ArticleSensors (Basel, Switzerland)2018
Comparison of Data Preprocessing Approaches for Applying Deep Learning to Human Activity Recognition in the Context of Industry 4.0.
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 28 papers.
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Who cites it
28 citing papers in PubMed.
- Sensor-based human activity recognition using wrist and ankle wearables.Frontiers in bioengineering and biotechnology · 2026Article
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- Data Reconstruction Methods in Multi-Feature Fusion CNN Model for Enhanced Human Activity Recognition.Sensors (Basel, Switzerland) · 2025Article
- Automated Pipeline for Robust Cat Activity Detection Based on Deep Learning and Wearable Sensor Data.Sensors (Basel, Switzerland) · 2024Article
- Kinematic Analysis of Human Gait in Healthy Young Adults Using IMU Sensors: Exploring Relevant Machine Learning Features for Clinical Applications.Bioengineering (Basel, Switzerland) · 2024Article
- Recognition of sports and daily activities through deep learning and convolutional block attention.PeerJ. Computer science · 2024Article
- A Multi-Layer Classifier Model XR-KS of Human Activity Recognition for the Problem of Similar Human Activity.Sensors (Basel, Switzerland) · 2023Article
- Continual Deep Learning for Time Series Modeling.Sensors (Basel, Switzerland) · 2023Review
- A Light-Weight Artificial Neural Network for Recognition of Activities of Daily Living.Sensors (Basel, Switzerland) · 2023Article
- TN-GAN-Based Pet Behavior Prediction through Multiple-Dimension Time-Series Augmentation.Sensors (Basel, Switzerland) · 2023Article
- Quantum cyber-physical systems.Scientific reports · 2022Article
- Predicting Human Motion Signals Using Modern Deep Learning Techniques and Smartphone Sensors.Sensors (Basel, Switzerland) · 2021Article
- A Novel Elderly Tracking System Using Machine Learning to Classify Signals from Mobile and Wearable Sensors.International journal of environmental research and public health · 2021Article
- Big Machinery Data Preprocessing Methodology for Data-Driven Models in Prognostics and Health Management.Sensors (Basel, Switzerland) · 2021Article
- Human-Machine Integration in Processes within Industry 4.0 Management.Sensors (Basel, Switzerland) · 2021Article
- A Framework for Maternal Physical Activities and Health Monitoring Using Wearable Sensors.Sensors (Basel, Switzerland) · 2021Article
- INIM: Inertial Images Construction with Applications to Activity Recognition.Sensors (Basel, Switzerland) · 2021Article
- Inertial Measurement Unit Sensors in Assistive Technologies for Visually Impaired People, a Review.Sensors (Basel, Switzerland) · 2021Review
- Activity-Aware Vital SignMonitoring Based on a Multi-Agent Architecture.Sensors (Basel, Switzerland) · 2021Article
- Moving the Lab into the Mountains: A Pilot Study of Human Activity Recognition in Unstructured Environments.Sensors (Basel, Switzerland) · 2021Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
According to the Industry 4.0 paradigm, all objects in a factory, including people, are equipped with communication capabilities and integrated into cyber-physical systems (CPS). Human activity recognition (HAR) based on wearable sensors provides a method to connect people to CPS. Deep learning has shown surpassing performance in HAR. Data preprocessing is an important part of deep learning projects and takes up a large part of the whole analytical pipeline. Data segmentation and data transformation are two critical steps of data preprocessing. This study analyzes the impact of segmentation methods on deep learning model performance, and compares four data transformation approaches. An experiment with HAR based on acceleration data from multiple wearable devices was conducted. The multichannel method, which treats the data for the three axes as three overlapped color channels, produced the best performance. The highest overall recognition accuracy achieved was 97.20% for eight daily activities, based on the data from seven wearable sensors, which outperformed most of the other machine learning techniques. Moreover, the multichannel approach was applied to three public datasets and produced satisfying results for multi-source acceleration data. The proposed method can help better analyze workers’ activities and help to integrate people into CPS.
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Registered trials
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.