Shrestha et al., 2019 - Google Patents
Cross-frequency classification of indoor activities with dnn transfer learningShrestha et al., 2019
View PDF- Document ID
- 7208509156862019669
- Author
- Shrestha A
- Murphy C
- Johnson I
- Anbulselvam A
- Fioranelli F
- Le Kernec J
- Gurbuz S
- Publication year
- Publication venue
- 2019 IEEE Radar Conference (RadarConf)
External Links
Snippet
Remote, non-contact recognition of human motion and activities is central to health monitoring in assisted living facilities, but current systems face the problems of training compatibility, minimal training data sets and a lack of interoperability between radar sensors …
- 230000000694 effects 0 title abstract description 33
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/62—Methods or arrangements for recognition using electronic means
- G06K9/6267—Classification techniques
- G06K9/6268—Classification techniques relating to the classification paradigm, e.g. parametric or non-parametric approaches
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/36—Image preprocessing, i.e. processing the image information without deciding about the identity of the image
- G06K9/46—Extraction of features or characteristics of the image
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/62—Methods or arrangements for recognition using electronic means
- G06K9/6217—Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/62—Methods or arrangements for recognition using electronic means
- G06K9/68—Methods or arrangements for recognition using electronic means using sequential comparisons of the image signals with a plurality of references in which the sequence of the image signals or the references is relevant, e.g. addressable memory
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/00362—Recognising human body or animal bodies, e.g. vehicle occupant, pedestrian; Recognising body parts, e.g. hand
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
- G01S7/02—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
- G01S7/41—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00 using analysis of echo signal for target characterisation; Target signature; Target cross-section
- G01S7/415—Identification of targets based on measurements of movement associated with the target
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/20—Image acquisition
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
- G01S13/02—Systems using reflection of radio waves, e.g. primary radar systems; Analogous systems
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
- G01S13/66—Radar-tracking systems; Analogous systems where the wavelength or the kind of wave is irrelevant
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N99/00—Subject matter not provided for in other groups of this subclass
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computer systems based on biological models
- G06N3/02—Computer systems based on biological models using neural network models
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S3/00—Direction-finders for determining the direction from which infrasonic, sonic, ultrasonic, or electromagnetic waves, or particle emission, not having a directional significance, are being received
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Gurbuz et al. | Radar-based human-motion recognition with deep learning: Promising applications for indoor monitoring | |
Shrestha et al. | Cross-frequency classification of indoor activities with dnn transfer learning | |
Cao et al. | Radar‐ID: human identification based on radar micro‐Doppler signatures using deep convolutional neural networks | |
Kim et al. | Hand gesture recognition using micro-Doppler signatures with convolutional neural network | |
Yang et al. | Omnidirectional motion classification with monostatic radar system using micro-Doppler signatures | |
Skaria et al. | Deep-learning methods for hand-gesture recognition using ultra-wideband radar | |
Ding et al. | Non-contact human motion recognition based on UWB radar | |
Du et al. | Segmented convolutional gated recurrent neural networks for human activity recognition in ultra-wideband radar | |
US11301672B2 (en) | Radar-based methods and apparatus for communication and interpretation of sign languages | |
Chakraborty et al. | Application of DNN for radar micro-doppler signature-based human suspicious activity recognition | |
Kim et al. | Human detection based on time-varying signature on range-Doppler diagram using deep neural networks | |
Li et al. | Hierarchical sensor fusion for micro-gesture recognition with pressure sensor array and radar | |
Yang et al. | Multiscenario open-set gait recognition based on radar micro-Doppler signatures | |
Shi et al. | Robust gait recognition based on deep cnns with camera and radar sensor fusion | |
Qiao et al. | Human activity classification based on micro-Doppler signatures separation | |
Shah et al. | Data portability for activities of daily living and fall detection in different environments using radar micro-doppler | |
Sadreazami et al. | TL-FALL: Contactless indoor fall detection using transfer learning from a pretrained model | |
Wang et al. | Negative latency recognition method for fine-grained gestures based on terahertz radar | |
Li et al. | Multi-domains based human activity classification in radar | |
Le et al. | A fast and compact deep Gabor network for micro-Doppler signal processing and human motion classification | |
Faisal et al. | Human activity recognition from FMCW radar signals utilizing cross-terms free WVD | |
Zhang et al. | Radar recognition of multiple micro‐drones based on their micro‐Doppler signatures via dictionary learning | |
Niazi et al. | Radar-based efficient gait classification using Gaussian prototypical networks | |
Jung et al. | Digit Recognition Using FMCW and UWB Radar Sensors: A Transfer Learning Approach | |
Kim et al. | Classification of human activity on water through micro-Dopplers using deep convolutional neural networks |