scholarly journals Feature fusion using deep learning for smartphone based human activity recognition

2021 ◽  
Author(s):  
Dipanwita Thakur ◽  
Suparna Biswas
Keyword(s):  
Deep Learning ◽  
Activity Recognition ◽  
Human Activity ◽  
Feature Fusion ◽  
Human Activity Recognition

scholarly journals Feature Fusion of a Deep-Learning Algorithm into Wearable Sensor Devices for Human Activity Recognition

Sensors ◽  
2021 ◽  
Vol 21 (24) ◽  
pp. 8294
Author(s):  
Chih-Ta Yen ◽  
Jia-Xian Liao ◽  
Yi-Kai Huang
Keyword(s):  
Deep Learning ◽  
Activity Recognition ◽  
Human Activity ◽  
Feature Fusion ◽  
Learning Algorithm ◽  
Human Activity Recognition ◽  
The Self ◽  
Wearable Device ◽  
Deep Learning Algorithm ◽  
Recorded Data

This paper presents a wearable device, fitted on the waist of a participant that recognizes six activities of daily living (walking, walking upstairs, walking downstairs, sitting, standing, and laying) through a deep-learning algorithm, human activity recognition (HAR). The wearable device comprises a single-board computer (SBC) and six-axis sensors. The deep-learning algorithm employs three parallel convolutional neural networks for local feature extraction and for subsequent concatenation to establish feature fusion models of varying kernel size. By using kernels of different sizes, relevant local features of varying lengths were identified, thereby increasing the accuracy of human activity recognition. Regarding experimental data, the database of University of California, Irvine (UCI) and self-recorded data were used separately. The self-recorded data were obtained by having 21 participants wear the device on their waist and perform six common activities in the laboratory. These data were used to verify the proposed deep-learning algorithm on the performance of the wearable device. The accuracy of these six activities in the UCI dataset and in the self-recorded data were 97.49% and 96.27%, respectively. The accuracies in tenfold cross-validation were 99.56% and 97.46%, respectively. The experimental results have successfully verified the proposed convolutional neural network (CNN) architecture, which can be used in rehabilitation assessment for people unable to exercise vigorously.

scholarly journals Coarse-Fine Convolutional Deep-Learning Strategy for Human Activity Recognition

Sensors ◽  
2019 ◽  
Vol 19 (7) ◽  
pp. 1556 ◽  
Author(s):  
Carlos Avilés-Cruz ◽  
Andrés Ferreyra-Ramírez ◽  
Arturo Zúñiga-López ◽  
Juan Villegas-Cortéz
Keyword(s):  
Deep Learning ◽  
Activity Recognition ◽  
Human Activity ◽  
Human Activities ◽  
Learning Strategy ◽  
Feature Fusion ◽  
Human Activity Recognition ◽  
Laying Performance ◽  
Benchmark Datasets ◽  
Local Feature Extraction

In the last decade, deep learning techniques have further improved human activity recognition (HAR) performance on several benchmark datasets. This paper presents a novel framework to classify and analyze human activities. A new convolutional neural network (CNN) strategy is applied to a single user movement recognition using a smartphone. Three parallel CNNs are used for local feature extraction, and latter they are fused in the classification task stage. The whole CNN scheme is based on a feature fusion of a fine-CNN, a medium-CNN, and a coarse-CNN. A tri-axial accelerometer and a tri-axial gyroscope sensor embedded in a smartphone are used to record the acceleration and angle signals. Six human activities successfully classified are walking, walking-upstairs, walking-downstairs, sitting, standing and laying. Performance evaluation is presented for the proposed CNN.

scholarly journals Study on Human Activity Recognition Using Semi-Supervised Active Transfer Learning

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2760
Author(s):  
Seungmin Oh ◽  
Akm Ashiquzzaman ◽  
Dongsu Lee ◽  
Yeonggwang Kim ◽  
Jinsul Kim
Keyword(s):  
Deep Learning ◽  
Transfer Learning ◽  
Activity Recognition ◽  
Human Activity ◽  
Random Sampling ◽  
Data Gathering ◽  
Human Activity Recognition ◽  
Learning Models ◽  
Active Transfer ◽  
Existing Data

In recent years, various studies have begun to use deep learning models to conduct research in the field of human activity recognition (HAR). However, there has been a severe lag in the absolute development of such models since training deep learning models require a lot of labeled data. In fields such as HAR, it is difficult to collect data and there are high costs and efforts involved in manual labeling. The existing methods rely heavily on manual data collection and proper labeling of the data, which is done by human administrators. This often results in the data gathering process often being slow and prone to human-biased labeling. To address these problems, we proposed a new solution for the existing data gathering methods by reducing the labeling tasks conducted on new data based by using the data learned through the semi-supervised active transfer learning method. This method achieved 95.9% performance while also reducing labeling compared to the random sampling or active transfer learning methods.

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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