Exploring Orientation Invariant Heuristic Features with Variant Window Length of 1D-CNN-LSTM in Human Activity Recognition

Abstract

Many studies have explored divergent deep neural networks in human activity recognition (HAR) using a single accelerometer sensor. Multiple types of deep neural networks, such as convolutional neural networks (CNN), long short-term memory (LSTM), or their hybridization (CNN-LSTM), have been implemented. However, the sensor orientation problem poses challenges in HAR, and the length of windows as inputs for the deep neural networks has mostly been adopted arbitrarily. This paper explores the effect of window lengths with orientation invariant heuristic features on the performance of 1D-CNN-LSTM in recognizing six human activities; sitting, lying, walking and running at three different speeds using data from an accelerometer sensor encapsulated into a smartphone. Forty-two participants performed the six mentioned activities by keeping smartphones in their pants pockets with arbitrary orientation. We conducted an inter-participant evaluation using 1D-CNN-LSTM architecture. We found that the average accuracy of the classifier was saturated to 80 ± 8.07% for window lengths greater than 65 using only four selected simple orientation invariant heuristic features. In addition, precision, recall and F1-measure in recognizing stationary activities such as sitting and lying decreased with increment of window length, whereas we encountered an increment in recognizing the non-stationary activities.

The effectiveness of simple heuristic features in sensor orientation and placement problems in human activity recognition using a single smartphone accelerometer

“…A more detailed explanation of the features can be found in [ 26 ]. Although they introduced the nine features mentioned and used them to eliminate the orientational effect, in a previous study [ 69 ], we found the first four features and to be the most significant and effective in reducing the orientational effect. Therefore, for our study, we only used the first four features.…”

“…That means each window had 65 samples, and two consecutive windows had 64 samples in common. We used the window length of 65, because we found it to be both computational and time-efficient in our previous study [ 69 ]. The information for the validation approach is organized in Table 7 .…”

Identification of Air-Writing Tamil Alphabetical Vowel Characters

“…However, this includes the investigation to test the selected features with a variety of sensor placement locations, such as waist, wrist, chest, and ankle, sampling frequency, and the performance of the dataset. The purpose of [20] is to investigate the impact of window lengths with orientation invariant heuristic features on the performance of 1D-CNN-LSTM using data from 42 participants to recognize six human activities: sitting, lying down, walking, and running at three different speeds using information gathered from the Samsung Galaxy s7 smartphone's accelerometer sensor with the preinstalled Ethica application.…”

“…The impact of the one-dimensional convolutional neural network (1D CNN) technique on the field of air-writing recognition has been substantial. Numerous studies have been conducted to investigate the application of deep convolutional neural networks (CNNs) in the field of air-writing recognition, and these investigations have consistently proved the efficacy of such networks [20], [29].…”

“…Furthermore, some SBR techniques are used for gesture recognition, which may incur high computation complexities [18]. The Recurrent Neural Networks (RNNs) and their variants, such as the Long Short-Term Memory (LSTM) algorithm, are effective for the recognition of gesture sequences with low computation costs [12], [19], [20], [21].…”