Review—The Development of Wearable Polymer-Based Sensors: Perspectives

Harito, Christian; Utari, Listya; Yuliarto, Brian; Purwanto, Setyo; Zaidi, Syed S.J.; Bavykin, Dmitry V.; Marken, Frank; Walsh, Frank C.

doi:10.31224/osf.io/kjrhq

Cited by 3 publications

(4 citation statements)

References 147 publications

(38 reference statements)

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“…Although the coverage of the scientific literature reported for various humidity-sensing materials appears to be broad in scope, there are limited reports on PANI/biopolymer composites as potential humidity sensors. Various review articles [ 30 , 64 ] indicate that the humidity-sensing performance of PANI-based composites have received good coverage for PANI in combination with metal additives (e.g., Co, Cd, Ag), metal oxides (e.g., TiO 2 , WO 3 , V 2 O 5 ) and metal chalcogenides (e.g., CdS, CdSe). Some of these metals that have been developed as sensors may suffer from limitations due to material cost and the relative availability of materials, in some cases.…”

Section: Discussion and Knowledge Gapsmentioning

confidence: 99%

“…As shown in Table 1 , the most appropriate sensors for general daily life are resistive and capacitive types since they operate at room temperature and allow the measurement of RH with good accuracy. Resistive sensors are practical, cost-effective, and suitable for everyday use, and can be subdivided into two categories: polymer-based sensors and ceramic sensors [ 30 ]. Capacitive sensors possess the ability to measure RH with higher precision, but they are relatively expensive.…”

Section: Introductionmentioning

confidence: 99%

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Polyaniline/Biopolymer Composite Systems for Humidity Sensor Applications: A Review

et al. 2021

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The development of polyaniline (PANI)/biomaterial composites as humidity sensor materials represents an emerging area of advanced materials with promising applications. The increasing attention to biopolymer materials as desiccants for humidity sensor components can be explained by their sustainability and propensity to absorb water. This review represents a literature survey, covering the last decade, which is focused on the interrelationship between the core properties and moisture responsiveness of multicomponent polymer/biomaterial composites. This contribution provides an overview of humidity-sensing materials and the corresponding sensors that emphasize the resistive (impedance) type of PANI devices. The key physicochemical properties that affect moisture sensitivity include the following: swelling, water vapor adsorption capacity, porosity, electrical conductivity, and enthalpies of adsorption and vaporization. Some key features of humidity-sensing materials involve the response time, recovery time, and hysteresis error. This work presents a discussion on various types of humidity-responsive composite materials that contain PANI and biopolymers, such as cellulose, chitosan and structurally related systems, along with a brief overview of carbonaceous and ceramic materials. The effect of additive components, such as polyvinyl alcohol (PVA), for film fabrication and their adsorption properties are also discussed. The mechanisms of hydration and proton transfer, as well as the relationship with conductivity is discussed. The literature survey on hydration reveals that the textural properties (surface area and pore structure) of a material, along with the hydrophile–lipophile balance (HLB) play a crucial role. The role of HLB is important in PANI/biopolymer materials for understanding hydration phenomena and hydrophobic effects. Fundamental aspects of hydration studies that are relevant to humidity sensor materials are reviewed. The experimental design of humidity sensor materials is described, and their relevant physicochemical characterization methods are covered, along with some perspectives on future directions in research on PANI-based humidity sensors.

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Section: Discussion and Knowledge Gapsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Polyaniline/Biopolymer Composite Systems for Humidity Sensor Applications: A Review

et al. 2021

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“…Many sensors can be applied for HAR including accelerometers, gyroscopes, magnetometer, and radio-frequency identification [8]. Because of their robustness, diversity, availability, and wide acceptance among the population, wearable sensors are one of the most common approaches for HAR applications [9].…”

Section: Introductionmentioning

confidence: 99%

Deep Neural Networks for Human Activity Recognition With Wearable Sensors: Leave-One-Subject-Out Cross-Validation for Model Selection

2020

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Human Activity Recognition (HAR) has been attracting significant research attention because of the increasing availability of environmental and wearable sensors for collecting HAR data. In recent years, deep learning approaches have demonstrated a great success due to their ability to model complex systems. However, these models are often evaluated on the same subjects as those used to train the model; thus, the provided accuracy estimates do not pertain to new subjects. Occasionally, one or a few subjects are selected for the evaluation, but such estimates highly depend on the subjects selected for the evaluation. Consequently, this paper examines how well different machine learning architectures make generalizations based on a new subject(s) by using Leave-One-Subject-Out Cross-Validation (LOSOCV). Changing the subject used for the evaluation in each fold of the cross-validation, LOSOCV provides subject-independent estimate of the performance for new subjects. Six feed forward and convolutional neural network (CNN) architectures as well as four pre-processing scenarios have been considered. Results show that CNN architecture with two convolutions and one-dimensional filter accompanied by a sliding window and vector magnitude, generalizes better than other architectures. For the same CNN, the accuracy improves from 85.1% when evaluated with LOSOCV to 99.85% when evaluated with the traditional 10-fold crossvalidation, demonstrating the importance of using LOSOCV for the evaluation.

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“…Due to its stretchability, elasticity, and modulus match with human skin, polydimethylsiloxane (PDMS) is one of the most commonly used polymers in the fabrication of stretch sensors [ 11 , 75 , 81 , 85 , 89 , 100 , 140 , 150 , 151 , 152 , 153 , 154 , 155 , 156 , 157 , 158 , 159 , 160 ]. Lee et al [ 11 ], Yang et al [ 85 ], and Zou et al [ 100 ] have each fabricated a stretch sensor comprised of a thin film that utilizes a PDMS substrate.…”

Section: External Wearable Sensing Technologiesmentioning

confidence: 99%

Fatigue Testing of Wearable Sensing Technologies: Issues and Opportunities

et al. 2021

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Standards for the fatigue testing of wearable sensing technologies are lacking. The majority of published fatigue tests for wearable sensors are performed on proof-of-concept stretch sensors fabricated from a variety of materials. Due to their flexibility and stretchability, polymers are often used in the fabrication of wearable sensors. Other materials, including textiles, carbon nanotubes, graphene, and conductive metals or inks, may be used in conjunction with polymers to fabricate wearable sensors. Depending on the combination of the materials used, the fatigue behaviors of wearable sensors can vary. Additionally, fatigue testing methodologies for the sensors also vary, with most tests focusing only on the low-cycle fatigue (LCF) regime, and few sensors are cycled until failure or runout are achieved. Fatigue life predictions of wearable sensors are also lacking. These issues make direct comparisons of wearable sensors difficult. To facilitate direct comparisons of wearable sensors and to move proof-of-concept sensors from “bench to bedside,” fatigue testing standards should be established. Further, both high-cycle fatigue (HCF) and failure data are needed to determine the appropriateness in the use, modification, development, and validation of fatigue life prediction models and to further the understanding of how cracks initiate and propagate in wearable sensing technologies.

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Review—The Development of Wearable Polymer-Based Sensors: Perspectives

Cited by 3 publications

References 147 publications

Polyaniline/Biopolymer Composite Systems for Humidity Sensor Applications: A Review

Polyaniline/Biopolymer Composite Systems for Humidity Sensor Applications: A Review

Deep Neural Networks for Human Activity Recognition With Wearable Sensors: Leave-One-Subject-Out Cross-Validation for Model Selection

Fatigue Testing of Wearable Sensing Technologies: Issues and Opportunities

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