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  1. Book ; Online ; Thesis: An automatic and multi-modal system for continuous pain intensity monitoring based on analyzing data from five sensor modalities

    Othman, Ehsan [Verfasser] / Hamadi, Ayoub al- [Gutachter] / Wendemuth, Andreas [Gutachter]

    2023  

    Author's details Ehsan Abdulraheem Mohammed Othman ; Gutachter: Ayoub Hamadi, Andreas Wendemuth
    Keywords Medizin, Gesundheit ; Medicine, Health
    Subject code sg610
    Language English
    Publisher Universitätsbibliothek Otto-von-Guericke-Universität
    Publishing place Magdeburg
    Document type Book ; Online ; Thesis
    Database Digital theses on the web

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  2. Article: Automated Electrodermal Activity and Facial Expression Analysis for Continuous Pain Intensity Monitoring on the X-ITE Pain Database.

    Othman, Ehsan / Werner, Philipp / Saxen, Frerk / Al-Hamadi, Ayoub / Gruss, Sascha / Walter, Steffen

    Life (Basel, Switzerland)

    2023  Volume 13, Issue 9

    Abstract: This study focuses on improving healthcare quality by introducing an automated system that continuously monitors patient pain intensity. The system analyzes the Electrodermal Activity (EDA) sensor modality modality, compares the results obtained from ... ...

    Abstract This study focuses on improving healthcare quality by introducing an automated system that continuously monitors patient pain intensity. The system analyzes the Electrodermal Activity (EDA) sensor modality modality, compares the results obtained from both EDA and facial expressions modalities, and late fuses EDA and facial expressions modalities. This work extends our previous studies of pain intensity monitoring via an expanded analysis of the two informative methods. The EDA sensor modality and facial expression analysis play a prominent role in pain recognition; the extracted features reflect the patient's responses to different pain levels. Three different approaches were applied: Random Forest (RF) baseline methods, Long-Short Term Memory Network (LSTM), and LSTM with the sample-weighting method (LSTM-SW). Evaluation metrics included Micro average F1-score for classification and Mean Squared Error (MSE) and intraclass correlation coefficient (ICC [3, 1]) for both classification and regression. The results highlight the effectiveness of late fusion for EDA and facial expressions, particularly in almost balanced datasets (Micro average F1-score around 61%, ICC about 0.35). EDA regression models, particularly LSTM and LSTM-SW, showed superiority in imbalanced datasets and outperformed guessing (where the majority of votes indicate no pain) and baseline methods (RF indicates Random Forest classifier (RFc) and Random Forest regression (RFr)). In conclusion, by integrating both modalities or utilizing EDA, they can provide medical centers with reliable and valuable insights into patients' pain experiences and responses.
    Language English
    Publishing date 2023-08-29
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2662250-6
    ISSN 2075-1729
    ISSN 2075-1729
    DOI 10.3390/life13091828
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  3. Article ; Online: An Automatic System for Continuous Pain Intensity Monitoring Based on Analyzing Data from Uni-, Bi-, and Multi-Modality.

    Othman, Ehsan / Werner, Philipp / Saxen, Frerk / Fiedler, Marc-André / Al-Hamadi, Ayoub

    Sensors (Basel, Switzerland)

    2022  Volume 22, Issue 13

    Abstract: Pain is a reliable indicator of health issues; it affects patients' quality of life when not well managed. The current methods in the clinical application undergo biases and errors; moreover, such methods do not facilitate continuous pain monitoring. For ...

