Biosignal monitoring using wearables: Observations and opportunities

Athavale, Yashodhan; Krishnan, Sridhar

doi:10.1016/j.bspc.2017.03.011

Cited by 90 publications

(96 citation statements)

References 9 publications

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“…In both cases no proof of feasibility can be found. Nonetheless, scholarly literature has already developed frameworks/taxonomies and aims to give an overview of the topic of WSHT [16]- [21]. Despite their different foci, many of these reviews call for testing the accuracy or effectiveness in connection with real users.…”

Section: Introductionmentioning

confidence: 99%

Transforming Personal Healthcare through Technology - A Systematic Literature Review of Wearable Sensors for Medical Application

Witte¹,

Zarnekow²

2019

Proceedings of the Annual Hawaii International Conference on System Sciences

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Wearable Sensor Health Technology (WSHT) captures, analyzes and aggregates physiological data to improve personal well-being. Recently the technology market is flooded with wearable sensors that measure health-related data and have a high user adoption. Nevertheless, these devices are almost exclusively used for fitness purposes and the healthcare sector still faces the challenge of constantly increasing costs. To respond to the necessary but rare use of WSHT in professional healthcare, we aim to identify the most promising areas for future medical implementation. Therefore, we performed a systematic literature search and reviewed 97 papers with regard to disease treatment, application area, vital parameter measurement and target patient. As a result, we could identify five potential areas for further research: (RA1) concentration on widespread diseases, (RA2) expansion of WSHT's functionality, (RA3) diversity of vital parameter measurements, (RA4) proactive analysis of sensor data for preventive purposes and (RA5) promoting patient adoption through enhanced usability.

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Section: Introductionmentioning

confidence: 99%

Transforming Personal Healthcare through Technology - A Systematic Literature Review of Wearable Sensors for Medical Application

Witte¹,

Zarnekow²

2019

Proceedings of the Annual Hawaii International Conference on System Sciences

View full text Add to dashboard Cite

show abstract

“…Wearables are devices embedded with accelerometers, gryoscopes, light and pressure sensors, for capturing and analyzing streaming physiological data from an individual during daily activity. Unlike smart phones or tablets, these devices can be comfortably worn on different body regions throughout the day, and can be used for various applications such as fitness monitoring, behavior tracking and vital signs analysis for critical disorders such as stroke, falls or seizures [ 6 ].…”

Section: Introductionmentioning

confidence: 99%

“…From our prior survey [ 6 ], we found that many currently available wearables such as Apple Watch TM and FitBit TM have embedded sensors for collecting and analyzing basic human activity parameters such as step counts, pulse rate, temperature and sleep times for fitness awareness. We also investigated into their respective SDKs (software development kits), which described how physiological data is collected, analyzed and shared with service providers for decision generation.…”

Section: Introductionmentioning

confidence: 99%

“…As per a survey [ 8 ], considering that only about 90 out of 600 currently available wearables are being used for medical applications, we can see a clear potential for their usage in long-term, home-based health monitoring applications. Even though these numbers present a promising future for wearable-based health monitoring solutions, our review indicates that there still exist some crucial hurdles before implementing health monitoring devices and applications in real-time [ 6 ]. These include: Focusing on developing physiological signal analysis algorithms which promote edge computing approaches [ 4 , 5 , 6 , 9 ].…”

Section: Introductionmentioning

confidence: 99%

“…Even though these numbers present a promising future for wearable-based health monitoring solutions, our review indicates that there still exist some crucial hurdles before implementing health monitoring devices and applications in real-time [ 6 ]. These include: Focusing on developing physiological signal analysis algorithms which promote edge computing approaches [ 4 , 5 , 6 , 9 ]. That is, the data acquisition, compression and analysis must be done at the device level without having the need to transmit long, streaming data to cloud services.…”

Section: Introductionmentioning

confidence: 99%

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A Device-Independent Efficient Actigraphy Signal-Encoding System for Applications in Monitoring Daily Human Activities and Health

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2018

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Actigraphs for personalized health and fitness monitoring is a trending niche market and fit aptly in the Internet of Medical Things (IoMT) paradigm. Conventionally, actigraphy is acquired and digitized using standard low pass filtering and quantization techniques. High sampling frequencies and quantization resolution of various actigraphs can lead to memory leakage and unwanted battery usage. Our systematic investigation on different types of actigraphy signals yields that lower levels of quantization are sufficient for acquiring and storing vital movement information while ensuring an increase in SNR, higher space savings, and in faster time. The objective of this study is to propose a low-level signal encoding method which could improve data acquisition and storage in actigraphs, as well as enhance signal clarity for pattern classification. To further verify this study, we have used a machine learning approach which suggests that signal encoding also improves pattern recognition accuracy. Our experiments indicate that signal encoding at the source results in an increase in SNR (signal-to-noise ratio) by at least 50–90%, coupled with a bit rate reduction by 50–80%, and an overall space savings in the range of 68–92%, depending on the type of actigraph and application used in our study. Consistent improvements by lowering the quantization factor also indicates that a 3-bit encoding of actigraphy data retains most prominent movement information, and also results in an increase of the pattern recognition accuracy by at least 10%.

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This review offers an illuminating journey through the historical evolution and modern‐day applications of liquid metals, presenting a comprehensive view of their significance in diverse fields. Tracing the trajectory from mercury applications to contemporary innovations, the paper explores their pivotal role in industry and research. The analysis spans electrical switches, mechanical applications, electrodes, chemical synthesis, energy storage, thermal transport, electronics, and biomedicine. Each section examines the intricacies of liquid metal integration, elucidating their contributions to technological advancements and societal progress. Moreover, the review critically appraises the challenges and prospects inherent in liquid metal applications, addressing issues of recycling, corrosion management, device stability, economic feasibility, translational hurdles, and market dynamics. By delving into these complexities, the paper advances scholarly understanding and offers actionable insights for researchers, engineers, and policymakers. It aims to catalyze innovation, foster interdisciplinary collaboration, and promote liquid metal‐enabled solutions for societal needs. Through its comprehensive analysis and forward‐looking perspective, this review serves as a guide for navigating the landscape of liquid metal applications, bridging historical legacies with contemporary challenges, and highlighting the transformative potential of liquid metals in shaping future technologies.

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Biosignal monitoring using wearables: Observations and opportunities

Cited by 90 publications

References 9 publications

Transforming Personal Healthcare through Technology - A Systematic Literature Review of Wearable Sensors for Medical Application

Transforming Personal Healthcare through Technology - A Systematic Literature Review of Wearable Sensors for Medical Application

A Device-Independent Efficient Actigraphy Signal-Encoding System for Applications in Monitoring Daily Human Activities and Health

The Liquid Metal Age: A Transition From Hg to Ga

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