Golestani et al., 2020 - Google Patents
A comparison of machine learning classifiers for human activity recognition using magnetic induction-based motion signalsGolestani et al., 2020
- Document ID
- 1977556837343721980
- Author
- Golestani N
- Moghaddam M
- Publication year
- Publication venue
- 2020 14th European Conference on Antennas and Propagation (EuCAP)
External Links
Snippet
Human activity recognition (HAR) is a growing research field with a wide range of applications. Magnetic induction-based human activity recognition system (MI-HAR) is a wearable-based HAR system proposed for capturing human motions and detecting activities …
- 230000000694 effects 0 title abstract description 21
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
- G06K9/62—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
- G06K9/6232—Extracting features by transforming the feature space, e.g. multidimensional scaling; Mappings, e.g. subspace methods
- G06K9/6247—Extracting features by transforming the feature space, e.g. multidimensional scaling; Mappings, e.g. subspace methods based on an approximation criterion, e.g. principal component analysis
-
- 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
- G06K9/6228—Selecting the most significant subset of features
-
- 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
- G06K9/00221—Acquiring or recognising human faces, facial parts, facial sketches, facial expressions
- G06K9/00268—Feature extraction; Face representation
- G06K9/00281—Local features and components; Facial parts ; Occluding parts, e.g. glasses; Geometrical relationships
-
- 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
- G06K9/00362—Recognising human body or animal bodies, e.g. vehicle occupant, pedestrian; Recognising body parts, e.g. hand
-
- 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
- G06K9/00335—Recognising movements or behaviour, e.g. recognition of gestures, dynamic facial expressions; Lip-reading
-
- 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
- G06K9/00496—Recognising patterns in signals and combinations thereof
-
- 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
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Coyle et al. | A time-series prediction approach for feature extraction in a brain-computer interface | |
Frank et al. | Activity and gait recognition with time-delay embeddings | |
US8935195B2 (en) | Method of identification and devices thereof | |
Chen et al. | Classification of human activity based on radar signal using 1-D convolutional neural network | |
KR101293446B1 (en) | Electroencephalography Classification Method for Movement Imagination and Apparatus Thereof | |
Aljarrah et al. | Human activity recognition using PCA and BiLSTM recurrent neural networks | |
Chikhaoui et al. | Towards automatic feature extraction for activity recognition from wearable sensors: a deep learning approach | |
Dehzangi et al. | IMU-based robust human activity recognition using feature analysis, extraction, and reduction | |
CN103886341A (en) | Gait behavior recognition method based on feature combination | |
Espinosa et al. | Application of convolutional neural networks for fall detection using multiple cameras | |
Al-Assaf | Surface myoelectric signal analysis: dynamic approaches for change detection and classification | |
Meena et al. | Gender recognition using in-built inertial sensors of smartphone | |
Zheng et al. | L-sign: Large-vocabulary sign gestures recognition system | |
Li et al. | The novel recognition method with optimal wavelet packet and LSTM based recurrent neural network | |
Golestani et al. | A comparison of machine learning classifiers for human activity recognition using magnetic induction-based motion signals | |
Saidani et al. | An efficient human activity recognition using hybrid features and transformer model | |
Nguyen et al. | A potential approach for emotion prediction using heart rate signals | |
Sharma et al. | A transformer based approach for activity detection | |
Ali et al. | Motor imagery eeg classification using fine-tuned deep convolutional efficientnetb0 model | |
Ren et al. | PDCHAR: Human activity recognition via multi-sensor wearable networks using two-channel convolutional neural networks | |
Shukla et al. | An experimental analysis of motor imagery EEG signals using feature extraction and classification methodologies | |
Zainudin et al. | Activity recognition using one-versus-all strategy with Relief-F and self-adaptive algorithm | |
Jeong et al. | Physical workout classification using wrist accelerometer data by deep convolutional neural networks | |
Kumar | Heart disease detection using radial basis function classifier | |
Assam et al. | Activity recognition from sensors using dyadic wavelets and hidden markov model |