scholarly journals Respiration Based Non-Invasive Approach for Emotion Recognition Using Impulse Radio Ultra Wide Band Radar and Machine Learning

Sensors ◽  
2021 ◽  
Vol 21 (24) ◽  
pp. 8336
Author(s):  
Hafeez Ur Rehman Siddiqui ◽  
Hina Fatima Shahzad ◽  
Adil Ali Saleem ◽  
Abdul Baqi Khan Khan Khakwani ◽  
Furqan Rustam ◽  
...  

Emotion recognition gained increasingly prominent attraction from a multitude of fields recently due to their wide use in human-computer interaction interface, therapy, and advanced robotics, etc. Human speech, gestures, facial expressions, and physiological signals can be used to recognize different emotions. Despite the discriminating properties to recognize emotions, the first three methods have been regarded as ineffective as the probability of human’s voluntary and involuntary concealing the real emotions can not be ignored. Physiological signals, on the other hand, are capable of providing more objective, and reliable emotion recognition. Based on physiological signals, several methods have been introduced for emotion recognition, yet, predominantly such approaches are invasive involving the placement of on-body sensors. The efficacy and accuracy of these approaches are hindered by the sensor malfunctioning and erroneous data due to human limbs movement. This study presents a non-invasive approach where machine learning complements the impulse radio ultra-wideband (IR-UWB) signals for emotion recognition. First, the feasibility of using IR-UWB for emotion recognition is analyzed followed by determining the state of emotions into happiness, disgust, and fear. These emotions are triggered using carefully selected video clips to human subjects involving both males and females. The convincing evidence that different breathing patterns are linked with different emotions has been leveraged to discriminate between different emotions. Chest movement of thirty-five subjects is obtained using IR-UWB radar while watching the video clips in solitude. Extensive signal processing is applied to the obtained chest movement signals to estimate respiration rate per minute (RPM). The RPM estimated by the algorithm is validated by repeated measurements by a commercially available Pulse Oximeter. A dataset is maintained comprising gender, RPM, age, and associated emotions which are further used with several machine learning algorithms for automatic recognition of human emotions. Experiments reveal that IR-UWB possesses the potential to differentiate between different human emotions with a decent accuracy of 76% without placing any on-body sensors. Separate analysis for male and female participants reveals that males experience high arousal for happiness while females experience intense fear emotions. For disgust emotion, no large difference is found for male and female participants. To the best of the authors’ knowledge, this study presents the first non-invasive approach using the IR-UWB radar for emotion recognition.

Plant Methods ◽  
2019 ◽  
Vol 15 (1) ◽  
Author(s):  
Adnan Zahid ◽  
Hasan T. Abbas ◽  
Aifeng Ren ◽  
Ahmed Zoha ◽  
Hadi Heidari ◽  
...  

Abstract Background The demand for effective use of water resources has increased because of ongoing global climate transformations in the agriculture science sector. Cost-effective and timely distributions of the appropriate amount of water are vital not only to maintain a healthy status of plants leaves but to drive the productivity of the crops and achieve economic benefits. In this regard, employing a terahertz (THz) technology can be more reliable and progressive technique due to its distinctive features. This paper presents a novel, and non-invasive machine learning (ML) driven approach using terahertz waves with a swissto12 material characterization kit (MCK) in the frequency range of 0.75 to 1.1 THz in real-life digital agriculture interventions, aiming to develop a feasible and viable technique for the precise estimation of water content (WC) in plants leaves for 4 days. For this purpose, using measurements observations data, multi-domain features are extracted from frequency, time, time–frequency domains to incorporate three different machine learning algorithms such as support vector machine (SVM), K-nearest neighbour (KNN) and decision-tree (D-Tree). Results The results demonstrated SVM outperformed other classifiers using tenfold and leave-one-observations-out cross-validation for different days classification with an overall accuracy of 98.8%, 97.15%, and 96.82% for Coffee, pea shoot, and baby spinach leaves respectively. In addition, using SFS technique, coffee leaf showed a significant improvement of 15%, 11.9%, 6.5% in computational time for SVM, KNN and D-tree. For pea-shoot, 21.28%, 10.01%, and 8.53% of improvement was noticed in operating time for SVM, KNN and D-Tree classifiers, respectively. Lastly, baby spinach leaf exhibited a further improvement of 21.28% in SVM, 10.01% in KNN, and 8.53% in D-tree in overall operating time for classifiers. These improvements in classifiers produced significant advancements in classification accuracy, indicating a more precise quantification of WC in leaves. Conclusion Thus, the proposed method incorporating ML using terahertz waves can be beneficial for precise estimation of WC in leaves and can provide prolific recommendations and insights for growers to take proactive actions in relations to plants health monitoring.


