A Method of Constructing Robotics Service Platform for Assisting Handicapped or Elderly People

Yin, Zuozhong; Wang, Dianpeng; Liu, Jihong

doi:10.1155/2020/4259175

Cited by 3 publications

(11 citation statements)

References 11 publications

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“…Sensory environments refer to places and spaces designed to support the processing of sensory information through multiple senses to enable disadvantaged groups, such as children with autism [98], to partake in several activities and improve their well-being. In two distinguished instances (e.g., [67,92]), services were invoked via robots. Overall, two types of services were composed, namely, software and web services (28 studies) and IoT services (16 studies).…”

Section: Services and Domains Of Compositionmentioning

confidence: 99%

“…Social needs [85] Personalization of services as per the culture and lifestyle of disabled users [77] Reduction of social isolation [83] Autonomy to survive in isolation; social inclusion [80] Individual profling to help users in social contexts [92] Social function services, e.g., inclusion; psychological comfort…”

Section: Studiesmentioning

confidence: 99%

“…In control fow patterns, the order of executing services and activities is specifed, whereas, in data fow patterns, data passing from one service to the next is defned. Composition concerns that were considered included security (8 studies, [66,75,76,85,91,92,96,100]), quality of services (5 studies, [68,71,83,84,107]), and exception handling (3 studies, [68,70,90]). One of the key advantages of SOA is its ability to promote the reuse of artifacts and techniques.…”

Section: Service Composition Approachesmentioning

confidence: 99%

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A Systematic Literature Review on Service Composition for People with Disabilities: Taxonomies, Solutions, and Open Research Challenges

Namoun

Tufail

Nawaz

et al. 2023

Computational Intelligence and Neuroscience

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Integrating smart heterogeneous objects, IoT devices, data sources, and software services to produce new business processes and functionalities continues to attract considerable attention from the research community due to its unraveled advantages, including reusability, adaptation, distribution, and pervasiveness. However, the exploitation of service-oriented computing technologies (e.g., SOC, SOA, and microservice architectures) by people with special needs is underexplored and often overlooked. Furthermore, the existing challenges in this area are yet to be identified clearly. This research study presents a rigorous literature survey of the recent advances in service-oriented composition approaches and solutions for disabled people, their domains of application, and the major challenges, covering studies published between January 2010 and October 2022. To this end, we applied the systematic literature review (SLR) methodology to retrieve and collate only the articles presenting and discussing service composition solutions tailored to produce digitally accessible services for consumption by people who suffer from an impairment or loss of some physical or mental functions. We searched six renowned bibliographic databases, particularly IEEE Xplore, Web of Science, Springer Link, ACM Library, ScienceDirect, and Google Scholar, to synthesize a final pool of 38 related articles. Our survey contributes a comprehensive taxonomy of service composition solutions, techniques, and practices that are utilized to create assistive technologies and services. The seven-facet taxonomy helps researchers and practitioners to quickly understand and analyze the fundamental conceptualizations and characteristics of accessible service composition for people with disabilities. Key findings showed that services are fused to assist disabled persons to carry out their daily activities, mainly in smart homes and ambient intelligent environments. Despite the emergence of immersive technologies (e.g., wearable computing), user-service interactions are enabled primarily through tactile and speech modalities. Service descriptions mainly incorporate functional features (e.g., performance, latency, and cost) of service quality, largely ignoring accessibility features. Moreover, the outstanding research problems revolve around (1) the unavailability of assistive services datasets, (2) the underspecification of accessibility aspects of disabilities, (3) the weak adoption of accessible and universal design practices, (4) the abstraction of service composition approaches, and (5) the rare experimental testing of composition approaches with disabled users. We conclude our survey with a set of guidelines to realize effective assistive service composition in IoT and cloud environments. Researchers and practitioners are advised to create assistive services that support the social relationships of disabled users and model their accessibility needs as part of the quality of service (QoS). Moreover, they should exploit AI/ML models to address the evolving requirements of disabled users in their unique environments. Furthermore, weaknesses of service composition solutions and research challenges are exposed as notable opportunities for future research.