    Abstract Pain is a reliable indicator of health issues; it affects patients' quality of life when not well managed. The current methods in the clinical application undergo biases and errors; moreover, such methods do not facilitate continuous pain monitoring. For this purpose, the recent methodologies in automatic pain assessment were introduced, which demonstrated the possibility for objectively and robustly measuring and monitoring pain when using behavioral cues and physiological signals. This paper focuses on introducing a reliable automatic system for continuous monitoring of pain intensity by analyzing behavioral cues, such as facial expressions and audio, and physiological signals, such as electrocardiogram (ECG), electromyogram (EMG), and electrodermal activity (EDA) from the X-ITE Pain Dataset. Several experiments were conducted with 11 datasets regarding classification and regression; these datasets were obtained from the database to reduce the impact of the imbalanced database problem. With each single modality (Uni-modality) experiment, we used a Random Forest [RF] baseline method, a Long Short-Term Memory (LSTM) method, and a LSTM using a sample weighting method (called LSTM-SW). Further, LSTM and LSTM-SW were used with fused modalities (two modalities = Bi-modality and all modalities = Multi-modality) experiments. Sample weighting was used to downweight misclassified samples during training to improve the performance. The experiments' results confirmed that regression is better than classification with imbalanced datasets, EDA is the best single modality, and fused modalities improved the performance significantly over the single modality in 10 out of 11 datasets.
    MeSH term(s) Electrocardiography ; Humans ; Neural Networks, Computer ; Pain ; Pain Measurement/methods ; Quality of Life
    Language English
    Publishing date 2022-07-01
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2052857-7
    ISSN 1424-8220 ; 1424-8220
    ISSN (online) 1424-8220
    ISSN 1424-8220
    DOI 10.3390/s22134992
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  4. Article: Classification networks for continuous automatic pain intensity monitoring in video using facial expression on the X-ITE Pain Database

    Othman, Ehsan / Werner, Philipp / Saxen, Frerk / Al-Hamadi, Ayoub / Gruss, Sascha / Walter, Steffen

    Journal of Visual Communication and Image Representation

    2023  

    Abstract: So far, the current methods in the clinical application do not facilitate continuous monitoring for pain and are unreliable, especially for vulnerable patients. In contrast, several automated methods have been proposed for this task by using facial ... ...

    Title translation Klassifizierungsnetzwerke für die kontinuierliche automatische Überwachung der Schmerzintensität in Videos anhand von Gesichtsausdrücken in der X-ITE Pain Database
    Abstract So far, the current methods in the clinical application do not facilitate continuous monitoring for pain and are unreliable, especially for vulnerable patients. In contrast, several automated methods have been proposed for this task by using facial features that were extracted independently from every frame of a given sequence. However, the obtained results were poor due to the failure to represent movement dynamics. To solve this problem, this work introduces three distinct methods regarding classification to monitor continuous pain intensity: (1) A Random Forest classifier (RFc) baseline method, (2) Long-Short Term Memory (LSTM) method, and (3) LSTM using sample weighting method (LSTM-SW). In this study, we conducted experiments with 11 datasets regarding classification, then compared results to regression results in Othman et al. (2021). Experimental results showed that the LSTM & LSTM-SW methods for continuous automatic pain intensity recognition performed better than guessing and RFc except with small datasets such as the reduced tonic datasets.
    Keywords Facial Expressions ; Gesichtsausdruck ; Guessing ; Kurzzeitgedächtnis ; Machine Learning ; Maschinelles Lernen ; Methodologie ; Methodology ; Monitoring ; Pain ; Prediction ; Raten ; Schmerz ; Short Term Memory ; Vorhersage ; Überwachen
    Language English
    Document type Article
    ISSN 1047-3203
    ISSN 1047-3203
    DOI 10.1016/j.jvcir.2022.103743
    Database PSYNDEX

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  5. Article ; Online: Automatic vs. Human Recognition of Pain Intensity from Facial Expression on the X-ITE Pain Database.

    Othman, Ehsan / Werner, Philipp / Saxen, Frerk / Al-Hamadi, Ayoub / Gruss, Sascha / Walter, Steffen

    Sensors (Basel, Switzerland)

    2021  Volume 21, Issue 9

    Abstract: Prior work on automated methods demonstrated that it is possible to recognize pain intensity from frontal faces in videos, while there is an assumption that humans are very adept at this task compared to machines. In this paper, we investigate whether ... ...

