Human health monitoring mobile phone application by using the wireless nanosensor based embedded system

Kumar, K. Dinesh

doi:10.1109/icices.2013.6508277

Cited by 6 publications

(3 citation statements)

References 7 publications

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“…Despite some works have already proposed initial analysis on features and capabilities of such a network architecture (see for example contributions presented in [11][12][13]15,44]), more accurate studies need to be done before considering BANNETs a mature technology. In this context, network architecture, communication paradigm, energy harvesting schemes, and protocol stacks are just the first very challenging aspects that the research community is called to investigate.…”

Section: Bannet: When Iont Meets the Nano-medicine Fieldmentioning

confidence: 99%

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On the design of an energy-harvesting protocol stack for Body Area Nano-NETworks

Piro

Boggia

Grieco

2015

Nano Communication Networks

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a b s t r a c tBody Area Nano-NETworks (BANNETs) consist of integrated nano-machines, diffused in the human body for collecting diagnostic information and tuning medical treatments. Endowed with communication capabilities, such nano-metric devices can interact with each other and the external micro/macro world, thus enabling advanced health-care services (e.g., therapeutic, monitoring, sensing, and telemedicine tasks). Due to limited computational and communication capabilities of nano-devices, as well as their scarce energy availability, the design of powerful BANNET systems represents a very challenging research activity for upcoming years. Starting from the most significant and recent findings of the research community, this work provides a further step ahead by proposing a hierarchical network architecture, which integrates a BANNET and a macro-scale healthcare monitoring system and two different energy-harvesting protocol stacks that regulate the communication among nano-devices during the execution of advanced nano-medical applications. The effectiveness of devised solutions and the comparison with the common flooding-based communication technique have been evaluated through computer simulations. Results highlight pros and cons of considered approaches and pave the way for future activities in the Internet of Nano-Things and nano-medical research fields.

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Section: Bannet: When Iont Meets the Nano-medicine Fieldmentioning

confidence: 99%

“…In line with [3,[11][12][13]15,44], the health-care monitoring system considered in this work is composed by a BAN-NET, a set of external monitoring devices, and a remote health-care server (see Fig. 2).…”

Section: System Architecture and Energy-harvesting Protocols For Bannetsmentioning

confidence: 99%

On the design of an energy-harvesting protocol stack for Body Area Nano-NETworks

Piro

Boggia

Grieco

2015

Nano Communication Networks

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“…Ref. [19] designed a human health monitoring mobile phone application by using a wireless nanosensor-based embedded system. In the proposed system, a nanosensor was placed in the mobile phone, and a smartphone application displays the body condition and alerts the patient to the causes of any problems and how to overcome them without the need for physician guidance.…”

mentioning

confidence: 99%

Using Mobiles to Monitor Respiratory Diseases

et al. 2020

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In this work, a mobile application is developed to assist patients suffering from chronic obstructive pulmonary disease (COPD) or Asthma that will reduce the dependency on hospital and clinic based tests and enable users to better manage their disease through increased self-involvement. Due to the pervasiveness of smartphones, it is proposed to make use of their built-in sensors and ever increasing computational capabilities to provide patients with a mobile-based spirometer capable of diagnosing COPD or asthma in a reliable and cost effective manner. Data collected using an experimental setup consisting of an airflow source, an anemometer, and a smartphone is used to develop a mathematical model that relates exhalation frequency to air flow rate. This model allows for the computation of two key parameters known as forced vital capacity (FVC) and forced expiratory volume in one second (FEV1) that are used in the diagnosis of respiratory diseases. The developed platform has been validated using data collected from 25 subjects with various conditions. Results show that an excellent match is achieved between the FVC and FEV1 values computed using a clinical spirometer and those returned by the model embedded in the mobile application.

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