Stimuli-Responsive Smart Materials for Wearable Technology in Healthcare: History
View Latest Version
Please note this is an old version of this entry, which may differ significantly from the current revision.
Contributor:
Valentina Trovato
,
Silvia Sfameni
,
Giulia Rando
,
Giuseppe Rosace
,
Sebania Libertino
,
Ada Ferri
,
Maria Rosaria Plutino

Thanks to the Internet of Things (IoT), the demand for the development of miniaturized and wearable sensors has skyrocketed. Among them, novel sensors for wearable medical devices are mostly needed. Wearable sensors can monitor physiological parameters in a non-invasive way, thus strongly reducing but not fully avoiding any reactions. With the goal of smart health monitoring, nanosized sol–gel precursors, bringing coupling agents into their chemical structure, were used to modify halochromic dyestuffs, both minimizing leaching from the treated surfaces and increasing photostability for the development of stimuli-responsive sensors.

  • wearable sensors
  • pH sensors
  • sol–gel technique
  • health monitoring

1. Introduction

The world’s aging populations are putting national healthcare systems under pressure due to rising hospitalization costs. On the other hand, the current pandemic has induced several countries to promote home-care programs that allow the elderly and patients with chronic conditions to be monitored remotely and are user and environmentally “friendly”, thus making periodic checks more pleasant and timely and providing clinical interventions only when their medical conditions require. For this purpose, in recent years, large investments have been made in both the research and industrial fields to provide innovative wearable devices and technologies able to monitor health conditions [1][2] and/or environmental parameters [3][4][5], resulting in the emergence of an important market segment. In fitness and wellness, the discrete and unobtrusive real-time monitoring of specific physiological, biomedical, and biomechanical parameters of athletes is of great interest in order to improve training compliance and/or competition performance [6]. In the same way, in the medical field, real-time and high-throughput devices for monitoring patients’ physiological parameters are even more desirable [7]. Unfortunately, the continuous monitoring of chronic medical conditions using implantable devices has significant drawbacks, such as the biodegradation of the sensing element and the patient’s immune system response to the host, e.g., changing diffusional barriers [8]. Wearable sensors can monitor physiological parameters in a non-invasive way, thus strongly reducing but not fully avoiding any reactions. They have attracted increasing interest and have been employed in many biomedical applications [9][10]. Moreover, they could potentially monitor both the wearer’s and the environmental parameters, thus acting as an intermediate between the body and surrounding conditions.
The main obstacle to the availability of the full technology, i.e., wearable systems and sensors for continuous health monitoring, is the lack of sensors [11][12]. The research is now focused on this issue since wearable body sensor networks (BSN) are an emerging medical sensor technology for achieving the unobtrusive monitoring of vital parameters in real time.
To minimize system failures when using such sensors, the sensing head must be in contact with the skin surface, and a fixing material (such as an elastic bandage or a tightly fitted undergarment) is used to keep it in place at the skin surface to avoid misplacement and background noise. Unfortunately, the use of a fixing material at the skin surface can cause measurement errors [13]. Another approach for in vivo and remote monitoring is the use of wireless micro-devices for signal transmission [14][15]. They are non-degradable and the electronics must be coated with biocompatible materials. Non-degradable micro-devices must be removed after the monitoring period and are not recommended for the post-operation control of physiological parameters, but rather are more indicated in chronic or long-term disease monitoring. Wearable electronics must be miniaturized, low-powered, and made of biocompatible materials in order to minimize the impact on the daily activities of the wearer. Through the integration of novel technologies, flexible support, such as textiles, can be equipped with information and power transmission capabilities and sensing functions as an infrastructure for embedded wearable microsystems [16][17][18].
Of paramount importance is the monitoring of the elderly and people with chronic conditions participating in “aging in place” programs. Wearable sensors have been used to monitor the recovery of patients after abdominal surgery [19]. The first step to achieving continuous monitoring in everyday life is correctly identifying the activities of daily living (ADL), i.e., walking, sitting, standing, etc. Many approaches have been followed mainly using accelerometers [20], for example, for step counting in patients with Parkinson’s disease [21]. An “in-shoe” pressure and acceleration sensor system [22] was developed to classify an arm movement coupled to the above-mentioned activities.
Several research projects have suggested that the continuous monitoring of ADL activities increases exercise compliance in populations at risk. As an example, the continuous monitoring of physical activities in obese individuals stimulates an active lifestyle, thus reducing clinical interventions [23][24].
Another use of the long-term monitoring of physiological data is for the diagnosis and treatment of cardiovascular diseases. Typical commercial technologies include the long-term monitoring of heart rate, oxygen saturation, blood pressure, body temperature, respiratory rate, and galvanic skin response, but some of these technologies are quite unpleasant. For this reason, clinical studies are currently validating wearable sensor platforms with the aim of improving the clinical management of patients with congestive heart failure [25]. For instance, the MIT Media Laboratory developed LiveNet, a system that measures 3D acceleration, electrocardiogram (ECG), electromyogram (EMG), and galvanic skin conductance for monitoring Parkinson’s symptoms and detecting epileptic seizures [26]. A custom data logger, “LifeGuard”, was developed to monitor the health status of individuals in extreme environments (space and terrestrial) [27].
