SBOT: A Social Media Based Object Tracking System

Chu, Edward T.-H.; Lin, K. H.; Chen, Shih‐Yu; Hsu, Jiun; Wu, Hsien-Huang P.

doi:10.1109/indo-taiwanican48429.2020.9181331

Cited by 4 publications

(7 citation statements)

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“…Finally, the search result is returned to the user through Line’s interface (Step 4). DBOS is an extension of our previous work, the SBOT [ 7 ], which combined the popular social media app Line with an indoor positioning system. In Figure 2 , Steps 1, 2, and 3 are the new content in this work, while Step 4 is covered by the SBOT previously.…”

Section: Methodsmentioning

confidence: 99%

“…The user interface, shown in Figure 1 , was built on top of the SBOT. For readers who are interested in how the SBOT returns the search result, please refer to [ 7 ].…”

Section: Methodsmentioning

confidence: 99%

“…In other words, if the user query is mapped to more than one user intent or an incorrect user intent, the identification fails. It should be noted that this work is an extension of our previous work SBOT [ 7 ], which combined the popular social media app Line with an indoor object tracking system. For readers who are interested in the way how the indoor positioning system returns the location of a specific object when a user asked for the object, please refer to [ 7 ].…”

Section: Methodsmentioning

confidence: 99%

“…In this paper, DBOS, i.e., dialog-based object query system, is proposed, which simulates a real conversation with users via the Line messaging app’s chatbot interface [ 6 ]. DBOS is an extension of our previous work SBOT (social media-based object tracking system) [ 7 ], which combines the popular social media app Line with an indoor object tracking system. Inside DBOS, there is an intent table, stored at a remote server, that lists possible query intents in a nurse’s daily working scenario, such as search for a device, search for all devices, search for inventory, search for available devices, and so on.…”

Section: Introductionmentioning

confidence: 99%

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DBOS: A Dialog-Based Object Query System for Hospital Nurses

Chu

Huang

2020

Sensors

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Due to the advance of indoor positioning technology, it is now possible to trace mobile medical equipment (such as electrocardiography machines, patient monitors, and so on) being moved around a hospital ward. With the support of an object tracking system, nurses can easily locate and find a device, especially when they prepare for a shift change or a medical treatment. As nurses usually face high workloads, it is highly desirable to provide nurses with a user-friendly search interface integrated into a popular mobile app that they use daily. For this, DBOS, a dialog-based object query system, is proposed, which simulates a real conversation with users via the Line messaging app’s chatbot interface. A hybrid method that combines cosine similarity (CS) and term frequency–inverse document frequency (TF-IDF) is used to determine user intent. The result is returned to the user through Line’s interface. To evaluate the applicability of DBOS, 70 search queries given by a head nurse were tested. DBOS was compared with CS, TF-IDF, and Facebook Wit.ai respectively. The experiment results show that DBOS outperforms the abovementioned methods and can achieve a 92.8% accuracy in identifying user intent.

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Section: Methodsmentioning

confidence: 99%

“…The user interface, shown in Figure 1 , was built on top of the SBOT. For readers who are interested in how the SBOT returns the search result, please refer to [ 7 ].…”

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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DBOS: A Dialog-Based Object Query System for Hospital Nurses

Chu

Huang

2020

Sensors

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“…For example, a scheme called Social-Loc has been proposed that integrates the physical traces of an individual posted through social media (e.g., check-ins to a particular shop in a shopping mall) with RSSI signals from WiFI routers to potentially derive the exact location of individual users within a building [140]. Chu et al designed SBOT, a social media and sensor networkdriven indoor localization scheme which combines user statuses and updates posted though social media (using text mining techniques) with telemetry data from smartphone sensors (e.g., altitude, speed, and heading of the users) to pinpoint the location of the users [142]. Liu et al proposed a socialdriven IoT system consisting of backpacks equipped with 2D laser scanners and inertial measurement units augmented with historical social network traces of the users to perform indoor localization and visualization of complex environments such as a staircases or corridors [141].…”

Section: Automatic License Plate Recognition Using Crowdsensing and P...mentioning

confidence: 99%

A Survey on Social-Physical Sensing: An Emerging Sensing Paradigm that Explores the Collective Intelligence of Humans and Machine

Rashid¹,

Wei²,

Wang³

2021

Preprint

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Propelled by the omnipresence of versatile data capture, communication, and computing technologies, physical sensing has revolutionized the avenue for spontaneously capturing and interpreting the real-world phenomenon. Despite its virtues, various limitations (e.g., high application specificity, partial autonomy, and sparse coverage) hinder physical sensing's effectiveness in critical scenarios such as disaster response and urban anomaly detection. Meanwhile, social sensing is contriving as a pervasive sensing paradigm that leverages the observations from human participants equipped with portable devices and ubiquitous Internet connectivity (i.e., through social media or crowdsensing apps) to perceive the occurrences in the environment. While social sensing possesses a plethora of benefits, it also inherently suffers from a few drawbacks (e.g., inconsistent reliability, uncertain data provenance, and limited sensing availability). Motivated by the complementary virtues of both physical and social sensing, social-physical sensing (SPS) is protruding as an emerging sensing paradigm that tightly integrates the social and physical sensors at an unprecedented scale. The vision of SPS centers on mitigating the individual weaknesses of physical and social sensing while exploiting their collective strengths in reconstructing the "state of the world", both physically and socially. While a good amount of interesting SPS applications has been explored, several important unsolved challenges and open research questions prevail in the way of developing innovative and practical SPS systems, which require careful scrutiny and research to address. In this paper, we provide a comprehensive survey of SPS, with an emphasis on its definition and key enablers, state-of-the-art applications, potential research challenges, and the road-map for future work. While existing survey papers related to sensing have discussed sensing schemes that are partly related to SPS, they do not explicitly provide an extensive overview of the SPS paradigm or the state-of-the-art SPS schemes. This paper intends to bridge this knowledge gap by thoroughly examining the various aspects of SPS crucial for building potent SPS systems.

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