A New Method for Road Surface Detection

Islam, Md. Mushfiqul; Islam, Md. Milon; Hasan, Md. Kamrul

doi:10.1109/ceeict.2018.8628125

Cited by 5 publications

(3 citation statements)

References 8 publications

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“…increased number of patients related to epidemics. Various assistant devices [16][17][18][19] are developed to support humanity in different applications like fall detection for elderly [20,21], staircase detection [22,23], path hole as well as road surface smoothness detection [24][25][26][27], and any types of hindrances detection [28,29] using modern technologies. The recent development of respiratory support systems assists infected patients for initial treatment.…”

Section: Introductionmentioning

confidence: 99%

Breathing Aid Devices to Support Novel Coronavirus (COVID-19)Infected Patients

et al. 2020

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Novel coronavirus (COVID-19), an ongoing pandemic, is threatening the whole population all over the world including the nations having high or low resource health infrastructure. The number of infection as well as death cases are increasing day by day, and outperforming all the records of previously found infectious diseases. This pandemic is imposing specific pressures on the medical system almost the whole globe. The respiration problem is the main complication that a COVID-19 infected patient faced generally. It is a matter of hope that the recent deployment of small-scale technologies like 3D printer, microcontroller, ventilator, Continuous Positive Airway Pressure (CPAP) are mostly used to resolve the problem associated with medical equipment's for breathing. This paper aims to overview the existing technologies which are frequently used to support the infected patients for respiration. We described the most recent developed breathing aid devices such as oxygen therapy devices, ventilator, and CPAP throughout the review. A comparative analysis among the developed devices with necessary challenges and possible future directions are also outlined for the proper selection of affordable technologies. It is expected that this paper would be of great help to the experts who would like to contribute in this area.

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Section: Introductionmentioning

confidence: 99%

Breathing Aid Devices to Support Novel Coronavirus (COVID-19)Infected Patients

et al. 2020

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“…In most cases a guide dog cannot notify the user precisely for example if there is a hump on the road then the guide dog cannot realize it to the user. Recently, various works have been done in developing electronic travel aid based on sensors and actuators [5][6][7][8], computer vision [9,10], deep learning [11,12], and machine learning [13] for providing a flexible interaction [14,15] with environment.…”

Section: Introductionmentioning

confidence: 99%

Obstacle and Fall Detection to Guide the Visually Impaired People with Real Time Monitoring

et al. 2020

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To assist the visually impaired people to travel independently without external aid and monitoring real-time location information of these individuals, a wearable electronic device is presented in this paper. The system is able to detect the obstacles in front of the user, humps on the ground, moving objects. In addition, the system detects the sudden fall and informs the user's guardian. The system is comprised of ultrasonic sensors, a PIR motion sensor, an accelerometer, a smartphone application, a microcontroller, and a data transmission device. The microcontroller transmits the data to the user's smartphone via Bluetooth module. The smartphone application generates audible instructions to navigate the user properly. The application also updates the current location of the user to keep track and notifies the guardians when the user falls down or in distress. The developed system obtained an accuracy of about 98.34% when the obstacle is 50 cm away from the user. The system is proved to be very effective and efficient for users to navigate using precise speech instructions. Overall, the developed system will make the visually impaired people and their guardian's feel much safer and confident.

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“…But comparing with a typical web camera, such depth cameras are expensive and the environment can affect to obtain a better result, which is limitations. In recent years, several human device interaction frameworks are developed based on sensor technology [12][13][14][15], computer vision [16][17][18][19], deep learning [20], smartphone [21,22] and Internet of Things (IoT) [23][24][25] for different purposes.…”

Section: Introductionmentioning

confidence: 99%

An Efficient Human Computer Interaction through Hand Gesture Using Deep Convolutional Neural Network

Islam

2020

SN COMPUT. SCI.

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This paper focuses on the achievement of effective human-computer interaction using only webcam by continuous locating or tracking and recognizing the hand region. We detected the region of interest (ROI) in the captured image range and classify hand gestures for specific tasks. Firstly, background subtraction is used based on the main frame captured by webcam, and some preprocessing are done, and then YCrCb skin segmentation is used on RGB subtracted image. The ROI is detected using Haar cascade classifier for hand palm detection. Next, kernelized correlation filters tracking algorithm is used to avoid noise or background influences for tracking the ROI, and the median-flow tracking algorithm is used for depth tracking. The ROI is converted to a binary channel (black and white), resized to 54 × 54. Then gesture recognition is done using a 2D convolutional neural network (CNN) by entering the preprocessed ROI on the architecture. Two predictions are made based on skin segmented frame and image dilated frame, and gesture is recognized from the maximum value of those two predictions. The tracking and recognition process is continued until the ROI is presented on the frames. Finally, after validation, the proposed system has successfully obtained a recognition rate of 98.44%, which is usable for the practical and real-time application. Keywords Human-computer interaction • Hand gesture detection • Hand tracking • Convolutional neural network This article is part of the topical collection "Advances in Computational Approaches for Artificial Intelligence, Image Processing, IoT and Cloud Applications" guest edited by Bhanu Prakash K N and M. Shivakumar.

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A New Method for Road Surface Detection

Cited by 5 publications

References 8 publications

Breathing Aid Devices to Support Novel Coronavirus (COVID-19)Infected Patients

Breathing Aid Devices to Support Novel Coronavirus (COVID-19)Infected Patients

Obstacle and Fall Detection to Guide the Visually Impaired People with Real Time Monitoring

An Efficient Human Computer Interaction through Hand Gesture Using Deep Convolutional Neural Network

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