The system has already been validated in hostile environments with good results. A wearable system to monitor brain activity using a non-invasive approach with fNIRS (functional near-infrared spectroscopy) has been recently developed [28][29] and patented [30] under the EU commission-funded project ASTONISH. The project AMON, again funded by the EU, developed a wrist-worn device capable of monitoring ECG, blood pressure, blood oxygen saturation, and skin temperature to monitor patients with cardio-respiratory problems [31]. Other projects worth mentioning that received grants from the European Commission are [32] MyHeart, WEALTHY, and MagIC. These projects were based on the idea of developing garments with wearable sensors for the health monitoring of people in home and community settings.
Wearable sensing systems can be broadly classified into two main categories: electronic devices fixed in various ways onto the fabric and chemical sensors fully integrated into the textile itself. The second category, certainly challenging but intriguing, allows for the production of a “smart” textile thanks to the full integration of the sensing system. The simplest approach is fabricating colorimetric sensors that cause a detectable color change in the cloth. This is achieved using halochromic dyes and they have been used for employees working with chemicals; the color change immediately alerts the workers of a potentially harmful chemical leak without requiring power sources [33][34][35][36]. Their main drawbacks are the relatively low stability and pH sensitivity, which are quite important at high gas concentrations. To overcome the last limit, most textile sensors have been fabricated with a large surface area, i.e., through electrospinning, thus improving the pH response [37][38][39]. For example, Agarwal et al. [40] developed universal pH sensing nanofibrous sensors using various halochromic dyes with Nylon 6; Pakolpakçıl et al. [41] used natural halochromic dye with sodium alginate and a polyvinyl alcohol mixture to formulate pH-indicating nanofibrous sensors; Guinovart et al. fabricated an electropolymerized polyaniline (PANi)-conducting polymer for the production of a bandage-based wearable potentiometric sensor for monitoring wounds’ pH [42]; Kassal et al. developed a wireless RFID-based smart bandage for the optical determination of pH by embedding covalently modified cellulose particles with a pH indicator dye in a biocompatible hydrogel [43]; Getmeyer et al. [44] detected gaseous NH3 and HCl with nanofibrous sensors fabricated using electrospinning and sol–gel methods; and Jeevarathinam et al. [45] and Suleymanov et al. [46] used an aggregation-induced emission of dyestuff.
To commercialize smart textiles, the main areas that need to be addressed are service life, washability, productivity, and production costs while maintaining a high detection performance with respect to liquid or gaseous alkalis and acids.

2. Flexible Electronics

Over the past ten years, the Internet of Things (IoT) has seen exponential growth as markets recognize the true potential of real-time data acquisition for a range of applications in entertainment, knowledge dissemination, defense, the environment, and healthcare [47][48]. Since the real-time monitoring of physiologically relevant indicators is essential not only in urgent hospital settings but also during regular daily activities, the medical applications of the IoT have attracted the most attention [49]. Such ongoing essential information can notify the user of health problems so they can take preventative measures and avoid life-threatening medical conditions.
Wearable sensors could become a key component of the IoT in healthcare since they offer new ways to monitor people continuously and give the wearer individualized access to crucial physiological information about their health [50]. Due to the advancement of flexible electronics, wearable sensors are no longer just confined to on-body applications but may also be integrated with other surfaces, such as those of buildings or vehicles, for far wider applications. Flexible electronics have been theoretically possible for many years. In theory, anything long or thin can stretch to become flexible. Although flexible cables and wiring are the best examples, it was not until the space race that silicon wafers used in satellite solar cells were thinned to increase their power-to-weight ratio, allowing for some warping. This concept permitted the first flexible solar cells in the 1960s [51]. The development of conductive polymers [52], organic semiconductors, and amorphous silicon [51][53] led to significant advancements in flexibility and processability over the ensuing decades, and as a result, they were used as a base for electronic devices in applications that required properties such as bending, rolling, folding, and stretching that could not be met by conventional electronics [54].
New materials and fabrication methods that enable the direct production of high-performance, scalable electronic devices on flexible substrates are currently of significant interest. This interest now includes qualities such as stretchability and the capacity to heal, which can be attained by using elastomeric substrates with strong molecular connections. [55][56]. In a similar vein, biocompatibility and biodegradability have been made possible by polymers that have no negative effects on the body and can degrade into smaller constituent parts after use [57][58][59]. Devices that can conform to dynamic, complex surfaces, such as those found in biological systems and soft robotics that are inspired by nature, are now possible thanks to recent advancements.
