Using affective human–machine interface to increase the operation performance in virtual construction crane training system: A novel approach

Rezazadeh, Iman; Wang, Xiangyu; Firoozabadi, Mohammad; Golpayegani, Mohammad Reza Hashemi

doi:10.1016/j.autcon.2010.10.005

Cited by 73 publications

(35 citation statements)

References 12 publications

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“…Three recording channels are considered in this study Channel 1 is located on Frontalis muscle: above the eyebrows with 2cm inter-electrodes distance and channel 2 and 3 are placed on right and left Temporalis muscle face and one ground electrode on the boney part of the left wrist (Rezazadeh et al, 2010).…”

Section: Subject Preparation and Electrodes Placementmentioning

confidence: 99%

“…In this step three threshold lines are calculated which are from the features of three normal channels achieved in the preceding step (T 1 , T 2 , T 3 ) (2) (Rezazadeh et al, 2010):…”

Section: Emg Signal Processingmentioning

confidence: 99%

“…Then, it was used by many researchers in the field of data classification and clustering (Abd Elaal et al, 2010). It is obviously useful in biomedical signal processing like in EMG applications (Ajiboye and Weir, 2005; and specifically in facial EMG classification (Rezazadeh et al, 2010;Hamedi et al, 2011). One of the most profitable properties of fuzzy logic systems is that contradictions in the data can be tolerated.…”

Section: Introductionmentioning

confidence: 99%

“…AR, MDF, FR, AC, MMF, MMNF and MNF), Time-Frequency Domain (e.g., STFT, WT and BTFD) and Evaluation (PE). There are many applications of them in SEMG field (Firoozabadi et al, 2008;Rezazadeh et al, 2010;Tkach et al, 2010;Khokhar et al, 2010;Huang et al, 2005;Kiguchi et al, 2003;Fukuda et al, 2003;Vuskovic and Du, 2002;Liejun et al, 2009;Hamedi et al, 2011). Root Mean Square (RMS) and Bilinear TimeFrequency Distribution (BTFD) features are chosen in this study.…”

Section: Introductionmentioning

confidence: 99%

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Surface Electromyography-Based Facial Expression Recognition in Bi-Polar Configuration

Hamedi

Salleh²,

Swee³

et al. 2011

Journal of Computer Science

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Problem statement: Facial expression recognition has been improved recently and it has become a significant issue in diagnostic and medical fields, particularly in the areas of assistive technology and rehabilitation. Apart from their usefulness, there are some problems in their applications like peripheral conditions, lightening, contrast and quality of video and images. Approach: Facial Action Coding System (FACS) and some other methods based on images or videos were applied. This study proposed two methods for recognizing 8 different facial expressions such as natural (rest), happiness in three conditions, anger, rage, gesturing 'a' like in apple word and gesturing no by pulling up the eyebrows based on Three-channels in Bi-polar configuration by SEMG. Raw signals were processed in three main steps (filtration, feature extraction and active features selection) sequentially. Processed data was fed into Support Vector Machine and Fuzzy C-Means classifiers for being classified into 8 facial expression groups. Results: 91.8 and 80.4% recognition ratio had been achieved for FCM and SVM respectively. Conclusion: The confirmed enough accuracy and power in this field of study and FCM showed its better ability and performance in comparison with SVM. It's expected that in near future, new approaches in the frequency bandwidth of each facial gesture will provide better results.

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Section: Subject Preparation and Electrodes Placementmentioning

confidence: 99%

“…In this step three threshold lines are calculated which are from the features of three normal channels achieved in the preceding step (T 1 , T 2 , T 3 ) (2) (Rezazadeh et al, 2010):…”

Section: Emg Signal Processingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Surface Electromyography-Based Facial Expression Recognition in Bi-Polar Configuration

Hamedi

Salleh²,

Swee³

et al. 2011

Journal of Computer Science

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“…• Channel 3: based on [8], one pair of electrodes was placed on the subject's forehead region, close to the Fp1 and Fp2 regions in the international 10-20 electrode placement system for EEG recording. This channel is responsible for extracting the affective measures and cues based on the ACE scheme.…”

Section: B)electrode Placementmentioning

confidence: 99%

Controlling a virtual forehand prosthesis using an adaptive and affective Human-Machine Interface

Rezazadeh

Firoozabadi

Golpayegani

et al. 2011

2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society

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Abstract-This paper presents the design of an adaptable Human-Machine Interface (HMI) for controlling virtual forearm prosthesis. Direct physical performance measures (obtained score and completion time) for the requested tasks were calculated. Furthermore, bioelectric signals from the forehead were recorded using one pair of electrodes placed on the frontal region of the subject head to extract the mental (affective) measures while performing the tasks. By employing the proposed algorithm and above measures, the proposed HMI can adapt itself to the subject's mental states, thus improving the usability of the interface. The quantitative results from 15 subjects show that the proposed HMI achieved better physical performance measures in comparison to a conventional non-adaptive myoelectric controller (p < 0.001).

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Virtual Learning for Workers in Robot Deployed Construction Sites

Moon

Becerik-Gerber

Soibelman

2018

Advances in Informatics and Computing in Civil and Construction Engineering

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Using affective human–machine interface to increase the operation performance in virtual construction crane training system: A novel approach

Cited by 73 publications

References 12 publications

Surface Electromyography-Based Facial Expression Recognition in Bi-Polar Configuration

Surface Electromyography-Based Facial Expression Recognition in Bi-Polar Configuration

Controlling a virtual forehand prosthesis using an adaptive and affective Human-Machine Interface

Virtual Learning for Workers in Robot Deployed Construction Sites

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