Moein Razavi | Texas A&M University (original) (raw)

Papers by Moein Razavi

Every day many lives are taken or individuals are handicapped due to the existence of mines remai... more Every day many lives are taken or individuals are handicapped due to the existence of mines remaining from wartime. The following robot is an intelligence system designed for detecting mines in an area. This robot is able to indicate the location of mines.

Proceedings of the Human Factors and Ergonomics Society Annual Meeting

Anxiety disorder is the most common mental health disorder in the United States. One of the key f... more Anxiety disorder is the most common mental health disorder in the United States. One of the key factors that leads to the development and aggravation of anxiety disorders is mental stress. In this study, we reviewed publications that used physiological responses and symptoms to assess mental stress. This review found that mental stress affects heart rate, hear rate variability, blood pressure, and skin conductance. Fuzzy logic, time series, and Poincare plots are prominent data analysis tools for physiological data. Most studies used a threshold (e.g., Poincaré plot) or variance (e.g., moving average models) to distinguish stress from normal conditions. The variations and thresholds, however, might fluctuate across various activities and individuals. Moreover, most research evaluated lab-generated stress data, which may be biased. Therefore, more naturalistic studies should be conducted for future research.

Journal of Construction Engineering and Management, 2021

AbstractRepeated exposure to hazards in road construction work zones often generates worker habit... more AbstractRepeated exposure to hazards in road construction work zones often generates worker habituation to risks associated with those hazards, a key causal factor in workplace accidents. Understan...

Proceedings of the Human Factors and Ergonomics Society Annual Meeting

The goal of this paper is to review the literature on machine learning (ML) and big data applicat... more The goal of this paper is to review the literature on machine learning (ML) and big data applications for mental health, emphasizing current research and practical implementations. To explore the field of ML in mental health, we used a scoping review process. The literature identified application domains of detection and prediction of stress as a contributor to mental health disorders. We evaluated the articles and data on the mental health application, machine learning approach, type of data (sensor, survey, etc.), and type of sensors. Most studies extracted features before developing AI-based stress detection algorithms. Findings revealed that heart rate, heart rate variability, and skin conductance features are the key indicators of stress. Moreover, among AI stress-detection methods, Random Forest and Neural Networks show promising results.

arXiv: Robotics, Dec 2, 2021

In-pipe robots are promising solutions for condition assessment, leak detection, water quality mo... more In-pipe robots are promising solutions for condition assessment, leak detection, water quality monitoring in a variety of other tasks in pipeline networks. Smart navigation is an extremely challenging task for these robots as a result of highly uncertain and disturbing environment for operation. Wireless communication to control these robots during operation is not feasible if the pipe material is metal since the radio signals are destroyed in the pipe environment, and hence, this challenge is still unsolved. In this paper, we introduce a method for smart navigation for our previously designed in-pipe robot [1] based on particle filtering and a two-phase motion controller. The robot is given the map of the operation path with a novel approach and the particle filtering determines the straight and non-straight configurations of the pipeline. In the straight paths, the robot follows a linear quadratic regulator (LQR) and proportionalintegral-derivative (PID) based controller that stabilizes the robot and tracks a desired velocity. In non-straight paths, the robot follows the trajectory that a motion trajectory generator block plans for the robot. The proposed method is a promising solution for smart navigation without the need for wireless communication and capable of inspecting long distances in water distribution systems.

2021 6th International Conference on Mechanical Engineering and Robotics Research (ICMERR), 2021

Water distribution systems (WDS) carry potable water with millions of miles of pipelines and deli... more Water distribution systems (WDS) carry potable water with millions of miles of pipelines and deliver purified water to residential areas. The incidents in the WDS cause leak and water loss, which imposes pressure gradient and public health crisis. Hence, utility managers need to assess the condition of pipelines periodically and localize the leak location (in case it is reported). In our previous works, we designed and developed a size-adaptable modular in-pipe robot [1] and controlled its motion in in-service WDS. However, due to the linearization of the dynamical equations of the robot, the stabilizer controller which is a linear quadratic regulator (LQR) cannot stabilize the large deviations of the stabilizing states due to the presence of obstacles that fails the robot during operation. To this aim, we design a "self-rescue" mechanism for the robot in which three auxiliary gear-motors retract and extend the arm modules with the designed controller towards a reliable motion in the negotiation of large obstacles and nonstraight configurations. Simulation results show that the proposed mechanism along with the motion controller enables the robot to have an improved motion in pipelines.

