PlaIMoS: A Remote Mobile Healthcare Platform to Monitor Cardiovascular and Respiratory Variables

Miramontes, Ramses; Aquino, Raúl; Flores, Arturo; Rodríguez, Guillermo; Anguiano, Rafael; Ríos, Arturo; Edwards, Arthur

doi:10.3390/s17010176

Cited by 35 publications

(24 citation statements)

References 18 publications

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“…The changing of patient location from one place to another can provide a future opportunity to continue the current study to further understand and capture the relevant challenges of the patient's location, especially in practical applications. [68],fall detection [115],human activities [87],fall detection [116],cardiovascular and respiratory [50],fall detection [90],fall detection [117],human activities [118],fall detection [119],human activities [24],fall detection [71],patient temperature [120],human activities [121],elderly living Proposed S-BFDA Estimated battery life (h) Figure 27. Comparison between battery life of the proposed S-BFDA and previous system.…”

Section: Discussionmentioning

confidence: 99%

“…The previous works are plotted on the x-axis, and the estimated battery life of the sensor node is plotted on the y-axis, as shown in Figure 27. For a fair comparison between the proposed FDS and previous works [24,50,68,71,87,90,[115][116][117][118][119][120][121], the battery life of the proposed systems in the previous works was recalculated based on 1000 mAh. Most of these works are similar to our proposed system because they use different fall detection algorithms to monitor elderly fall events or activities.…”

Section: Power Consumption Comparisonmentioning

confidence: 99%

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Accurate Fall Detection and Localization for Elderly People Based on Neural Network and Energy-Efficient Wireless Sensor Network

et al. 2018

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Falls are the main source of injury for elderly patients with epilepsy and Parkinson's disease. Elderly people who carry battery powered health monitoring systems can move unhindered from one place to another according to their activities, thus improving their quality of life. This paper aims to detect when an elderly individual falls and to provide accurate location of the incident while the individual is moving in indoor environments such as in houses, medical health care centers, and hospitals. Fall detection is accurately determined based on a proposed sensor-based fall detection algorithm, whereas the localization of the elderly person is determined based on an artificial neural network (ANN). In addition, the power consumption of the fall detection system (FDS) is minimized based on a data-driven algorithm. Results show that an elderly fall can be detected with accuracy levels of 100% and 92.5% for line-of-sight (LOS) and non-line-of-sight (NLOS) environments, respectively. In addition, elderly indoor localization error is improved with a mean absolute error of 0.0094 and 0.0454 m for LOS and NLOS, respectively, after the application of the ANN optimization technique. Moreover, the battery life of the FDS is improved relative to conventional implementation due to reduced computational effort. The proposed FDS outperforms existing systems in terms of fall detection accuracy, localization errors, and power consumption.Energies 2018, 11, 2866 2 of 32 to detect emergency conditions and enable caregivers to respond efficiently. A fall is one of the key factors that can lead to injuries and decrease quality of life, at times resulting in the death of elderly persons. People's rate of falling increases with their age. Falls occur frequently in medical health care centers, hospitals, or houses, with approximately 30% of falls causing injury. Falls in hospitals occur in the rooms of the patients (84%) and during transfer from one place to another (19%). Furthermore, the majority of falls occur in areas adjacent to chairs and beds [2]. Most people who experience falls need special care in a nursing home or hospital, thereby restricting their life activities. The hazard issues of fall or slight fall, especially of the elderly, can be aggravated by chronic diseases, such as osteoporosis, delirium, and dementia [3]. The degree of danger from a fall for aging persons is frequently decided by the location of the fall, time of fall detection, duration and time of transfer and rescue services. Therefore, automatic detection of elderly people's falls along with the locations of the incident is important so that medical rescue staff can be dispatched immediately and so that the family of the elderly can be informed about the incident through a specific wireless network or mobile telephone.The development of microelectromechanical technologies allows the integration of different sensors, and a wireless network is commonly used. Wireless sensor networks (WSNs) comprise a number of tiny and small sensor nodes which are deployed ...

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Section: Discussionmentioning

confidence: 99%

Section: Power Consumption Comparisonmentioning

confidence: 99%

Accurate Fall Detection and Localization for Elderly People Based on Neural Network and Energy-Efficient Wireless Sensor Network

et al. 2018

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“…This has the benefit of taking into consideration the indirectly of all propagation features in the channel through the actual area measurements. While, the related research works evaluate both outdoor and indoor environments based on ZigBee wireless technology [2], [23][24][25][26][27], these works did not truthfully fit to our application (i.e. medical application).…”

Section: Path Loss Model Based On Lnsmmentioning

confidence: 99%

Path-loss modelling for WSN deployment in indoor and outdoor environments for medical applications

Fakhri¹,

Gharghan²,

Mohammed³

2018

IJET

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Wireless sensor networks (WSNs) and their applications have received significantly interested in the last few years. In WSN, knowing an accurate path-loss model as well as packet delivery should be taken into account for the successful distribution of several nodes in the network. This paper presents a path-loss modeling and performance evaluation of the ZigBee wireless standard. Received signal strength indicator (RSSI) measurements were achieved in outdoor and indoor environments to derive the path-loss based on Log-Normal Shadowing Model (LNSM). The path-loss parameters such as standard deviation and path loss exponents were estimated over point-to-point ZigBee WSN. In addition, the variances of received RSSI values and standard deviation for these values have been investigated. Furthermore, the data packets received is measured practically. Results revealed that the LNSM can be estimated to reflect the channel losses in both outdoor and indoor environments for medical application. The data delivery was achieved successfully of 100% in outdoor which better than indoor due to multipath propagation and shadowing. Moreover, the data packets delivery of the current work outperformed previous work.

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“…Examples include remote monitoring of the elderly after transfer to a community care setting (164, 165). Sophisticated systems have been developed and trialed for measuring multiple physiological parameters (166), and it could be envisaged that such systems could be adapted and used in high-risk women to identify changes in uterine activity, for example, suggestive of early onset of labor (164). …”

Section: Precision Refinement Using New Technologiesmentioning

confidence: 99%

Applying Precision Public Health to Prevent Preterm Birth

Newnham

Kemp

White

et al. 2017

Front. Public Health

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Preterm birth (PTB) is one of the major health-care challenges of our time. Being born too early is associated with major risks to the child with potential for serious consequences in terms of life-long disability and health-care costs. Discovering how to prevent PTB needs to be one of our greatest priorities. Recent advances have provided hope that a percentage of cases known to be related to risk factors may be amenable to prevention; but the majority of cases remain of unknown cause, and there is little chance of prevention. Applying the principle of precision public health may offer opportunities previously unavailable. Presented in this article are ideas that may improve our abilities in the fields of studying the effects of migration and of populations in transition, public health programs, tobacco control, routine measurement of length of the cervix in mid-pregnancy by ultrasound imaging, prevention of non-medically indicated late PTB, identification of pregnant women for whom treatment of vaginal infection may be of benefit, and screening by genetics and other “omics.” Opening new research in these fields, and viewing these clinical problems through a prism of precision public health, may produce benefits that will affect the lives of large numbers of people.

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PlaIMoS: A Remote Mobile Healthcare Platform to Monitor Cardiovascular and Respiratory Variables

Cited by 35 publications

References 18 publications

Accurate Fall Detection and Localization for Elderly People Based on Neural Network and Energy-Efficient Wireless Sensor Network

Accurate Fall Detection and Localization for Elderly People Based on Neural Network and Energy-Efficient Wireless Sensor Network

Path-loss modelling for WSN deployment in indoor and outdoor environments for medical applications

Applying Precision Public Health to Prevent Preterm Birth

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