An Uphill Safety Controller With Deep Learning-Based Ramp Detection for Intelligent Wheelchairs

Wu, Bing-Fei; Chen, Yung-Shin; Huang, Chun-Hsi; Chang, Po-Ju

doi:10.1109/access.2018.2839729

Cited by 12 publications

(8 citation statements)

References 19 publications

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“…To address this issue, various studies and services have been presented. Studies from [ 3 , 4 , 5 , 6 , 7 ] attempted to improve and enhance the hardware utility and performance of a wheelchair. For example, Favey et al and Arnay et al [ 3 , 4 ] developed new sensors to increase the driving quality of electric wheelchairs, while studies from [ 5 , 6 , 7 ] focused on the development of motors and controllers to address various issues while driving through uphill, ramp, and stairs.…”

Section: Introductionmentioning

confidence: 99%

“…Studies from [ 3 , 4 , 5 , 6 , 7 ] attempted to improve and enhance the hardware utility and performance of a wheelchair. For example, Favey et al and Arnay et al [ 3 , 4 ] developed new sensors to increase the driving quality of electric wheelchairs, while studies from [ 5 , 6 , 7 ] focused on the development of motors and controllers to address various issues while driving through uphill, ramp, and stairs. In addition, there have been studies to facilitate wheelchair control by sensing surface electromyography (sEMG) signals from the human arm to detect gestures [ 8 ] or by using printed pressure sensor units to identify and inform irregular and improper posture to prevent sitting-related health issues [ 9 ].…”

Section: Introductionmentioning

confidence: 99%

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Classification of the Sidewalk Condition Using Self-Supervised Transfer Learning for Wheelchair Safety Driving

Yoon

Kim

Jeong

2022

Sensors

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The demand for wheelchairs has increased recently as the population of the elderly and patients with disorders increases. However, society still pays less attention to infrastructure that can threaten the wheelchair user, such as sidewalks with cracks/potholes. Although various studies have been proposed to recognize such challenges, they mainly depend on RGB images or IMU sensors, which are sensitive to outdoor conditions such as low illumination, bad weather, and unavoidable vibrations, resulting in unsatisfactory and unstable performance. In this paper, we introduce a novel system based on various convolutional neural networks (CNNs) to automatically classify the condition of sidewalks using images captured with depth and infrared modalities. Moreover, we compare the performance of training CNNs from scratch and the transfer learning approach, where the weights learned from the natural image domain (e.g., ImageNet) are fine-tuned to the depth and infrared image domain. In particular, we propose applying the ResNet-152 model pre-trained with self-supervised learning during transfer learning to leverage better image representations. Performance evaluation on the classification of the sidewalk condition was conducted with 100% and 10% of training data. The experimental results validate the effectiveness and feasibility of the proposed approach and bring future research directions.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Classification of the Sidewalk Condition Using Self-Supervised Transfer Learning for Wheelchair Safety Driving

Yoon

Kim

Jeong

2022

Sensors

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“…One of the oldest and most commonly used means of supporting the mobility of disabled people are wheelchairs. Wheelchair's structures are dynamically developed in many directions, e.g., in order to increase the range of overcoming terrain obstacles (thresholds [5], stairs [6][7][8], elevations [9], ditches characterized by large differences in height [10]), both weight and dimensions reduction [11], frame reinforcements [12], technologies supporting propulsion [13], systems enabling the transmission of driving force from hands to wheels of a wheelchair with various degrees of dysfunction [14], structural improvements increasing the safety of movement [15,16], systems facilitating manual driving of the wheelchair, e.g., gears [8,17]. The group that has been most developed in recent years consists of electric wheelchairs with the possibility of supporting the manual propulsion [18][19][20].…”

Section: Introductionmentioning

confidence: 99%

An Analytical Modelling of Demand for Driving Torque of a Wheelchair with Electromechanical Drive

et al. 2021

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This study aimed at analysing the influence of the position of the centre of gravity variability and the movement velocity on the demand for a torque and work time coverage of a wheelchair with an electromechanical drive. The variable parameter in the study was the configuration of the wheelchair, namely changes in the position of the batteries which changed the weight distribution. An analytical model describing the demand for torque was used in the analysis. The set of equations was introduced into the numerical calculation software. Simulations were carried out which allowed it to analyse selected parameters of the wheelchair dynamics. An increase in the torque demand was observed due to the increase in the mass of the system from 427.7 N to 533.1 N, ranging from 6.1% to 31.6% at the simulated velocity v7 = 4.2 m/s. The increase in the demand for torque due to the increase in velocity of the wheelchair from v2 = 1.05 m/s to v7 = 4.2 m/s ranged from 25.9% to 31.6% compared to the reference velocity v1 = 0.525 m/s. The centre of gravity of the wheelchair structure localization has a non-linear impact on the analysed values. At the same time, it was not possible to define its nature—this issue remains open and requires further research.

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“…This problem has motivated us to design a warning system to track rented powered wheelchairs in public spaces within the event area to prevent them from being removed from the area. (6)(7)(8)(9)(10) We utilized Arduino, the Quantum Geographic Information System (QGIS), and Python as the software in this study. (11,12) Arduino was used to drive high-frequency RFID, QGIS was used to design the topographic map of the event area, and Python was used to calculate the relative coordinates.…”

Section: Introductionmentioning

confidence: 99%

Designing Positioning Coordinates of Powered Wheelchairs with Radio Frequency Identification

Lu¹,

Wu²,

Tai³

et al. 2021

Sensors and Materials

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We combine radio frequency identification (RFID) and the Quantum Geographic Information System (QGIS) to develop a positioning and warning system for powered wheelchairs rented for activities within a specific area. First, we begin with the use of Arduino to drive ultrahighfrequency (UHF) RFID to read an Electronic Product Code (EPC) tag and take the information of the tag to design an area boundary detection warning function for powered wheelchairs in use. Because the Global Positioning System (GPS) takes more time to start and is greatly affected by the weather and location, we use the relative position of the powered wheelchair to design the positioning system instead of the GPS chip positioning system. By combining the coordinate map of the active area designed by Python and the QGIS, the current position of the wheelchair relative to the point where it was rented is calculated. Finally, the Arduino and QGIS programs are integrated through Python to establish the positioning coordinate system of the powered wheelchair with RFID identification.

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An Uphill Safety Controller With Deep Learning-Based Ramp Detection for Intelligent Wheelchairs

Cited by 12 publications

References 19 publications

Classification of the Sidewalk Condition Using Self-Supervised Transfer Learning for Wheelchair Safety Driving

Classification of the Sidewalk Condition Using Self-Supervised Transfer Learning for Wheelchair Safety Driving

An Analytical Modelling of Demand for Driving Torque of a Wheelchair with Electromechanical Drive

Designing Positioning Coordinates of Powered Wheelchairs with Radio Frequency Identification

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