New applications for these next-generation flexible electronics include flexible lighting and display technologies for consumer electronics, architecture, and textiles; wearables with sensors that track people’s habits and health; implantable electronics for better medical imaging and diagnostics; and enhancing the functionality of robots and unmanned aircraft with light-weight and conformable energy-harvesting devices and sensor technologies.
Depending on the context, flexibility could mean several things. From the folding, twisting, stretching, and deforming needed for devices in electronic skin to bending and rolling, enabling better handling of large-area photovoltaics while maintaining device performance and dependability. Although there has been some early progress and significant discoveries, the field of flexible electronics still faces numerous obstacles before becoming a part of everyday life. This offers a tremendous opportunity for scientific research and development to gain significantly and quickly more insights and progresses into the field of wearable sensors.
Despite the advantages of wearable sensors, research and development in this area have advanced unevenly. Early research efforts concentrated on developing wearable sensors that can measure temperature, body motion, or ECG. Current wearable devices frequently track the user’s physical activities and vital indicators (such as heart rate) [41][60]. However, in order to fully understand a wearer’s health, performance, or stress at the molecular level, continuous chemical parameter monitoring is essential. In light of the importance of wearable sensors, various groups have recently discussed significant advancements in this area of research [61].
Thus, devices that can potentially monitor wound healing [62], electrolytes [63], metabolites [64], heavy metals [65], and toxic gases [66] directly on the body in various biofluids, such as sweat [67], tears [68], and saliva [69] have been demonstrated.
Electronic textiles cover a wide range of textile items that can incorporate electronics, including filaments and fibers. They differ from conventional electronic approaches in that they are physically flexible and have a characteristic size [70]. Electronic fabrics’ unique qualities make them easily adaptable to the sensing needs of wearable chemosensors. Scientists have created a variety of methods and materials for the design and production of smart textiles with a range of features and functions over the past ten years. These electronic textiles are constructed using a variety of different processes, including weaving, sewing, and embroidery.
Wang et al. [71] have developed a wearable, flexible, and stretchable glove-based electrochemical biosensor for the detection of organophosphorus chemicals, presenting the first design for performing fingertip enzymatic assays. The sampling and biosensing processes are carried out by the glove-based sensor using different fingers; the enzyme is fixed to the index finger and the thumb is used to collect residue. An enzyme-immobilized biosensing detection finger, a sampling finger, and wireless real-time data transmission to a smartphone are all included in this adaptable, wearable “lab-on-a-glove”.
Chemical coatings for creating electronic fabrics have recently been developed (e-textiles) [72] and have undergone intensive research to create the next generation of wearable electronics. These electronics have exciting potential applications in portable military equipment, medical monitoring gadgets, and smart fabrics with built-in electronics. In order to achieve this, the deposition of carbon nanotubes (CNTs) onto textile fabrics has been researched. CNTs are ideal for smart textiles due to their exceptional aspect ratio, incredible structural flexibility, high mechanical qualities, and excellent thermal and electrical conductivity [73]. Using photoplethysmography (PPG), a popular method for gathering key physiological data, such as heart rate, respiration cycles, and blood oxygen saturation, the potential use of CNT-treated cotton fabrics as conductive materials for signal transmission has been investigated [74].

3. Wearable Sensors

Physiological measurements of interest in rehabilitation and health monitoring include blood pressure, blood oxygen saturation, heart and respiratory rates, and muscle activity. The aim is to provide an indication of health status that can also be useful to formulate a diagnosis. The growth of the wearable technology field allows for performing continuous monitoring of physiological parameters at home, whereas before, this was possible only in a hospital setting.
The main issue to address is the sensor stability and response accuracy over time. Chemical sensors need to be in contact with bio-fluids to operate, thus exposing the device to biofouling, oxidation, or chemical changes. In this perspective, optical and electrical sensors are considered more robust, especially if they are fabricated using proven materials. The sensing device must be comfortable for the user, hence many approaches to producing robust and “comfortable” devices have been proposed. As an example, a “silicon flexible skin” obtained using silicon islands integrated with boron-doped strain gauges and metal pads was proposed by Katragadda et al. [75]. This approach would allow exploiting the mature silicon technologies fully.
The natural evolution of such an approach brought by flexible electronics has experienced tremendous growth thanks to the recent advances in printable materials and techniques [76]. In fact, silicon ICs, which can be used for real-time data processing, wireless communication, and display visualization can be integrated into platforms. Thanks to these new products that are more easily integrated into textiles and with the progressive miniaturization of Si-based devices, large-scale setups designed for clinical or laboratory use are becoming portable. The integration of optical sensing systems with consumer electronic goods is already a fact; a good example is wrist-mounted wearables. They can measure heart rate, heart rate variability, and blood oxygenation [77]. Among the physiological parameters, heart rate is one of the most often measured, with healthy individuals having a heart rate of 60–100 beats per min (at ≈21 mmHg pulse pressure) [78]. Heart rate can be measured by PPG-based technologies [79][80], sound-based techniques [81], etc.
