Human activity data discovery from triaxial accelerometer sensor: Non-supervised learning sensitivity to feature extraction parametrization

Human activity recognition via triaxial accelerometers can provide valuable information for evaluating functional abilities. In this paper, we present an accelerometer sensor-based approach for human activity recognition. Our proposed recognition method used a hierarchical scheme, where the recognition of ten activity classes was divided into five distinct classification problems. Every classifier used the Least Squares Support Vector Machine (LS-SVM) and Naive Bayes (NB) algorithm to distinguish different activity classes. The activity class was recognized based on the mean, variance, entropy of magnitude, and angle of triaxial accelerometer signal features. Our proposed activity recognition method recognized ten activities with an average accuracy of 95.6% using only a single triaxial accelerometer.

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Human Activity Recognition from Triaxial Accelerometer Data - Feature Extraction and Selection Methods for Clustering of Physical Activities

Proceedings of the International Conference on Bio-inspired Systems and Signal Processing ◽

10.5220/0004749801550162 ◽

2014 ◽

Keyword(s):

Feature Extraction ◽

Activity Recognition ◽

Human Activity ◽

Human Activity Recognition ◽

Physical Activities ◽

Accelerometer Data ◽

Selection Methods ◽

Triaxial Accelerometer ◽

Feature Extraction And Selection

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Structured optimal graph based sparse feature extraction for semi-supervised learning

Signal Processing ◽

10.1016/j.sigpro.2020.107456 ◽

2020 ◽

Vol 170 ◽

pp. 107456 ◽

Cited By ~ 49

Author(s):

Zhonghua Liu ◽

Zhihui Lai ◽

Weihua Ou ◽

Kaibing Zhang ◽

Ruijuan Zheng

Keyword(s):

Feature Extraction ◽

Supervised Learning ◽

Optimal Graph

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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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Target Detection Network for SAR Images Based on Semi-Supervised Learning and Attention Mechanism

Remote Sensing ◽

10.3390/rs13142686 ◽

2021 ◽

Vol 13 (14) ◽

pp. 2686

Author(s):

Di Wei ◽

Yuang Du ◽

Lan Du ◽

Lu Li

Keyword(s):

Feature Extraction ◽

Supervised Learning ◽

Target Detection ◽

Scene Recognition ◽

Attention Mechanism ◽

Sar Image ◽

Target Level ◽

Sar Images ◽

Training Samples ◽

Pass Through

The existing Synthetic Aperture Radar (SAR) image target detection methods based on convolutional neural networks (CNNs) have achieved remarkable performance, but these methods require a large number of target-level labeled training samples to train the network. Moreover, some clutter is very similar to targets in SAR images with complex scenes, making the target detection task very difficult. Therefore, a SAR target detection network based on a semi-supervised learning and attention mechanism is proposed in this paper. Since the image-level label simply marks whether the image contains the target of interest or not, which is easier to be labeled than the target-level label, the proposed method uses a small number of target-level labeled training samples and a large number of image-level labeled training samples to train the network with a semi-supervised learning algorithm. The proposed network consists of a detection branch and a scene recognition branch with a feature extraction module and an attention module shared between these two branches. The feature extraction module can extract the deep features of the input SAR images, and the attention module can guide the network to focus on the target of interest while suppressing the clutter. During the semi-supervised learning process, the target-level labeled training samples will pass through the detection branch, while the image-level labeled training samples will pass through the scene recognition branch. During the test process, considering the help of global scene information in SAR images for detection, a novel coarse-to-fine detection procedure is proposed. After the coarse scene recognition determining whether the input SAR image contains the target of interest or not, the fine target detection is performed on the image that may contain the target. The experimental results based on the measured SAR dataset demonstrate that the proposed method can achieve better performance than the existing methods.

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