Info
- Utilizzare la checkbox di selezione a fianco di ciascun documento per attivare le funzionalità di stampa, invio email, download nei formati disponibili del (i) record.
Intelligent Sensors for Human Motion Analysis
| Intelligent Sensors for Human Motion Analysis |
| Autore | Krzeszowski Tomasz |
| Pubbl/distr/stampa | MDPI - Multidisciplinary Digital Publishing Institute, 2022 |
| Descrizione fisica | 1 online resource (382 p.) |
| Soggetto topico |
History of engineering and technology
Technology: general issues |
| Soggetto non controllato |
3D human mesh reconstruction
3D human pose estimation 3D multi-person pose estimation absolute poses action units aggregation function anomaly detection artifact classification artifact detection artificial intelligence assessment Azure Kinect balance Berg Balance Scale BILSTM biometrics camera-centric coordinates computer vision convolutional neural networks COVID-19 cyber-physical systems data augmentation deep learning deep neural network deep-learning development diagnosis elderly EMG F-Formation facial expression recognition facial landmarks fall risk detection features fusion features selection FFNN FMCW fuzzy inference gait analysis gait parameters gait recognition gap filling generalization graph convolutional networks grey wolf optimization GRU human action recognition human motion analysis human motion modelling human tracking Kinect v2 kinematics knowledge measure LSTM machine learning markerless markerless motion capture MFCC modular sensing unit motion capture movement tracking n/a neural networks optical sensing principle particle swarm optimization pattern recognition plantar pressure measurement pose estimation posture detection precedence indicator recognition reconstruction regularized discriminant analysis RGB-D sensors robot rule induction skeletal data socially occupied space telemedicine time series classification vital sign whale optimization algorithm XGBoost Zed 2i |
| ISBN | 3-0365-5074-7 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910619469003321 |
Krzeszowski Tomasz
|
||
| MDPI - Multidisciplinary Digital Publishing Institute, 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Wearable Sensors & Gait
| Wearable Sensors & Gait |
| Pubbl/distr/stampa | MDPI - Multidisciplinary Digital Publishing Institute, 2023 |
| Descrizione fisica | 1 online resource (254 p.) |
| Soggetto topico | Technology: general issues |
| Soggetto non controllato |
3-D gait analysis
aerobic agreement of measurements analysis assessment biomechanics clinical measurement cycling data histogram decision tree diabetic foot distance estimation functional scales gait gait analysis gait cycle gait disturbance gait parameters gait retraining gait speed ground reaction force hierarchical cluster analysis human running IMU inertial measurement unit inertial sensor unit inertial sensors injury kinematics load monitoring loaded sprint machine learning malleolar fractures markerless mobile power meter monitoring foot temperature muscle activation music therapy on-ankle path loss orientation-invariant Parkinson's disease performance physiology portable gait rhythmogram power principal component analysis random forest regression RSSI running running biomechanics running conditions sensor fusion sled-push smart wearable spatiotemporal parameters step length estimation strike length estimation stroke surface electromyography synergy team-sports technology testing training triathletes two-term Gaussian distribution VO2max walking wearable wearable device wearable devices wearable sensor wearables |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910743270703321 |
| MDPI - Multidisciplinary Digital Publishing Institute, 2023 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||