A Wearable Fall Detection System Based on Body Area Networks

Blunda, Luigi La; Gutierrez-Madronal, Lorena; Wagner, Matthias; Medina‐Bulo, Inmaculada

doi:10.1109/access.2020.3032497

Cited by 16 publications

(11 citation statements)

References 26 publications

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“…In addition to the AWGN channel model, the IEEE model CM 3, which targets wearable WBANs and includes multipath fading, is applied as a channel model [28]. For comparison, the computer simulation also evaluates the case where the (11,5) shortened Reed-Solomon (RS) code and the (31,25) RS code are applied to the PHR and PSDU, respectively, as error correction codes with coding rates almost equal to those of the BCH codes used in IEEE 802.15.6. The computer simulator was constructed in MATLAB.…”

Section: Computer Simulation Parametersmentioning

confidence: 99%

“…The system involves wearable, wireless vital sign sensors or medical robots. One type of technology supporting the development of the IoMT system is wireless body area networks (WBANs), which flexibly connect biosensors placed near the surface of the body [ 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 ]. A WBAN is a wireless network formed by connecting small sensors located on the surface, inside, and in the immediate vicinity of the body by wireless communication.…”

Section: Introductionmentioning

confidence: 99%

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Toward Dependable Internet of Medical Things: IEEE 802.15.6 Ultra-Wideband Physical Layer Utilizing Superorthogonal Convolutional Code

Takabayashi

Tanaka

Sakakibara

2022

Sensors

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Wireless body area networks (WBANs) are attracting attention as a very important technology for realizing an Internet of Medical Things (IoMT). IEEE 802.15.6 is well known as one of the international standards for WBANs for the IoMT. This article proposes the combination of the IEEE 802.15.6 ultra-wideband (UWB) physical layer (PHY) with a super orthogonal convolutional code (SOOC) and evaluates its performance as a dependable WBAN. Numerical results show that sufficient dependability cannot be obtained with the error-correcting code specified in IEEE 802.15.6 when applying the single pulse option, while both high energy efficiency and dependability can be obtained by applying an SOCC. In addition, it is confirmed that higher dependability can be obtained by combining an SOCC with a Reed–Solomon (RS) code with a coding rate that is almost the same as the error correction code specified in the standard. Furthermore, the results indicate that high dependability and energy efficiency can be obtained by adjusting the SOCC coding rate and UWB PHY parameters, even in the burst pulse option. The SOCC-applied UWB PHY of this research satisfies the high requirements of the IoMT.

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Section: Computer Simulation Parametersmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Toward Dependable Internet of Medical Things: IEEE 802.15.6 Ultra-Wideband Physical Layer Utilizing Superorthogonal Convolutional Code

Takabayashi

Tanaka

Sakakibara

2022

Sensors

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“…The hardware component consisted of a wearable belt with ECG sensor attached to a Arduino board. The wearable prototype was designed after taking several features in account as explained in [31]. The device should have been able to record the signals continuously and should be adaptable to the experiment protocol which requires a lot of movement including falls.…”

Section: Experimental Setup and Data Collectionmentioning

confidence: 99%

“…The readings of a 3-lead ECG are comparable to a 12-lead ECG in our experimental set up. The design of the wearable device is discussed in detail in [31].…”

Section: Experimental Setup and Data Collectionmentioning

confidence: 99%

Fall Detection from Electrocardiogram (ECG) Signals and Classification by Deep Transfer Learning

et al. 2021

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Fall is a prominent issue due to its severe consequences both physically and mentally. Fall detection and prevention is a critical area of research because it can help elderly people to depend less on caregivers and allow them to live and move more independently. Using electrocardiograms (ECG) signals independently for fall detection and activity classification is a novel approach used in this paper. An algorithm has been proposed which uses pre-trained convolutional neural networks AlexNet and GoogLeNet as a classifier between the fall and no fall scenarios using electrocardiogram signals. The ECGs for both falling and no falling cases were obtained as part of the study using eight volunteers. The signals are pre-processed using an elliptical filter for signal noises such as baseline wander and power-line interface. As feature extractors, frequency-time representations (scalograms) were obtained by applying a continuous wavelet transform on the filtered ECG signals. These scalograms were used as inputs to the neural network and a significant validation accuracy of 98.08% was achieved in the first model. The trained model is able to distinguish ECGs with a fall activity from an ECG with a no fall activity with an accuracy of 98.02%. For the verification of the robustness of the proposed algorithm, our experimental dataset was augmented by adding two different publicly available datasets to it. The second model can classify fall, daily activities and no activities with an accuracy of 98.44%. These models were developed by transfer learning from the domain of real images to the medical images. In comparison to traditional deep learning approaches, the transfer learning not only avoids “reinventing the wheel,” but also presents a lightweight solution to otherwise computationally heavy problems.

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“…For example, sensor nodes for measuring blood oxygen saturation and heart rate, designed based on the near-infrared dynamic spectrum method for WBANs, were proposed in [11]. The authors of [12] designed a prototype to facilitate reliable fall detection and to classify several fall types and human activities. In [13], a trust-based communication scheme to ensure the reliability and privacy of WBANs was proposed for remote patient monitoring.…”

Section: Introductionmentioning

confidence: 99%

Toward an Advanced Human Monitoring System Based on a Smart Body Area Network for Industry Use

2021

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This research provides a study on a smart body area network (SmartBAN) physical layer (PHY), as an of the Internet of medical things (IoMT) technology, for an advanced human monitoring system in industrial use. The SmartBAN provides a new PHY and a medium access control (MAC) layer, improving its performance and providing very low-latency emergency information transmission with low energy consumption compared with other wireless body area network (WBAN) standards. On the other hand, IoMT applications are expected to become more advanced with smarter wearable devices, such as augmented reality-based human monitoring and work support in a factory. Therefore, it is possible to develop more advanced human monitoring systems for industrial use by combining the SmartBAN with multimedia devices. However, the SmartBAN PHY is not designed to transmit multimedia information such as audio and video. To address this issue, multilevel phase shift keying (PSK) modulation is applied to the SmartBAN PHY, and the symbol rate is improved by setting the roll-off rate appropriately to realize the system. The numerical results show that a sufficient link budget, receiver sensitivity and fade margin were obtained even when those approaches were applied to the SmartBAN PHY. The results indicate that these techniques are required for high-quality audio or video transmission, as well as vital sign data transmission, in a SmartBAN.

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A Wearable Fall Detection System Based on Body Area Networks

Cited by 16 publications

References 26 publications

Toward Dependable Internet of Medical Things: IEEE 802.15.6 Ultra-Wideband Physical Layer Utilizing Superorthogonal Convolutional Code

Toward Dependable Internet of Medical Things: IEEE 802.15.6 Ultra-Wideband Physical Layer Utilizing Superorthogonal Convolutional Code

Fall Detection from Electrocardiogram (ECG) Signals and Classification by Deep Transfer Learning

Toward an Advanced Human Monitoring System Based on a Smart Body Area Network for Industry Use

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