Walking Motion Real-time Detection Based on Walking Stick Equipped with MPU and Raspberry Pi

2019 ◽  
Author(s):  
Junyi Wang ◽  
Mohamad Saada ◽  
Haibin Cai ◽  
Qinggang Meng
Keyword(s):  
Real Time ◽  
Raspberry Pi ◽  
Walking Motion ◽  
Real Time Detection ◽  
Walking Stick

scholarly journals Mobility Assistive Device for the Visually Impaired

2020 ◽  
Vol 6 (12) ◽  
pp. 129-133
Author(s):  
Puru Malhotra and Vinay Kumar Saini
Keyword(s):  
Real Time ◽  
Structural Design ◽  
Visually Impaired ◽  
Assistive Device ◽  
Raspberry Pi ◽  
Traditional Use ◽  
Smart Glasses ◽  
Visually Impaired People ◽  
Impaired People ◽  
Walking Stick

he paper is aimed at the design of a mobility assistive device to help the visually impaired. The traditional use of a walking stick proposes its own drawbacks and limitations. Our research is motivated by the inability of the visually impaired people to ambulate and we have made an attempt to restore their independence and reduce the trouble of carrying a stick around. We offer a hands-free wearable glass which finds it utility in real-time navigation. The design of the smart glasses includes the integration of various sensors with raspberry pi. The paper presents a detailed account of the various components and the structural design of the glasses. The novelty of our work lies in providing a complete pipeline for analysis of surroundings in real-time and hence a better solution for navigating during the day to day activities using audio instructions as output.

scholarly journals Road surface real-time detection based on Raspberry Pi and recurrent neural networks

2021 ◽  
pp. 014233122110033
Author(s):  
Junyi Wang ◽  
Qinggang Meng ◽  
Peng Shang ◽  
Mohamad Saada
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Real Time ◽  
Recurrent Neural Networks ◽  
Road Surface ◽  
Machine Learning Algorithms ◽  
Raspberry Pi ◽  
Accuracy Rate ◽  
Surface Detection ◽  
Real Time Detection

This paper focuses on road surface real-time detection by using tripod dolly equipped with Raspberry Pi 3 B+, MPU 9250, which is convenient to collect road surface data and realize real-time road surface detection. Firstly, six kinds of road surfaces data are collected by utilizing Raspberry Pi 3 B+ and MPU 9250. Secondly, the classifiers can be obtained by adopting several machine learning algorithms, recurrent neural networks (RNN) and long short-term memory (LSTM) neural networks. Among the machine learning classifiers, gradient boosting decision tree has the highest accuracy rate of 97.92%, which improves by 29.52% compared with KNN with the lowest accuracy rate of 75.60%. The accuracy rate of LSTM neural networks is 95.31%, which improves by 2.79% compared with RNN with the accuracy rate of 92.52%. Finally, the classifiers are embedded into the Raspberry Pi to detect the road surface in real time, and the detection time is about one second. This road surface detection system could be used in wheeled robot-car and guiding the robot-car to move smoothly.

Real Time Detection and Automatic Detection of Images, Obstacles, Color, Distance by a Live Streaming Automatic Robot Using Raspberry Pi

2019 ◽  
Vol 11 (1) ◽  
pp. 49-53 ◽  
Author(s):  
Shweta Yadav ◽  
K. Bhanu Prasad ◽  
Ravi Shankar Singhal ◽  
Nivedita ◽  
Ushaben Keshwal
Keyword(s):  
Real Time ◽  
Automatic Detection ◽  
Raspberry Pi ◽  
Live Streaming ◽  
Real Time Detection

Real-Time Detection of Drowsiness Related Lane Departures Using Steering Wheel Angle

2012 ◽  
Author(s):  
Anthony D. McDonald ◽  
Chris Schwarz ◽  
John D. Lee ◽  
Timothy L. Brown
Keyword(s):  
Real Time ◽  
Steering Wheel ◽  
Real Time Detection

PROTOTYPE ALAT PEMANTAU SALURAN AIR BAWAH TANAH DENGAN MENGGUNAKAN WEBCAM C170 BERBASIS RASPBERRY PI

2015 ◽  
Vol 1 (1) ◽  
pp. 37-45
Author(s):  
Irwansyah Irwansyah ◽  
Hendra Kusumah ◽  
Muhammad Syarif
Keyword(s):  
Real Time ◽  
Monitoring System ◽  
Public Awareness ◽  
Underground Water ◽  
Raspberry Pi ◽  
Laptop Computer ◽  
Web Browser ◽  
Disaster Monitoring ◽  
The Times ◽  
Human Desire

Along with the times, recently there have been found tool to facilitate human’s work. Electronics is one of technology to facilitate human’s work. One of human desire is being safe, so that people think to make a tool which can monitor the surrounding condition without being monitored with people’s own eyes. Public awareness of the underground water channels currently felt still very little so frequent floods. To avoid the flood disaster monitoring needs to be done to underground water channels.This tool is controlled via a web browser. for the components used in this monitoring system is the Raspberry Pi technology where the system can take pictures in real time with the help of Logitech C170 webcam camera. web browser and Raspberry Pi make everyone can control the devices around with using smartphone, laptop, computer and ipad. This research is expected to be able to help the users in knowing the blockage on water flow and monitored around in realtime.

Real-time Detection of Aortic Valve in Echocardiography using Convolutional Neural Networks

2020 ◽  
Vol 16 (5) ◽  
pp. 584-591 ◽  
Author(s):  
Muhammad Hanif Ahmad Nizar ◽  
Chow Khuen Chan ◽  
Azira Khalil ◽  
Ahmad Khairuddin Mohamed Yusof ◽  
Khin Wee Lai
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Heart Disease ◽  
Aortic Valve ◽  
Real Time ◽  
Convolutional Neural Networks ◽  
Valvular Heart Disease ◽  
Detection System ◽  
Processing Unit ◽  
Real Time Detection

Background: Valvular heart disease is a serious disease leading to mortality and increasing medical care cost. The aortic valve is the most common valve affected by this disease. Doctors rely on echocardiogram for diagnosing and evaluating valvular heart disease. However, the images from echocardiogram are poor in comparison to Computerized Tomography and Magnetic Resonance Imaging scan. This study proposes the development of Convolutional Neural Networks (CNN) that can function optimally during a live echocardiographic examination for detection of the aortic valve. An automated detection system in an echocardiogram will improve the accuracy of medical diagnosis and can provide further medical analysis from the resulting detection. Methods: Two detection architectures, Single Shot Multibox Detector (SSD) and Faster Regional based Convolutional Neural Network (R-CNN) with various feature extractors were trained on echocardiography images from 33 patients. Thereafter, the models were tested on 10 echocardiography videos. Results: Faster R-CNN Inception v2 had shown the highest accuracy (98.6%) followed closely by SSD Mobilenet v2. In terms of speed, SSD Mobilenet v2 resulted in a loss of 46.81% in framesper- second (fps) during real-time detection but managed to perform better than the other neural network models. Additionally, SSD Mobilenet v2 used the least amount of Graphic Processing Unit (GPU) but the Central Processing Unit (CPU) usage was relatively similar throughout all models. Conclusion: Our findings provide a foundation for implementing a convolutional detection system to echocardiography for medical purposes.

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