Deep learning for human-computer interaction

interactions ◽  
2021 ◽  
Vol 28 (1) ◽  
pp. 78-82
Author(s):  
Huy Viet Le ◽  
Sven Mayer ◽  
Niels Henze
Keyword(s):  
Deep Learning ◽  
Human Computer Interaction ◽  
Computer Interaction

scholarly journals Simultaneous Hand Gesture Classification and Finger Angle Estimation via a Novel Dual-Output Deep Learning Model

Sensors ◽  
2020 ◽  
Vol 20 (10) ◽  
pp. 2972
Author(s):  
Qinghua Gao ◽  
Shuo Jiang ◽  
Peter B. Shull
Keyword(s):  
Deep Learning ◽  
Human Computer Interaction ◽  
Data Augmentation ◽  
Experimental Testing ◽  
Learning Model ◽  
Hand Gesture ◽  
Angle Estimation ◽  
Gesture Classification ◽  
Deep Learning Model ◽  
Computer Interaction

Hand gesture classification and finger angle estimation are both critical for intuitive human–computer interaction. However, most approaches study them in isolation. We thus propose a dual-output deep learning model to enable simultaneous hand gesture classification and finger angle estimation. Data augmentation and deep learning were used to detect spatial-temporal features via a wristband with ten modified barometric sensors. Ten subjects performed experimental testing by flexing/extending each finger at the metacarpophalangeal joint while the proposed model was used to classify each hand gesture and estimate continuous finger angles simultaneously. A data glove was worn to record ground-truth finger angles. Overall hand gesture classification accuracy was 97.5% and finger angle estimation R 2 was 0.922, both of which were significantly higher than shallow existing learning approaches used in isolation. The proposed method could be used in applications related to the human–computer interaction and in control environments with both discrete and continuous variables.

scholarly journals Towards Hybrid Model for Human-Computer Interaction in Latvian

2020 ◽  
Author(s):  
Inguna Skadiņa ◽  
Didzis Goško
Keyword(s):  
Deep Learning ◽  
Human Computer Interaction ◽  
Hybrid Approach ◽  
Training Data ◽  
Student Service ◽  
Knowledge Based ◽  
Learning Techniques ◽  
Data Driven Approach ◽  
The University ◽  
Computer Interaction

Human-computer interaction, especially in form of dialogue systems and chatbots, has become extremely popular during the last decade. The dominant approach in the recent development of practical virtual assistants is the application of deep learning techniques. However, in case of less resourced language (or domain), the application of deep learning could be very complicated due to the lack of necessary training data. In this paper, we discuss possibility to apply hybrid approach to dialogue modelling by combining data-driven approach with the knowledge-based approach. Our hypothesis is that by combining different agents (general domain chatbot, frequently asked questions module and goal oriented virtual assistant) into single virtual assistant we can facilitate adequacy and fluency of the conversation. We investigate suitability of different widely used techniques in less resourced settings. We demonstrate feasibility of our approach for morphologically rich less resourced language Latvian through initial virtual assistant prototype for the student service of the University of Latvia.

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