Deep Learning Based Stair Detection and Statistical Image Filtering for Autonomous Stair Climbing

2019 ◽  
Author(s):  
Unmesh Patil ◽  
Aniket Gujarathi ◽  
Akshay Kulkarni ◽  
Aman Jain ◽  
Lokeshkumar Malke ◽  
...  
Keyword(s):  
Deep Learning ◽  
Image Filtering ◽  
Stair Climbing

scholarly journals Classification of Gait Type Based on Deep Learning Using Various Sensors with Smart Insole

Sensors ◽  
2019 ◽  
Vol 19 (8) ◽  
pp. 1757 ◽  
Author(s):  
Sung-Sin Lee ◽  
Sang Tae Choi ◽  
Sang-Il Choi
Keyword(s):  
Deep Learning ◽  
Sensor Array ◽  
Measurement Data ◽  
Sensor Arrays ◽  
Stair Climbing ◽  
Hill Climbing ◽  
Feature Maps ◽  
Classification Rate ◽  
Smart Insole ◽  
Type Classification

In this paper, we proposed a gait type classification method based on deep learning using a smart insole with various sensor arrays. We measured gait data using a pressure sensor array, an acceleration sensor array, and a gyro sensor array built into a smart insole. Features of gait pattern were then extracted using a deep convolution neural network (DCNN). In order to accomplish this, measurement data of continuous gait cycle were divided into unit steps. Pre-processing of data were then performed to remove noise followed by data normalization. A feature map was then extracted by constructing an independent DCNN for data obtained from each sensor array. Each of the feature maps was then combined to form a fully connected network for gait type classification. Experimental results for seven types of gait (walking, fast walking, running, stair climbing, stair descending, hill climbing, and hill descending) showed that the proposed method provided a high classification rate of more than 90%.

Deep Learning-Based Stair Segmentation and Behavioral Cloning for Autonomous Stair Climbing

2019 ◽  
Vol 13 (04) ◽  
pp. 497-512
Author(s):  
Navid Panchi ◽  
Khush Agrawal ◽  
Unmesh Patil ◽  
Aniket Gujarathi ◽  
Aman Jain ◽  
...  
Keyword(s):  
Deep Learning ◽  
Mechanical Design ◽  
Semantic Segmentation ◽  
Industrial Applications ◽  
Stair Climbing ◽  
Urban Search And Rescue ◽  
Rescue Operation ◽  
Detection Techniques ◽  
Learning Technique ◽  
Behavioral Cloning

Mobile robots are widely used in the surveillance industry, for military and industrial applications. In order to carry out surveillance tasks like urban search and rescue operation, the ability to traverse stairs is of immense significance. This paper presents a deep learning-based approach for semantic segmentation of stairs, behavioral cloning for stair alignment, and a novel mechanical design for an autonomous stair climbing robot. The main objective is to solve the problem of locomotion over staircases with the proposed implementation. Alignment of a robot with stairs in an image is a traditional problem, and the most recent approaches are centered around hand-crafted texture-based Gabor filters and stair detection techniques. However, we could arrive at a more scalable and robust pipeline for alignment schemes. The proposed deep learning technique eliminates the need for manual tuning of parameters of the edge detector, the Hough accumulator and PID constants. The empirical results and architecture of stair alignment pipeline are demonstrated in this paper.

scholarly journals Classification of Initial Stages of Alzheimer’s Disease through Pet Neuroimaging Modality and Deep Learning: Quantifying the Impact of Image Filtering Approaches

Mathematics ◽  
2021 ◽  
Vol 9 (23) ◽  
pp. 3101
Author(s):  
Ahsan Bin Tufail ◽  
Yong-Kui Ma ◽  
Mohammed K. A. Kaabar ◽  
Ateeq Ur Rehman ◽  
Rahim Khan ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Image Processing ◽  
Alzheimer's Disease ◽  
Deep Learning ◽  
Binary Classification ◽  
Image Filtering ◽  
Clinical Settings ◽  
Gaussian Filtering ◽  
3D Cnn ◽  
The Impact