Wearable PPG sensors are commercially available (e.g., Apple Watch, Fitbit, and Samsung Gear) and instantaneously monitor heart rate. They are mainly formed by a light-emitting diode (that can be easily miniaturized [82]) and a photodetector. The physical principle is based on the optical properties of the tissues and blood. Most of human biological tissues transmit, reflect, and scatter, according to the Beer–Lambert law, visible and near-infrared (NIR) light [83].
One of the main drawbacks of these devices is signal distortion due to motion artifacts, which can be more challenging if application during physical activity is envisioned. To overcome this problem, short wavelengths (400–600 nm) are quite often selected for LEDs used for PPG measurements. Such light does not penetrate the tissue deeply, hence does not reveal much cardiac activity and blood vessel information, but the measurements are less affected by artifacts due to movement thanks to the short light path.
On the other hand, information on blood vessel status and heart activity can be obtained only using long wavelengths since their penetration in the tissues is deeper. Recently, Lee and coworkers [84] showed a strong reduction of motion artifacts by combining multiple LED wavelengths (visible and IR) coupled with an algorithm for signal processing.
By implementing a more complex system, functional near-infrared spectroscopy (fNIRS) can be made wearable. The physical principle behind this technique is the same as PPG. It is known that hemoglobin in its two states, oxygenated (O2Hb) and deoxygenated (HHb), is the main chromophore absorbing light in the NIR spectral range with different spectra [85]. These characteristics allow the monitoring of the backscattered NIR light to determine the blood oxygenation value [86].
By performing a continuous analysis, the back-scattered photons provide information related to the dynamical changes of O2Hb and HHb concentrations in the blood (both hemodynamic and metabolic). If applied to the brain, fNIRS can provide insights into neural activity [87]. Measurements are performed by irradiating the scalp at two different wavelengths in the range of 700–950 nm (one above and one below 800 nm) and collecting the light in a photomultiplier tube. Optical fibers are used both to irradiate the scalp surface and collect the back-scattered light [88].
Silicon photomultipliers (SiPM) [89][90][91], which are proposed as optical detectors in many sensing systems [92][93][94][95], and miniaturized LEDs are combined in a potentially portable system [96][97][98][99] operating in continuous waves. For each cycle, each detector collects the signal coming from the LEDs placed on the scalp (each LED is switched to a frequency of 20 Hz). The system performs measurements of the O2Hb and HHb concentrations in the brain region defined by the LED-SiPM position, and the relative distances between the emitter and the receiver regulate the penetration depth. In a recent patent [30], the same authors proposed the use of an optimized circuit and algorithm for optimal system operation to be used in diffused optical tomography (Figure 1).
Molecules 27 05709 g003 550
Figure 1. Details of SiPMs (top left and bottom right) and LEDs (top right and bottom left) for diffused optical tomography/functional near-infrared spectroscopy (DOT/fNIRS).

4. Smart Material Applications in Healthcare Technology

Table 1 summarizes the different categories of chromogenic sensing systems and smart materials described in the previous paragraphs, thus emphasizing the characteristics and recent applications in healthcare of chromogenic wearable sensors.
Table 1. Smart materials together with the main characteristics and some recent examples of their application in healthcare technology.
Smart Material Characteristics Applications Refs.
Chromogenic Reversible, reusable, solid-state applications, stimuli-responsive, color-change detection to the naked eye Eye patch biosensors for biomarkers in human tears, glucose monitoring, sweat monitoring [100][101][102]
Photochromic Color-changing capacity when exposed to light and sunlight (IR and UV radiations) can alter their optical characteristics; high temperatures can accelerate the material decomposition Fluorimetric multi-sensing of sweat biomarkers, UV indicators, temperature, and sweat pH sensing [103][104][105]
Thermochromic Different color states at different temperatures, versatile Human movement monitoring (strain), body temperature [106][107][108]
Electrochromic Sensitive to redox reactions, they have to feature a fast response to injection and ejection processes, coloration efficiency, high contrast level, specified life cycle, and write–erase efficiency Skin temperature and wrist movement, alarm system for smart contact lenses, glucose sensing [109][110][111]
Ionochromic Color-changing ability by inducing ionic species in an ionic state, versatile, selective, different commercial applications pH monitoring, acid gas sensing, ammonia gas detection [112][113]
Mechanochromic Optical-changing properties when subjected to mechanical stimuli, low pressure-responsive ability, versatile Volatile organic compound (VOC) detection, subtle and large human motion sensing [114][115]
Solvatochromic Display different colors depending on the solvent in which they are dissolved, versatile, highly sensitive Lactate sensing, sweat analysis [116][117]
Biochromic Changes color through biochemical or hydrolysis reactions upon exposure to a biological stimulus, can be exploited for various biological potential applications Sweat sensing, glucose detection, in vitro perspiration monitoring [118][119][120]
In light of the versatility and sensitivity of these systems for sensor devices, in the next paragraphs, thorough attention is devoted to the employment of halochromic materials in wearable and healthcare applications.

