ArticleNature communications2020
Human activity recognition using magnetic induction-based motion signals and deep recurrent neural networks.
Article in Nature communications, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 23 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
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
23 citing papers in PubMed.
- The peak shifting electricity consumption management and influencing factors of smart grid from recurrent neural network model and deep learning.Scientific reports · 2026Article
- Flexible Tactile Sensing Systems: Challenges in Theoretical Research Transferring to Practical Applications.Nano-micro letters · 2025Review
- Radar Signal Processing and Its Impact on Deep Learning-Driven Human Activity Recognition.Sensors (Basel, Switzerland) · 2025Article
- A novel approach for joint indoor localization and activity recognition using a hybrid CNN-GRU and MRF framework.PloS one · 2025Article
- Biomimetic Wearable Sensors: Emerging Combination of Intelligence and Electronics.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Review
- 3D Printed Conformal Strain and Humidity Sensors for Human Motion Prediction and Health Monitoring via Machine Learning.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2023Article
- Amphibious epidermal area networks for uninterrupted wireless data and power transfer.Nature communications · 2023Article
- A novel WGF-LN based edge driven intelligence for wearable devices in human activity recognition.Scientific reports · 2023Article
- Upper limb modeling and motion extraction based on multi-space-fusion.Scientific reports · 2023Article
- Skin-interfaced electronics: A promising and intelligent paradigm for personalized healthcare.Biomaterials · 2023Review
- SDHAR-HOME: A Sensor Dataset for Human Activity Recognition at Home.Sensors (Basel, Switzerland) · 2022Article
- Non-Invasive Data Acquisition and IoT Solution for Human Vital Signs Monitoring: Applications, Limitations and Future Prospects.Sensors (Basel, Switzerland) · 2022Review
- WiFi Signal-Based Gesture Recognition Using Federated Parameter-Matched Aggregation.Sensors (Basel, Switzerland) · 2022Article
- Human Motion Enhancement via Tobit Kalman Filter-Assisted Autoencoder.IEEE access : practical innovations, open solutions · 2022Article
- Predicting Human Motion Signals Using Modern Deep Learning Techniques and Smartphone Sensors.Sensors (Basel, Switzerland) · 2021Article
- A Motion Capturing and Energy Harvesting Hybridized Lower-Limb System for Rehabilitation and Sports Applications.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2021Article
- Wearable magnetic induction-based approach toward 3D motion tracking.Scientific reports · 2021Article
- Domain randomization-enhanced deep learning models for bird detection.Scientific reports · 2021Article
- Syntactic model-based human body 3D reconstruction and event classification via association based features mining and deep learning.PeerJ. Computer science · 2021Article
- A wearable motion capture device able to detect dynamic motion of human limbs.Nature communications · 2020Article
Corrections and comments
- Erratum issued
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Recognizing human physical activities using wireless sensor networks has attracted significant research interest due to its broad range of applications, such as healthcare, rehabilitation, athletics, and senior monitoring. There are critical challenges inherent in designing a sensor-based activity recognition system operating in and around a lossy medium such as the human body to gain a trade-off among power consumption, cost, computational complexity, and accuracy. We introduce an innovative wireless system based on magnetic induction for human activity recognition to tackle these challenges and constraints. The magnetic induction system is integrated with machine learning techniques to detect a wide range of human motions. This approach is successfully evaluated using synthesized datasets, laboratory measurements, and deep recurrent neural networks.
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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.