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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Traditional sensors typically rely on materials such as silicon, metals, or metal oxides and often exhibit slow response times and limited sensitivity, particularly when detecting low-concentration gases or weak signals.
“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]
The Substrate materials section (page unavailable) describes metals and semiconductors in wearable sensors, and the Sensing unit section identifies silicon-based accelerometers. The Sensing unit section also notes poor signal-to-noise ratio as a limitation for biochemical wearables, but it does not say that sensors made from these materials typically respond slowly or have limited sensitivity to low-concentration gases or weak signals. Its reference to slow development of biochemical sensors is not a claim about sensor response time. (LLM, conf=0.83)
Experimental — accuracy not yet measuredTraditional sensors often face high energy consumption and large size, which are disadvantageous in modern devices that require portability and low energy consumption.
“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]
The Sensing unit section identifies high power consumption and the size of active systems as challenges for optical wearable sensors. The Power unit section also emphasizes miniaturization and energy needs in wearables. The claim extends those specific concerns to traditional sensors generally, which the cited passages do not establish; page numbers are unavailable in our copy. (LLM, conf=0.87)
Experimental — accuracy not yet measuredMultimodal fusion is a key driver of deep learning's advantage over traditional machine learning.
“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]
Integrating complementary data from different sensors is extremely important for improving prediction accuracy, which makes multimodal fusion technology highly valued in HMIP research.
“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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Stiffness Matters: Fine-Tuned Hydrogel Elasticity Alters Chondrogenic Redifferentiation
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Flagged first, then by citations
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“Additionally, traditional sensors often face issues such as high energy consumption and large size, which are particularly dis-advantageous in modern devices that require portability and low energy consumption [4].”
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- 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
- 284
- 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 › Engineering › Electrical and Electronic Engineering › 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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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.
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