Energy efficient wearable sensor node for IoT-based fall detection systems

Gia, Tuan Nguyen; Sarker, Victor Kathan; Tcarenko, Igor; Rahmani, Amir M.; Westerlund, Tomi; Liljeberg, Pasi; Tenhunen, Hannu

doi:10.1016/j.micpro.2017.10.014

Cited by 117 publications

(36 citation statements)

References 20 publications

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“…. Power consumption trend of the power saving technique in (Ignjatovic et al, 2012) A brief comparison of the proposed work in this research with the related work mentioned in (Gia et al, 2018), (Lombardo et al, 2012), and (Ignjatovic et al, 2012) is summarized in table 5 Table 5. Comparison between power consumption between proposed system and other systems from literature…”

Section: Testing Resultsmentioning

confidence: 99%

“…After running the developed robot that is equipped with the aforementioned Chips, the total sensor power consumption was registered to be around 660µA, and both controllers consumed 3 mA, which summed up to a total of 3.66 mA, compared to the originally needed 340 mAs, the 13.1mA recorded in the work of (Gia et al, 2018), and the 31.65mA consumed in the research in (Lombardo et al, 2012 This is a strong indication that the system's working time per charge went up from 8 hours and 48 minutes to 34 days (816 hours), while the similar technique that was developed in (Gia et al, 2018) made the system operable for 228 hours (9 days and 12 hours), and 106 hours (4 days and 5 hours) for the controlling system in (Ignjatovic et al, 2012). Figure 3 shows the trend of power consumption of the proposed system, while the trend of the power saving technique mentioned in (Gia et al, 2018) is plotted in figure 4, and the trend for the Wireless Filters Monitoring System (WFMS) of power consumption is shown in figure 5.…”

Section: Testing Resultsmentioning

confidence: 99%

“…Figure 3 shows the trend of power consumption of the proposed system, while the trend of the power saving technique mentioned in (Gia et al, 2018) is plotted in figure 4, and the trend for the Wireless Filters Monitoring System (WFMS) of power consumption is shown in figure 5. .…”

Section: Testing Resultsmentioning

confidence: 99%

“…The authors of (Gia et al, 2018) developed a wearable device in an IoT system, where small chip was embedded inside the smart device to monitor falls for elderly people. While the authors in (Lombardo et al, 2012) focused the attention in power saving on the WiFi adapter in a Wireless Sensor Network, where an ATTINY 2313V chip was installed within an FPGA system to monitor and control energy consumption.…”

Section: The Case Studymentioning

confidence: 99%

“…Elderly Fall detection (Gia et al, 2018) FPGA for WSN (Lombardo et al, 2012) WFMS (Ignjatovic et al, 2012) Proposed System The Proposed algorithm proved to be very efficient and can increase battery life (while keeping same system performance) to up to 98%. Though we believe the comparison might not be fair, given that we are comparing the overall performance of the system (where the WiFi adapter is only one of several parts of the system), yet the comparison numbers were in favor of the developed system of this research.…”

Section: Modementioning

confidence: 99%

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A Power Saving Hybrid Technique for IoT

Al-Hamouz

2018

MAS

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Most IoT applications usually contain a battery inside, which provides good mobility for the system, but also limits lifetime of the application in general. A lot of work concentrated on power saving algorithms for software, and hardware solutions to provide better results. The proposed solution uses a hybrid technique that enables the IoT system to save power by installing a secondary low current microcontroller to put sensors and other devices in sleep mode, and depending on the IoT device's application, it will be awakened by a needed trigger that is tuned according to the application (such as certain sensors readings, or time based trigger). This will provide high system flexibility.

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Section: Testing Resultsmentioning

confidence: 99%

Section: Testing Resultsmentioning

confidence: 99%

Section: Testing Resultsmentioning

confidence: 99%

Section: The Case Studymentioning

confidence: 99%

Section: Modementioning

confidence: 99%

See 3 more Smart Citations

A Power Saving Hybrid Technique for IoT

Al-Hamouz

2018

MAS

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Seamlessly Integrable Optoelectronics for Clinical Grade Wearables

Polat

2021

Adv Materials Technologies

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Health and fitness wearables present viable solutions for public wellbeing by providing remote personal control and clinical intervention through telemedicine networks. Due to their noninvasive and continuous vital sign monitoring, optoelectronic wearables are incorporated in number of studies to identify the onset and progression of the Coronavirus pandemic, and institutions deployed patient surveillance networks based on them. Today's optoelectronic wearables rely on rigid light emitting and sensing technologies that are vulnerable to motion artifacts due to discontinuous skin contact. However, wearable‐based digital health systems require the extraction of clinical grade data for the actionable outcomes. Therefore, optoelectronic technologies that provide clinical grade data through seamless skin integration are in high demand. The seamlessly integrable optoelectronic technologies for wearable based digital health solutions are discussed here. The review of highlighted wearable optoelectronic materials and technologies provides a benchmark, therefore helps to define pathways through the realization of clinical grade wearables rising as strong contenders for the public health strategies.

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Design of an energy‐efficient IoT device with optimized data management in sports person health monitoring application

Qiu

Liu

Muthu³

et al. 2021

Trans Emerging Tel Tech

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The improvements in health habits, technological advances, and the proliferation of healthy living activities have contributed to the comprehensive extension of sports person health assessment. Since the internet of things (IoT) device requires energy‐optimized wearable devices, it has been observed that the demanding factors include energy efficiency factor in Sports Person Health Monitoring wearable device. Hence in this paper, IoT‐based Hierarchical Health Monitoring Model (IoT‐HHMM) is proposed to improve the efficiency factor by minimizing the energy consumption to achieve effective assessment of sports person health monitoring wearables. The complexity of limited resources and usage of energy is optimized by introducing the Optimal Energy‐Efficient Resource Assignment Algorithm. Likewise, a cloud computing technique is implemented using Probabilistic Radial Basis Function Neural Network to ensure effective prediction and classification in healthcare data management, which is considered as a significant factor in wearable IoT devices for Sports Person Health Monitoring. The result indicates that the proposed IoT‐HHMM achieves a high accuracy ratio of 98.4%, a sensitivity ratio of 92.5%, a performance ratio of 96.7% when compared to traditional approaches.

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Energy efficient wearable sensor node for IoT-based fall detection systems

Cited by 117 publications

References 20 publications

A Power Saving Hybrid Technique for IoT

A Power Saving Hybrid Technique for IoT

Seamlessly Integrable Optoelectronics for Clinical Grade Wearables

Design of an energy‐efficient IoT device with optimized data management in sports person health monitoring application

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