Evaluation of incentives for body area network-based healthcare systems

Aflaki, S.; Meratnia, Nirvana; Baratchi, Mitra; Havinga, Paul J.M.

doi:10.1109/issnip.2013.6529843

Cited by 12 publications

(12 citation statements)

References 22 publications

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“…Mining these mobility data provides the opportunity of extracting valuable knowledge regarding behavioral habits such as frequent paths [4]. This knowledge leads to design of various environmental or healthcare services [5]. An interesting topic in analyzing such patterns is finding the existence of social ties between entities and describing the purpose of such links.…”

Section: Introductionmentioning

confidence: 99%

On the use of mobility data for discovery and description of social ties

Baratchi

Meratnia

Havinga

2013

Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining

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Ever-increasing emergence of location-aware ubiquitous devices has facilitated collection of timestamped mobility data. This large volume of data not only provides trajectory information but also information about social interaction between individuals. Unlike trajectory representation and discovery, discovery of social ties and interactions hidden in mobility data has not yet been fully explored. To identify such interaction, social network analysis has been recently used. However, compared with data from emails, phone calls, and messages, which are commonly used for social network analysis, mobility data convey less information about interaction between entities. Therefore, identifying the type of tie between two entities using only mobility data is a great challenge. In this paper, we propose a method for measuring the strength and type of social ties between people only based on their spatio-temporal correlations. Using mutual information metric, we propose utilization of two types of measures for identifying the purpose of being in a certain location. Our experimental results using a location-aware sensing device show that our method can identify different social ties between various entities successfully.

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Section: Introductionmentioning

confidence: 99%

On the use of mobility data for discovery and description of social ties

Baratchi

Meratnia

Havinga

2013

Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining

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“…Once a hierarchy is achieved, an overall solution can be found by building a multi-attribute cost function and evaluating it according to the set of the objectives. AHP is a 30-year-old concept, used so far in various areas such as project management and organisation [28], mathematical programming [22], Geographic Information Systems (GIS) [40], logistics [24], vascular surgery [35], and incentive to use Body Area Networks (BAN) [1].…”

mentioning

confidence: 99%

Location Based Services Analysis Through Analytical Hierarchical Processes: An e-Health-Based Case Study

Lohan

Silva

Basiri

et al. 2017

Multi-Technology Positioning

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Wireless localisation has become a valuable information for a large number of location based or location-aware services [2]. Significant economic value can be generated with an increased granularity of the location information (i.e., knowing the location at block or room level instead of street or district level) as shown, for example, in [2]. LBS are generally offered to the users by an LBS provider, using one or more localisation technologies, which can belong or not to the LBS provider. For example, an LBS provider can use the information coming from the WiFi network owned by a Mall owner and provide position location information to the user. In this example, the LBS provider and the technology provider (i.e., the owner(s) of the WiFi network) are two distinct units. In another example, a cellular operator can use the information provided by its own cells or base stations to provide the user location. In this example, the LBS provider and the technology provider are the same unit, namely the cellular operator.

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“…For example, in [90], the authors use real-time musical feedback to motivate users to achieve their exercise goals. We do not go into detail on feedback applications, as those can be designed in different ways, for example, interactive games [91]. However, details of the interaction with users for assistive feedback using mobile phones can be found in [48,10,37].…”

Section: Discussionmentioning

confidence: 99%

Sitting is the new smoking: online complex human activity recognition with smartphones and wearables

Shoaib¹

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Evaluation of incentives for body area network-based healthcare systems

Cited by 12 publications

References 22 publications

On the use of mobility data for discovery and description of social ties

On the use of mobility data for discovery and description of social ties

Location Based Services Analysis Through Analytical Hierarchical Processes: An e-Health-Based Case Study

Sitting is the new smoking: online complex human activity recognition with smartphones and wearables

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