Alzheimer’s disease (AD) is a leading health concern affecting the elderly population worldwide. It is defined by amyloid plaques, neurofibrillary tangles, and neuronal loss. Neuroimaging modalities such as positron emission tomography (PET) and magnetic resonance imaging are routinely used in clinical settings to monitor the alterations in the brain during the course of progression of AD. Deep learning techniques such as convolutional neural networks (CNNs) have found numerous applications in healthcare and other technologies. Together with neuroimaging modalities, they can be deployed in clinical settings to learn effective representations of data for different tasks such as classification, segmentation, detection, etc. Image filtering methods are instrumental in making images viable for image processing operations and have found numerous applications in image-processing-related tasks. In this work, we deployed 3D-CNNs to learn effective representations of PET modality data to quantify the impact of different image filtering approaches. We used box filtering, median filtering, Gaussian filtering, and modified Gaussian filtering approaches to preprocess the images and use them for classification using 3D-CNN architecture. Our findings suggest that these approaches are nearly equivalent and have no distinct advantage over one another. For the multiclass classification task between normal control (NC), mild cognitive impairment (MCI), and AD classes, the 3D-CNN architecture trained using Gaussian-filtered data performed the best. For binary classification between NC and MCI classes, the 3D-CNN architecture trained using median-filtered data performed the best, while, for binary classification between AD and MCI classes, the 3D-CNN architecture trained using modified Gaussian-filtered data performed the best. Finally, for binary classification between AD and NC classes, the 3D-CNN architecture trained using box-filtered data performed the best.

Deep Learning

2009 ◽  
Author(s):  
Stellan Ohlsson
Keyword(s):  
Deep Learning

Can Users Judge the Stair-Climbing Abilities of Two-Wheeled Self-Balancing Robots?

2012 ◽  
Author(s):  
Keith S. Jones ◽  
Elizabeth A. Schmidlin ◽  
Noah J. Wheeler
Keyword(s):  
Stair Climbing

Look at that—the positive effect of video observation on physical stair climbing performance.

2018 ◽  
Vol 33 (8) ◽  
pp. 1195-1201
Author(s):  
Dorothée Altmeier ◽  
Otmar Bock ◽  
Daniel Memmert
Keyword(s):  
Stair Climbing ◽  
Video Observation ◽  
Positive Effect

Intelligence artificielle : le futur de l’Orthodontie ?

2019 ◽  
Vol 53 (3) ◽  
pp. 281-294
Author(s):  
Jean-Michel Foucart ◽  
Augustin Chavanne ◽  
Jérôme Bourriau
Keyword(s):  
Big Data ◽  
Deep Learning ◽  
Intelligence Artificielle ◽  
Set Up

Nombreux sont les apports envisagés de l’Intelligence Artificielle (IA) en médecine. En orthodontie, plusieurs solutions automatisées sont disponibles depuis quelques années en imagerie par rayons X (analyse céphalométrique automatisée, analyse automatisée des voies aériennes) ou depuis quelques mois (analyse automatique des modèles numériques, set-up automatisé; CS Model +, Carestream Dental™). L’objectif de cette étude, en deux parties, est d’évaluer la fiabilité de l’analyse automatisée des modèles tant au niveau de leur numérisation que de leur segmentation. La comparaison des résultats d’analyse des modèles obtenus automatiquement et par l’intermédiaire de plusieurs orthodontistes démontre la fiabilité de l’analyse automatique; l’erreur de mesure oscillant, in fine, entre 0,08 et 1,04 mm, ce qui est non significatif et comparable avec les erreurs de mesures inter-observateurs rapportées dans la littérature. Ces résultats ouvrent ainsi de nouvelles perspectives quand à l’apport de l’IA en Orthodontie qui, basée sur le deep learning et le big data, devrait permettre, à moyen terme, d’évoluer vers une orthodontie plus préventive et plus prédictive.

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