Sensor-data augmentation for human activity recognition with time-warping and data masking

Recent trends in ubiquitous computing have led to a proliferation of studies that focus on human activity recognition (HAR) utilizing inertial sensor data that consist of acceleration, orientation and angular velocity. However, the performances of such approaches are limited by the amount of annotated training data, especially in fields where annotating data is highly time-consuming and requires specialized professionals, such as in healthcare. In image classification, this limitation has been mitigated by powerful oversampling techniques such as data augmentation. Using this technique, this work evaluates to what extent transforming inertial sensor data into movement trajectories and into 2D heatmap images can be advantageous for HAR when data are scarce. A convolutional long short-term memory (ConvLSTM) network that incorporates spatiotemporal correlations was used to classify the heatmap images. Evaluation was carried out on Deep Inertial Poser (DIP), a known dataset composed of inertial sensor data. The results obtained suggest that for datasets with large numbers of subjects, using state-of-the-art methods remains the best alternative. However, a performance advantage was achieved for small datasets, which is usually the case in healthcare. Moreover, movement trajectories provide a visual representation of human activities, which can help researchers to better interpret and analyze motion patterns.

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Similarity Embedding Networks for Robust Human Activity Recognition

ACM Transactions on Knowledge Discovery from Data ◽

10.1145/3448021 ◽

2021 ◽

Vol 15 (6) ◽

pp. 1-17

Author(s):

Chenglin Li ◽

Carrie Lu Tong ◽

Di Niu ◽

Bei Jiang ◽

Xiao Zuo ◽

...

Keyword(s):

Activity Recognition ◽

Human Activity ◽

Short Term Memory ◽

Real Space ◽

Human Activity Recognition ◽

Sensor Data ◽

Activity Data ◽

Extensive Evaluation ◽

Sensor Signals ◽

Public Datasets

Deep learning models for human activity recognition (HAR) based on sensor data have been heavily studied recently. However, the generalization ability of deep models on complex real-world HAR data is limited by the availability of high-quality labeled activity data, which are hard to obtain. In this article, we design a similarity embedding neural network that maps input sensor signals onto real vectors through carefully designed convolutional and Long Short-Term Memory (LSTM) layers. The embedding network is trained with a pairwise similarity loss, encouraging the clustering of samples from the same class in the embedded real space, and can be effectively trained on a small dataset and even on a noisy dataset with mislabeled samples. Based on the learned embeddings, we further propose both nonparametric and parametric approaches for activity recognition. Extensive evaluation based on two public datasets has shown that the proposed similarity embedding network significantly outperforms state-of-the-art deep models on HAR classification tasks, is robust to mislabeled samples in the training set, and can also be used to effectively denoise a noisy dataset.

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Non-Linear Chaotic Features-Based Human Activity Recognition

Electronics ◽

10.3390/electronics10020111 ◽

2021 ◽

Vol 10 (2) ◽

pp. 111

Author(s):

Pengjia Tu ◽

Junhuai Li ◽

Huaijun Wang ◽

Ting Cao ◽

Kan Wang

Keyword(s):

Activity Recognition ◽

Human Activity ◽

Human Activity Recognition ◽

Human Motion ◽

Sensor Data ◽

Largest Lyapunov Exponent ◽

Optimal Delay ◽

Motion Time ◽

Non Linear ◽

Optimal Delay Time

Human activity recognition (HAR) has vital applications in human–computer interaction, somatosensory games, and motion monitoring, etc. On the basis of the human motion accelerate sensor data, through a nonlinear analysis of the human motion time series, a novel method for HAR that is based on non-linear chaotic features is proposed in this paper. First, the C-C method and G-P algorithm are used to, respectively, compute the optimal delay time and embedding dimension. Additionally, a Reconstructed Phase Space (RPS) is formed while using time-delay embedding for the human accelerometer motion sensor data. Subsequently, a two-dimensional chaotic feature matrix is constructed, where the chaotic feature is composed of the correlation dimension and largest Lyapunov exponent (LLE) of attractor trajectory in the RPS. Next, the classification algorithms are used in order to classify and recognize the two different activity classes, i.e., basic and transitional activities. The experimental results show that the chaotic feature has a higher accuracy than traditional time and frequency domain features.

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