RFID RSS Fingerprinting System for Wearable Human Activity Recognition

Shuaieb, Wafa; Oguntala, George; Alabdullah, Ali A. S.; Obeidat, H. A.; Asif, Rameez; Abd‐Alhameed, Raed A.; Bin-Melha, Mohammed S.; Kara-Zaïtri, Chakib

doi:10.3390/fi12020033

Cited by 13 publications

(5 citation statements)

References 32 publications

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“…Task speed time has also been calculated for evaluating the Bimodal-Shared Control model. [9]. The classification accuracy is defined as the ratio of true classes Vs total classes Task speed is the total time to complete the application task.…”

Section: ░ 3 Application Results and Discussionmentioning

confidence: 99%

Bimodal-Shared Control Interface for Assisted Mobility Application

Kumari

Mathew

Kumar

2023

IJEER

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The paper presents (Electro-oculography) EOG- (Electroencephalography) EEG- Radio Frequency Identification (RFID) based Bimodal-Shared Control Interface for mobility assistance application by controlling a mobile robotic arm. EOG-EEG based bio-signal based bimodal interface has been used to move the robot following a predefined path to reach at an object placed at initial predefined position (Zone 1). RFID has been used as shared control interface for object identification and for sending trigger signal to gripper arm to pick the object and place it at another predefined position (Zone 2) automatically. A threshold based algorithm has been developed for horizontal eye movement (HEOG) detection. Minimum Energy Combination (MEC) method has been used to recognize the Steady State Visual Evoked Potential (SSVEP) brain pattern of EEG. Combining shared control with bio-signal based bimodal system has improved the classification accuracy and increased the number of commands without giving extra effort by user. This reduces the chances of fatigue in users due to continuously performing the same task. Classification accuracy and Information Transfer Rate (ITR) have been calculated as performance parameters and compared with previous literatures for evaluation.

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Section: ░ 3 Application Results and Discussionmentioning

confidence: 99%

Bimodal-Shared Control Interface for Assisted Mobility Application

Kumari

Mathew

Kumar

2023

IJEER

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“…Wearables approaches are proposed in [10,24,46,51,52,53,54,55,56,57,58,59] to obtain occupancy information as a product of tasks completed by other systems which can be used to track the occupancy location. ML model can obtain signal intensity from statically positioned beacons in a target space to obtain a fine-grained occupant location and achieve the location accuracy of five meters.…”

Section: Literature Reviewmentioning

confidence: 99%

Comparative Analysis of Machine Learning Methods for Non-Intrusive Indoor Occupancy Detection and Estimation

Aliero¹,

Pasha²,

Toosi³

et al. 2021

Preprint

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Occupancy-driven application research has been active research for a decade that focuses on improving or replacing new building infrastructure to improve building energy efficiency. Existing approaches for HVAC energy saving are putting more emphasis on occupancy detection, estimation, and localization to trade-off between energy consumption and thermal comfort satisfaction. In a non-intrusive approach, various sensors, actuators, and analytic data methods are commonly used to process data from occupant surroundings and trigger appropriate action to achieve the task. However, the performance of the non-intrusive approach reported in the literature is relatively poor due to the lack of quality of dataset used in model training and expropriate choice of machine learning model. This study proposed a non-intrusive approach that to improve the collection and quality of dataset using data pre-processing. The study collected a training dataset using various sensors installed in the building and developed a model using five machine learning models to determine occupant’s presence and estimate their number in the building. The proposed solution is tested in the living room with a prototype system integrated with various sensors designed to obtain occupant surrounding environmental datasets. The model’s prediction results obtained indicate that it is possible for the proposed solution to obtain data, process, and predict the occupant number with high accuracy (73.6 -99.7% using random forest).

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“…The second aspect that goes with this is that human action is different and the detection of particular actions thus necessitates a precise choice of sensors and their placement. A few challenges are the collection of data and the selection of sensor measurements under realistic conditions (Tang et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

“…In Shuaieb et al (2020), the authors suggest a price-efficient, radio frequency identification (RFID)-dependent interior position scheme that implements received signal strength data of inactive RFID tags. This system employs RFID tags positioned at diverse locations on the aimed body.…”

Section: Introductionmentioning

confidence: 99%

Computer Vision with Optimal Deep Stacked Autoencoder-based Fall Activity Recognition for Disabled Persons in the IoT Environment

Alabdulkreem,

Marzouk,

Alduhayyem

et al. 2023

Journal of Disability Research

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Remote monitoring of fall conditions or actions and the daily life of disabled victims is one of the indispensable purposes of contemporary telemedicine. Artificial intelligence and Internet of Things (IoT) techniques that include deep learning and machine learning methods are now implemented in the field of medicine for automating the detection process of diseased and abnormal cases. Many other applications exist that include the real-time detection of fall accidents in older patients. Owing to the articulated nature of human motion, it is unimportant to find human action with a higher level of accuracy for every application. Likewise, finding human activity is required to automate a system to monitor and find suspicious activities while executing surveillance. In this study, a new Computer Vision with Optimal Deep Stacked Autoencoder Fall Activity Recognition (CVDSAE-FAR) for disabled persons is designed. The presented CVDSAE-FAR technique aims to determine the occurrence of fall activity among disabled persons in the IoT environment. In this work, the densely connected networks model can be exploited for feature extraction purposes. Besides, the DSAE model receives the feature vectors and classifies the activities effectually. Lastly, the fruitfly optimization method can be used for the automated parameter tuning of the DSAE method which leads to enhanced recognition performance. The simulation result analysis of the CVDSAE-FAR approach is tested on a benchmark dataset. The extensive experimental results emphasized the supremacy of the CVDSAE-FAR method compared to recent approaches.

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RFID RSS Fingerprinting System for Wearable Human Activity Recognition

Cited by 13 publications

References 32 publications

Bimodal-Shared Control Interface for Assisted Mobility Application

Bimodal-Shared Control Interface for Assisted Mobility Application

Comparative Analysis of Machine Learning Methods for Non-Intrusive Indoor Occupancy Detection and Estimation

Computer Vision with Optimal Deep Stacked Autoencoder-based Fall Activity Recognition for Disabled Persons in the IoT Environment

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