Empirical Evaluation on Deep Learning of Depth Feature for Human Activity Recognition

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.

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Location-based Daily Human Activity Recognition using Hybrid Deep Learning Network

2021 18th International Joint Conference on Computer Science and Software Engineering (JCSSE) ◽

10.1109/jcsse53117.2021.9493807 ◽

2021 ◽

Author(s):

Sakorn Mekruksavanich ◽

Chanon Promsakon ◽

Anuchit Jitpattanakul

Keyword(s):

Deep Learning ◽

Activity Recognition ◽

Human Activity ◽

Human Activity Recognition ◽

Learning Network ◽

Deep Learning Network

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

Sensors ◽

10.3390/s18113910 ◽

2018 ◽

Vol 18 (11) ◽

pp. 3910 ◽

Cited By ~ 11

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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