Sensors ◽  
2020 ◽  
Vol 20 (9) ◽  
pp. 2479 ◽  
Author(s):  
Faheem Khan ◽  
Asim Ghaffar ◽  
Naeem Khan ◽  
Sung Ho Cho

Non-invasive remote health monitoring plays a vital role in epidemiological situations such as SARS outbreak (2003), MERS (2015) and the recently ongoing outbreak of COVID-19 because it is extremely risky to get close to the patient due to the spread of contagious infections. Non-invasive monitoring is also extremely necessary in situations where it is difficult to use complicated wired connections, such as ECG monitoring for infants, burn victims or during rescue missions when people are buried during building collapses/earthquakes. Due to the unique characteristics such as higher penetration capabilities, extremely precise ranging, low power requirement, low cost, simple hardware and robustness to multipath interferences, Impulse Radio Ultra Wideband (IR-UWB) technology is appropriate for non-invasive medical applications. IR-UWB sensors detect the macro as well as micro movement inside the human body due to its fine range resolution. The two vital signs, i.e., respiration rate and heart rate, can be measured by IR-UWB radar by measuring the change in the magnitude of signal due to displacement caused by human lungs, heart during respiration and heart beating. This paper reviews recent advances in IR- UWB radar sensor design for healthcare, such as vital signs measurements of a stationary human, vitals of a non-stationary human, vital signs of people in a vehicle, through the wall vitals measurement, neonate’s health monitoring, fall detection, sleep monitoring and medical imaging. Although we have covered many topics related to health monitoring using IR-UWB, this paper is mainly focused on signal processing techniques for measurement of vital signs, i.e., respiration and heart rate monitoring.


Author(s):  
Daniele Apiletti ◽  
Elena Baralis ◽  
Giulia Bruno ◽  
Tania Cerquitelli

Current advances in sensing devices and wireless technologies are providing a high opportunity for improving care quality and reducing the medical costs. This chapter presents the architecture of a mobile healthcare system and provides an overview of mobile health applications. Furthermore, it proposes a framework for patient monitoring that performs real-time stream analysis of data collected by means of non-invasive body sensors. It evaluates a patient’s health conditions by analyzing different physiological signals to identify anomalies and activate alarms in risk situations.


2020 ◽  
Vol 55 ◽  
pp. 101646 ◽  
Author(s):  
J.A. Domínguez-Jiménez ◽  
K.C. Campo-Landines ◽  
J.C. Martínez-Santos ◽  
E.J. Delahoz ◽  
S.H. Contreras-Ortiz

Sensors ◽  
2019 ◽  
Vol 19 (16) ◽  
pp. 3475
Author(s):  
Hodam Kim ◽  
Laehyun Kim ◽  
Chang-Hwan Im

Internet gaming disorder in adolescents and young adults has become an increasing public concern because of its high prevalence rate and potential risk of alteration of brain functions and organizations. Cue exposure therapy is designed for reducing or maintaining craving, a core factor of relapse of addiction, and is extensively employed in addiction treatment. In a previous study, we proposed a machine-learning-based method to detect craving for gaming using multimodal physiological signals including photoplethysmogram, galvanic skin response, and electrooculogram. Our previous study demonstrated that a craving for gaming could be detected with a fairly high accuracy; however, as the feature vectors for the machine-learning-based detection of the craving of a user were selected based on the physiological data of the user that were recorded on the same day, the effectiveness of the reuse of the machine learning model constructed during the previous experiments, without any further calibration sessions, was still questionable. This “high test-retest reliability” characteristic is of importance for the practical use of the craving detection system because the system needs to be repeatedly applied to the treatment processes as a tool to monitor the efficacy of the treatment. We presented short video clips of three addictive games to nine participants, during which various physiological signals were recorded. This experiment was repeated with different video clips on three different days. Initially, we investigated the test-retest reliability of 14 features used in a craving detection system by computing the intraclass correlation coefficient. Then, we classified whether each participant experienced a craving for gaming in the third experiment using various classifiers—the support vector machine, k-nearest neighbors (kNN), centroid displacement-based kNN, linear discriminant analysis, and random forest—trained with the physiological signals recorded during the first or second experiment. Consequently, the craving/non-craving states in the third experiment were classified with an accuracy that was comparable to that achieved using the data of the same day; thus, demonstrating a high test-retest reliability and the practicality of our craving detection method. In addition, the classification performance was further enhanced by using both datasets of the first and second experiments to train the classifiers, suggesting that an individually customized game craving detection system with high accuracy can be implemented by accumulating datasets recorded on different days under different experimental conditions.


Author(s):  
Madam Chakradar ◽  
Alok Aggarwal ◽  
Xiaochun Cheng ◽  
Anuj Rani ◽  
Manoj Kumar ◽  
...  

2021 ◽  
Vol 11 (11) ◽  
pp. 4945
Author(s):  
Axel Sepúlveda ◽  
Francisco Castillo ◽  
Carlos Palma ◽  
Maria Rodriguez-Fernandez

Affect detection combined with a system that dynamically responds to a person’s emotional state allows an improved user experience with computers, systems, and environments and has a wide range of applications, including entertainment and health care. Previous studies on this topic have used a variety of machine learning algorithms and inputs such as audial, visual, or physiological signals. Recently, a lot of interest has been focused on the last, as speech or video recording is impractical for some applications. Therefore, there is a need to create Human–Computer Interface Systems capable of recognizing emotional states from noninvasive and nonintrusive physiological signals. Typically, the recognition task is carried out from electroencephalogram (EEG) signals, obtaining good accuracy. However, EEGs are difficult to register without interfering with daily activities, and recent studies have shown that it is possible to use electrocardiogram (ECG) signals for this purpose. This work improves the performance of emotion recognition from ECG signals using wavelet transform for signal analysis. Features of the ECG signal are extracted from the AMIGOS database using a wavelet scattering algorithm that allows obtaining features of the signal at different time scales, which are then used as inputs for different classifiers to evaluate their performance. The results show that the proposed algorithm for extracting features and classifying the signals obtains an accuracy of 88.8% in the valence dimension, 90.2% in arousal, and 95.3% in a two-dimensional classification, which is better than the performance reported in previous studies. This algorithm is expected to be useful for classifying emotions using wearable devices.


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