Proceedings ArticleDOI
Smartwatch-based Human Activity Recognition Using Hybrid LSTM Network
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An HAR framework that employs spatial-temporal features that are automatically extracted from data obtained from smartwatch sensors is proposed, and it was indicated by the results that the baseline models are outperformed by the proposed hybrid deep learning model.Abstract:
As a result of the rapid development of wearable sensor technology, the use of smartwatch sensors for human activity recognition (HAR) has recently become a popular area of research. Currently, a large number of mobile applications, such as healthcare monitoring, sport performance tracking, etc., are applying the results of major HAR research studies. In this paper, an HAR framework that employs spatial-temporal features that are automatically extracted from data obtained from smartwatch sensors is proposed. The hybrid deep learning approach is used in the framework through the employment of Long Short-Term Memory Networks and the Convolutional Neural Network, eliminating the need for the manual extraction of features. The advantage of tuning the hyperparameters of each of the considered networks by Bayesian optimization is also utilized. It was indicated by the results that the baseline models are outperformed by the proposed hybrid deep learning model, which has an average accuracy of 96.2% and an F-measure of 96.3%.read more
Citations
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Journal ArticleDOI
Biometric User Identification Based on Human Activity Recognition Using Wearable Sensors: An Experiment Using Deep Learning Models
TL;DR: A novel framework for multi-class wearable user identification, with a basis in the recognition of human behavior through the use of deep learning models, is presented, and the proposed framework’s effectiveness was demonstrated.
Proceedings ArticleDOI
A Multichannel CNN-LSTM Network for Daily Activity Recognition using Smartwatch Sensor Data
TL;DR: In this paper, a hybrid model called a multichannel CNN-LSTM network was proposed to solve the human behavior recognition problem in the context of smartwatch accelerometer data.
Proceedings ArticleDOI
Recognition of Real-life Activities with Smartphone Sensors using Deep Learning Approaches
TL;DR: In this article, three deep learning models were used to investigate real-life activities using smartphone sensors in this study, and the Att-CNN-LSTM network was introduced as a hybrid DL model to handle the human activity recognition challenge using an attention mechanism.
Journal ArticleDOI
Understanding LSTM Network Behaviour of IMU-Based Locomotion Mode Recognition for Applications in Prostheses and Wearables
TL;DR: In this paper, a dataset of six locomotive activities (walking, stopped, stairs and ramps) from 22 non-amputee subjects is collected, capturing both steady-state and transitions between activities in natural environments.
Proceedings ArticleDOI
Deep Learning Approach for Complex Activity Recognition using Heterogeneous Sensors from Wearable Device
TL;DR: In this article, a combination of two inertial measurement units outperforms employing either an accelerometer or a gyroscope by utilizing four deep learning classifiers to recognize complex human activity (CHA).
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Scikit-learn: Machine Learning in Python
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