Efficient Smartphone-Based Human Activity Recognition Using Convolutional Neural Network

Dhanraj, Surya; De, Suddhasil; Dash, Dinesh

doi:10.1109/icit48102.2019.00061

Cited by 17 publications

(8 citation statements)

References 46 publications

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“…In [51], the authors suggested a HAR framework using a CNN architecture for the "UCI-HAR" public dataset. Moreover, the authors calculated the training and testing times of their approach as 3.4274 seconds and 372.6 ms, respectively.…”

Section: A Deep Learning For Harmentioning

confidence: 99%

“…Utilizing the UCI-HAR dataset, we perform exhaustive experiments on various DL approaches, such as a CNN, LSTM, an AE, CNN-LSTM, a convolutional AE, and the proposed method (ConvAE-LSTM). [51], where the computational times for training and testing are 3.4274 s and 372.6 ms, respectively, when using the CNN with the UCI dataset. The testing accuracy of ConvAE-LSTM is 98.14%, which is much higher than that of other popular DL approaches.…”

Section: ) Uci Datasetmentioning

confidence: 99%

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ConvAE-LSTM: Convolutional Autoencoder Long Short-Term Memory Network for Smartphone-Based Human Activity Recognition

Thakur

Biswas

et al. 2022

IEEE Access

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The self-regulated recognition of human activities from time-series smartphone sensor data is a growing research area in smart and intelligent health care. Deep learning (DL) approaches have exhibited improvements over traditional machine learning (ML) models in various domains, including human activity recognition (HAR). Several issues are involved with traditional ML approaches; these include handcrafted feature extraction, which is a tedious and complex task involving expert domain knowledge, and the use of a separate dimensionality reduction module to overcome overfitting problems and hence provide model generalization. In this article, we propose a DL-based approach for activity recognition with smartphone sensor data, i.e., accelerometer and gyroscope data. Convolutional neural networks (CNNs), autoencoders (AEs), and long short-term memory (LSTM) possess complementary modeling capabilities, as CNNs are good at automatic feature extraction, AEs are used for dimensionality reduction and LSTMs are adept at temporal modeling. In this study, we take advantage of the complementarity of CNNs, AEs, and LSTMs by combining them into a unified architecture. We explore the proposed architecture, namely, "ConvAE-LSTM", on four different standard public datasets (WISDM, UCI, PAMAP2, and OPPORTUNITY). The experimental results indicate that our novel approach is practical and provides relative smartphone-based HAR solution performance improvements in terms of computational time, accuracy, F1-score, precision, and recall over existing state-of-the-art methods.

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Section: A Deep Learning For Harmentioning

confidence: 99%

Section: ) Uci Datasetmentioning

confidence: 99%

ConvAE-LSTM: Convolutional Autoencoder Long Short-Term Memory Network for Smartphone-Based Human Activity Recognition

Thakur

Biswas

et al. 2022

IEEE Access

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show abstract

“…By contrast, the present study only required 600 iterations to obtain superior accuracy. Reference [32], like us, used CNN, but we differed from it in that after research we excluded the pooling layers and obtained higher accuracy.…”

Section: H the Comparisons Of Several Models Of The Open Datasetmentioning

confidence: 96%

“…After 10 repetitions, the average accuracy of the open database was 95.08%, and the mean accuracy of the database we recorded was 87.88%. [20] 93.70% LSTM-CNN [28] 95.78% Bidir-LSTM [31] 93.79% EHARS [32] 93.92% CNN-LSTM [33] 92.13% CNN-LSTM [34] 93.40% Ours 95.99%…”

Section: G K-fold Cross-validation In Both Open Dataset and Data Thimentioning

confidence: 99%

Human Daily Activity Recognition Performed Using Wearable Inertial Sensors Combined With Deep Learning Algorithms

2020

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This study proposed a wearable device capable of recognizing six human daily activities (walking, walking upstairs, walking downstairs, sitting, standing, and lying) through a deep learning algorithm. Existing wearable devices are mainly watches or wristbands, and almost none are to be worn on the waist. Wearable devices in the forms of watches and wristbands are unfriendly to patients who are critically ill, such as patients undergoing dialysis. Patients undergoing dialysis have artificial blood vessels on their arm, and they cannot perform intense exercise. For this type of users, general hand wearable devices cannot correctly identify wearers' activities. Therefore, we proposed a waist wearable device and these types of daily life activities to assess their exercise. The hardware of the wearable device consisted of an inertial sensor, which included a microcontroller, a three-axis accelerometer, and a three-axis gyroscope. The activity recognition algorithm of the software used motion signals acquisition, signal normalization, and a feature learning method. The feature learning method was based on a 1D convolutional neural network that automatically performed feature extraction and classification from raw data. One part of the experimental data was from the dataset of the University of California (UCI), and the other part was recorded by this study. To capture the data recorded, the wearable inertial sensing device was attached to the waists of 21 experimental participants who performed six common movements in a laboratorial environment, and the subsequent records were collected to verify the validity of the proposed deep learning algorithm in relation to the inertial sensor of the wearable device. For the six common activities in the UCI dataset and the data recorded, the recognition rates in the training sample reached 98.93% and 97.19%, respectively, and the recognition rates in the testing sample were 95.99% and 93.77%, respectively.

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“…Using multiple layers in the network, the deep learning procedure identifies optimal features from the raw data itself, without human intervention [ 20 ]. Some studies show that this approach can yield highly accurate results in activity classification [ 21 , 22 , 23 ]. However, there are limitations and challenges in the application.…”

Section: Introductionmentioning

confidence: 99%

Smartphone-Based Activity Recognition Using Multistream Movelets Combining Accelerometer and Gyroscope Data

Huang

Yan

Onnela

2022

Sensors

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Physical activity patterns can reveal information about one’s health status. Built-in sensors in a smartphone, in comparison to a patient’s self-report, can collect activity recognition data more objectively, unobtrusively, and continuously. A variety of data analysis approaches have been proposed in the literature. In this study, we applied the movelet method to classify the activities performed using smartphone accelerometer and gyroscope data, which measure a phone’s acceleration and angular velocity, respectively. The movelet method constructs a personalized dictionary for each participant using training data and classifies activities in new data with the dictionary. Our results show that this method has the advantages of being interpretable and transparent. A unique aspect of our movelet application involves extracting unique information, optimally, from multiple sensors. In comparison to single-sensor applications, our approach jointly incorporates the accelerometer and gyroscope sensors with the movelet method. Our findings show that combining data from the two sensors can result in more accurate activity recognition than using each sensor alone. In particular, the joint-sensor method reduces errors of the gyroscope-only method in differentiating between standing and sitting. It also reduces errors in the accelerometer-only method when classifying vigorous activities.

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Efficient Smartphone-Based Human Activity Recognition Using Convolutional Neural Network

Cited by 17 publications

References 46 publications

ConvAE-LSTM: Convolutional Autoencoder Long Short-Term Memory Network for Smartphone-Based Human Activity Recognition

ConvAE-LSTM: Convolutional Autoencoder Long Short-Term Memory Network for Smartphone-Based Human Activity Recognition

Human Daily Activity Recognition Performed Using Wearable Inertial Sensors Combined With Deep Learning Algorithms

Smartphone-Based Activity Recognition Using Multistream Movelets Combining Accelerometer and Gyroscope Data

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