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A stress detection system based on physiological signals and fuzzy logic, Industrial Electronics
- IEEE Transactions on PP(99
, 2011
"... Abstract—A stress-detection system is proposed based on phys-iological signals. Concretely, galvanic skin response (GSR) and heart rate (HR) are proposed to provide information on the state of mind of an individual, due to their nonintrusiveness and noninvasiveness. Furthermore, specific psychologic ..."
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Abstract—A stress-detection system is proposed based on phys-iological signals. Concretely, galvanic skin response (GSR) and heart rate (HR) are proposed to provide information on the state of mind of an individual, due to their nonintrusiveness and noninvasiveness. Furthermore, specific psychological experiments were designed to induce properly stress on individuals in order to acquire a database for training, validating, and testing the proposed system. Such system is based on fuzzy logic, and it described the behavior of an individual under stressing stimuli in terms of HR and GSR. The stress-detection accuracy obtained is 99.5 % by acquiring HR and GSR during a period of 10 s, and what is more, rates over 90 % of success are achieved by decreasing that acquisition period to 3–5 s. Finally, this paper comes up with a proposal that an accurate stress detection only requires two physiological signals, namely, HR and GSR, and the fact that the proposed stress-detection system is suitable for real-time applications. Index Terms—Biometrics, fuzzy logic, galvanic skin response (GSR), heart rate (HR), physiological signals, stress detection, stress template. I.
AMMON: A Speech Analysis Library for Analyzing Affect, Stress, and Mental Health on Mobile Phones
"... The human voice encodes a wealth of information about emotion, mood and mental state. With mobile phones this information is potentially available to a host of applications. In this paper we describe the AMMON (Affective and Mentalhealth MONitor) library, a low footprint C library designed for widel ..."
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The human voice encodes a wealth of information about emotion, mood and mental state. With mobile phones this information is potentially available to a host of applications. In this paper we describe the AMMON (Affective and Mentalhealth MONitor) library, a low footprint C library designed for widely available phones. The library incorporates both core features for emotion recognition (from the Interspeech 2009 emotion recognition challenge), and the most important features for mental health analysis (glottal timing features). To comfortably run the library on feature phones (the most widely-used class of phones today), we implemented most of the routines in fixed-point arithmetic, and minimized computational and memory footprint. While there are still floating-point routines to be revised in fixed-point, on identical test data, emotion and mental stress classification accuracy was indistinguishable from a state-of-the-art reference system running on a PC. 1
Removal of Respiratory Influences From Heart Rate Variability in Stress Monitoring
"... Abstract—This paper addresses a major weakness of traditional heart-rate-variability (HRV) analysis for the purpose of moni-toring stress: sensitivity to respiratory influences. To address this issue, a linear system-identification model of the cardiorespiratory system using commercial heart rate mo ..."
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Abstract—This paper addresses a major weakness of traditional heart-rate-variability (HRV) analysis for the purpose of moni-toring stress: sensitivity to respiratory influences. To address this issue, a linear system-identification model of the cardiorespiratory system using commercial heart rate monitors and respiratory sensors was constructed. Subtraction of respiratory driven fluc-tuations in heart rate leads to a residual signal where the effects of mental stress become more salient. We experimentally vali-dated the effectiveness of this method on a binary discrimination problem with two conditions: mental stress of subjects performing cognitive tasks and a relaxation condition. In the process, we also propose a normalization method that can be used to compen-sate for ventilation differences between paced and spontaneous breathing. Our results suggest that, by separating respiration influences, the residual HRV has more discrimination power than traditional HRV analysis for the purpose of monitoring mental stress/load. Index Terms—Heart rate variability, mental stress, respiratory sinus arrhythmia, system identification, wearable sensors. I.
Removal of subject-dependent and activity-dependent variation in physiological measures of stress
- In Pervasive Computing Technologies for Healthcare, Proceedings of the 6th International Conference on
, 2012
"... Abstract—The ability to monitor stress levels in daily life can provide valuable information to patients and their caretakers, help identify potential stressors, determine appropriate interventions, and monitor their effectiveness. Wearable sensor technology makes it now possible to measure non-inva ..."
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Abstract—The ability to monitor stress levels in daily life can provide valuable information to patients and their caretakers, help identify potential stressors, determine appropriate interventions, and monitor their effectiveness. Wearable sensor technology makes it now possible to measure non-invasively a number of physiological correlates of stress, from skin conductance to heart rate variability. These measures, however, show large individual differences and are also correlated with the physical activity of the subject. In this paper, we propose two multivariate signal processing techniques to reduce the effect of both forms of interference. The first method is an unsupervised technique that removes any systematic variation that is orthogonal to the dependent variable, in this case physiological stress. In contrast, the second method is a supervised technique that first projects the data into a subspace that emphasizes these systematic variations, and then removes them from the data. The two methods were validated on an experimental dataset containing physiological recordings from multiple subjects performing physical and/or mental activities. When compared to z-score normalization, the standard method for removing individual differences, our methods can reduce stress prediction errors by as much as 50%.
