ArticleSensors (Basel, Switzerland)2018
Divide and Conquer-Based 1D CNN Human Activity Recognition Using Test Data Sharpening.
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 24 papers.
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Who cites it
24 citing papers in PubMed.
- Efficient human activity recognition on edge devices using DeepConv LSTM architectures.Scientific reports · 2025Article
- An Algorithm for Mining the Living Habits of Elderly People Living Alone Based on AIoT.Sensors (Basel, Switzerland) · 2025Article
- Achieving More with Less: A Lightweight Deep Learning Solution for Advanced Human Activity Recognition (HAR).Sensors (Basel, Switzerland) · 2024Article
- Detection of Multiple Respiration Patterns Based on 1D SNN from Continuous Human Breathing Signals and the Range Classification Method for Each Respiration Pattern.Sensors (Basel, Switzerland) · 2023Article
- X-CHAR: A Concept-based Explainable Complex Human Activity Recognition Model.Proceedings of the ACM on interactive, mobile, wearable and ubiquitous technologies · 2023Article
- A Deep Learning-Based Semantic Segmentation Model Using MCNN and Attention Layer for Human Activity Recognition.Sensors (Basel, Switzerland) · 2023Article
- Automated Student Classroom Behaviors' Perception and Identification Using Motion Sensors.Bioengineering (Basel, Switzerland) · 2023Article
- Low-Cost CO Sensor Calibration Using One Dimensional Convolutional Neural Network.Sensors (Basel, Switzerland) · 2023Article
- Enhancing COVID-19 tracking apps with human activity recognition using a deep convolutional neural network and HAR-images.Neural computing & applications · 2023Article
- Vehicle target detection method based on improved YOLO V3 network model.PeerJ. Computer science · 2023Article
- Improving Inertial Sensor-Based Activity Recognition in Neurological Populations.Sensors (Basel, Switzerland) · 2022Article
- Novel spatiotemporal feature extraction parallel deep neural network for forecasting confirmed cases of coronavirus disease 2019.Socio-economic planning sciences · 2022Article
- Sensor-Based Human Activity Recognition Using Adaptive Class Hierarchy.Sensors (Basel, Switzerland) · 2021Article
- Towards a Clustering Guided Hierarchical Framework for Sensor-Based Activity Recognition.Sensors (Basel, Switzerland) · 2021Article
- Image Generation for 2D-CNN Using Time-Series Signal Features from Foot Gesture Applied to Select Cobot Operating Mode.Sensors (Basel, Switzerland) · 2021Article
- Non-Destructive Detection Pilot Study of Vegetable Organic Residues Using VNIR Hyperspectral Imaging and Deep Learning Techniques.Sensors (Basel, Switzerland) · 2021Article
- Hyperparameter Optimization Method Based on Harmony Search Algorithm to Improve Performance of 1D CNN Human Respiration Pattern Recognition System.Sensors (Basel, Switzerland) · 2020Article
- Classification of VLF/LF Lightning Signals Using Sensors and Deep Learning Methods.Sensors (Basel, Switzerland) · 2020Article
- Article
- An Energy-Efficient Method for Human Activity Recognition with Segment-Level Change Detection and Deep Learning.Sensors (Basel, Switzerland) · 2019Article
Corrections and comments
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Authors and funding
2 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Human Activity Recognition (HAR) aims to identify the actions performed by humans using signals collected from various sensors embedded in mobile devices. In recent years, deep learning techniques have further improved HAR performance on several benchmark datasets. In this paper, we propose one-dimensional Convolutional Neural Network (1D CNN) for HAR that employs a divide and conquer-based classifier learning coupled with test data sharpening. Our approach leverages a two-stage learning of multiple 1D CNN models; we first build a binary classifier for recognizing abstract activities, and then build two multi-class 1D CNN models for recognizing individual activities. We then introduce test data sharpening during prediction phase to further improve the activity recognition accuracy. While there have been numerous researches exploring the benefits of activity signal denoising for HAR, few researches have examined the effect of test data sharpening for HAR. We evaluate the effectiveness of our approach on two popular HAR benchmark datasets, and show that our approach outperforms both the two-stage 1D CNN-only method and other state of the art approaches.
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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.