Cornell University - arXiv, Oct 28, 2021

Deep Neural Networks (DNN) are computationally intensive to train. It consists of a large number ... more Deep Neural Networks (DNN) are computationally intensive to train. It consists of a large number of multidimensional dot products between many weights and input vectors. However, there can be significant similarity among input vectors. If one input vector is similar to another, its computations with the weights are similar to those of the other and, therefore, can be skipped by reusing the already-computed results. We propose a novel scheme, called MERCURY, to exploit input similarity during DNN training in a hardware accelerator. MERCURY uses Random Projection with Quantization (RPQ) to convert an input vector to a bit sequence, called Signature. A cache (MCACHE) stores signatures of recent input vectors along with the computed results. If the Signature of a new input vector matches that of an already existing vector in the MCACHE, the two vectors are found to have similarities. Therefore, the already-computed result is reused for the new vector. To the best of our knowledge, MERCURY is the first work that exploits input similarity using RPQ for accelerating DNN training in hardware. The paper presents a detailed design, workflow, and implementation of the MERCURY. Our experimental evaluation with twelve different deep learning models shows that MERCURY saves a significant number of computations and speeds up the model training by an average of 1.97× with an accuracy similar to the baseline system.

IEEE Access

Pipelines are backbone of the transportation of gases and liquids such as oil, gasoline, water, a... more Pipelines are backbone of the transportation of gases and liquids such as oil, gasoline, water, and sewage. However, pipelines are constantly aging and sustaining damage, which may result in significant resource loss and environmental contamination. Pipelines must be inspected and maintained on a regular basis for effective functioning and to avoid cost overrun. Due to the fact that pipes are often located underground and they have different sizes and configurations, inspection including condition assessment, leak detection, and fluid quality monitoring of pipelines are challenging. For this purpose, in-pipe robots have shown promising solutions to reach the inaccessible parts of pipeline networks. In this paper, we first categorize the mechanical systems of in-pipe robots. Then, we review four missions performed by these robots, including localization, mapping, navigation, and inspection, along with the core methods used in each mission. Further, since image processing is a common and important approach to accomplish all the mentioned missions, we decided to dedicate a separate section for reviewing comprehensive categorization of image processing techniques. We also provide the list sensors used in in-pipe robots classified by the mission and the method of use.

ArXiv, 2021

Water distribution systems (WDS) carry potable water with millions of miles of pipelines and deli... more Water distribution systems (WDS) carry potable water with millions of miles of pipelines and deliver purified water to residential areas. The incidents in the WDS cause leak and water loss, which imposes pressure gradient and public health crisis. Hence, utility managers need to assess the condition of pipelines periodically and localize the leak location (in case it is reported). In our previous works, we designed and developed a size-adaptable modular in-pipe robot [1] and controlled its motion in in-service WDS. However, due to the linearization of the dynamical equations of the robot, the stabilizer controller which is a linear quadratic regulator (LQR) cannot stabilize the large deviations of the stabilizing states due to the presence of obstacles that fails the robot during operation. To this aim, we design a “self-rescue” mechanism for the robot in which three auxiliary gear-motors retract and extend the arm modules with the designed controller towards a reliable motion in th...

