Fuzzy Algorithm for Human Drowsiness Detection Devices

Dziţac, Simona; Vesselényi, Tiberiu; Popper, Laurenţiu; Moga, Ioan; Secui, Călin D.

doi:10.24846/v19i4y201010

Cited by 6 publications

(4 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…D node = D processing + D queuing + D transmission + D propagation (14) where D transmission = Segment Length / Rate (15) and D queuing = D transmission * (Length of queue) (…”

Section: Resultsmentioning

confidence: 99%

A Fuzzy Logic Architecture for Rehabilitation Robotic Systems

Bouteraa

Abdallah

Elmogy

et al. 2020

INT J COMPUT COMMUN, Int. J. Comput. Commun. Control

View full text Add to dashboard Cite

Robots are highly incorporated in rehabilitation in the last decade to compensate lost functions in disabled individuals. By controlling the rehabilitation robots from far, many benefits are achieved. These benefits include but not restricted to minimum hospital stays, decreasing cost, and increasing the level of care. The main goal of this work is to have an effective solution to take care of patients from far. Tackling the problem of the remote control of rehabilitation robots is undergoing and highly challenging. In this paper, a remote wrist rehabilitation system is presented. The developed system is a sophisticated robot ensuring the two wrist movements (Flexion /extension and abduction/adduction). Additionally, the proposed system provides a software interface enabling the physiotherapists to control the rehabilitation process remotely. The patient’s safety during the therapy is achieved through the integration of a fuzzy controller in the system control architecture. The fuzzy controller is employed to control the robot action according to the pain felt by the patient. By using fuzzy logic approach, the system can adapt effectively according to the patients’ conditions. The Queue Telemetry Transport Protocol (MQTT) is considered to overcome the latency during the human robot interaction. Based on a Kinect camera, the control technique is made gestural. The physiotherapist gestures are detected and transmitted to the software interface to be processed and be sent to the robot. The acquired measurements are recorded in a database that can be used later to monitor patient progress during the treatment protocol. The obtained experimental results show the effectiveness of the developed remote rehabilitation system.

show abstract

“…D node = D processing + D queuing + D transmission + D propagation (14) where D transmission = Segment Length / Rate (15) and D queuing = D transmission * (Length of queue) (…”

Section: Resultsmentioning

confidence: 99%

A Fuzzy Logic Architecture for Rehabilitation Robotic Systems

Bouteraa

Abdallah

Elmogy

et al. 2020

INT J COMPUT COMMUN, Int. J. Comput. Commun. Control

View full text Add to dashboard Cite

show abstract

“…Fuzzy logic methods can make very effective inferences in such a context [12], specifically to model sleep onset [16]. Such an approach works because it enables us to use membership functions to model data features.…”

Section: Soft Computing Approachmentioning

confidence: 99%

“…The above limitations of the hard computing approach can be solved by applying soft computing methods [12][13][14][15][16][17]. Physiological and behavioral measurements [18][19][20] captured in the field without driver cooperation are extremely fuzzy.…”

Section: Introductionmentioning

confidence: 99%

Applying evolutionary methods for early prediction of sleep onset

Malcangi

2015

Neural Comput & Applic

View full text Add to dashboard Cite

Driving safety can be achieved by predicting imminent falling asleep at the wheel. Several methods of early detection have been investigated by continuous monitoring of physiological and behavioral parameters. Requirements for noninvasive, unattended, personal adaptation need to be met, along with the effectiveness of the detection method, in order to perform reliably when applied. Because wakefulness and sleep are reflected in several human physiological conditions, such as cardiac activity, breathing, movement, and galvanic skin conductance, captured bioelectric signal features were extracted. A fuzzy decision-fusion logic was tuned to make inferences about oncoming driver fatigue and drowsiness. The evolving fuzzy neural network paradigm was applied to the previous developed framework to improve reliability while keeping target system complexity low.

show abstract

“…Recently, the development of sensor technologies, especially wearable sensors, has advanced numerous design for activity recognition [4,8,18,22]. Wang et al [35] used a single waist-worn triaxial accelerometer to recognize six activities based on a hidden Markov model.…”

Section: Introductionmentioning

confidence: 99%

Wearable System for Daily Activity Recognition Using Inertial and Pressure Sensors of a Smart Band and Smart Shoes

Truong

You

et al. 2020

INT J COMPUT COMMUN, Int. J. Comput. Commun. Control

View full text Add to dashboard Cite

Human Activity Recognition (HAR) is a challenging task in the field of human-related signal processing. Owing to the development of wearable sensing technology, an emerging research approach in HAR is to identify user-performed tasks by using data collected from wearable sensors. In this paper, we propose a novel system for monitoring and recognizing daily living activities using an off-the-shelf smart band and two smart shoes. The system aims at providing a useful tool for solving problems regarding body part placement, fusion of multimodal sensors and feature selection for a specific set of activities. The system collects inertial and plantar pressure data at wrist and foot to analyze and then, extract, select important features for recognition. We construct and compare two predictive models of classifying activities from the reduced feature set. A comparison of the classification for each wearable device and a fusion scheme is provided to identify the best body part for activity recognition: either the wrist or the feet. This comparison also demonstrated the effective HAR performance of the proposed system.

show abstract

Fuzzy Algorithm for Human Drowsiness Detection Devices

Cited by 6 publications

References 7 publications

A Fuzzy Logic Architecture for Rehabilitation Robotic Systems

A Fuzzy Logic Architecture for Rehabilitation Robotic Systems

Applying evolutionary methods for early prediction of sleep onset

Wearable System for Daily Activity Recognition Using Inertial and Pressure Sensors of a Smart Band and Smart Shoes

Contact Info

Product

Resources

About