    Abstract Prior work on automated methods demonstrated that it is possible to recognize pain intensity from frontal faces in videos, while there is an assumption that humans are very adept at this task compared to machines. In this paper, we investigate whether such an assumption is correct by comparing the results achieved by two human observers with the results achieved by a Random Forest classifier (RFc) baseline model (called RFc-BL) and by three proposed automated models. The first proposed model is a Random Forest classifying descriptors of Action Unit (AU) time series; the second is a modified MobileNetV2 CNN classifying face images that combine three points in time; and the third is a custom deep network combining two CNN branches using the same input as for MobileNetV2 plus knowledge of the RFc. We conduct experiments with X-ITE phasic pain database, which comprises videotaped responses to heat and electrical pain stimuli, each of three intensities. Distinguishing these six stimulation types plus no stimulation was the main 7-class classification task for the human observers and automated approaches. Further, we conducted reduced 5-class and 3-class classification experiments, applied Multi-task learning, and a newly suggested sample weighting method. Experimental results show that the pain assessments of the human observers are significantly better than guessing and perform better than the automatic baseline approach (RFc-BL) by about 1%; however, the human performance is quite poor due to the challenge that pain that is ethically allowed to be induced in experimental studies often does not show up in facial reaction. We discovered that downweighting those samples during training improves the performance for all samples. The proposed RFc and two-CNNs models (using the proposed sample weighting) significantly outperformed the human observer by about 6% and 7%, respectively.
    MeSH term(s) Databases, Factual ; Facial Expression ; Humans ; Neural Networks, Computer ; Pain ; Pain Measurement
    Language English
    Publishing date 2021-05-10
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2052857-7
    ISSN 1424-8220 ; 1424-8220
    ISSN (online) 1424-8220
    ISSN 1424-8220
    DOI 10.3390/s21093273
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  6. Article ; Online: Predicting Group Contribution Behaviour in a Public Goods Game from Face-to-Face Communication.

    Othman, Ehsan / Saxen, Frerk / Bershadskyy, Dmitri / Werner, Philipp / Al-Hamadi, Ayoub / Weimann, Joachim

    Sensors (Basel, Switzerland)

    2019  Volume 19, Issue 12

    Abstract: Experimental economic laboratories run many studies to test theoretical predictions with actual human behaviour, including public goods games. With this experiment, participants in a group have the option to invest money in a public account or to keep it. ...

    Abstract Experimental economic laboratories run many studies to test theoretical predictions with actual human behaviour, including public goods games. With this experiment, participants in a group have the option to invest money in a public account or to keep it. All the invested money is multiplied and then evenly distributed. This structure incentivizes free riding, resulting in contributions to the public goods declining over time. Face-to-face Communication (FFC) diminishes free riding and thus positively affects contribution behaviour, but the question of how has remained mostly unknown. In this paper, we investigate two communication channels, aiming to explain what promotes cooperation and discourages free riding. Firstly, the facial expressions of the group in the 3-minute FFC videos are automatically analysed to predict the group behaviour towards the end of the game. The proposed automatic facial expressions analysis approach uses a new group activity descriptor and utilises random forest classification. Secondly, the contents of FFC are investigated by categorising strategy-relevant topics and using meta-data. The results show that it is possible to predict whether the group will fully contribute to the end of the games based on facial expression data from three minutes of FFC, but deeper understanding requires a larger dataset. Facial expression analysis and content analysis found that FFC and talking until the very end had a significant, positive effect on the contributions.
    MeSH term(s) Communication ; Cooperative Behavior ; Facial Expression ; Game Theory ; Humans ; Interpersonal Relations ; Social Behavior
    Language English
    Publishing date 2019-06-21
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2052857-7
    ISSN 1424-8220 ; 1424-8220
    ISSN (online) 1424-8220
    ISSN 1424-8220
    DOI 10.3390/s19122786
    Database MEDical Literature Analysis and Retrieval System OnLINE

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