This entry is adapted from the peer-reviewed paper 10.3390/molecules27175709

References

  1. Chen, S.; Qi, J.; Fan, S.; Qiao, Z.; Yeo, J.C.; Lim, C.T. Flexible Wearable Sensors for Cardiovascular Health Monitoring. Adv. Healthc. Mater. 2021, 10, 2100116.
  2. Adeniyi, E.A.; Ogundokun, R.O.; Awotunde, J.B. IoMT-Based Wearable Body Sensors Network Healthcare Monitoring System. In IoT in Healthcare and Ambient Assisted Living; Marques, G., Bhoi, A.K., de Albuquerque, V.H.C., Hareesha, K.S., Eds.; Studies in Computational Intelligence; Springer: Singapore, 2021; Volume 933, pp. 103–121. ISBN 978-981-15-9897-5.
  3. Helbig, C.; Ueberham, M.; Becker, A.M.; Marquart, H.; Schlink, U. Wearable Sensors for Human Environmental Exposure in Urban Settings. Curr. Pollut. Rep. 2021, 7, 417–433.
  4. Rebelo, P.; Costa-Rama, E.; Seguro, I.; Pacheco, J.G.; Nouws, H.P.A.; Cordeiro, M.N.D.S.; Delerue-Matos, C. Molecularly imprinted polymer-based electrochemical sensors for environmental analysis. Biosens. Bioelectron. 2021, 172, 112719.
  5. Lee, G.; Wei, Q.; Zhu, Y. Emerging Wearable Sensors for Plant Health Monitoring. Adv. Funct. Mater. 2021, 31, 2106475.
  6. Farrokhi, A.; Farahbakhsh, R.; Rezazadeh, J.; Minerva, R. Application of Internet of Things and artificial intelligence for smart fitness: A survey. Comput. Netw. 2021, 189, 107859.
  7. Khoshmanesh, F.; Thurgood, P.; Pirogova, E.; Nahavandi, S.; Baratchi, S. Wearable sensors: At the frontier of personalised health monitoring, smart prosthetics and assistive technologies. Biosens. Bioelectron. 2021, 176, 112946.
  8. Kotanen, C.N.; Moussy, F.G.; Carrara, S.; Guiseppi-Elie, A. Implantable enzyme amperometric biosensors. Biosens. Bioelectron. 2012, 35, 14–26.
  9. Yang, Y.-L.; Chuang, M.-C.; Lou, S.-L.; Wang, J. Thick-film textile-based amperometric sensors and biosensors. Analyst 2010, 135, 1230–1234.
  10. Yao, S.; Swetha, P.; Zhu, Y. Nanomaterial-Enabled Wearable Sensors for Healthcare. Adv. Healthc. Mater. 2018, 7, 1700889.
  11. Pinna, G.D.; Maestri, R.; Mortara, A.; Johnson, P.; Andrews, D.; Ponikowski, P.; Witkowski, T.; La Rovere, M.T.; Sleight, P. Long-term time-course of nocturnal breathing disorders in heart failure patients. Eur. Respir. J. 2010, 35, 361–367.
  12. Ledet, E.H.; D’Lima, D.; Westerhoff, P.; Szivek, J.A.; Wachs, R.A.; Bergmann, G. Implantable sensor technology: From research to clinical practice. J. Am. Acad. Orthop. Surg. 2012, 20, 383–392.
  13. Deng, Z.-S.; Liu, J. Effect of fixing material on skin-contact temperature measurement by wearable sensor. In Proceedings of the 2008 5th International Summer School and Symposium on Medical Devices and Biosensors, Hong Kong, China, 1–3 June 2008; pp. 137–140.
  14. Fu, X.; Chen, W.; Ye, S.; Tu, Y.; Tang, Y.; Li, D.; Chen, H.; Jiang, K. A Wireless Implantable Sensor Network System for In Vivo Monitoring of Physiological Signals. IEEE Trans. Inf. Technol. Biomed. 2011, 15, 577–584.
  15. Kassal, P.; Steinberg, M.D.; Steinberg, I.M. Wireless chemical sensors and biosensors: A review. Sens. Actuators B Chem. 2018, 266, 228–245.
  16. Lam Po Tang, S.; Stylios, G.K. An overview of smart technologies for clothing design and engineering. Int. J. Cloth. Sci. Technol. 2006, 18, 108–128.
  17. Tröster, G. The Agenda of Wearable Healthcare. IMIA Yearb. Med. Inform. 2005, 14, 125–138.
  18. Kim, J.; Campbell, A.S.; de Ávila, B.E.-F.; Wang, J. Wearable biosensors for healthcare monitoring. Nat. Biotechnol. 2019, 37, 389–406.
