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Wearable Chemical Sensors: Present Challenges and Future Prospects
Abstract
Wearable sensors have received considerable interest over the past decade owing to their tremendous promise for monitoring the wearers’ health, fitness, and their surroundings. However, only limited attention has been directed at developing wearable chemical sensors that offer more comprehensive information about a wearer’s well-being. The development of wearable chemical sensors faces multiple challenges on various fronts. This perspective reviews key challenges and technological gaps impeding the successful realization of effective wearable chemical sensor systems, related to materials, power, analytical procedure, communication, data acquisition, processing, and security. Size, rigidity, and operational requirements of present chemical sensors are incompatible with wearable technology. Sensor stability and on-body sensor surface regeneration constitute key analytical challenges. Similarly, present wearable power sources are incapable of meeting the requirements for wearable electronics owing to their low energy densities and slow recharging. Several energy-harvesting methodologies have inherent issues, including inconsistent power supply and limited stability. There are also major challenges pertaining to handling and securing the big data generated by wearable sensors. These include achieving high data transfer rates and efficient data mining. Efforts must also be made toward developing next generation cryptologic algorithms for ensuring data security and user privacy. The challenges facing the field of wearable chemical sensors, and wearable sensors, in general, can thus be addressed only by a multidisciplinary approach where researchers from diverse fields work in unison. The article discusses these challenges and their potential solutions along with future prospects.
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“Wearable biosensors face many fundamental challenges and technological gaps related to the scope, validation, stability and accuracy, along with power (Box 1)1,42,157-167, communication (Box 2)1,168 and security/privacy issues (Box 3)1,169-172”
From Wearable biosensors for healthcare monitoring (2019) · cites this paper as [1]
In the citing paper
“Researchers have focused on a combination approaches that involve the development of both the power sources and the design of more energy-efficient devices and adaptive algorithms to decrease energy demands.”
From Wearable biosensors for healthcare monitoring (2019) · cites this paper as [1]
In the citing paper
“The emergence of IC fabrication strategies, flexible electronics, device design, and e-garments has revolutionized the development of soft electronics and biological and chemical sensor technologies toward advancing sports medicine.123–127”
From Wearable sensors for monitoring the physiological and biochemical profile of the athlete (2019) · cites this paper as [123]
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Metadata
- Authors
- Bandodkar, A. J., Jeerapan, I., & Wang, J.
- Affiliations
- University of California San Diego (US)
- Corresponding author
- Wang, Joseph
- Venue
- ACS Sensors
- Publisher
- American Chemical Society (ACS)
- Published
- 2016-05-11
- Volume
- 1
- Issue
- 5
- Pages
- 464-482
- ISSN
- 2379-3694 (ISSN-L)
- OpenAlex
- W2346517120
- Semantic Scholar
- 0a2ab0ac9bd399ade5d2539455300284ffb7c989
- Cited by
- 778
- References
- 249
- Citations by year
- 2026: 54 · 2025: 63 · 2024: 62 · 2023: 66 · 2022: 66 · 2021: 104 · 2020: 106 · 2019: 112 · 2018: 73 · 2017: 56 · 2016: 16
- FWCI
- 41.29
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- ACS Sensors (publishedVersion)
- Topics
- Physical Sciences › Engineering › Biomedical Engineering › Advanced Sensor and Energy Harvesting Materials
- Physical Sciences › Chemistry › Analytical Chemistry › Electrochemical sensors and biosensors
- Physical Sciences › Chemistry › Analytical Chemistry › Analytical Chemistry and Sensors
- Keywords
- wearable sensors, health monitoring, sensor materials, wearable power sources, energy harvesting, sensor stability, data security, user privacy, data acquisition and processing, data rate
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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