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24 results on '"Hoda, Mohamad"'

4. EVOKE: Emotion Enabled Virtual Avatar Mapping Using Optimized Knowledge Distillation

Author

Nadeem, Maryam, Imam, Raza, Al-Refai, Rouqaiah, Chkir, Meriem, Hoda, Mohamad, Saddik, Abdulmotaleb El, Nadeem, Maryam, Imam, Raza, Al-Refai, Rouqaiah, Chkir, Meriem, Hoda, Mohamad, and Saddik, Abdulmotaleb El

Abstract

As virtual environments continue to advance, the demand for immersive and emotionally engaging experiences has grown. Addressing this demand, we introduce Emotion enabled Virtual avatar mapping using Optimized KnowledgE distillation (EVOKE), a lightweight emotion recognition framework designed for the seamless integration of emotion recognition into 3D avatars within virtual environments. Our approach leverages knowledge distillation involving multi-label classification on the publicly available DEAP dataset, which covers valence, arousal, and dominance as primary emotional classes. Remarkably, our distilled model, a CNN with only two convolutional layers and 18 times fewer parameters than the teacher model, achieves competitive results, boasting an accuracy of 87% while demanding far less computational resources. This equilibrium between performance and deployability positions our framework as an ideal choice for virtual environment systems. Furthermore, the multi-label classification outcomes are utilized to map emotions onto custom-designed 3D avatars., Comment: Presented at IEEE 42nd International Conference on Consumer Electronics (ICCE) 2024

Published
2024

7. ECOPPA: Extensible Context Ontology for Persuasive Physical-Activity Applications

Author

Hoda, Mohamad, Montaghami, Valeh, Al Osman, Hussein, El Saddik, Abdulmotaleb, Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Rocha, Álvaro, editor, and Guarda, Teresa, editor

Published
2018
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10. Recovery Prediction in the Framework of Cloud-Based Rehabilitation Exergame

Author

Hoda, Mohamad, Dong, Haiwei, El Saddik, Abdulmotaleb, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Kobsa, Alfred, editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Weikum, Gerhard, editor, Stephanidis, Constantine, editor, and Antona, Margherita, editor

Published
2014
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19. SHECARE: Shared Haptic Environment on the Cloud for Arm Rehabilitation Exercises

Author

Hoda, Mohamad

Subjects
Exergames, Cloud Compting, Stroke Rehabilitation, Virtual Reality, Auto Regressive Integrated Moving Average, Haptic, Dynamic Time Warping
Abstract

It is well known that home exercise is as good as rehab center. Unfortunately, passive devices such as dumbbells, elastic bands, stress balls and tubing that have been widely used for home-based arm rehabilitation do not provide therapists with the information needed to monitor the patient’s progress, identify any impairment, and suggest treatments. Moreover, the lack of interactivity of these devices turns the rehabilitation exercises into a boring, unpleasant task. In this thesis, we introduce a family of home-based post-stroke rehabilitation systems aimed at solving the aforementioned problems. We call such applications: “Shared Haptic Environment on the Cloud for Arm Rehabilitation Exercises (SHECARE)”. The systems combine recent rehabilitation approaches with efficient, yet affordable skeleton tracking input technologies, and multimodal interactive computer environment. In addition, the systems provide a real-time feedback to the stroke patients, summarize the feedback after each session, and predict the overall recovery progress. Moreover, these systems show a new style of home-based rehabilitation approach that motivate the patients by engaging the whole family and friends in the rehabilitation process and allow the therapists to remotely assess the progress of the patients and adjust the training strategy accordingly. Two mathematical models have been presented in this thesis. The first model is developed to find the relationship between upper extremity kinematics and the associated forces/strength. The second model is used to evaluate the medical condition of the stroke patients and predict their recovery progress depending on their performance history. The objective assessments, clinical tests, and the subjective assessments, usability studies have shown the feasibility of the proposed systems for rehabilitation in stroke patients with upper limb motor dysfunction.

Published
2016
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20. Predicting muscle forces measurements from kinematics data using kinect in stroke rehabilitation.

Author

Hoda, Mohamad, Hoda, Yehya, Hafidh, Basim, and El Saddik, Abdulmotaleb

Subjects
KINEMATICS, REHABILITATION, LEAST squares, VIRTUAL reality, MULTIPLE regression analysis
Abstract

Muscle strength is mostly measured by wearable devices. However, wearing such devices is a tedious, unpleasant, and sometimes impossible task for stroke patients. In this paper, a mathematical model is proposed to estimate the strength of the upper limb muscles of a stroke patient by using Microsoft Kinect sensor. A prototype exergame is designed and developed to mimic real post-stroke rehabilitation exercises. Least-square regression matrix is used to find the relation between the kinematics of the upper limb and the strength of the corresponding muscles. Kinect sensor is used along with a force sensing resistors (FSR) glove and two straps to collect both, real-time upper limb joints data and the strength of muscles of the subjects while they are performing the exercises. The prototype of this system is tested on five stroke patients and eight healthy subjects. Results show that there is no statistically significant difference between the measured and the estimated values of the upper-limb muscles of the stroke patients. Thus, the proposed method is useful in estimating the strength of the muscles of stroke patient without the need to wear any devices. [ABSTRACT FROM AUTHOR]

Published
2018
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