  19. Aziz, O.; Atallah, L.; Lo, B.; Elhelw, M.; Wang, L.; Yang, G.Z.; Darzi, A. A pervasive body sensor network for measuring postoperative recovery at home. Surg. Innov. 2007, 14, 83–90.
  20. Mathie, M.J.; Coster, A.C.; Lovell, N.H.; Celler, B.G.; Lord, S.R.; Tiedemann, A. A pilot study of long-term monitoring of human movements in the home using accelerometry. J. Telemed. Telecare 2004, 10, 144–151.
  21. Giansanti, D.; Maccioni, G.; Morelli, S. An experience of health technology assessment in new models of care for subjects with Parkinson’s disease by means of a new wearable device. Telemed. J. E Health 2008, 14, 467–472.
  22. Sazonov, E.S.; Fulk, G.; Sazonova, N.; Schuckers, S. Automatic recognition of postures and activities in stroke patients. Conf. Proc. IEEE Eng. Med. Biol. Soc. 2009, 2009, 2200–2203.
  23. Amft, O.; Troster, G. Recognition of dietary activity events using on-body sensors. Artif. Intell. Med. 2008, 42, 121–136.
  24. Benedetti, M.G.; Di Gioia, A.; Conti, L.; Berti, L.; Esposti, L.D.; Tarrini, G.; Melchionda, N.; Giannini, S. Physical activity monitoring in obese people in the real life environment. J. Neuroeng. Rehabil. 2009, 6, 47.
  25. Merilahti, J.; Pärkkä, J.; Antila, K.; Paavilainen, P.; Mattila, E.; Malm, E.J.; Saarinen, A.; Korhonen, I. Compliance and technical feasibility of long-term health monitoring with wearable and ambient technologies. J. Telemed. Telecare 2009, 15, 302–309.
  26. Sung, M.; Marci, C.; Pentland, A. Wearable feedback systems for rehabilitation. J. Neuroeng. Rehabil. 2005, 2, 17.
  27. Mundt, C.W.; Montgomery, K.N.; Udoh, U.E.; Barker, V.N.; Thonier, G.C.; Tellier, A.M.; Ricks, R.D.; Darling, R.B.; Cagle, Y.D.; Cabrol, N.A.; et al. A multiparameter wearable physiologic monitoring system for space and terrestrial applications. IEEE Trans. Inf. Technol. Biomed. 2005, 9, 382–391.
  28. Chiarelli, A.M.; Libertino, S.; Zappasodi, F.; Mazzillo, M.; Di Pompeo, F.; Merla, A.; Lombardo, S.; Fallica, G. Characterization of a fiberless, multichannel optical probe for continuous-wave functional near-infrared spectroscopy based on silicon photomultipliers detectors: In-vivo assessment of primary sensorimotor response. Neurophotonics 2017, 4, 035002.
  29. Maira, G.; Chiarelli, A.M.; Brafa, S.; Libertino, S.; Fallica, G.; Merla, A.; Lombardo, S. Imaging System Based on Silicon Photomultipliers and Light Emitting Diodes for Functional Near-Infrared Spectroscopy. Appl. Sci. 2020, 10, 1068.
  30. Lombardo, S.A.; Maira, G.A.; Libertino, S.; Merla, A.; Chiarelli, A.M. Circuit, Procedure, and Algorithm to Operate SiPM Photodetectors in Optimal Conditions for fNIRS / DOT Systems. Italian Patent n. 10201900016424, 16 September 2019.
  31. Anliker, U.; Ward, J.A.; Lukowicz, P.; Troster, G.; Dolveck, F.; Baer, M.; Keita, F.; Schenker, E.B.; Catarsi, F.; Coluccini, L.; et al. AMON: A wearable multiparameter medical monitoring and alert system. IEEE Trans. Inf. Technol. Biomed. 2004, 8, 415–427.
  32. Patel, S.; Park, H.; Bonato, P.; Chan, L.; Rodgers, M. A Review of Wearable Sensors and Systems with Application in Rehabilitation. J. Neuro Eng. Rehabil. 2012, 9, 21.
  33. Park, Y.K.; Oh, H.J.; Bae, J.H.; Lim, J.Y.; Lee, H.D.; Hong, S.I.; Son, H.S.; Kim, J.H.; Lim, S.J.; Lee, W. Colorimetric Textile Sensor for the Simultaneous Detection of NH3 and HCl Gases. Polymers 2020, 12, 2595.
  34. Van der Schueren, L.; De Clerck, K. Coloration and application of pH-sensitive dyes on textile materials. Color. Technol. 2012, 128, 82–90.
  35. Owyeung, R.E.; Panzer, M.J.; Sonkusale, S.R. Colorimetric gas sensing washable threads for smart textiles. Sci. Rep. 2019, 9, 5607.
