A Heuristic Approach to Overcome Architectural Barriers Using a Robotic Wheelchair

Candiotti, Jorge; Daveler, Brandon; Kamaraj, Deepan C.; Chung, Cheng S.; Cooper, Rosemarie; Grindle, Garrett G.

doi:10.1109/tnsre.2019.2934387

Cited by 23 publications

(11 citation statements)

References 28 publications

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“…These features provide indoor/outdoor maneuvering and curb negotiation to improve accessibility. A previous study explained the sequential steps to ascend and descend curbs in an automated process ( Figure 1 ) [ 29 ]. Similar to RWs previously discussed, the MEBot must meet two assumptions prior to curb negotiation: the wheelchair must be aligned perpendicular to the curb for full contact of the wheels with the curb, and the curb height and approach angle must be known.…”

Section: Methodsmentioning

confidence: 99%

Automated Curb Recognition and Negotiation for Robotic Wheelchairs

Sivakanthan

Castagno

Candiotti

et al. 2021

Sensors

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Common electric powered wheelchairs cannot safely negotiate architectural barriers (i.e., curbs) which could injure the user and damage the wheelchair. Robotic wheelchairs have been developed to address this issue; however, proper alignment performed by the user is needed prior to negotiating curbs. Users with physical and/or sensory impairments may find it challenging to negotiate such barriers. Hence, a Curb Recognition and Negotiation (CRN) system was developed to increase user’s speed and safety when negotiating a curb. This article describes the CRN system which combines an existing curb negotiation application of a mobility enhancement robot (MEBot) and a plane extraction algorithm called Polylidar3D to recognize curb characteristics and automatically approach and negotiate curbs. The accuracy and reliability of the CRN system were evaluated to detect an engineered curb with known height and 15 starting positions in controlled conditions. The CRN system successfully recognized curbs at 14 out of 15 starting positions and correctly determined the height and distance for the MEBot to travel towards the curb. While the MEBot curb alignment was 1.5 ± 4.4°, the curb ascending was executed safely. The findings provide support for the implementation of a robotic wheelchair to increase speed and reduce human error when negotiating curbs and improve accessibility.

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Section: Methodsmentioning

confidence: 99%

Automated Curb Recognition and Negotiation for Robotic Wheelchairs

Sivakanthan

Castagno

Candiotti

et al. 2021

Sensors

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“…e success recognition times and rates as well as the mean values are as shown in Table 1. e results indicate that the average success recognition rate can reach 91% to 96% in a quiet environment (20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30), no matter which language is used or what the sex of the subject is. e average success recognition rate is 81% to 84% in a relatively quiet environment (30)(31)(32)(33)(34)(35)(36)(37)(38)(39)(40).…”

Section: Voice Control System Evaluationmentioning

confidence: 99%

“…e voice control system is tested by one male and one female users in different environments. First, voice Complexity command training is conducted in quiet environment (20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30). Each command training is conducted twice and stored.…”

Section: Voice Control System Evaluationmentioning

confidence: 99%

“…Various research studies about different aspects of powered wheelchairs have been conducted [15][16][17][18][19][20][21]. With the development of human-computer interface hardware and sensor processing algorithms, the smart wheelchairs give people with lower limb motor dysfunction not only mobility but also the necessary rehabilitation and daily living help [22][23][24]. Doyoung and colleagues proposed a wheelchair integrated lower limb rehabilitation system [25] that is composed of an electrical wheelchair, a lifter, and an LLE.…”

Section: Introductionmentioning

confidence: 99%

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Development and Evaluation of a Rehabilitation Wheelchair with Multiposture Transformation and Smart Control

Cao

Wang

et al. 2021

Complexity

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Stroke and other neurological disorders have an effect on mobility which has a significant impact on independence and quality of life. The core rehabilitation requirements for patients with lower limb motor dysfunction are gait training, restand, and mobility. In this work, we introduce a newly developed multifunctional wheelchair that we call “ReChair” and evaluated its performance preliminarily. ReChair seamlessly integrates the mobility, gait training, and multiposture transformation. ReChair driving and multiposture transformation are done using the voice, button, and mobile terminal control. This work describes the functional requirements, mechanical structure, and control system and the overall evaluation of ReChair including the kinematic simulation of the multiposture transformation and passive lower limb rehabilitation training to quantitatively verify the motion capability of ReChair, the voice control system evaluation that shows how the voice recognition system is suitable for home environment, the sensorless speed detection test results that indicate how the wheel speeds measured by sensorless method are appropriate for travelling control, and the passive and balance training test results that show how the lower limb rehabilitation training in daily life by ReChair is convenient. Finally, the experimental results show that ReChair meets the patients’ requirements and has practical significance. It is cost-effective, easy to use, and supports multiple control modes to adapt to different rehabilitation phases.

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“…Robotic wheelchairs provide users with autonomy, enhanced mobility and safety ( 21 ). With an appropriate mechanical structure for the robotic wheelchair, architectural barriers may be overcome, including curb ascending and descending ( 22 ). In terms of control, a robotic wheelchair may hierarchically combine: (i) low-level functions (e.g., obstacle/collision avoidance, corridor centering) and (ii) high-level functions (e.g., directing the wheelchair) ( 23 ).…”

Section: Physically-assistive Robotsmentioning

confidence: 99%

The Upcoming Role for Nursing and Assistive Robotics: Opportunities and Challenges Ahead

Christoforou

Avgousti

Ramdani

et al. 2020

Front. Digit. Health

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As an integral part of patient care, nursing is required to constantly adapt to changes in the healthcare system, as well as the wider financial and societal environment. Among the key factors driving these changes is the aging of population. Combined with an existing shortage of nursing and caregiving professionals, accommodating for the patients and elderly needs within hospitals, elderly-care facilities and at a home setting, becomes a societal challenge. Amongst the technological solutions that have evolved in response to these developments, nursing and assistive robotics claim a pivotal role. The objective of the present study is to provide an overview of today's landscape in nursing and assistive robotics, highlighting the benefits associated with adopting such solutions in standard clinical practice. At the same time, to identify existing challenges and limitations that essentially outline the area's future directions. Beyond technological innovation, the manuscript also investigates the end-users' angle, being a crucial parameter in the success of robotics solutions operating within a healthcare environment. In this direction, the results of a survey designed to capture the nursing professionals' perspective toward more informed robotics design and development are presented.

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A Heuristic Approach to Overcome Architectural Barriers Using a Robotic Wheelchair

Cited by 23 publications

References 28 publications

Automated Curb Recognition and Negotiation for Robotic Wheelchairs

Automated Curb Recognition and Negotiation for Robotic Wheelchairs

Development and Evaluation of a Rehabilitation Wheelchair with Multiposture Transformation and Smart Control

The Upcoming Role for Nursing and Assistive Robotics: Opportunities and Challenges Ahead

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