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Section: Services and Domains Of Compositionmentioning

confidence: 99%

Section: Studiesmentioning

confidence: 99%

Section: Service Composition Approachesmentioning

confidence: 99%

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A Systematic Literature Review on Service Composition for People with Disabilities: Taxonomies, Solutions, and Open Research Challenges

Namoun

Tufail

Nawaz

et al. 2023

Computational Intelligence and Neuroscience

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“…The service selection process is non-trivial when the context and requirements are difficult to determine, e.g., people with disabilities. Therefore, researchers have devised various approaches for people with disabilities and for different environments, e.g., ambient assisted living [ 21 ], older adults [ 22 ], IoT [ 23 , 24 ], handicapped or elderly people [ 25 ], IoT and smart cities [ 26 , 27 ], and virtual education [ 28 ]. In a recent study [ 21 ], the authors presented an autonomous solution for service selection to assist elderly people in emergencies.…”

Section: Introductionmentioning

confidence: 99%

“…These fragments are then ranked according to user needs (complying with the user profile information), and the top fragments are selected to form an integrated solution. Similarly, service selection was achieved in a robotics environment, as discussed in [ 25 ], whereby the demands of elderly or handicapped people are analyzed to determine the appropriate robotic service to fulfill user requirements. The authors did not reveal details of their robotic service selection algorithm in their study.…”

Section: Introductionmentioning

confidence: 99%

A Two-Phase Machine Learning Framework for Context-Aware Service Selection to Empower People with Disabilities

Namoun

Sen

Tufail

et al. 2022

Sensors

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The use of software and IoT services is increasing significantly among people with special needs, who constitute 15% of the world’s population. However, selecting appropriate services to create a composite assistive service based on the evolving needs and context of disabled user groups remains a challenging research endeavor. Our research applies a scenario-based design technique to contribute (1) an inclusive disability ontology for assistive service selection, (2) semi-synthetic generated disability service datasets, and (3) a machine learning (ML) framework to choose services adaptively to suit the dynamic requirements of people with special needs. The ML-based selection framework is applied in two complementary phases. In the first phase, all available atomic tasks are assessed to determine their appropriateness to the user goal and profiles, whereas in the subsequent phase, the list of service providers is narrowed by matching their quality-of-service factors against the context and characteristics of the disabled person. Our methodology is centered around a myriad of user characteristics, including their disability profile, preferences, environment, and available IT resources. To this end, we extended the widely used QWS V2.0 and WS-DREAM web services datasets with a fusion of selected accessibility features. To ascertain the validity of our approach, we compared its performance against common multi-criteria decision making (MCDM) models, namely AHP, SAW, PROMETHEE, and TOPSIS. The findings demonstrate superior service selection accuracy in contrast to the other methods while ensuring accessibility requirements are satisfied.

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A Personalized Travel Route Recommendation Model Using Deep Learning in Scenic Spots Intelligent Service Robots

Tang

2022

Journal of Robotics

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This paper proposes a personalized tourist interest demand recommendation model based on deep neural network. Firstly, the basic information data and comment text data of tourism service items are obtained by crawling the relevant website data. Furthermore, word segmentation and word vector transformation are carried out through Jieba word segmentation tool and Skip-gram model, the semantic information between different data is deeply characterized, and the problem of very high vector sparsity is solved. Then, the corresponding features are obtained by using the feature extraction ability of DNN’s in-depth learning. On this basis, the user’s score on tourism service items is predicted through the model until a personalized recommendation list is generated. Finally, through simulation experiments, the recommendation accuracy and average reciprocal ranking of the proposed algorithm model and the other two algorithms in three different databases are compared and analyzed. The results show that the overall performance of the proposed algorithm is better than the other two comparison algorithms.

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A Method of Constructing Robotics Service Platform for Assisting Handicapped or Elderly People

Cited by 3 publications

References 11 publications

A Systematic Literature Review on Service Composition for People with Disabilities: Taxonomies, Solutions, and Open Research Challenges

A Systematic Literature Review on Service Composition for People with Disabilities: Taxonomies, Solutions, and Open Research Challenges

A Two-Phase Machine Learning Framework for Context-Aware Service Selection to Empower People with Disabilities

A Personalized Travel Route Recommendation Model Using Deep Learning in Scenic Spots Intelligent Service Robots

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