scholarly journals Sequential Human Activity Recognition Based on Deep Convolutional Network and Extreme Learning Machine Using Wearable Sensors

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Jian Sun ◽  
Yongling Fu ◽  
Shengguang Li ◽  
Jie He ◽  
Cheng Xu ◽  
...  
Keyword(s):  
Activity Recognition ◽  
Human Activity ◽  
Temporal Dynamics ◽  
Expert Knowledge ◽  
Wearable Sensors ◽  
Human Activity Recognition ◽  
Sensor Data ◽  
Testing Time ◽  
Convolutional Network ◽  
Low Efficiency

Human activity recognition (HAR) problems have traditionally been solved by using engineered features obtained by heuristic methods. These methods ignore the time information of the streaming sensor data and cannot achieve sequential human activity recognition. With the use of traditional statistical learning methods, results could easily plunge into the local minimum other than the global optimal and also face the problem of low efficiency. Therefore, we propose a hybrid deep framework based on convolution operations, LSTM recurrent units, and ELM classifier; the advantages are as follows: (1) does not require expert knowledge in extracting features; (2) models temporal dynamics of features; and (3) is more suitable to classify the extracted features and shortens the runtime. All of these unique advantages make it superior to other HAR algorithms. We evaluate our framework on OPPORTUNITY dataset which has been used in OPPORTUNITY challenge. Results show that our proposed method outperforms deep nonrecurrent networks by 6%, outperforming the previous reported best result by 8%. When compared with neural network using BP algorithm, testing time reduced by 38%.

scholarly journals Human Activity Recognition based on Wearable Sensors using Multiscale DCNN Ensemble

2019 ◽  
Author(s):  
Jessica Sena ◽  
William Robson Schwartz
Keyword(s):  
Activity Recognition ◽  
Human Activity ◽  
Wearable Sensors ◽  
Human Activity Recognition ◽  
Sensor Data ◽  
Human Gait ◽  
Multi Temporal ◽  
Previous State ◽  
Simple Movement ◽  
Kernel Approach

Sensor-based Human Activity Recognition (HAR) provides valuable knowledge to many areas. Recently, wearable devices have gained space as a relevant source of data. However, there are two issues: large number of heterogeneous sensors available and the temporal nature of the sensor data. To handle these issues, we propose a multimodal approach that processes each sensor separately and, through an ensemble of Deep Convolution Neural Networks (DCNN), extracts information from multiple temporal scales of the sensor data. In this ensemble, we use a convolutional kernel with a different height for each DCNN. Considering that the number of rows in the sensor data reflects the data captured over time, each kernel height reflects a temporal scale from which we can extract patterns. Consequently, our approach is able to extract information from simple movement patterns such as a wrist twist when picking up a spoon, to complex movements such as the human gait. This multimodal and multi-temporal approach outperforms previous state-of-the-art works in seven important datasets using two different protocols. In addition, we demonstrate that the use of our proposed set of kernels improves sensor-based HAR in another multi-kernel approach, the widely employed inception network.

scholarly journals Dimensionality Reduction for Human Activity Recognition Using Google Colab

Information ◽  
2020 ◽  
Vol 12 (1) ◽  
pp. 6
Author(s):  
Sujan Ray ◽  
Khaldoon Alshouiliy ◽  
Dharma P. Agrawal
Keyword(s):  
Deep Learning ◽  
Activity Recognition ◽  
Human Activity ◽  
Hybrid Approach ◽  
Principal Component ◽  
Human Activity Recognition ◽  
Mobile Sensing ◽  
Sensor Data ◽  
Testing Time ◽  
Machine Learning Models

