A learning-based control architecture for an assistive robot providing social engagement during cognitively stimulating activities

Chan, Jeanie; Nejat, Goldie

doi:10.1109/icra.2011.5980426

Cited by 12 publications

(18 citation statements)

References 15 publications

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“…Building upon the works of Bohus and Horvitz on multiparty engagement in open-world dialog [36], Louie and associates on multi-user planning and scheduling architecture [24], and state of the art SAR architectures [20][28][30], we proposed the mathematical models of ROCARE, a robotic coach architecture, for augmenting elder care. This architecture is capable of one-on-one and multi-user interactions between a SAR and older adults.…”

Section: Resultsmentioning

confidence: 99%

“…There are several existing SAR architectures designed for behavior intervention for children with autism spectrum disorder [28], functional intervention or companion purpose for older adults [13][20][24][29][30][31][32], as well as other applications [33][34]. All these architectures including ours have component(s) or module(s) dedicated to sensing and actuation, which provide the interface between SAR systems and targeted users; and decision making, for system behavior adaptation.…”

Section: Introductionmentioning

confidence: 99%

“…First, in Sensing we modified and extended the Sensory Input Recognition and Analysis Modules proposed by Chan and Nejat [29] [30] and the User State and Activity State Modules presented by Louie et al [24]. The implicit state submodule in ROCARE is dedicated to implicit channel of communication between the SAR system and users, whereas the explicit state submodule is dedicated to explicit channel of communication.…”

Section: Introductionmentioning

confidence: 99%

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A Robotic Coach Architecture for Elder Care (ROCARE) Based on Multi-User Engagement Models

Fan

Bian

Zheng

et al. 2017

IEEE Trans. Neural Syst. Rehabil. Eng.

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The aging population with its concomitant medical conditions, physical and cognitive impairments, at a time of strained resources, establishes the urgent need to explore advanced technologies that may enhance function and quality of life. Recently, robotic technology, especially socially assistive robotics has been investigated to address the physical, cognitive, and social needs of older adults. Most system to date have predominantly focused on one-on-one human robot interaction (HRI). In this paper, we present a multi-user engagement-based robotic coach system architecture (ROCARE). ROCARE is capable of administering both one-on-one and multi-user HRI, providing implicit and explicit channels of communication, and individualized activity management for long-term engagement. Two preliminary feasibility studies, a one-on-one interaction and a triadic interaction with two humans and a robot, were conducted and the results indicated potential usefulness and acceptance by older adults, with and without cognitive impairment.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Robotic Coach Architecture for Elder Care (ROCARE) Based on Multi-User Engagement Models

Fan

Bian

Zheng

et al. 2017

IEEE Trans. Neural Syst. Rehabil. Eng.

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“…The level of difficulty of the game is also changed based on the number of incorrect matches the person has made in the last n rounds. The reader is referred to [14] for more details on the MAXQ learning approach. The overall behavior of the robot is physically implemented by the actuator control module.…”

Section: B Robot Control Architecturementioning

confidence: 99%

“…In this section, we provide a brief description of the functionality of each module within the control architecture. For further details of the modules, readers are referred to [13], [14]. The Activity State module monitors the state of the memory game during the interaction utilizing images captured by the 2D webcam.…”

Section: B Robot Control Architecturementioning

confidence: 99%

Minimizing task-induced stress in cognitively stimulating activities using an intelligent socially assistive robot

Chan

Nejat

2011

2011 Ro-Man

Self Cite

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Dementia is currently a growing epidemic, bringing forth severe health, social, and economic strains. As an alternative to pharmacological measures, current research supports the effectiveness of using cognitive training interventions to slow the decline of or even improve brain functioning in persons with dementia. However, implementing and sustaining these interventions on a long-term basis can be challenging as they demand considerable resources and people. Our research focuses on investigating the potential use of robotic assistants to allow for these interventions to become more accessible to users and caregivers. Namely, the aim of our work is to develop socially assistive robots that can provide cognitive and social stimulation for persons with dementia. In this paper, we study the social interaction attributes of the human-like robot, Brian 2.0, during a one-on-one personcentered cognitively stimulating activity to determine if the robot is capable of minimizing task-induced stress by providing assistance, encouragement, and celebration, while adapting its behavior to a user state during the course of the activity. Our preliminary study shows that the social intelligence of Brian 2.0 is effective in engaging individuals in a cognitively stimulating game while minimizing stress during gameplay.

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Action Recognition Based on Efficient Deep Feature Learning in the Spatio-Temporal Domain

Husain

Dellen

Torras

2016

IEEE Robot. Autom. Lett.

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Abstract-Hand-crafted feature functions are usually designed based on the domain knowledge of a presumably controlled environment and often fail to generalize, as the statistics of realworld data cannot always be modeled correctly. Data-driven feature learning methods, on the other hand, have emerged as an alternative that often generalize better in uncontrolled environments. We present a simple, yet robust, 2D convolutional neural network extended to a concatenated 3D network that learns to extract features from the spatio-temporal domain of raw video data. The resulting network model is used for contentbased recognition of videos. Relying on a 2D convolutional neural network allows us to exploit a pretrained network as a descriptor that yielded the best results on the largest and challenging ILSVRC-2014 dataset. Experimental results on commonly used benchmarking video datasets demonstrate that our results are state-of-the-art in terms of accuracy and computational time without requiring any preprocessing (e.g., optic flow) or a priori knowledge on data capture (e.g., camera motion estimation), which makes it more general and flexible than other approaches. Our implementation is made available.

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A learning-based control architecture for an assistive robot providing social engagement during cognitively stimulating activities

Cited by 12 publications

References 15 publications

A Robotic Coach Architecture for Elder Care (ROCARE) Based on Multi-User Engagement Models

A Robotic Coach Architecture for Elder Care (ROCARE) Based on Multi-User Engagement Models

Minimizing task-induced stress in cognitively stimulating activities using an intelligent socially assistive robot

Action Recognition Based on Efficient Deep Feature Learning in the Spatio-Temporal Domain

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