ArXiv, 2021

The COVID-19 disease spreads swiftly, and nearly three months after the first positive case was c... more The COVID-19 disease spreads swiftly, and nearly three months after the first positive case was confirmed in China, Coronavirus started to spread all over the United States. Some states and counties reported high number of positive cases and deaths, while some reported lower COVID-19 related cases and mortality. In this paper, the factors that could affect the risk of COVID-19 infection and mortality were analyzed in county level. An innovative method by using K-means clustering and several classification models is utilized to determine the most critical factors. Results showed that mean temperature, percent of people below poverty, percent of adults with obesity, air pressure, population density, wind speed, longitude, and percent of uninsured people were the most significant attributes

10 Background: The human mind is multimodal. Yet most behavioral studies rely on century-old 11 m... more 10 Background: The human mind is multimodal. Yet most behavioral studies rely on century-old 11 measures such as task accuracy and latency. To create a better understanding of human behavior 12 and brain functionality, we should introduce other measures and analyze behavior from various 13 aspects. However, it is technically complex and costly to design and implement the experiments 14 that record multiple measures. To address this issue, a platform that allows synchronizing multiple 15 measures from human behavior is needed. 16 Method: This paper introduces an opensource platform named OpenSync, which can be used to 17 synchronize multiple measures in neuroscience experiments. This platform helps to automatically 18 integrate, synchronize and record physiological measures (e.g., electroencephalogram (EEG), 19 galvanic skin response (GSR), eye-tracking, body motion, etc.), user input response (e.g., from 20 mouse, keyboard, joystick, etc.), and task-related information (stimulus mar...

The COVID-19 disease spreads swiftly, and nearly three months after the first positive case was c... more The COVID-19 disease spreads swiftly, and nearly three months after the first positive case was confirmed in China, Coronavirus started to spread all over the United States. Some states and counties reported an extremely high number of positive cases and deaths, while some reported too few COVID-19 related cases and mortality. In this paper, the factors that could affect the transmission of COVID-19 and its risk-level in different counties have been determined and analyzed. Using Pearson Correlation, Kmeans clustering, and several classification models, the most critical ones were determined. Results showed that mean temperature, percent of people below poverty, percent of adults with obesity, air pressure, percentage of rural areas, and percent of uninsured people in each county were the most significant and effective attributes.

2021 IEEE International Conference on Electro Information Technology (EIT)

In-pipe robots are promising solutions for condition assessment, leak detection, water quality mo... more In-pipe robots are promising solutions for condition assessment, leak detection, water quality monitoring in a variety of other tasks in pipeline networks. Smart navigation is an extremely challenging task for these robots as a result of highly uncertain and disturbing environment for operation. Wireless communication to control these robots during operation is not feasible if the pipe material is metal since the radio signals are destroyed in the pipe environment, and hence, this challenge is still unsolved. In this paper, we introduce a method for smart navigation for our previously designed in-pipe robot [1] based on particle filtering and a two-phase motion controller. The robot is given the map of the operation path with a novel approach and the particle filtering determines the straight and non-straight configurations of the pipeline. In the straight paths, the robot follows a linear quadratic regulator (LQR) and proportionalintegral-derivative (PID) based controller that stabilizes the robot and tracks a desired velocity. In non-straight paths, the robot follows the trajectory that a motion trajectory generator block plans for the robot. The proposed method is a promising solution for smart navigation without the need for wireless communication and capable of inspecting long distances in water distribution systems.

A handful of research has demonstrated the potential of mouse tracking for emotion assessment. Ye... more A handful of research has demonstrated the potential of mouse tracking for emotion assessment. Yet, theoretical and empirical bases for this approach remain opaque. If emotion influences motor control (e.g., controlling the movement of the computer mouse), how does that happen? What experimental situations constrain or promote the purported connection? To address these questions, we examined how prior emotional experience (viewing emotional photos) would influence participants' motor activity, as measured by the movement of the computer cursor. Results from two experiments indicate that emotional experience impacts both temporal (peak velocity) and spatial characteristics (deviation of the trajectory) of the cursor motion. But there are clear gender differences; for male participants, emotion influenced temporal features (peak velocity) but this impact was absent in female participants. It is suggested that emotion intervenes motor vigor and decision-making processes differently...

