ArticleSensors (Basel, Switzerland)2019
Virtual Sensors for Optimal Integration of Human Activity Data.
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 5 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
5 citing papers in PubMed.
- Facial Expression Recognition from Multi-Perspective Visual Inputs and Soft Voting.Sensors (Basel, Switzerland) · 2022Article
- Improved Accuracy in Predicting the Best Sensor Fusion Architecture for Multiple Domains.Sensors (Basel, Switzerland) · 2021Article
- An Optimization Approach to Multi-Sensor Operation for Multi-Context Recognition.Sensors (Basel, Switzerland) · 2021Article
- Choosing the Best Sensor Fusion Method: A Machine-Learning Approach.Sensors (Basel, Switzerland) · 2020Article
- Multi-Sensor Fusion for Activity Recognition-A Survey.Sensors (Basel, Switzerland) · 2019Review
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Sensors are becoming more and more ubiquitous as their price and availability continue to improve, and as they are the source of information for many important tasks. However, the use of sensors has to deal with noise and failures. The lack of reliability in the sensors has led to many forms of redundancy, but simple solutions are not always the best, and the precise way in which several sensors are combined has a big impact on the overall result. In this paper, we discuss how to deal with the combination of information coming from different sensors, acting thus as "virtual sensors", in the context of human activity recognition, in a systematic way, aiming for optimality. To achieve this goal, we construct meta-datasets containing the "signatures" of individual datasets, and apply machine-learning methods in order to distinguish when each possible combination method could be actually the best. We present specific results based on experimentation, supporting our claims of optimality.
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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.