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Creators/Authors contains: "Zand, Manizheh"

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  1. ; ; ; ; (, PETRA '24: Proceedings of the 17th International Conference on PErvasive Technologies Related to Assistive Environments, Association for Computing Machinery (ACM))
    Accepted Manuscript Full Text Available
  2. ; ; ; (, IEEE)
    Full Text Available 
  3. ; ; ; (, IEEE DataPort)
    A multimodal dataset is presented for the cognitive fatigue assessment of physiological minimally invasive sensory data of Electrocardiography (ECG) and Electrodermal Activity (EDA) and self-reporting scores of cognitive fatigue during HRI. Data were collected from 16 non-STEM participants, up to three visits each, during which the subjects interacted with a robot to prepare a meal and get ready for work. For some of the visits, a well-established cognitive test was used to induce cognitive fatigue. The developed cognitive fatigue assessment framework filtered noise from the raw signals, extracted relevant features, and applied machine learning regression algorithms, such as Support Vector Regression (SVR), Gradient Boosting Machine (GBM), and Random Forest Regressor (RFR) for estimating the Cognitive Fatigue (CF) level. 
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  4. ; ; (, Companion of the 2023 ACM/IEEE International Conference on Human-Robot Interaction (HRI ’23 Companion))
    The paper proposes a robot learning framework that empowers a robot to automatically generate a sequence of actions from unstructured spoken language. The robot learning framework was able to distinguish between instructions and unrelated conversations. Data were collected from 25 participants, who were asked to instruct the robot to perform a collaborative cooking task while being interrupted and distracted. The system was able to identify the sequence of instructed actions for a cooking task with the accuracy of 92.85 ± 3.87%. 
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    Full Text Available 
  5. ; ; (, 2023 9th International Conference on Automation, Robotics and Applications (ICARA))
    Robots have the potential to assist people in daily tasks, such as cooking a meal. Communicating with the robots verbally and in an unstructured way is important, as spoken language is the main form of communication for humans. This paper proposes a novel framework that automatically generates robot actions from unstructured speech. The proposed framework was evaluated by collecting data from 15 participants preparing their meals while seating on a chair in a randomly disrupted environment. The system can identify and respond to a task sequence while the user may be engaged in unrelated conversations, even if the user’s speech might be unstructured and grammatically incorrect. The accuracy of the proposed system is 98.6%, which is a very promising finding. 
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    Full Text Available