UWB Platform for Vital Signs Detection and Monitoring

Čuljak, Ivana; Mihaldinec, Hrvoje; Kovačić, Zdravko; Cifrek, Mario; Džapo, Hrvoje

doi:10.1007/978-981-10-9023-3_7

Cited by 9 publications

(11 citation statements)

References 9 publications

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“…In contrast to employing these purposely built devices, researchers have also focused on how Decawave's low cost, off-the-shelf UWB radios can be utilized for these applications, as also done in this work. For example, the authors in [35] demonstrated heart rate monitoring by analyzing the CIR between a DWM1000 radio placed on the chest and another placed on the back of a person. Passive localization using Decawave's radios was investigated in [36].…”

Section: Related Workmentioning

confidence: 99%

A Multi-Static Radar Network with Ultra-Wideband Radio-Equipped Devices

Ledergerber

D’Andrea

2020

Sensors

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A growing number of devices, from car key fobs to mobile phones to WiFi-routers, are equipped with ultra-wideband radios. In the network formed by these devices, communicating modules often estimate the channel impulse response to employ a matched filter to decode transmitted data or to accurately time stamp incoming messages when estimating the time-of-flight for localization. This paper investigates how such measurements of the channel impulse response can be utilized to augment existing ultra-wideband communication and localization networks to a multi-static radar network. The approach is experimentally evaluated using off-the-shelf hardware and simple, distributed filtering, and shows that a tag-free human walking in the space equipped with ultra-wideband modules can be tracked in real time. This opens the door for various location-based smart home applications, ranging from smart audio and light systems to elderly monitoring and security systems.

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Section: Related Workmentioning

confidence: 99%

A Multi-Static Radar Network with Ultra-Wideband Radio-Equipped Devices

Ledergerber

D’Andrea

2020

Sensors

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“…The working principle of the existing radar-based approaches is based on the electromagnetic energy propagated towards and through the body and its reflection on border surfaces between tissues with different wave propagation properties [ 20 , 21 , 22 , 24 , 32 , 47 , 73 , 74 , 75 , 76 , 77 , 78 , 79 , 80 , 81 , 82 , 83 , 84 ]. Lately, another approach based on the transversal propagation method proved to be an alternative viable solution for using UWB in vital signs monitoring applications [ 85 , 86 , 87 ]. It is based on the assumption that heart and lung motions are continuously changing the communication channel properties and modulating the observed signal power on the receiver side [ 85 ].…”

Section: State Of the Art: Emerging Technologies For The Wbs Applimentioning

confidence: 99%

Wireless Body Sensor Communication Systems Based on UWB and IBC Technologies: State-of-the-Art and Open Challenges

Čuljak

Vasić

Mihaldinec

et al. 2020

Sensors

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In recent years there has been an increasing need for miniature, low-cost, commercially accessible, and user-friendly sensor solutions for wireless body area networks (WBAN), which has led to the adoption of new physical communication interfaces providing distinctive advantages over traditional wireless technologies. Ultra-wideband (UWB) and intrabody communication (IBC) have been the subject of intensive research in recent years due to their promising characteristics as means for short-range, low-power, and low-data-rate wireless interfaces for interconnection of various sensors and devices placed on, inside, or in the close vicinity of the human body. The need for safe and standardized solutions has resulted in the development of two relevant standards, IEEE 802.15.4 (for UWB) and IEEE 802.15.6 (for UWB and IBC), respectively. This paper presents an in-depth overview of recent studies and advances in the field of application of UWB and IBC technologies for wireless body sensor communication systems.

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“…OpenCV [18] is an open-source computer vision and machine learning library. It is a common infrastructure in computer vision with more than 2500 optimized algorithms and lots of built-in functions.…”

Section: The Methods and Proposed Systemmentioning

confidence: 99%

“…A single 128 × 128 grayscale image shrinks the feature vector element size from 49,152 to 16,384, which is three times less than the color image. The cv2.COLORBRG2GRAY function from OpenCV [18] transformed the color image to grayscale. We inserted the numerical values of gray images into NumPy [22] arrays.…”

Section: The Methods and Proposed Systemmentioning

confidence: 99%