Human activity recognition with inertial sensors using a deep learning approach

Deep learning techniques have been widely applied to Human Activity Recognition (HAR), but a specific challenge appears when HAR systems are trained and tested with different subjects. This paper describes and evaluates several techniques based on deep learning algorithms for adapting and selecting the training data used to generate a HAR system using accelerometer recordings. This paper proposes two alternatives: autoencoders and Generative Adversarial Networks (GANs). Both alternatives are based on deep neural networks including convolutional layers for feature extraction and fully-connected layers for classification. Fast Fourier Transform (FFT) is used as a transformation of acceleration data to provide an appropriate input data to the deep neural network. This study has used acceleration recordings from hand, chest and ankle sensors included in the Physical Activity Monitoring Data Set (PAMAP2) dataset. This is a public dataset including recordings from nine subjects while performing 12 activities such as walking, running, sitting, ascending stairs, or ironing. The evaluation has been performed using a Leave-One-Subject-Out (LOSO) cross-validation: all recordings from a subject are used as testing subset and recordings from the rest of the subjects are used as training subset. The obtained results suggest that strategies using autoencoders to adapt training data to testing data improve some users’ performance. Moreover, training data selection algorithms with autoencoders provide significant improvements. The GAN approach, using the generator or discriminator module, also provides improvement in selection experiments.

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Human Activity Recognition Based on Residual Network and BiLSTM

Sensors ◽

10.3390/s22020635 ◽

2022 ◽

Vol 22 (2) ◽

pp. 635

Author(s):

Yong Li ◽

Luping Wang

Keyword(s):

Deep Learning ◽

Activity Recognition ◽

Human Activity ◽

Inertial Sensors ◽

Human Activity Recognition ◽

Activity Data ◽

Multidimensional Signals ◽

Proposed Model ◽

Public Datasets ◽

Residual Block

Due to the wide application of human activity recognition (HAR) in sports and health, a large number of HAR models based on deep learning have been proposed. However, many existing models ignore the effective extraction of spatial and temporal features of human activity data. This paper proposes a deep learning model based on residual block and bi-directional LSTM (BiLSTM). The model first extracts spatial features of multidimensional signals of MEMS inertial sensors automatically using the residual block, and then obtains the forward and backward dependencies of feature sequence using BiLSTM. Finally, the obtained features are fed into the Softmax layer to complete the human activity recognition. The optimal parameters of the model are obtained by experiments. A homemade dataset containing six common human activities of sitting, standing, walking, running, going upstairs and going downstairs is developed. The proposed model is evaluated on our dataset and two public datasets, WISDM and PAMAP2. The experimental results show that the proposed model achieves the accuracy of 96.95%, 97.32% and 97.15% on our dataset, WISDM and PAMAP2, respectively. Compared with some existing models, the proposed model has better performance and fewer parameters.

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Hybrid Features Enabled Adaptive Butterfly Based Deep Learning Approach for Human Activity Recognition

10.1007/978-981-16-5078-9_30 ◽

2021 ◽

pp. 341-363

Author(s):

Anagha Deshpande ◽

Krishna K. Warhade

Keyword(s):

Deep Learning ◽

Activity Recognition ◽

Human Activity ◽

Human Activity Recognition ◽

Learning Approach ◽

Hybrid Features

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A Statistical and Deep Learning Approach for Human Activity Recognition

2019 3rd International Conference on Trends in Electronics and Informatics (ICOEI) ◽

10.1109/icoei.2019.8862610 ◽

2019 ◽

Cited By ~ 1

Author(s):

Abdul Kadar Muhammad Masum ◽

Mohammad Emdad Hossain ◽

Asma Humayra ◽

Sanzeeda Islam ◽

Arnab Barua ◽

...

Keyword(s):

Deep Learning ◽

Activity Recognition ◽

Human Activity ◽

Human Activity Recognition ◽

Learning Approach

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Assessing impacts of data volume and data set balance in using deep learning approach to human activity recognition

2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) ◽

10.1109/bibm.2017.8217821 ◽

2017 ◽

Cited By ~ 1

Author(s):

Haipeng Chen ◽

Fuhai Xiong ◽

Dihong Wu ◽

Lingxiang Zheng ◽

Ao Peng ◽

...

Keyword(s):

Deep Learning ◽

Activity Recognition ◽

Human Activity ◽

Human Activity Recognition ◽

Learning Approach ◽

Data Set ◽

Data Volume

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Out-of-Distribution Detection of Human Activity Recognition with Smartwatch Inertial Sensors

Sensors ◽

10.3390/s21051669 ◽

2021 ◽

Vol 21 (5) ◽

pp. 1669

Author(s):

Philip Boyer ◽

David Burns ◽

Cari Whyne

Keyword(s):

Deep Learning ◽

Activity Recognition ◽

Human Activity ◽

Healthy Subjects ◽

Inertial Sensors ◽

Human Activity Recognition ◽

Training Data ◽

Learning Approaches ◽

Statistical Features ◽

Better Than

Out-of-distribution (OOD) in the context of Human Activity Recognition (HAR) refers to data from activity classes that are not represented in the training data of a Machine Learning (ML) algorithm. OOD data are a challenge to classify accurately for most ML algorithms, especially deep learning models that are prone to overconfident predictions based on in-distribution (IIN) classes. To simulate the OOD problem in physiotherapy, our team collected a new dataset (SPARS9x) consisting of inertial data captured by smartwatches worn by 20 healthy subjects as they performed supervised physiotherapy exercises (IIN), followed by a minimum 3 h of data captured for each subject as they engaged in unrelated and unstructured activities (OOD). In this paper, we experiment with three traditional algorithms for OOD-detection using engineered statistical features, deep learning-generated features, and several popular deep learning approaches on SPARS9x and two other publicly-available human activity datasets (MHEALTH and SPARS). We demonstrate that, while deep learning algorithms perform better than simple traditional algorithms such as KNN with engineered features for in-distribution classification, traditional algorithms outperform deep learning approaches for OOD detection for these HAR time series datasets.

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