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A systematic review of emotion recognition using cardio-based signals
Sayed Ismail Sharifah Noor Masidayu,Ab. Aziz Nor Azlina,Ibrahim Siti Zainab,Mohamad Mohd Saberi 한국통신학회 2024 ICT Express Vol.10 No.1
There is a growing demand for emotion recognition systems (ERS) to be adopted in everyday life from various fields, particularly automotive, education, and social security. Recently, the use of cardio-based physiological signals, electrocardiogram (ECG), and photoplethysmogram (PPG) in ERS has yielded promising results. Furthermore, the development of wearable devices equipped with cardio-based physiological sensors has significantly aided towards the adoption of ERS in daily life. This paper systematically reviews emotion recognition using cardio-based physiological signals, encompassing emotion models, emotion elicitation methods, and ERS development methods, emphasizing feature extraction, feature selection methods, feature dimension reduction methods, and classifiers. A summary and comparison of recent studies are presented to highlight existing studies’ gaps and suggest future research for better ERS especially using cardio-based signals.
Syed Nazir Hussain,Azlan Abd Aziz,Md. Jakir Hossen,Nor Azlina Ab Aziz,G. Ramana Murthy,Fajaruddin Bin Mustakim 한국정보처리학회 2022 Journal of information processing systems Vol.18 No.1
Adopting Internet of Things (IoT)-based technologies in smart homes helps users analyze home applianceselectricity consumption for better overall cost monitoring. The IoT application like smart home system (SHS)could suffer from large missing values gaps due to several factors such as security attacks, sensor faults, orconnection errors. In this paper, a novel framework has been proposed to predict large gaps of missing valuesfrom the SHS home appliances electricity consumption time-series datasets. The framework follows a series ofsteps to detect, predict and reconstruct the input time-series datasets of missing values. A hybrid convolutionalneural network-long short term memory (CNN-LSTM) neural network used to forecast large missing valuesgaps. A comparative experiment has been conducted to evaluate the performance of hybrid CNN-LSTM withits single variant CNN and LSTM in forecasting missing values. The experimental results indicate a performancesuperiority of the CNN-LSTM model over the single CNN and LSTM neural networks.