Background: The human mind is multimodal. Yet most behavioral studies rely on century-old measure... more Background: The human mind is multimodal. Yet most behavioral studies rely on century-old measures such as task accuracy and latency. To create a better understanding of human behavior and brain functionality, we should introduce other measures and analyze behavior from various aspects. However, it is technically complex and costly to design and implement the experiments that record multiple measures. To address this issue, a platform that allows synchronizing multiple measures from human behavior is needed. Method: This paper introduces an opensource platform named OpenSync, which can be used to synchronize multiple measures in neuroscience experiments. This platform helps to automatically integrate, synchronize and record physiological measures (e.g., electroencephalogram (EEG), galvanic skin response (GSR), eye-tracking, body motion, etc.), user input response (e.g., from mouse, keyboard, joystick, etc.), and task-related information (stimulus markers). In this paper, we explain ...

SN Computer Science

The COVID-19 pandemic has caused many shutdowns in different industries around the world. Sectors... more The COVID-19 pandemic has caused many shutdowns in different industries around the world. Sectors such as infrastructure construction and maintenance projects have not been suspended due to their significant effect on people's routine life. In such projects, workers work close together that makes a high risk of infection. The World Health Organization recommends wearing a face mask and practicing physical distancing to mitigate the virus's spread. In this paper, we developed a computer vision system to automatically detect the violation of face mask wearing and physical distancing among construction workers to assure their safety on infrastructure projects during the pandemic. For the face mask detection, we collected and annotated 1000 images, including different types of face mask wearing, and added them to a pre-existing face mask dataset to develop a dataset of 1853 images and increased the dataset to 3300 images by data augmentation. Then, we trained and tested multiple Tensorflow state-of-the-art object detection models on the face mask dataset and chose the Faster R-CNN Inception ResNet V2 network that yielded the accuracy of 99.8%. For physical distance detection, we employed the Faster R-CNN Inception V2 to detect people. A transformation matrix was used to eliminate the camera angle's effect on the object distances on the image. The Euclidian distance used the pixels of the transformed image to compute the actual distance between people. A threshold of six feet was considered to capture physical distance violation. We also used transfer learning for training the model. The final model was applied on four videos of road maintenance projects in Houston, TX, that effectively detected the face mask and physical distance. We recommend that construction owners use the proposed system to enhance construction workers' safety in the pandemic situation.

2019 8th International Conference on Affective Computing and Intelligent Interaction Workshops and Demos (ACIIW)

Every day many lives are taken or individuals are handicapped due to the existence of mines remai... more Every day many lives are taken or individuals are handicapped due to the existence of mines remaining from wartime. The following robot is an intelligence system designed for detecting mines in an area. This robot is able to indicate the location of mines.

Proceedings of the Human Factors and Ergonomics Society Annual Meeting

Anxiety disorder is the most common mental health disorder in the United States. One of the key f... more Anxiety disorder is the most common mental health disorder in the United States. One of the key factors that leads to the development and aggravation of anxiety disorders is mental stress. In this study, we reviewed publications that used physiological responses and symptoms to assess mental stress. This review found that mental stress affects heart rate, hear rate variability, blood pressure, and skin conductance. Fuzzy logic, time series, and Poincare plots are prominent data analysis tools for physiological data. Most studies used a threshold (e.g., Poincaré plot) or variance (e.g., moving average models) to distinguish stress from normal conditions. The variations and thresholds, however, might fluctuate across various activities and individuals. Moreover, most research evaluated lab-generated stress data, which may be biased. Therefore, more naturalistic studies should be conducted for future research.

Journal of Construction Engineering and Management, 2021

AbstractRepeated exposure to hazards in road construction work zones often generates worker habit... more AbstractRepeated exposure to hazards in road construction work zones often generates worker habituation to risks associated with those hazards, a key causal factor in workplace accidents. Understan...

