Title

From the Ground Up: Identification through a Floor based system

Poster Number

06C

Lead Author Major

Computer Engineering

Lead Author Status

Senior

Second Author Major

Electrical Engineering

Second Author Status

Senior

Format

Poster Presentation

Faculty Mentor Name

Fadi Muheidat

Faculty Mentor Email

fmuheidat@pacific.edu

Faculty Mentor Department

Electrical and Computer Engineering

Additional Mentors

Lo’ai A. Tawalbeh, Ph.D., IEEE SM Department of Computing and Cyber Security, Texas A&M University- San Antonio, TX, 78224, USA Ltawalbeh@tamusa.edu

Abstract/Artist Statement

Current advances in sensor design technologies and computing power (computational and artificial intelligence) have made it possible to build smart assistive living systems that can improve the lives of everyday people. Because older adults want to live in the comfort of their own home, there is a need to monitor their health status, detect emergency situations, and notify health care providers. We have improved a floor based monitoring system, which we call the smart carpet, originally to detect falls, but we can take advantage of the continual 24/7 monitoring capability to get important information on gait, fall detection, and counting the number of people traversing the carpet. Recently, we studied the characteristics of the waveform of the scavenged signal from the sensors and used computational intelligence, feature extractions, and classifications to identify people. In this paper, we used Dynamic Time Warping (DTW) to help improve on walk identification, compared with the Mel Frequency Cepstral Coefficient (MFCC) feature extraction methods. Results showed that our system identifies walks using a dynamic time warping algorithm and KNN classifier with 86% precision, 76% recall, and 81% accuracy. We also present a cooperative cloudlet mobile computing model for eldercare and medical applications where the decisions are very time sensitive. The sensors data will be sent to the nearest cloudlet for analysis and extracting real-time decisions in minimal delay. Users can obtain these results and make decisions by accessing the cloud through their mobile devices and in a real time manner.

Location

DeRosa University Center Ballroom

Start Date

27-4-2018 12:30 PM

End Date

27-4-2018 2:30 PM

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Apr 27th, 12:30 PM Apr 27th, 2:30 PM

From the Ground Up: Identification through a Floor based system

DeRosa University Center Ballroom

Current advances in sensor design technologies and computing power (computational and artificial intelligence) have made it possible to build smart assistive living systems that can improve the lives of everyday people. Because older adults want to live in the comfort of their own home, there is a need to monitor their health status, detect emergency situations, and notify health care providers. We have improved a floor based monitoring system, which we call the smart carpet, originally to detect falls, but we can take advantage of the continual 24/7 monitoring capability to get important information on gait, fall detection, and counting the number of people traversing the carpet. Recently, we studied the characteristics of the waveform of the scavenged signal from the sensors and used computational intelligence, feature extractions, and classifications to identify people. In this paper, we used Dynamic Time Warping (DTW) to help improve on walk identification, compared with the Mel Frequency Cepstral Coefficient (MFCC) feature extraction methods. Results showed that our system identifies walks using a dynamic time warping algorithm and KNN classifier with 86% precision, 76% recall, and 81% accuracy. We also present a cooperative cloudlet mobile computing model for eldercare and medical applications where the decisions are very time sensitive. The sensors data will be sent to the nearest cloudlet for analysis and extracting real-time decisions in minimal delay. Users can obtain these results and make decisions by accessing the cloud through their mobile devices and in a real time manner.