RestHAR: Residual Feature Learning Transformer for Human Activity Recognition from Multi-sensor Data

2021 ◽  
Author(s):  
Debaditya Shome
Keyword(s):  
Activity Recognition ◽  
Human Activity ◽  
Feature Learning ◽  
Human Activity Recognition ◽  
Sensor Data

scholarly journals Similarity Embedding Networks for Robust Human Activity Recognition

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.

scholarly journals Non-Linear Chaotic Features-Based Human Activity Recognition

Electronics ◽  
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.

Human Activity Recognition from Body Sensor Data using Deep Learning

2018 ◽  
Vol 42 (6) ◽  
Author(s):  
Mohammad Mehedi Hassan ◽  
Shamsul Huda ◽  
Md Zia Uddin ◽  
Ahmad Almogren ◽  
Majed Alrubaian
Keyword(s):  
Deep Learning ◽  
Activity Recognition ◽  
Human Activity ◽  
Human Activity Recognition ◽  
Sensor Data

scholarly journals Asymmetric Residual Neural Network for Accurate Human Activity Recognition

Information ◽  
2019 ◽  
Vol 10 (6) ◽  
pp. 203 ◽  
Author(s):  
Jun Long ◽  
Wuqing Sun ◽  
Zhan Yang ◽  
Osolo Ian Raymond
Keyword(s):  
Activity Recognition ◽  
Human Activity ◽  
Time Window ◽  
Research Problem ◽  
Human Activity Recognition ◽  
Sensor Data ◽  
Practical Applications ◽  
Long Time ◽  
Benchmark Datasets ◽  
High Level

Human activity recognition (HAR) using deep neural networks has become a hot topic in human–computer interaction. Machines can effectively identify human naturalistic activities by learning from a large collection of sensor data. Activity recognition is not only an interesting research problem but also has many real-world practical applications. Based on the success of residual networks in achieving a high level of aesthetic representation of automatic learning, we propose a novel asymmetric residual network, named ARN. ARN is implemented using two identical path frameworks consisting of (1) a short time window, which is used to capture spatial features, and (2) a long time window, which is used to capture fine temporal features. The long time window path can be made very lightweight by reducing its channel capacity, while still being able to learn useful temporal representations for activity recognition. In this paper, we mainly focus on proposing a new model to improve the accuracy of HAR. In order to demonstrate the effectiveness of the ARN model, we carried out extensive experiments on benchmark datasets (i.e., OPPORTUNITY, UniMiB-SHAR) and compared the results with some conventional and state-of-the-art learning-based methods. We discuss the influence of networks parameters on performance to provide insights about its optimization. Results from our experiments show that ARN is effective in recognizing human activities via wearable datasets.

Sign in / Sign up

Export Citation Format

Share Document