Human activity recognition (HAR) is a classification task that involves predicting the movement of a person based on sensor data. As we can see, there has been a huge growth and development of smartphones over the last 10–15 years—they could be used as a medium of mobile sensing to recognize human activity. Nowadays, deep learning methods are in a great demand and we could use those methods to recognize human activity. A great way is to build a convolutional neural network (CNN). HAR using Smartphone dataset has been widely used by researchers to develop machine learning models to recognize human activity. The dataset has two parts: training and testing. In this paper, we propose a hybrid approach to analyze and recognize human activity on the same dataset using deep learning method on cloud-based platform. We have applied principal component analysis on the dataset to get the most important features. Next, we have executed the experiment for all the features as well as the top 48, 92, 138, and 164 features. We have run all the experiments on Google Colab. In the experiment, for the evaluation of our proposed methodology, datasets are split into two different ratios such as 70–10–20% and 80–10–10% for training, validation, and testing, respectively. We have set the performance of CNN (70% training–10% validation–20% testing) with 48 features as a benchmark for our work. In this work, we have achieved maximum accuracy of 98.70% with CNN. On the other hand, we have obtained 96.36% accuracy with the top 92 features of the dataset. We can see from the experimental results that if we could select the features properly then not only could the accuracy be improved but also the training and testing time of the model.

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.

scholarly journals Improving Human Activity Recognition Performance by Data Fusion and Feature Engineering

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 692
Author(s):  
Jingcheng Chen ◽  
Yining Sun ◽  
Shaoming Sun
Keyword(s):  
Feature Selection ◽  
Activity Recognition ◽  
Human Activity ◽  
Joint Angle ◽  
Recognition Performance ◽  
Wearable Sensors ◽  
Estimation Algorithm ◽  
Recognition System ◽  
Human Activity Recognition ◽  
Time Frequency

Human activity recognition (HAR) is essential in many health-related fields. A variety of technologies based on different sensors have been developed for HAR. Among them, fusion from heterogeneous wearable sensors has been developed as it is portable, non-interventional and accurate for HAR. To be applied in real-time use with limited resources, the activity recognition system must be compact and reliable. This requirement can be achieved by feature selection (FS). By eliminating irrelevant and redundant features, the system burden is reduced with good classification performance (CP). This manuscript proposes a two-stage genetic algorithm-based feature selection algorithm with a fixed activation number (GFSFAN), which is implemented on the datasets with a variety of time, frequency and time-frequency domain features extracted from the collected raw time series of nine activities of daily living (ADL). Six classifiers are used to evaluate the effects of selected feature subsets from different FS algorithms on HAR performance. The results indicate that GFSFAN can achieve good CP with a small size. A sensor-to-segment coordinate calibration algorithm and lower-limb joint angle estimation algorithm are introduced. Experiments on the effect of the calibration and the introduction of joint angle on HAR shows that both of them can improve the CP.

scholarly journals Iss2Image: A Novel Signal-Encoding Technique for CNN-Based Human Activity Recognition

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 3910 ◽  
Author(s):  
Taeho Hur ◽  
Jaehun Bang ◽  
Thien Huynh-The ◽  
Jongwon Lee ◽  
Jee-In Kim ◽  
...  
Keyword(s):  
Deep Learning ◽  
Activity Recognition ◽  
Human Activity ◽  
Expert Knowledge ◽  
Inertial Sensor ◽  
Human Activity Recognition ◽  
Learning Technology ◽  
Sensor Signal ◽  
Signal Encoding ◽  
The Right

The most significant barrier to success in human activity recognition is extracting and selecting the right features. In traditional methods, the features are chosen by humans, which requires the user to have expert knowledge or to do a large amount of empirical study. Newly developed deep learning technology can automatically extract and select features. Among the various deep learning methods, convolutional neural networks (CNNs) have the advantages of local dependency and scale invariance and are suitable for temporal data such as accelerometer (ACC) signals. In this paper, we propose an efficient human activity recognition method, namely Iss2Image (Inertial sensor signal to Image), a novel encoding technique for transforming an inertial sensor signal into an image with minimum distortion and a CNN model for image-based activity classification. Iss2Image converts real number values from the X, Y, and Z axes into three color channels to precisely infer correlations among successive sensor signal values in three different dimensions. We experimentally evaluated our method using several well-known datasets and our own dataset collected from a smartphone and smartwatch. The proposed method shows higher accuracy than other state-of-the-art approaches on the tested datasets.

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