ArticleSensors (Basel, Switzerland)2019
Neural Network Ensembles for Sensor-Based Human Activity Recognition Within Smart Environments.
Article in Sensors (Basel, Switzerland), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed, 75 citations in OpenAlex.
- The Lifespan of Human Activity Recognition Systems for Smart Homes.Sensors (Basel, Switzerland) · 2023Article
- Sensor-Based Activity Recognition Using Frequency Band Enhancement Filters and Model Ensembles.Sensors (Basel, Switzerland) · 2023Article
- Stochastic Recognition of Human Physical Activities via Augmented Feature Descriptors and Random Forest Model.Sensors (Basel, Switzerland) · 2022Article
- A Survey of Human Activity Recognition in Smart Homes Based on IoT Sensors Algorithms: Taxonomies, Challenges, and Opportunities with Deep Learning.Sensors (Basel, Switzerland) · 2021Review
- Intelligent system for human activity recognition in IoT environment.Complex & intelligent systems · 2021Article
- Framework for Intelligent Swimming Analytics with Wearable Sensors for Stroke Classification.Sensors (Basel, Switzerland) · 2021Article
- A Risk-Based IoT Decision-Making Framework Based on Literature Review with Human Activity Recognition Case Studies.Sensors (Basel, Switzerland) · 2021Review
- Age-group determination of living individuals using first molar images based on artificial intelligence.Scientific reports · 2021Article
- A Robust Feature Extraction Model for Human Activity Characterization Using 3-Axis Accelerometer and Gyroscope Data.Sensors (Basel, Switzerland) · 2020Article
- Discovering Human Activities from Binary Data in Smart Homes.Sensors (Basel, Switzerland) · 2020Article
- Using Rough Sets to Improve Activity Recognition Based on Sensor Data.Sensors (Basel, Switzerland) · 2020Article
- Radial Basis Function Neural Network with Localized Stochastic-Sensitive Autoencoder for Home-Based Activity Recognition.Sensors (Basel, Switzerland) · 2020Article
- A Model-Based Design Floating-Point Accumulator. Case of Study: FPGA Implementation of a Support Vector Machine Kernel Function.Sensors (Basel, Switzerland) · 2020Article
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In this paper, we focus on data-driven approaches to human activity recognition (HAR). Data-driven approaches rely on good quality data during training, however, a shortage of high quality, large-scale, and accurately annotated HAR datasets exists for recognizing activities of daily living (ADLs) within smart environments. The contributions of this paper involve improving the quality of an openly available HAR dataset for the purpose of data-driven HAR and proposing a new ensemble of neural networks as a data-driven HAR classifier. Specifically, we propose a homogeneous ensemble neural network approach for the purpose of recognizing activities of daily living within a smart home setting. Four base models were generated and integrated using a support function fusion method which involved computing an output decision score for each base classifier. The contribution of this work also involved exploring several approaches to resolving conflicts between the base models. Experimental results demonstrated that distributing data at a class level greatly reduces the number of conflicts that occur between the base models, leading to an increased performance prior to the application of conflict resolution techniques. Overall, the best HAR performance of 80.39% was achieved through distributing data at a class level in conjunction with a conflict resolution approach, which involved calculating the difference between the highest and second highest predictions per conflicting model and awarding the final decision to the model with the highest differential value.
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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.