Gesture-based telemanipulation of a humanoid robot for home service tasks

Yu, Ningbo; Xu, Chang; Wang, Kui; Yang, Zhuo; Liu, Jingtai

doi:10.1109/cyber.2015.7288241

Cited by 12 publications

(9 citation statements)

References 6 publications

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“…For example, NAO robots interacted with users in both verbal and nonverbal modes and provided face-to-face communication and physical touch. More than 80 publications reported findings about the applications of this humanoid robot among patients with various health issues, including improving communication skills for children with ASD (eg, [42,43]), reducing distress for cancer patients (eg, [50]), and providing functional and emotional support for the elderly (eg, [52,78]). NAO’s hybrid communication channels (eg, speech, eye contact, and body movement) contributed to users’ in-depth engagement to achieve desired behavior change.…”

Section: Discussionmentioning

confidence: 99%

Conversational Interfaces for Health: Bibliometric Analysis of Grants, Publications, and Patents

Xing¹,

Yu²,

Du³

et al. 2019

J Med Internet Res

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BackgroundConversational interfaces (CIs) in different modalities have been developed for health purposes, such as health behavioral intervention, patient self-management, and clinical decision support. Despite growing research evidence supporting CIs’ potential, CI-related research is still in its infancy. There is a lack of systematic investigation that goes beyond publication review and presents the state of the art from perspectives of funding agencies, academia, and industry by incorporating CI-related public funding and patent activities.ObjectiveThis study aimed to use data systematically extracted from multiple sources (ie, grant, publication, and patent databases) to investigate the development, research, and fund application of health-related CIs and associated stakeholders (ie, countries, organizations, and collaborators).MethodsA multifaceted search query was executed to retrieve records from 9 databases. Bibliometric analysis, social network analysis, and term co-occurrence analysis were conducted on the screened records.ResultsThis review included 42 funded projects, 428 research publications, and 162 patents. The total dollar amount of grants awarded was US $30,297,932, of which US $13,513,473 was awarded by US funding agencies and US $16,784,459 was funded by the Europe Commission. The top 3 funding agencies in the United States were the National Science Foundation, National Institutes of Health, and Agency for Healthcare Research and Quality. Boston Medical Center was awarded the largest combined grant size (US $2,246,437) for 4 projects. The authors of the publications were from 58 countries and 566 organizations; the top 3 most productive organizations were Northeastern University (United States), Universiti Teknologi MARA (Malaysia), and the French National Center for Scientific Research (CNRS; France). US researchers produced 114 publications. Although 82.0% (464/566) of the organizations engaged in interorganizational collaboration, 2 organizational research-collaboration clusters were observed with Northeastern University and CNRS as the central nodes. About 112 organizations from the United States and China filed 87.7% patents. IBM filed most patents (N=17). Only 5 patents were co-owned by different organizations, and there was no across-country collaboration on patenting activity. The terms patient, child, elderly, and robot were frequently discussed in the 3 record types. The terms related to mental and chronic issues were discussed mainly in grants and publications. The terms regarding multimodal interactions were widely mentioned as users’ communication modes with CIs in the identified records.ConclusionsOur findings provided an overview of the countries, organizations, and topic terms in funded projects, as well as the authorship, collaboration, content, and related information of research publications and patents. There is a lack of broad cross-sector partnerships among grant agencies, academia, and industry, particularly in the United States. Our results suggest a need to improve col...

