transform (DCT) and employed as the feature vector. Finally, extracted features are trained and tested with LSTM (long short-term memory) network, which is an artificial recurrent neural network (RNN) architecture. Experimental and benchmarking results indicate that proposed framework boosts the performance of LSTM by approximately 30% accuracy in low-dimensional feature space.

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