PRAXIS: Towards automatic cognitive assessment using gesture recognition
| Autor: | Francois Bremond, Jérémy Bourgeois, Philippe Robert, Emmanuelle Chapoulie, Jordi Gonzàlez, Pau Rodríguez, Adlen Kerboua, Michal Koperski, Farhood Negin |
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| Přispěvatelé: | Spatio-Temporal Activity Recognition Systems (STARS), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Computer Vision Center (Centre de visio per computador) (CVC), Universitat Autònoma de Barcelona (UAB), Computer Science Department, Université de Constantine 2 Abdelhamid Mehri [Constantine], Cognition Behaviour Technology (CobTek), Université Nice Sophia Antipolis (1965 - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre Hospitalier Universitaire de Nice (CHU Nice)-Institut Claude Pompidou [Nice] (ICP - Nice)-Université Côte d'Azur (UCA), ANR-15-IDEX-0001,UCA JEDI,Idex UCA JEDI(2015), Université Nice Sophia Antipolis (... - 2019) (UNS) |
| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
Computer science
business.industry Deep learning General Engineering [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV] 020207 software engineering 02 engineering and technology computer.software_genre Convolutional neural network 3. Good health Computer Science Applications Test (assessment) Artificial Intelligence Gesture recognition 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence Set (psychology) business computer Natural language processing Gesture |
| Zdroj: | Expert Systems with Applications Expert Systems with Applications, 2018, 106, pp.21-35. ⟨10.1016/j.eswa.2018.03.063⟩ Expert Systems with Applications, Elsevier, 2018, 106, pp.21-35. ⟨10.1016/j.eswa.2018.03.063⟩ Universitat Autònoma de Barcelona |
| ISSN: | 0957-4174 |
| DOI: | 10.1016/j.eswa.2018.03.063⟩ |
| Popis: | International audience; Praxis test is a gesture-based diagnostic test which has been accepted as diagnostically indicative of cortical pathologies such as Alzheimer's disease. Despite being simple, this test is oftentimes skipped by the clinicians. In this paper, we propose a novel framework to investigate the potential of static and dynamic upper-body gestures based on the Praxis test and their potential in a medical framework to automatize the test procedures for computer-assisted cognitive assessment of older adults. In order to carry out gesture recognition as well as correctness assessment of the performances we have recolected a novel challenging RGB-D gesture video dataset recorded by Kinect v2, which contains 29 specific gestures suggested by clinicians and recorded from both experts and patients performing the gesture set. Moreover, we propose a framework to learn the dynamics of upper-body gestures, considering the videos as sequences of short-term clips of gestures. Our approach first uses body part detection to extract image patches surrounding the hands and then, by means of a fine-tuned convolutional neural network (CNN) model, it learns deep hand features which are then linked to a long short-term memory to capture the temporal dependencies between video frames. We report the results of four developed methods using different modalities. The experiments show effectiveness of our deep learning based approach in gesture recognition and performance assessment tasks. Satisfaction of clinicians from the assessment reports indicates the impact of framework corresponding to the diagnosis. Keywords: Human computer interaction, Computer assisted diagnosis, cybercare industry applications, human factors engineering in medicine and biology, medical services, monitoring, patient monitoring computers and information processing, pattern recognition. |
| Databáze: | OpenAIRE |
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