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Project # 7
Assistive Robots in Multi-Sensory Cognitive Fatigue Assessment: Enhancing Activities of Daily Living for Individuals with Paralysis
| Team Lead: | Minhaz Bin Farukee |
| Other Team Members: | Maisha Maimuna | Enamul Karim | Ayon Roy |
| Faculty Advisor(s): | Dr. Fillia Makedon |
| Department: | CSE |
| Project Type: | Graduate Student |
| Title: | Assistive Robots in Multi-Sensory Cognitive Fatigue Assessment: Enhancing Activities of Daily Living for Individuals with Paralysis |
| Abstract: | The increasing integration of assistive robots in aiding individuals with paralysis underscores the need for systems that adapt to users' varying cognitive states. This work introduces a novel framework for cognitive fatigue assessment and adaptive robotic assistance in Activities of Daily Living (ADL), leveraging multi-sensory data collection and machine learning techniques. The proposed system employs a three-tiered adaptive framework that dynamically adjusts robotic assistance based on real-time cognitive fatigue levels, transitioning between Voice Command Mode (VCM), Semi-Autonomous Mode (SAM), and Fully Autonomous Mode (FAM). To evaluate its effectiveness, we conducted experiments with 11 participants, implementing a comprehensive data collection protocol that included EEG, ECG and EDA signals, along with behavioral markers. Cognitive fatigue was induced using the N-back task protocol and quantified via the Visual Analog Scale of Fatigue (VAS-F). Additionally, Large Language Models were integrated for natural language processing, achieving high accuracy in command interpretation across various categories. The system demonstrated acceptable performance in action-object identification and context-aware assistance. This research advances human-robot collaboration in assistive applications for individuals with paralysis and highlights future directions for improving command interpretation and contextual understanding. |
| Poster: | View Poster #7 (PDF) |
| Judges Scoring: | Submit Evaluation (judges only) |