Bio-inspired Topology of Wearable Sensor Fusion for Telemedical Application

Kańtoch, Eliasz; Grochala, Dominik; Kajor, Marcin

doi:10.1007/978-3-319-59063-9_59

Cited by 6 publications

(5 citation statements)

References 10 publications

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“…The power consumption of the proposed HVSMS using a DE algorithm was compared with the current drain of other systems outlined in existing studies [16,17,18,19,20,21,24,37,38,39,40,41,42] on patient vital signs monitoring systems (Figure 10) to confirm the proposed HVSMS. The performance of the current work in terms of current drain was accomplished by relying on the designed system in Section 3 and was confirmed by the implementation prototype (Section 3) and proposed DE algorithm in Section 4, and current consumption modeling (Section 5) and current measurements (Section 6.1).…”

Section: Power Consumption Comparison With Related Workmentioning

confidence: 85%

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Energy-Efficient Elderly Fall Detection System Based on Power Reduction and Wireless Power Transfer

Gharghan

Fakhrulddin

Al-Naji

et al. 2019

Sensors

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Elderly fall detection systems based on wireless body area sensor networks (WBSNs) have increased significantly in medical contexts. The power consumption of such systems is a critical issue influencing the overall practicality of the WBSN. Reducing the power consumption of these networks while maintaining acceptable performance poses a challenge. Several power reduction techniques can be employed to tackle this issue. A human vital signs monitoring system (HVSMS) has been proposed here to measure vital parameters of the elderly, including heart rate and fall detection based on heartbeat and accelerometer sensors, respectively. In addition, the location of elderly people can be determined based on Global Positioning System (GPS) and transmitted with their vital parameters to emergency medical centers (EMCs) via the Global System for Mobile Communications (GSM) network. In this paper, the power consumption of the proposed HVSMS was minimized by merging a data-event (DE) algorithm and an energy-harvesting-technique-based wireless power transfer (WPT). The DE algorithm improved HVSMS power consumption, utilizing the duty cycle of the sleep/wake mode. The WPT successfully charged the HVSMS battery. The results demonstrated that the proposed DE algorithm reduced the current consumption of the HVSMS to 9.35 mA compared to traditional operation at 85.85 mA. Thus, an 89% power saving was achieved based on the DE algorithm and the battery life was extended to 30 days instead of 3 days (traditional operation). In addition, the WPT was able to charge the HVSMS batteries once every 30 days for 10 h, thus eliminating existing restrictions involving the use of wire charging methods. The results indicate that the HVSMS current consumption outperformed existing solutions from previous studies.

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Section: Power Consumption Comparison With Related Workmentioning

confidence: 85%

“…Some studies [38,39,40,41,42] have proposed a power-saving algorithm for VSMS based on a variable rate sampling strategy. This proposed algorithm focused on controlling the hardware component sampling frequency, allowing a reduction in the proposed devices’ power consumption.…”

Section: Related Workmentioning

confidence: 99%

Energy-Efficient Elderly Fall Detection System Based on Power Reduction and Wireless Power Transfer

Gharghan

Fakhrulddin

Al-Naji

et al. 2019

Sensors

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“…The battery life time of the proposed monitoring system using Equation (6) could be extended to 374.855 h instead of 8 h when the traditional operation was used instead. We also compared the power consumption of the proposed monitoring system based on the sleep/wake scheme to the sixteen existing studies that employed different non-contact technologies such as GSM modem, ZigBee, and Bluetooth to communicate the vital signs of the patients [64][65][66][67][68][69][70][71][72][73][74][75][76][77][78][79]. The power consumption of different medical applications dissimilar to the breathing syndromes was considered for comparison because there are no previous studies similar to the current work focused on energy consumption.…”

Section: Results For Power Consumptionmentioning

confidence: 99%

A System for Monitoring Breathing Activity Using an Ultrasonic Radar Detection with Low Power Consumption

Al-Naji

Al-Askery

Gharghan

et al. 2019

JSAN

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Continuous monitoring of breathing activity plays a major role in detecting and classifying a breathing abnormality. This work aims to facilitate detection of abnormal breathing syndromes, including tachypnea, bradypnea, central apnea, and irregular breathing by tracking of thorax movement resulting from respiratory rhythms based on ultrasonic radar detection. This paper proposes a non-contact, non-invasive, low cost, low power consumption, portable, and precise system for simultaneous monitoring of normal and abnormal breathing activity in real-time using an ultrasonic PING sensor and microcontroller PIC18F452. Moreover, the obtained abnormal breathing syndrome is reported to the concerned physician’s mobile telephone through a global system for mobile communication (GSM) modem to handle the case depending on the patient’s emergency condition. In addition, the power consumption of the proposed monitoring system is reduced via a duty cycle using an energy-efficient sleep/wake scheme. Experiments were conducted on 12 participants without any physical contact at different distances of 0.5, 1, 2, and 3 m and the breathing rates measured with the proposed system were then compared with those measured by a piezo respiratory belt transducer. The experimental results illustrate the feasibility of the proposed system to extract breathing rate and detect the related abnormal breathing syndromes with a high degree of agreement, strong correlation coefficient, and low error ratio. The results also showed that the total current consumption of the proposed monitoring system based on the sleep/wake scheme was 6.936 mA compared to 321.75 mA when the traditional operation was used instead. Consequently, this led to a 97.8% of power savings and extended the battery life time from 8 h to approximately 370 h. The proposed monitoring system could be used in both clinical and home settings.

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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%

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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Bio-inspired Topology of Wearable Sensor Fusion for Telemedical Application

Cited by 6 publications

References 10 publications

Energy-Efficient Elderly Fall Detection System Based on Power Reduction and Wireless Power Transfer

Energy-Efficient Elderly Fall Detection System Based on Power Reduction and Wireless Power Transfer

A System for Monitoring Breathing Activity Using an Ultrasonic Radar Detection with Low Power Consumption

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

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