  36. Schoolaert, E.; Hoogenboom, R.; De Clerck, K. Colorimetric nanofibers as optical sensors. Adv. Funct. Mater. 2017, 27, 1702646.
  37. Qin, M.; Guo, H.; Dai, Z.; Yam, X.; Ning, X. Advances in flexible and wearable pH sensors for wound healing monitoring. J. Semicond. 2019, 40, 111607.
  38. Kim, S.H.; Bae, J.S. Halochromic chemosensor prepared by pyran-based nanofibers. Fibers Polym. 2013, 14, 1981–1984.
  39. Dargaville, T.R.; Farrugia, B.L.; Broadbent, J.A.; Pace, S.; Upton, Z.; Voelcker, N.H. Sensors and imaging for wound healing: A review. Biosens. Bioelectron. 2013, 41, 30–42.
  40. Agarwal, A.; Raheja, A.; Natarajan, T.S.; Chandra, T.S. Development of universal pH sensing electrospun nanofibers. Sens. Actuators B Chem. 2012, 161, 1097–1101.
  41. Pakolpakçıl, A.E.; Karaca, B.B. Investigation of a natural pH-indicator dye for nanofibrous wound dressings. In Proceedings of the IOP Conference Series: Materials Science and Engineering, AUTEX, Istanbul, Turkey, 20–22 June 2018; IOP Publishing: Bristol, UK, 2018; Volume 460, p. 012020.
  42. Guinovart, T.; Valdés-Ramírez, G.; Windmiller, J.R.; Andrade, F.J.; Wang, J. Bandage-Based Wearable Potentiometric Sensor for Monitoring Wound pH. Electroanalysis 2014, 26, 1345–1353.
  43. Kassal, P.; Zubak, M.; Scheipl, G.; Mohr, G.J.; Steinberg, M.D.; Murković Steinberg, I. Smart bandage with wireless connectivity for optical monitoring of pH. Sens. Actuators B Chem. 2017, 246, 455–460.
  44. Geltmeyer, J.; Vancoillie, G.; Steyaert, I.; Breyne, B.; Cousins, G.; Lava, K.; Hoogenboom, R.; De Buysser, K.; De Clerck, K. Dye modification of nanofibrous silicon oxide membranes for colorimetric HCl and NH3 sensing. Adv. Funct. Mater. 2016, 26, 5987–5996.
  45. Jeevarathinam, A.S.; Varathan, E.; Ravindran, E.; Somanathan, N.; Subramanian, V.; Mandal, A.B.; Sudha, J.D.; Ramakrishnan, R. A solution processable fluorene–fluorenone oligomer with aggregation induced emission enhancement. Chem. Commun. 2013, 49, 10742–10744.
  46. Suleymanov, A.A.; Doll, M.; Ruggi, A.; Scopelliti, R.; Fadaei-Tirani, F.; Severin, K. Synthesis of tetraarylethene luminogens by C−H vinylation of aromatic compounds with triazenes. Angew. Chem. Int. Ed. 2020, 59, 9957–9961.
  47. Atzori, L.; Iera, A.; Morabito, G.; Nitti, M. The Social Internet of Things (SIoT)—When social networks meet the Internet of Things: Concept, architecture and network characterization. Comput. Netw. 2012, 56, 3594–3608.
  48. Bandodkar, A.J.; Jeerapan, I.; Wang, J. Wearable Chemical Sensors: Present Challenges and Future Prospects. ACS Sens. 2016, 1, 464–482.
  49. Domingo, M.C. An overview of the Internet of Things for people with disabilities. J. Netw. Comput. Appl. 2012, 35, 584–596.
  50. Trung, T.Q.; Lee, N.-E. Flexible and Stretchable Physical Sensor Integrated Platforms for Wearable Human-Activity Monitoringand Personal Healthcare. Adv. Mater. 2016, 28, 4338–4372.
  51. Corzo, D.; Tostado-Blázquez, G.; Baran, D. Flexible electronics: Status, challenges and opportunities. Front. Electron. 2020, 1, 594003.
  52. Shirakawa, H.; Louis, E.J.; MacDiarmid, A.G.; Chiang, C.K.; Heeger, A.J. Synthesis of electrically conducting organic polymers: Halogen derivatives of polyacetylene, (CH). J. Chem. Soc. Chem. Commun. 1977, 16, 578–580.
  53. Chittick, R.C.; Alexander, J.H.; Sterling, H.F. The Preparation and Properties of Amorphous Silicon. J. Electrochem. Soc. 1969, 116, 77.
  54. Wong, W.S.; Salleo, A. Flexible Electronics: Materials and Applications; Springer Science & Business Media: Berlin/Heidelberg, Germany, 2009; Volume 11, ISBN 0387743634.
  55. Oh, J.Y.; Rondeau-Gagné, S.; Chiu, Y.-C.; Chortos, A.; Lissel, F.; Wang, G.-J.N.; Schroeder, B.C.; Kurosawa, T.; Lopez, J.; Katsumata, T.; et al. Intrinsically stretchable and healable semiconducting polymer for organic transistors. Nature 2016, 539, 411–415.