Proceedings of the Human Factors and Ergonomics Society Annual Meeting

The goal of this paper is to review the literature on machine learning (ML) and big data applicat... more The goal of this paper is to review the literature on machine learning (ML) and big data applications for mental health, emphasizing current research and practical implementations. To explore the field of ML in mental health, we used a scoping review process. The literature identified application domains of detection and prediction of stress as a contributor to mental health disorders. We evaluated the articles and data on the mental health application, machine learning approach, type of data (sensor, survey, etc.), and type of sensors. Most studies extracted features before developing AI-based stress detection algorithms. Findings revealed that heart rate, heart rate variability, and skin conductance features are the key indicators of stress. Moreover, among AI stress-detection methods, Random Forest and Neural Networks show promising results.

arXiv: Robotics, Dec 2, 2021

In-pipe robots are promising solutions for condition assessment, leak detection, water quality mo... more In-pipe robots are promising solutions for condition assessment, leak detection, water quality monitoring in a variety of other tasks in pipeline networks. Smart navigation is an extremely challenging task for these robots as a result of highly uncertain and disturbing environment for operation. Wireless communication to control these robots during operation is not feasible if the pipe material is metal since the radio signals are destroyed in the pipe environment, and hence, this challenge is still unsolved. In this paper, we introduce a method for smart navigation for our previously designed in-pipe robot [1] based on particle filtering and a two-phase motion controller. The robot is given the map of the operation path with a novel approach and the particle filtering determines the straight and non-straight configurations of the pipeline. In the straight paths, the robot follows a linear quadratic regulator (LQR) and proportionalintegral-derivative (PID) based controller that stabilizes the robot and tracks a desired velocity. In non-straight paths, the robot follows the trajectory that a motion trajectory generator block plans for the robot. The proposed method is a promising solution for smart navigation without the need for wireless communication and capable of inspecting long distances in water distribution systems.

2021 6th International Conference on Mechanical Engineering and Robotics Research (ICMERR), 2021

Water distribution systems (WDS) carry potable water with millions of miles of pipelines and deli... more Water distribution systems (WDS) carry potable water with millions of miles of pipelines and deliver purified water to residential areas. The incidents in the WDS cause leak and water loss, which imposes pressure gradient and public health crisis. Hence, utility managers need to assess the condition of pipelines periodically and localize the leak location (in case it is reported). In our previous works, we designed and developed a size-adaptable modular in-pipe robot [1] and controlled its motion in in-service WDS. However, due to the linearization of the dynamical equations of the robot, the stabilizer controller which is a linear quadratic regulator (LQR) cannot stabilize the large deviations of the stabilizing states due to the presence of obstacles that fails the robot during operation. To this aim, we design a "self-rescue" mechanism for the robot in which three auxiliary gear-motors retract and extend the arm modules with the designed controller towards a reliable motion in the negotiation of large obstacles and nonstraight configurations. Simulation results show that the proposed mechanism along with the motion controller enables the robot to have an improved motion in pipelines.

Cornell University - arXiv, Oct 28, 2021

Deep Neural Networks (DNN) are computationally intensive to train. It consists of a large number ... more Deep Neural Networks (DNN) are computationally intensive to train. It consists of a large number of multidimensional dot products between many weights and input vectors. However, there can be significant similarity among input vectors. If one input vector is similar to another, its computations with the weights are similar to those of the other and, therefore, can be skipped by reusing the already-computed results. We propose a novel scheme, called MERCURY, to exploit input similarity during DNN training in a hardware accelerator. MERCURY uses Random Projection with Quantization (RPQ) to convert an input vector to a bit sequence, called Signature. A cache (MCACHE) stores signatures of recent input vectors along with the computed results. If the Signature of a new input vector matches that of an already existing vector in the MCACHE, the two vectors are found to have similarities. Therefore, the already-computed result is reused for the new vector. To the best of our knowledge, MERCURY is the first work that exploits input similarity using RPQ for accelerating DNN training in hardware. The paper presents a detailed design, workflow, and implementation of the MERCURY. Our experimental evaluation with twelve different deep learning models shows that MERCURY saves a significant number of computations and speeds up the model training by an average of 1.97× with an accuracy similar to the baseline system.

