ArticleSensors (Basel, Switzerland)2024
Hand Gesture Recognition Using FSK Radar Sensors.
Article in Sensors (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Performance Optimization of Nonorthogonal MFSK for Symbol-by-Symbol Coherent Detection.Sensors (Basel, Switzerland) · 2026Article
- An Inclusive Offline Learning Platform Integrating Gesture Recognition and Local AI Models.Biomimetics (Basel, Switzerland) · 2025Article
- Radar-Based Gesture Recognition Using Adaptive Top-K Selection and Multi-Stream CNNs.Sensors (Basel, Switzerland) · 2025Article
- Real-Time Hand Gesture Recognition in Clinical Settings: A Low-Power FMCW Radar Integrated Sensor System with Multiple Feature Fusion.Sensors (Basel, Switzerland) · 2025Article
- RaGeoSense for smart home gesture recognition using sparse millimeter wave radar point clouds.Scientific reports · 2025Article
- Article
- Static and Dynamic Hand Gestures: A Review of Techniques of Virtual Reality Manipulation.Sensors (Basel, Switzerland) · 2024Review
- Sign language recognition based on dual-path background erasure convolutional neural network.Scientific reports · 2024Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Hand gesture recognition, which is one of the fields of human-computer interaction (HCI) research, extracts the user's pattern using sensors. Radio detection and ranging (RADAR) sensors are robust under severe environments and convenient to use for hand gestures. The existing studies mostly adopted continuous-wave (CW) radar, which only shows a good performance at a fixed distance, which is due to its limitation of not seeing the distance. This paper proposes a hand gesture recognition system that utilizes frequency-shift keying (FSK) radar, allowing for a recognition method that can work at the various distances between a radar sensor and a user. The proposed system adopts a convolutional neural network (CNN) model for the recognition. From the experimental results, the proposed recognition system covers the range from 30 cm to 180 cm and shows an accuracy of 93.67% over the entire range.
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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.