Network Topologies, Communication Protocols, and Standards

Espina, Javier; Falck, Thomas; Panousopoulou, Athanasia; Schmitt, L.; Mülhens, Oliver; Yang, Guang‐Zhong

doi:10.1007/978-1-4471-6374-9_5

Cited by 21 publications

(3 citation statements)

References 26 publications

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“…The sensor contained a 3D accelerometer to measure accelerations (AE2 g), a 3D gyroscope to measure angular velocities (AE300 deg/s) and a 3D magnetometer (AE2 Gauss) to measure orientation in the Earth's magnetic field [16]. Sampling frequency was 50 Hz and data was wirelessly transmitted to a nearby PC by means of a proprietary multipoint packetized radio protocol [17].…”

Section: Data Acquisitionmentioning

confidence: 99%

Sensitivity of sensor-based sit-to-stand peak power to the effects of training leg strength, leg power and balance in older adults

et al. 2014

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Increasing leg strength, leg power and overall balance can improve mobility and reduce fall risk. Sensor-based assessment of peak power during the sit-to-stand (STS) transfer may be useful for detecting changes in mobility and fall risk. Therefore, this study investigated whether sensor-based STS peak power and related measures are sensitive to the effects of increasing leg strength, leg power and overall balance in older adults. A further aim was to compare sensitivity between sensor-based STS measures and standard clinical measures of leg strength, leg power, balance, mobility and fall risk, following an exercise-based intervention. To achieve these aims, 26 older adults (age: 70-84 years) participated in an eight-week exercise program aimed at improving leg strength, leg power and balance. Before and after the intervention, performance on normal and fast STS transfers was evaluated with a hybrid motion sensor worn on the hip. In addition, standard clinical tests (isometric quadriceps strength, Timed Up and Go test, Berg Balance Scale) were performed. Standard clinical tests as well as sensor-based measures of peak power, maximal velocity and duration of normal and fast STS showed significant improvements. Sensor-based measurement of peak power, maximal velocity and duration of normal STS demonstrated a higher sensitivity (absolute standardized response mean (SRM): ≥ 0.69) to the effects of training leg strength, leg power and balance than standard clinical measures (absolute SRM: ≤ 0.61). Therefore, the presented sensor-based method appears to be useful for detecting changes in mobility and fall risk.

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Section: Data Acquisitionmentioning

confidence: 99%

Sensitivity of sensor-based sit-to-stand peak power to the effects of training leg strength, leg power and balance in older adults

et al. 2014

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“…Their sensors are tasked with data acquisition, information communication, and decision making with little to no user intervention [118]. Additionally, the nodes of WBS can be implanted, surfacemounted, or even invisible [77]. While surface-mounted WBS are in the form of Smartwatch [122], Fitbit [137], MOTOACTV [4] and Jawbone UP, implanted or embedded WBS have also started making appearances in global markets.…”

Section: Wearable Biometric Systems (Wbs)mentioning

confidence: 99%

A Survey on Modality Characteristics, Performance Evaluation Metrics, and Security for Traditional and Wearable Biometric Systems

2019

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Biometric research is directed increasingly towards Wearable Biometric Systems (WBS) for user authentication and identification. However, prior to engaging in WBS research, how their operational dynamics and design considerations differ from those of Traditional Biometric Systems (TBS) must be understood. While the current literature is cognizant of those differences, there is no effective work that summarizes the factors where TBS and WBS differ, namely, their modality characteristics, performance, security and privacy. To bridge the gap, this paper accordingly reviews and compares the key characteristics of modalities, contrasts the metrics used to evaluate system performance, and highlights the divergence in critical vulnerabilities, attacks and defenses for TBS and WBS. It further discusses how these factors affect the design considerations for WBS, the open challenges and future directions of research in these areas. In doing so, the paper provides a big-picture overview of the important avenues of challenges and potential solutions that researchers entering the field should be aware of. Hence, this survey aims to be a starting point for researchers in comprehending the fundamental differences between TBS and WBS before understanding the core challenges associated with WBS and its design. A. Sundararajan et al. • TBS consider each modality of the user as a separate entity while WBS consider the entire user (along with all of their individual modalities) as one [152] • TBS can be optionally connected to the Internet, while WBS are inherently online, exploiting the principles of Internet of Everything (IoE) [178] • The authentication and identification processes for TBS are static and user-initiated while for WBS are dynamic and autonomous

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“…Furthermore, BSNs [9] must ensure consistency in energy consumption management and allocation to load balance the energy for the small number of sensor nodes. The most widespread network topologies involve Bus, Ring, Mesh, and star [22]. Together Ring and Bus topologies do not qualify to be backbone on a complicated dynamic body area [1].…”

Section: Related Workmentioning

confidence: 99%

Developing a Powerful and Resilient Smart Body Sensor Network through Hypercube Interconnection

Almogren¹

2015

International Journal of Distributed Sensor Networks

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With recent advances in wireless sensor networks and embedded computing technologies, body sensor networks (BSNs) have become practically feasible. BSNs consist of a number of sensor nodes located and deployed over the human body. These sensors continuously gather vital sign data of the body area to be used in various intelligent systems in smart environments. This paper presents an intelligent design of the body sensor network based on virtual hypercube structure backbone termed as Smart BodyNet. The main purpose of the Smart BodyNet is to provide resilience for the BSN operation and reduce power consumption. Various experiments were carried out to show the performance of the Smart BodyNet design as compared to the state-of-the-art approaches.

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Network Topologies, Communication Protocols, and Standards

Cited by 21 publications

References 26 publications

Sensitivity of sensor-based sit-to-stand peak power to the effects of training leg strength, leg power and balance in older adults

Sensitivity of sensor-based sit-to-stand peak power to the effects of training leg strength, leg power and balance in older adults

A Survey on Modality Characteristics, Performance Evaluation Metrics, and Security for Traditional and Wearable Biometric Systems

Developing a Powerful and Resilient Smart Body Sensor Network through Hypercube Interconnection

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