IEEE Access

Pipelines are backbone of the transportation of gases and liquids such as oil, gasoline, water, a... more Pipelines are backbone of the transportation of gases and liquids such as oil, gasoline, water, and sewage. However, pipelines are constantly aging and sustaining damage, which may result in significant resource loss and environmental contamination. Pipelines must be inspected and maintained on a regular basis for effective functioning and to avoid cost overrun. Due to the fact that pipes are often located underground and they have different sizes and configurations, inspection including condition assessment, leak detection, and fluid quality monitoring of pipelines are challenging. For this purpose, in-pipe robots have shown promising solutions to reach the inaccessible parts of pipeline networks. In this paper, we first categorize the mechanical systems of in-pipe robots. Then, we review four missions performed by these robots, including localization, mapping, navigation, and inspection, along with the core methods used in each mission. Further, since image processing is a common and important approach to accomplish all the mentioned missions, we decided to dedicate a separate section for reviewing comprehensive categorization of image processing techniques. We also provide the list sensors used in in-pipe robots classified by the mission and the method of use.

ArXiv, 2021

Water distribution systems (WDS) carry potable water with millions of miles of pipelines and deli... more Water distribution systems (WDS) carry potable water with millions of miles of pipelines and deliver purified water to residential areas. The incidents in the WDS cause leak and water loss, which imposes pressure gradient and public health crisis. Hence, utility managers need to assess the condition of pipelines periodically and localize the leak location (in case it is reported). In our previous works, we designed and developed a size-adaptable modular in-pipe robot [1] and controlled its motion in in-service WDS. However, due to the linearization of the dynamical equations of the robot, the stabilizer controller which is a linear quadratic regulator (LQR) cannot stabilize the large deviations of the stabilizing states due to the presence of obstacles that fails the robot during operation. To this aim, we design a “self-rescue” mechanism for the robot in which three auxiliary gear-motors retract and extend the arm modules with the designed controller towards a reliable motion in th...

ArXiv, 2021

The COVID-19 disease spreads swiftly, and nearly three months after the first positive case was c... more The COVID-19 disease spreads swiftly, and nearly three months after the first positive case was confirmed in China, Coronavirus started to spread all over the United States. Some states and counties reported high number of positive cases and deaths, while some reported lower COVID-19 related cases and mortality. In this paper, the factors that could affect the risk of COVID-19 infection and mortality were analyzed in county level. An innovative method by using K-means clustering and several classification models is utilized to determine the most critical factors. Results showed that mean temperature, percent of people below poverty, percent of adults with obesity, air pressure, population density, wind speed, longitude, and percent of uninsured people were the most significant attributes

10 Background: The human mind is multimodal. Yet most behavioral studies rely on century-old 11 m... more 10 Background: The human mind is multimodal. Yet most behavioral studies rely on century-old 11 measures such as task accuracy and latency. To create a better understanding of human behavior 12 and brain functionality, we should introduce other measures and analyze behavior from various 13 aspects. However, it is technically complex and costly to design and implement the experiments 14 that record multiple measures. To address this issue, a platform that allows synchronizing multiple 15 measures from human behavior is needed. 16 Method: This paper introduces an opensource platform named OpenSync, which can be used to 17 synchronize multiple measures in neuroscience experiments. This platform helps to automatically 18 integrate, synchronize and record physiological measures (e.g., electroencephalogram (EEG), 19 galvanic skin response (GSR), eye-tracking, body motion, etc.), user input response (e.g., from 20 mouse, keyboard, joystick, etc.), and task-related information (stimulus mar...

The COVID-19 disease spreads swiftly, and nearly three months after the first positive case was c... more The COVID-19 disease spreads swiftly, and nearly three months after the first positive case was confirmed in China, Coronavirus started to spread all over the United States. Some states and counties reported an extremely high number of positive cases and deaths, while some reported too few COVID-19 related cases and mortality. In this paper, the factors that could affect the transmission of COVID-19 and its risk-level in different counties have been determined and analyzed. Using Pearson Correlation, Kmeans clustering, and several classification models, the most critical ones were determined. Results showed that mean temperature, percent of people below poverty, percent of adults with obesity, air pressure, percentage of rural areas, and percent of uninsured people in each county were the most significant and effective attributes.

