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End-to-end design of wearable sensors
Open accessAbstract
Wearable devices provide an alternative pathway to clinical diagnostics by exploiting various physical, chemical and biological sensors to mine physiological (biophysical and/or biochemical) information in real time (preferably, continuously) and in a non-invasive or minimally invasive manner. These sensors can be worn in the form of glasses, jewellery, face masks, wristwatches, fitness bands, tattoo-like devices, bandages or other patches, and textiles. Wearables such as smartwatches have already proved their capability for the early detection and monitoring of the progression and treatment of various diseases, such as COVID-19 and Parkinson disease, through biophysical signals. Next-generation wearable sensors that enable the multimodal and/or multiplexed measurement of physical parameters and biochemical markers in real time and continuously could be a transformative technology for diagnostics, allowing for high-resolution and time-resolved historical recording of the health status of an individual. In this Review, we examine the building blocks of such wearable sensors, including the substrate materials, sensing mechanisms, power modules and decision-making units, by reflecting on the recent developments in the materials, engineering and data science of these components. Finally, we synthesize current trends in the field to provide predictions for the future trajectory of wearable sensors.
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“However, traditional sensors typically rely on materials like silicon, metals, or metal oxides, which often exhibit slow response times and limited sensitivity, particularly when detecting lowconcentration gases or weak signals, requiring longer response times or stronger external signals [4,5].”
From Design Strategies and Emerging Applications of Perovskite-Based Sensors (2025) · cites this paper as [4]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
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“Additionally, traditional sensors often face issues such as high energy consumption and large size, which are particularly disadvantageous in modern devices that require portability and low energy consumption [4].”
From Design Strategies and Emerging Applications of Perovskite-Based Sensors (2025) · cites this paper as [4]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
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“Multimodal fusion is a key driver of deep learning's advantage over traditional machine learning 123,126-128 .”
From Recent advances in intelligent wearable systems: from multiscale biomechanical features towards human motion intent prediction (2026) · cites this paper as [126]
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In the citing paper
“Therefore, integrating complementary data from different sensors is extremely important for improving prediction accuracy, which makes multimodal fusion technology highly valued in HMIP research 123,126-128 .”
From Recent advances in intelligent wearable systems: from multiscale biomechanical features towards human motion intent prediction (2026) · cites this paper as [126]
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Metadata
- Authors
- Ates, H. C., Nguyen, P. Q., Gonzalez-Macia, L., Morales-Narváez, E., Güder, F., Collins, J. J., & Dincer, C.
- Affiliations
- University of Freiburg (DE)
- Harvard University (US)
- Imperial College London (GB)
- Broad Institute (US)
- Corresponding author
- Collins, James J.
- Dincer, Can
- ORCID
- Venue
- Nature Reviews Materials
- Published
- 2022-07-22
- Volume
- 7
- Issue
- 11
- Pages
- 887-907
- ISSN
- 2058-8437 (ISSN-L)
- PMC
- PMC9306444
- OpenAlex
- W4286560842
- Cited by
- 1,306
- References
- 285
- Citations by year
- 2026: 325 · 2025: 347 · 2024: 378 · 2023: 243 · 2022: 13
- FWCI
- 21.85
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Nature Reviews Materials (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Bill and Melinda Gates Foundation (OPP1212574)
- Deutsche Forschungsgemeinschaft (404478562, 446617142)
- Consejo Nacional de Ciencia y Tecnología (376135, 312271)
- Hansjörg Wyss Institute for Biologically Inspired Engineering, Harvard University
- Topics
- Physical Sciences › Engineering › Biomedical Engineering › Advanced Sensor and Energy Harvesting Materials
- Physical Sciences › Engineering › Biomedical Engineering › Non-Invasive Vital Sign Monitoring
- Physical Sciences › Chemistry › Analytical Chemistry › Electrochemical sensors and biosensors
- Keywords
- wearable sensors, wearable devices, physiological monitoring, biochemical markers, substrate materials, sensing mechanism, power modules, decision making units, smartwatches, non-invasive diagnosis, multimodal sensing, multiplexed measurement, health monitoring, COVID-19 detection, Parkinson's disease
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