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Section: Discussionmentioning

confidence: 99%

Conversational Interfaces for Health: Bibliometric Analysis of Grants, Publications, and Patents

Xing¹,

Yu²,

Du³

et al. 2019

J Med Internet Res

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“…The repeatability error is less than 0.17 mm. Thus, the Leap Motion device offers a portable, compact economic and reasonably precise sensor solution to realize various human-machine interaction applications in the field of AAL (ambient assisted living) and ADLs [ 30 , 31 , 32 ], especially for the elderly and disabled users.…”

Section: Methodsmentioning

confidence: 99%

“…It has overall 7 degrees of freedom (DOFs) movement capabilities, of which 3 DOFs are for rotation, 3 DOFs for translation and 1 DOF for grasp or grip. The 4 DOFs’ precise force feedback is provided during the active translational and grasping movements [ 30 ]. The movement range for grasping is 25 mm, with a resolution of 0.006 mm, and the force feedback is up to ±8.0 N, enabling dexterous manipulation and haptic interaction [ 4 , 31 ].…”

Section: Methodsmentioning

confidence: 99%

Fusion of Haptic and Gesture Sensors for Rehabilitation of Bimanual Coordination and Dexterous Manipulation

et al. 2016

Sensors

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Disabilities after neural injury, such as stroke, bring tremendous burden to patients, families and society. Besides the conventional constrained-induced training with a paretic arm, bilateral rehabilitation training involves both the ipsilateral and contralateral sides of the neural injury, fitting well with the fact that both arms are needed in common activities of daily living (ADLs), and can promote good functional recovery. In this work, the fusion of a gesture sensor and a haptic sensor with force feedback capabilities has enabled a bilateral rehabilitation training therapy. The Leap Motion gesture sensor detects the motion of the healthy hand, and the omega.7 device can detect and assist the paretic hand, according to the designed cooperative task paradigm, as much as needed, with active force feedback to accomplish the manipulation task. A virtual scenario has been built up, and the motion and force data facilitate instantaneous visual and audio feedback, as well as further analysis of the functional capabilities of the patient. This task-oriented bimanual training paradigm recruits the sensory, motor and cognitive aspects of the patient into one loop, encourages the active involvement of the patients into rehabilitation training, strengthens the cooperation of both the healthy and impaired hands, challenges the dexterous manipulation capability of the paretic hand, suits easy of use at home or centralized institutions and, thus, promises effective potentials for rehabilitation training.

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“…It has been considered a breakthrough for hand gesture controlled human-machine interaction. Due to its compact size, enhanced precision, and low cost, the Leap Motion technology benefits and enables realization of various human-machine interaction application in the field of AAL (Ambient Assisted Living) and activities of daily living [16], [17], especially for the elderly or disabled users.…”

Section: B Hardware Devicesmentioning

confidence: 99%

A bilateral rehabilitation method for arm coordination and manipulation function with gesture and haptic interfaces

Wang

et al. 2015

2015 IEEE International Conference on Robotics and Biomimetics (ROBIO)

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Disabilities after neural injury such as stroke bring tremendous burden to the patients, families and society. Besides the conventional constrained-induced training with the paretic arm, bilateral rehabilitation training involves both the ipsilateral and contralateral sides of the neural injury, fits well the fact that both arms are needed in common activities of daily living (ADLs), and can promote good functional recovery. In this work, a bilateral rehabilitation training method is proposed and implemented with the Leap Motion sensor and omega.7 haptic interface. The Leap Motion sensor detects the motion of the healthy hand, and the omega.7 device, according to the cooperative task paradigm, assists the paretic hand as much as needed with active force feedback to accomplish the manipulation task. A virtual scenario has been built up, and the motion and force data enables visual and audio feedback in real time as well as further analysis of the functional capabilities of the patient. This task-oriented bimanual training paradigm encourages active involvement of the patients into rehabilitation, strengthens cooperation of both the healthy and impaired hands, challenges the dexterous manipulation capability of the paretic hand, suits easy home use, and thus, promises effective rehabilitation therapy.

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Gesture-based telemanipulation of a humanoid robot for home service tasks

Cited by 12 publications

References 6 publications

Conversational Interfaces for Health: Bibliometric Analysis of Grants, Publications, and Patents

Conversational Interfaces for Health: Bibliometric Analysis of Grants, Publications, and Patents

Fusion of Haptic and Gesture Sensors for Rehabilitation of Bimanual Coordination and Dexterous Manipulation

A bilateral rehabilitation method for arm coordination and manipulation function with gesture and haptic interfaces

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