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Nabil Alshurafa
Researcher at Northwestern University
Publications - 106
Citations - 2202
Nabil Alshurafa is an academic researcher from Northwestern University. The author has contributed to research in topics: Wearable computer & Computer science. The author has an hindex of 24, co-authored 90 publications receiving 1580 citations. Previous affiliations of Nabil Alshurafa include University of California, Los Angeles & University of Chicago.
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Journal ArticleDOI
Monitoring eating habits using a piezoelectric sensor-based necklace
TL;DR: A novel food-intake monitoring system based around a wearable wireless-enabled necklace that includes an embedded piezoelectric sensor, small Arduino-compatible microcontroller, Bluetooth LE transceiver, and Lithium-Polymer battery is introduced.
Journal ArticleDOI
Wearable Food Intake Monitoring Technologies: A Comprehensive Review
TL;DR: A meticulous review of the latest sensing platforms and data analytic approaches to solve the challenges of food-intake monitoring, ranging from ear-based chewing and swallowing detection systems that capture eating gestures to wearable cameras that identify food types and caloric content through image processing techniques are presented.
Journal ArticleDOI
Designing a Robust Activity Recognition Framework for Health and Exergaming Using Wearable Sensors
Nabil Alshurafa,Wenyao Xu,Jason J. Liu,Ming-Chun Huang,Bobak J. Mortazavi,Christian K. Roberts,Majid Sarrafzadeh +6 more
TL;DR: A new robust stochastic approximation framework for enhanced classification of intensity-independent activity recognition of data where the class labels exhibit large variability, the data are of high dimensionality, and clustering algorithms are necessary is proposed.
Proceedings ArticleDOI
A dense pressure sensitive bedsheet design for unobtrusive sleep posture monitoring
Jason J. Liu,Wenyao Xu,Ming-Chun Huang,Nabil Alshurafa,Majid Sarrafzadeh,Nitin Raut,Behrooz Yadegar +6 more
TL;DR: The experimental results show that the proposed method enables reliable sleep posture recognition and offers better overall performance than state-of-the-art methods, achieving up to 83.0% precision and 83.2% recall on average.
Journal ArticleDOI
Recognition of Nutrition Intake Using Time-Frequency Decomposition in a Wearable Necklace Using a Piezoelectric Sensor
Nabil Alshurafa,Haik Kalantarian,Mohammad Pourhomayoun,Jason J. Liu,Shruti Sarin,Behnam Shahbazi,Majid Sarrafzadeh +6 more
TL;DR: Experimental results demonstrate promise in using time-frequency features, with high accuracy of distinguishing between food categories using spectrogram analysis and extracting key features representative of the unique swallow patterns of various foods.