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نتیجه جستجو - تعامل انسان و ماشین

تعداد مقالات یافته شده: 2
ردیف عنوان نوع
1 Vision–based framework for automatic interpretation of construction workers hand gestures
چارچوبی مبتنی بر بینایی برای تفسیر خودکار حرکات دست کارگران ساختمانی-2021
Construction robots have been recently developed to improve construction productivity and safety. One of the critical steps to make the robots work with human workers as teams is to provide a user-friendly interface to support their mutual interactions on construction sites. Compared with existing interfaces, hand gestures are easy to use, natural, and intuitive. This paper proposed a novel vision-based framework to capture and interpret the worker’s hand gestures as a human-robot interface in construction. The framework consists of three components: worker detection and tracking, recognition queues formulation, and hand gesture recognition. Its effectiveness on the hand gesture recognition was tested with field experiments and achieved the overall precision and recall of 87.0% and 66.7%. Also, a laboratory study was conducted to illustrate the use of the framework to interact with a robotic dump truck. Future work will integrate the proposed framework into robotic construction machines.
Keywords: Construction automation | Visual detection and tracking | Hand gesture recognition | Human-machine interaction
مقاله انگلیسی
2 Electroencephalogram-based emotion assessment system using ontology and data mining techniques
سیستم ارزیابی احساسات با استفاده از تکنیک داده کاوی و هستی شناسی و داده های مبتنی بر الکتروانسفالوگرام-2015
Article history:Received 9 April 2013Received in revised form 23 August 2014 Accepted 5 January 2015Available online 14 January 2015Keywords:Emotion assessment Human–machine interaction Electroencephalogram OntologyCurrently, emotion is considered as a critical aspect of human behavior; thus it should be embedded within the reasoning module in an intelligent system where the aim is to anticipate or respond to human reactions. Therefore, current research in data mining shows an increasing interest in emotion assessment for improving human–machine interaction. Based on the analysis of electroencephalogram (EEG) which derives from automatic nervous system responses, computers can assess user emotions and find corre- lations between significant EEG features extracted from the raw data and the human emotional states. With the advent of modern signal processing techniques, the evaluative power of human emotion derived from EEG is increased exponentially due to the huge number of features that are typically extracted from the EEG signals. Notwithstanding that the expanded set of features could allow computers to evaluate emotions in an accurate way, it is too complex a task to manage in a structured way and, for the reasons stated, methods and approaches to enable both EEG information management and evaluation are neces- sary to support emotion assessment. Starting from this consideration, this paper proposes an enhanced EEG-based emotion assessment system exploiting a collection of ontological models representing EEG feature sets and arousal–valence space (two-dimensional emotion scale), statistical tests capable of eval- uating the gender-specific correlations between EEG features and emotional states, and a classification methodology inferring arousal and valence levels. As will be shown in the experimental section where the proposed approach has been tested on a public dataset, the experimental results demonstrate that better performance in emotion assessment can be achieved using our framework as compared with other studies using the same dataset and with three other classification techniques.
Keywords: Emotion assessment | Human–machine interaction | Electroencephalogram | Ontology
مقاله انگلیسی
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