Content/Context-aware Multiple Camera Selection and Video Adaptation for the Support of m-Health Services

Moretti, Simone; Cicalo, Sergio; Mazzotti, Matteo; Tralli, V.; Chiani, Marco

doi:10.1016/j.procs.2014.12.028

Cited by 8 publications

(6 citation statements)

References 13 publications

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“…A study focused on providing healthcare services and treatment while travelling vehicles on the fly [164]. Another study presented the existence of first-aid services in an ambulance [165]. Moreover, another study introduced a new eldercare service that connects the clinic of a remote physical therapist and the home of an elderly [166].…”

Section: Server Based (Tier 3)mentioning

confidence: 99%

Real-Time Remote-Health Monitoring Systems: a Review on Patients Prioritisation for Multiple-Chronic Diseases, Taxonomy Analysis, Concerns and Solution Procedure

et al. 2019

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Remotely monitoring a patient's condition is a serious issue and must be addressed. Remote health monitoring systems (RHMS) in telemedicine refers to resources, strategies, methods and installations that enable doctors or other medical professionals to work remotely to consult, diagnose and treat patients. The goal of RHMS is to provide timely medical services at remote areas through telecommunication technologies. Through major advancements in technology, particularly in wireless networking, cloud computing and data storage, RHMS is becoming a feasible aspect of modern medicine. RHMS for the prioritisation of patients with multiple chronic diseases (MCDs) plays an important role in sustainably providing high-quality healthcare services. Further investigations are required to highlight the limitations of the prioritisation of patients with MCDs over a telemedicine environment. This study introduces a comprehensive and inclusive review on the prioritisation of patients with MCDs in telemedicine applications. Furthermore, it presents the challenges and open issues regarding patient prioritisation in telemedicine. The findings of this study are as follows: (1) The limitations and problems of existing patients' prioritisation with MCDs are presented and emphasised. (2) Based on the analysis of the academic literature, an accurate solution for remote prioritisation in a large scale of patients with MCDs was not presented. (3) There is an essential need to produce a new multiple-criteria decisionmaking theory to address the current problems in the prioritisation of patients with MCDs.

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Section: Server Based (Tier 3)mentioning

confidence: 99%

Real-Time Remote-Health Monitoring Systems: a Review on Patients Prioritisation for Multiple-Chronic Diseases, Taxonomy Analysis, Concerns and Solution Procedure

et al. 2019

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“…Furthermore, [57] provided healthcare services on-the-fly through vehicles in the case of travelling patients. First-aid operations and services that can be provided while the patient is in an ambulance were developed in [58]. Finally, [59] proposed a novel telehealth elderly healthcare service that connects remote physical therapists to seniors at home by providing verbal, auditory and visual cues to support correct exercise movements.…”

Section: B Tiermentioning

confidence: 99%

Fault-Tolerant mHealth Framework in the Context of IoT-Based Real-Time Wearable Health Data Sensors

et al. 2019

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The emerging technology breakthrough of the Internet of Things (IoT) is expected to offer promising solutions for indoor/outdoor healthcare, which may contribute significantly to human health and well-being. In this paper, we investigated the technologies of healthcare service applications in telemedicine architecture. We aimed to resolve a series of healthcare problems on the frequent failures in telemedicine architecture through IoT solutions, particularly the failures of wearable body sensors (Tier 1) and a medical center server (Tier 3). For improved generalisability, we demonstrated an effective research approach, the fault-tolerant framework on mHealth or the so-called FTF-mHealth-IoT in the context of IoT, to resolve essential problems in current investigations on healthcare services. First, we propose a risk local triage algorithm known as the risk-level localization triage (RLLT), which can exclude the control process of patient triage from the medical center by using mHealth and can warn about failures related to wearable sensors. RLLT performs this initial step towards detecting a patient's emergency case and then identifying the healthcare service package of the risk-level. Second, according to the risk-level package, our framework can aid decision makers in hospital selection through multi-criteria decision making (MCDM). Accordingly, mHealth can connect directly with the servers of distributed hospitals to ascertain available healthcare services for the risk-level package in those hospitals. The time of arrival of the patient at the hospital (TAH) is considered for each hospital to reach a final decision and select the appropriate institution in case of medical center failure. This paper used two datasets. The first dataset involved 572 patients with chronic heart disease. Their triage levels were evaluated using our RLLT algorithm. The second dataset included hospital healthcare services with two levels of availability within distributed hospitals to show variety when testing the proposed framework. The former dataset is an actual dataset of services collected from 12 hospitals located in the capital Baghdad, which represents the maximum level of availability. The latter is an assumption dataset of the services within the 12 hospitals located in the capital Kuala Lumpur, which represents the minimum level of availability. Subsequently, the hospitals were prioritized using a unique MCDM method for estimating small power consumption, namely, the analytic hierarchy process (AHP), based on a crossover between the ''healthcare services package/TAH'' of each hospital and the ''hospital list''. The results showed that the AHP is effective for solving hospital selection problems within mHealth. The implications of this study support the patients, organizations, and medical staff in a modern lifestyle.INDEX TERMS Telemedicine, Internet of thing, healthcare, triage, hospital selection, medical centre failure.

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“…In [5] a camera ranking algorithm, called Camera Ranking for Static Scenes (CRSS), has been proposed for low motion scenes. CRSS is based on the amount of pixels required to represent a unit area (1 square meter) at the target distance.…”

Section: Camera Ranking For Dynamic Scenes (Crds)mentioning

confidence: 99%

“…In this regard, wireless camera networks require careful design and implementation of efficient radio resource allocation and node scheduling policies. The objective is to select, in each moment, the best camera subset that is able to satisfy some specific criteria: in literature this is defined as camera selection for large camera networks [1], [2], [5]. Several camera ranking proposals are based on geometrical considerations: the placement and orientation of the nodes are taken into account to define camera selection cost metrics.…”

Section: Introductionmentioning

confidence: 99%

Video selection for visual sensor networks: A motion-based ranking algorithm

Moretti

Mazzotti

Chiani

2016

2016 24th European Signal Processing Conference (EUSIPCO)

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A Visual Sensor Network (VSN) is composed by several cameras, in general with different characteristics and orientations, which are used to cover a certain Area of Interest (AoI). To provide an optimal and autonomous exploitation of the VSN video streams, suitable algorithms are needed for selecting the cameras capable to guarantee the best video quality for the specific AoI in the scene. In this work, a novel content and context-aware camera ranking algorithm is proposed, with the goal to maximize the Quality of Experience (QoE) to the final user. The proposed algorithm takes into account the pose, camera resolution and frame rate, and the quantity of motion in the scene. Subjective tests are performed to compare the ranking of the algorithm with human ranking. Finally, the proposed ranking algorithm is compared with common objective video quality metrics and a previous ranking algorithm, confirming the validity of the approach.

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Content/Context-aware Multiple Camera Selection and Video Adaptation for the Support of m-Health Services

Cited by 8 publications

References 13 publications

Real-Time Remote-Health Monitoring Systems: a Review on Patients Prioritisation for Multiple-Chronic Diseases, Taxonomy Analysis, Concerns and Solution Procedure

Real-Time Remote-Health Monitoring Systems: a Review on Patients Prioritisation for Multiple-Chronic Diseases, Taxonomy Analysis, Concerns and Solution Procedure

Fault-Tolerant mHealth Framework in the Context of IoT-Based Real-Time Wearable Health Data Sensors

Video selection for visual sensor networks: A motion-based ranking algorithm

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