15+ MILLION TOP 1% MOST CITED SCIENTIST 12.2% AUTHORS AND EDITORS FROM TOP 500 UNIVERSITIES 6 Advances in Chinese Medicine Diagnosis: From Traditional Methods to Computational Models
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Speech Analysis Methodologies towards Unobtrusive Mental Health Monitoring
, 2012
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"... Abstract—Stress has been attributed to physiological and psychological demands that exceed the natural regulatory capacity of a person. Chronic stress is not only a catalyst for diseases such as hypertension, diabetes, insomnia but may also lead to social problems such as marriage breakups, suicide ..."
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Abstract—Stress has been attributed to physiological and psychological demands that exceed the natural regulatory capacity of a person. Chronic stress is not only a catalyst for diseases such as hypertension, diabetes, insomnia but may also lead to social problems such as marriage breakups, suicide and violence. Objective assessment of stress is difficult so self-reports are commonly used to indicate the severity of stress. However, empirical information on the validity of self-reports is limited. The present study investigated the authenticity and validity of different self-report surveys. An analysis, based on a three-pronged strategy, was performed on these surveys. It was concluded that although subjects are prone to systematic error in reporting, self-reports can provide a useful substitute for data modeling specifically in stress evaluation where other objective assessments such as determination of stress using only physiological response are difficult. I.
2012 6th International Conference on Pervasive Computing Technologies for Healthcare (PervasiveHealth) and Workshops Removal of Subject-Dependent and Activity-Dependent Variation in Physiological Measures of Stress
"... Abstract-The ability to monitor stress levels in daily life can provide valuable information to patients and their caretakers, help identify potential stressors, determine appropriate interventions, and monitor their effectiveness. Wearable sensor technology makes it now possible to measure non-inva ..."
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Abstract-The ability to monitor stress levels in daily life can provide valuable information to patients and their caretakers, help identify potential stressors, determine appropriate interventions, and monitor their effectiveness. Wearable sensor technology makes it now possible to measure non-invasively a number of physiological correlates of stress, from skin conductance to heart rate variability. These measures, however, show large individual differences and are also correlated with the physical activity of the subject. In this paper, we propose two multivariate signal processing techniques to reduce the effect of both forms of interference. The first method is an unsupervised technique that removes any systematic variation that is orthogonal to the dependent variable, in this case physiological stress. In contrast, the second method is a supervised technique that first projects the data into a subspace that emphasizes these systematic variations, and then removes them from the data. The two methods were validated on an experimental dataset containing physiological recordings from multiple subjects performing physical and/or mental activities. When compared to z-score normalization, the standard method for removing individual differences, our methods can reduce stress prediction errors by as much as 50%. Keywords- Wearable sensors, electrodermal activity, heart rate variability, mental stress, individual differences, noise cancellation I.
Techniques in Pattern Recognition for School Bullying Prevention: Review and Outlook
, 2014
"... School bullying is a serious problem among teenagers. With the development of sensor technology and pattern recognition algorithms, several approaches for detecting school bullying have been developed, namely speech emotion recognition, mental stress recog-nition, and activity recognition. This pape ..."
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School bullying is a serious problem among teenagers. With the development of sensor technology and pattern recognition algorithms, several approaches for detecting school bullying have been developed, namely speech emotion recognition, mental stress recog-nition, and activity recognition. This paper reviews some related work and makes some comparisons among these three aspects. The paper analyzes commonly used features and classifiers, and describes some examples. The Gaussian Mixture Model and the Double Threshold classifiers provided high accuracies in many experiments. By using a combined architecture of classifiers, the results could be further improved. According to the results of the experiments, the six basic emotions, high mental stress and irregular movements can be recognized with high accuracies. So the three types of pattern recognition can be used for school bullying detection effectively. And these techniques can be used on consumer devices such as smartphones to protect teenagers.
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"... Abstract- The paper presents a novel framework that includes an inhomogeneous (time-variant) Hidden Markov Model(HMM) and learning from data concepts. The framework either recognizes or estimates user contextual inferences called ’user states ’ within the concept of Human Activity Recognition (HAR) ..."
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Abstract- The paper presents a novel framework that includes an inhomogeneous (time-variant) Hidden Markov Model(HMM) and learning from data concepts. The framework either recognizes or estimates user contextual inferences called ’user states ’ within the concept of Human Activity Recognition (HAR) for future context-aware applications. Context-aware applications require continuous data acquisition and interpretation from one or more sensor reading(s). Therefore, device battery lifetimes need to be extended due to the fact that constantly running built-in sensors deplete device batteries rapidly. In this sense, a framework is constructed to fulfill requirements needed by applications and to prolong device battery lifetimes. The ultimate goal of this paper is to present an accurate user state representation model, and to maximize power efficiency while the model operates. Most importantly, this research intends to create and clarify a generic framework to guide the development of future context-aware applications. Moreover, topics such as user profile adaptability and variant sensory sampling operations are examined. The proposed framework is validated by simulations
