ArticleSensors (Basel, Switzerland)2018
A Robust Deep Learning Approach for Position-Independent Smartphone-Based Human Activity Recognition.
Article in Sensors (Basel, Switzerland), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- Machine Learning Techniques for Sensor-Based Human Activity Recognition with Data Heterogeneity-A Review.Sensors (Basel, Switzerland) · 2024Review
- Deep Neural Network for the Detections of Fall and Physical Activities Using Foot Pressures and Inertial Sensing.Sensors (Basel, Switzerland) · 2023Article
- Exploring Orientation Invariant Heuristic Features with Variant Window Length of 1D-CNN-LSTM in Human Activity Recognition.Biosensors · 2022Article
- Classifying Goliath Grouper (Sensors (Basel, Switzerland) · 2021Article
- Human Activity Recognition for Indoor Localization Using Smartphone Inertial Sensors.Sensors (Basel, Switzerland) · 2021Article
- Optimization of deep neural network-based human activity recognition for a wearable device.Heliyon · 2021Article
- New Sensor Data Structuring for Deeper Feature Extraction in Human Activity Recognition.Sensors (Basel, Switzerland) · 2021Article
- Out-of-Distribution Detection of Human Activity Recognition with Smartwatch Inertial Sensors.Sensors (Basel, Switzerland) · 2021Article
- Feature fusion using deep learning for smartphone based human activity recognition.International journal of information technology : an official journal of Bharati Vidyapeeth's Institute of Computer Applications and Management · 2021Article
- On the Challenges and Potential of Using Barometric Sensors to Track Human Activity.Sensors (Basel, Switzerland) · 2020Review
- Accelerometer-Based Human Activity Recognition for Patient Monitoring Using a Deep Neural Network.Sensors (Basel, Switzerland) · 2020Article
- Smartphone Motion Sensor-Based Complex Human Activity Identification Using Deep Stacked Autoencoder Algorithm for Enhanced Smart Healthcare System.Sensors (Basel, Switzerland) · 2020Article
- Fast Wearable Sensor-Based Foot-Ground Contact Phase Classification Using a Convolutional Neural Network with Sliding-Window Label Overlapping.Sensors (Basel, Switzerland) · 2020Article
- Human Activity Recognition using Inertial, Physiological and Environmental Sensors: A Comprehensive Survey.IEEE access : practical innovations, open solutions · 2020Article
- A Human Activity Recognition Algorithm Based on Stacking Denoising Autoencoder and LightGBM.Sensors (Basel, Switzerland) · 2019Article
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Authors and funding
3 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Recently, modern smartphones equipped with a variety of embedded-sensors, such as accelerometers and gyroscopes, have been used as an alternative platform for human activity recognition (HAR), since they are cost-effective, unobtrusive and they facilitate real-time applications. However, the majority of the related works have proposed a position-dependent HAR, i.e., the target subject has to fix the smartphone in a pre-defined position. Few studies have tackled the problem of position-independent HAR. They have tackled the problem either using handcrafted features that are less influenced by the position of the smartphone or by building a position-aware HAR. The performance of these studies still needs more improvement to produce a reliable smartphone-based HAR. Thus, in this paper, we propose a deep convolution neural network model that provides a robust position-independent HAR system. We build and evaluate the performance of the proposed model using the RealWorld HAR public dataset. We find that our deep learning proposed model increases the overall performance compared to the state-of-the-art traditional machine learning method from 84% to 88% for position-independent HAR. In addition, the position detection performance of our model improves superiorly from 89% to 98%. Finally, the recognition time of the proposed model is evaluated in order to validate the applicability of the model for real-time applications.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.