ArticleSensors (Basel, Switzerland)2020
Classifier-Based Data Transmission Reduction in Wearable Sensor Network for Human Activity Monitoring.
Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 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.
The trial behind it
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Who cites it
8 citing papers in PubMed.
- Exploiting neuro-inspired dynamic sparsity for energy-efficient intelligent perception.Nature communications · 2025Review
- A Multi-Agent Prediction Method for Data Sampling and Transmission Reduction in Internet of Things Sensor Networks.Sensors (Basel, Switzerland) · 2023Article
- Spectrum Evaluation in CR-Based Smart Healthcare Systems Using Optimizable Tree Machine Learning Approach.Sensors (Basel, Switzerland) · 2023Article
- Data transmission reduction formalization for cloud offloading-based IoT systems.Journal of cloud computing (Heidelberg, Germany) · 2023Article
- Ensemble of RNN Classifiers for Activity Detection Using a Smartphone and Supporting Nodes.Sensors (Basel, Switzerland) · 2022Article
- A Population-Based Iterated Greedy Algorithm for Maximizing Sensor Network Lifetime.Sensors (Basel, Switzerland) · 2022Article
- Machine Learning for Healthcare Wearable Devices: The Big Picture.Journal of healthcare engineering · 2022Review
- Data Transmission Reduction in Wireless Sensor Network for Spatial Event Detection.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
The recent development of wireless wearable sensor networks offers a spectrum of new applications in fields of healthcare, medicine, activity monitoring, sport, safety, human-machine interfacing, and beyond. Successful use of this technology depends on lifetime of the battery-powered sensor nodes. This paper presents a new method for extending the lifetime of the wearable sensor networks by avoiding unnecessary data transmissions. The introduced method is based on embedded classifiers that allow sensor nodes to decide if current sensor readings have to be transmitted to cluster head or not. In order to train the classifiers, a procedure was elaborated, which takes into account the impact of data selection on accuracy of a recognition system. This approach was implemented in a prototype of wearable sensor network for human activity monitoring. Real-world experiments were conducted to evaluate the new method in terms of network lifetime, energy consumption, and accuracy of human activity recognition. Results of the experimental evaluation have confirmed that, the proposed method enables significant prolongation of the network lifetime, while preserving high accuracy of the activity recognition. The experiments have also revealed advantages of the method in comparison with state-of-the-art algorithms for data transmission reduction.
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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.