ArticleSensors (Basel, Switzerland)2021
Optimizing Sensor Position with Virtual Sensors in Human Activity Recognition System Design.
Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 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
6 citing papers in PubMed.
- Optimizing Sensor and Data Selection on Lower Limbs via Deep Learning for Real-Time Human Activity Recognition.IEEE transactions on human-machine systems · 2026Article
- Simulation-driven design of smart gloves for gesture recognition.Scientific reports · 2024Article
- A Multi-Layer Classifier Model XR-KS of Human Activity Recognition for the Problem of Similar Human Activity.Sensors (Basel, Switzerland) · 2023Article
- HARE: Unifying the Human Activity Recognition Engineering Workflow.Sensors (Basel, Switzerland) · 2023Article
- A union of deep learning and swarm-based optimization for 3D human action recognition.Scientific reports · 2022Article
- Human Behavior Recognition Model Based on Feature and Classifier Selection.Sensors (Basel, Switzerland) · 2021Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
Human activity recognition (HAR) systems combined with machine learning normally serve users based on a fixed sensor position interface. Variations in the installing position will alter the performance of the recognition and will require a new training dataset. Therefore, we need to understand the role of sensor position in HAR system design to optimize its effect. In this paper, we designed an optimization scheme with virtual sensor data for the HAR system. The system is able to generate the optimal sensor position from all possible locations under a given sensor number. Utilizing virtual sensor data, the training dataset can be accessed at low cost. The system can help the decision-making process of sensor position selection with great accuracy using feedback, as well as output the classifier at a lower cost than a conventional training model.
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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.