Teaching Pepper Robot to Recognize Emotions of Traumatic Brain Injured Patients Using Deep Neural Networks

Ilyas, Chaudhary Muhammad Aqdus; Schmuck, Viktor; Haque, Muhammad Ahsanul; Nasrollahi, Kamal; Rehm, Matthias; Moeslund, Thomas B.

doi:10.1109/ro-man46459.2019.8956445

Cited by 8 publications

(3 citation statements)

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“…Regarding the computer resources used to recognize facial expressions, two implementations are reported using basic hardware computers [ 34 , 39 ] and computers with graphic cards [ 30 , 31 ]. It is worth noting the use of robotic platforms such as the NAO [ 27 , 28 , 33 , 38 ], R-50 Alice [ 35 , 40 ], Pepper [ 32 ], kiwi [ 44 ], and N-Maria [ 42 ].…”

Section: Discussion and Conclusionmentioning

confidence: 99%

“…In addition, Ilyas et al [ 32 ] proposed the traumatic brain injured facial expression recognition (TBI-FER) algorithm to enhance social interaction and assist trainers and physiotherapists of traumatic brain injured people using the Pepper robot. The algorithm implements the Supervised Descent Method (SDM) to detect the facial landmarks and track them.…”

Section: Algorithms Used For Face Recognition and Trackingmentioning

confidence: 99%

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Artificial Vision Algorithms for Socially Assistive Robot Applications: A Review of the Literature

Montaño-Serrano

Jacinto‐Villegas

Vilchis-González

et al. 2021

Sensors

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Today, computer vision algorithms are very important for different fields and applications, such as closed-circuit television security, health status monitoring, and recognizing a specific person or object and robotics. Regarding this topic, the present paper deals with a recent review of the literature on computer vision algorithms (recognition and tracking of faces, bodies, and objects) oriented towards socially assistive robot applications. The performance, frames per second (FPS) processing speed, and hardware implemented to run the algorithms are highlighted by comparing the available solutions. Moreover, this paper provides general information for researchers interested in knowing which vision algorithms are available, enabling them to select the one that is most suitable to include in their robotic system applications.

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Section: Discussion and Conclusionmentioning

confidence: 99%

Section: Algorithms Used For Face Recognition and Trackingmentioning

confidence: 99%

Artificial Vision Algorithms for Socially Assistive Robot Applications: A Review of the Literature

Montaño-Serrano

Jacinto‐Villegas

Vilchis-González

et al. 2021

Sensors

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show abstract

“…A teaching pepper robot using DL has been used for recognising facial expressions in TBI patients [24]. The main objective of this study was to accurately detect the emotions and facial expressions of TBI patients.…”

Section: Literature Surveymentioning

confidence: 99%

Detection of emotional and behavioural changes after traumatic brain injury: A comprehensive survey

Vutakuri

2023

Cognitive Comp and Systems

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Traumatic brain injury (TBI) can affect normal brain function and may be caused by a vehicle accident, falling, and so on. The purpose of this survey is to provide clear knowledge of TBI, the causes of TBI, the impacts of TBI, and the role of family members and friends in recovery. TBI affects the daily life of the patients, both physically and mentally. After TBI, the patients may experience many emotional and behavioural changes because of a lack of certain brain functions. These changes affect their personal and social relationships. On the other hand, these changes depend on the severity of the TBI (i.e. mild, moderate, or severe), which is measured using the Glasgow coma score. Generally, three processes are used for emotion recognition: preprocessing, feature extraction, and emotion recognition. Preprocessing is performed for landmark detection and pose normalisation, which improves the performance of emotion detection. Feature extraction and emotion recognition are performed by various deep learning techniques, such as convolution neural networks and long short‐term memory. These techniques recognise the behavioural and emotional changes (depression, anxiety, anger, personality changes etc.) of TBI patients using facial expressions. Family members and friends play an important role in TBI patients' recovery, the extent of which is based on the severity of the TBI. The care of family members and friends leads to quick recovery and rehabilitation of patients from TBI. Finally, testing is performed using Computed Tomography images, Magnetic Resonance Imaging images, Electroencephalography signals, and patient demographics, which together show that the deep learning methods achieve better performance in terms of accuracy, precision, recall, and F‐measure in recognising emotional and behavioural changes after TBI. The authors conclude with a summary of the future of emotional and behavioural change prediction methods for TBI patients.

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A Transformer Based Emotion Recognition Model for Social Robots Using Topographical Maps Generated from EEG Signals

Bethany,

Gupta

2024

Lecture Notes in Computer Science

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Teaching Pepper Robot to Recognize Emotions of Traumatic Brain Injured Patients Using Deep Neural Networks

Cited by 8 publications

References 50 publications

Artificial Vision Algorithms for Socially Assistive Robot Applications: A Review of the Literature

Artificial Vision Algorithms for Socially Assistive Robot Applications: A Review of the Literature

Detection of emotional and behavioural changes after traumatic brain injury: A comprehensive survey

A Transformer Based Emotion Recognition Model for Social Robots Using Topographical Maps Generated from EEG Signals

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