2021 IEEE International Conference on Electro Information Technology (EIT)

In-pipe robots are promising solutions for condition assessment, leak detection, water quality mo... more In-pipe robots are promising solutions for condition assessment, leak detection, water quality monitoring in a variety of other tasks in pipeline networks. Smart navigation is an extremely challenging task for these robots as a result of highly uncertain and disturbing environment for operation. Wireless communication to control these robots during operation is not feasible if the pipe material is metal since the radio signals are destroyed in the pipe environment, and hence, this challenge is still unsolved. In this paper, we introduce a method for smart navigation for our previously designed in-pipe robot [1] based on particle filtering and a two-phase motion controller. The robot is given the map of the operation path with a novel approach and the particle filtering determines the straight and non-straight configurations of the pipeline. In the straight paths, the robot follows a linear quadratic regulator (LQR) and proportionalintegral-derivative (PID) based controller that stabilizes the robot and tracks a desired velocity. In non-straight paths, the robot follows the trajectory that a motion trajectory generator block plans for the robot. The proposed method is a promising solution for smart navigation without the need for wireless communication and capable of inspecting long distances in water distribution systems.

A handful of research has demonstrated the potential of mouse tracking for emotion assessment. Ye... more A handful of research has demonstrated the potential of mouse tracking for emotion assessment. Yet, theoretical and empirical bases for this approach remain opaque. If emotion influences motor control (e.g., controlling the movement of the computer mouse), how does that happen? What experimental situations constrain or promote the purported connection? To address these questions, we examined how prior emotional experience (viewing emotional photos) would influence participants' motor activity, as measured by the movement of the computer cursor. Results from two experiments indicate that emotional experience impacts both temporal (peak velocity) and spatial characteristics (deviation of the trajectory) of the cursor motion. But there are clear gender differences; for male participants, emotion influenced temporal features (peak velocity) but this impact was absent in female participants. It is suggested that emotion intervenes motor vigor and decision-making processes differently...

Background: The human mind is multimodal. Yet most behavioral studies rely on century-old measure... more Background: The human mind is multimodal. Yet most behavioral studies rely on century-old measures such as task accuracy and latency. To create a better understanding of human behavior and brain functionality, we should introduce other measures and analyze behavior from various aspects. However, it is technically complex and costly to design and implement the experiments that record multiple measures. To address this issue, a platform that allows synchronizing multiple measures from human behavior is needed. Method: This paper introduces an opensource platform named OpenSync, which can be used to synchronize multiple measures in neuroscience experiments. This platform helps to automatically integrate, synchronize and record physiological measures (e.g., electroencephalogram (EEG), galvanic skin response (GSR), eye-tracking, body motion, etc.), user input response (e.g., from mouse, keyboard, joystick, etc.), and task-related information (stimulus markers). In this paper, we explain ...

SN Computer Science

The COVID-19 pandemic has caused many shutdowns in different industries around the world. Sectors... more The COVID-19 pandemic has caused many shutdowns in different industries around the world. Sectors such as infrastructure construction and maintenance projects have not been suspended due to their significant effect on people's routine life. In such projects, workers work close together that makes a high risk of infection. The World Health Organization recommends wearing a face mask and practicing physical distancing to mitigate the virus's spread. In this paper, we developed a computer vision system to automatically detect the violation of face mask wearing and physical distancing among construction workers to assure their safety on infrastructure projects during the pandemic. For the face mask detection, we collected and annotated 1000 images, including different types of face mask wearing, and added them to a pre-existing face mask dataset to develop a dataset of 1853 images and increased the dataset to 3300 images by data augmentation. Then, we trained and tested multiple Tensorflow state-of-the-art object detection models on the face mask dataset and chose the Faster R-CNN Inception ResNet V2 network that yielded the accuracy of 99.8%. For physical distance detection, we employed the Faster R-CNN Inception V2 to detect people. A transformation matrix was used to eliminate the camera angle's effect on the object distances on the image. The Euclidian distance used the pixels of the transformed image to compute the actual distance between people. A threshold of six feet was considered to capture physical distance violation. We also used transfer learning for training the model. The final model was applied on four videos of road maintenance projects in Houston, TX, that effectively detected the face mask and physical distance. We recommend that construction owners use the proposed system to enhance construction workers' safety in the pandemic situation.

2019 8th International Conference on Affective Computing and Intelligent Interaction Workshops and Demos (ACIIW)