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

Abstract

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.

Adapted Binary Particle Swarm Optimization for Efficient Features Selection in the Case of Imbalanced Sensor Data

“…Our approach 93.8% accuracy As can be seen in the table, the approach proposed in this article returns results comparable to the ones from the literature. The method presented in [33] returns much better results; however, in our approach we consider a maximum of six features and, in that approach, all features are used, therefore the two approaches are not directly comparable.…”

“…For example, the method presented in [33] performs significantly better,; however, it requires complex computational resources and image processing transformations.…”

“…The method presented in [33] considers the DaLiAc dataset from a different perspective than the other articles, which consider it as experimental support, converting the inertial sensor signals to images and then applying a convolutional neural network (CNN) [34] for images classification. Even if that method leads to better classification results than the traditional classification methods, it is also subject to the limitations of CNNs, such as the class imbalance, the overfitting and poor performance when the input data is small.…”

Heart Attack Detection in Colour Images Using Convolutional Neural Networks

“…Regardless of the event to be detected, it is possible to classify all publications into two principal approaches according to the acquisition sensors of the input data, which are non-visual and visual. For non-visual sensors, the use of accelerometers and gyroscopes [20][21][22] should be highlighted. Although these devices provide high precision, their main drawback is that they require that people wear them all the time, which is uncomfortable and not always possible.…”

An Energy-Efficient Method for Human Activity Recognition with Segment-Level Change Detection and Deep Learning

“…In recent years, deep neural networks (DNNs) have yielded satisfactory results in audio and visual recognition tasks because of their ability to systematically and automatically learn discriminative features from training samples [2,3]. Thus, many studies on HAR tasks using the superior power of DNNs have been reported [1,4,5,6,7,8,9,10,11]. Various DNNs such as convolutional neural networks (CNNs) [4,5,6,7,8], recurrent neural networks (RNNs) [9,10,11], and restricted Boltzmann machines (RBMs) [1] have been used to classify human activities and have shown impressive results in terms of their accuracy.…”