  56. Kang, J.; Son, D.; Wang, G.-J.N.; Liu, Y.; Lopez, J.; Kim, Y.; Oh, J.Y.; Katsumata, T.; Mun, J.; Lee, Y.; et al. Tough and Water-Insensitive Self-Healing Elastomer for Robust Electronic Skin. Adv. Mater. 2018, 30, 1706846.
  57. Bettinger, C.J.; Bao, Z. Biomaterials-based organic electronic devices. Polym. Int. 2010, 59, 563–567.
  58. Irimia-Vladu, M.; Troshin, P.A.; Reisinger, M.; Shmygleva, L.; Kanbur, Y.; Schwabegger, G.; Bodea, M.; Schwödiauer, R.; Mumyatov, A.; Fergus, J.W.; et al. Biocompatible and Biodegradable Materials for Organic Field-Effect Transistors. Adv. Funct. Mater. 2010, 20, 4069–4076.
  59. Liu, H.; Jian, R.; Chen, H.; Tian, X.; Sun, C.; Zhu, J.; Yang, Z.; Sun, J.; Wang, C. Application of Biodegradable and Biocompatible Nanocomposites in Electronics: Current Status and Future Directions. Nanomaterials 2019, 9, 950.
  60. Trung, T.Q.; Ramasundaram, S.; Hwang, B.-U.; Lee, N.-E. An All-Elastomeric Transparent and Stretchable Temperature Sensor for Body-Attachable Wearable Electronics. Adv. Mater. 2016, 28, 502–509.
  61. Kassal, P.; Steinberg, I.M. System Architectures in Wearable Electrochemical Sensors. Electroanalysis 2016, 28, 1149–1169.
  62. Milne, S.D.; Seoudi, I.; Al Hamad, H.; Talal, T.K.; Anoop, A.A.; Allahverdi, N.; Zakaria, Z.; Menzies, R.; Connolly, P. A wearable wound moisture sensor as an indicator for wound dressing change: An observational study of wound moisture and status. Int. Wound J. 2016, 13, 1309–1314.
  63. Bandodkar, A.J.; Molinnus, D.; Mirza, O.; Guinovart, T.; Windmiller, J.R.; Valdés-Ramírez, G.; Andrade, F.J.; Schöning, M.J.; Wang, J. Epidermal tattoo potentiometric sodium sensors with wireless signal transduction for continuous non-invasive sweat monitoring. Biosens. Bioelectron. 2014, 54, 603–609.
  64. Lee, H.; Choi, T.K.; Lee, Y.B.; Cho, H.R.; Ghaffari, R.; Wang, L.; Choi, H.J.; Chung, T.D.; Lu, N.; Hyeon, T.; et al. A graphene-based electrochemical device with thermoresponsive microneedles for diabetes monitoring and therapy. Nat. Nanotechnol. 2016, 11, 566–572.
  65. Lisak, G.; Arnebrant, T.; Ruzgas, T.; Bobacka, J. Textile-based sampling for potentiometric determination of ions. Anal. Chim. Acta 2015, 877, 71–79.
  66. Kim, Y.H.; Kim, S.J.; Kim, Y.-J.; Shim, Y.-S.; Kim, S.Y.; Hong, B.H.; Jang, H.W. Self-Activated Transparent All-Graphene Gas Sensor with Endurance to Humidity and Mechanical Bending. ACS Nano 2015, 9, 10453–10460.
  67. Gao, W.; Emaminejad, S.; Nyein, H.Y.Y.; Challa, S.; Chen, K.; Peck, A.; Fahad, H.M.; Ota, H.; Shiraki, H.; Kiriya, D.; et al. Fully integrated wearable sensor arrays for multiplexed in situ perspiration analysis. Nature 2016, 529, 509–514.
  68. Pankratov, D.; González-Arribas, E.; Blum, Z.; Shleev, S. Tear Based Bioelectronics. Electroanalysis 2016, 28, 1250–1266.
  69. Kim, J.; Imani, S.; de Araujo, W.R.; Warchall, J.; Valdés-Ramírez, G.; Paixão, T.R.L.C.; Mercier, P.P.; Wang, J. Wearable salivary uric acid mouthguard biosensor with integrated wireless electronics. Biosens. Bioelectron. 2015, 74, 1061–1068.
  70. Stoppa, M.; Chiolerio, A. Wearable Electronics and Smart Textiles: A Critical Review. Sensors 2014, 14, 11957–11992.
  71. Mishra, R.K.; Hubble, L.J.; Martín, A.; Kumar, R.; Barfidokht, A.; Kim, J.; Musameh, M.M.; Kyratzis, I.L.; Wang, J. Wearable Flexible and Stretchable Glove Biosensor for On-Site Detection of Organophosphorus Chemical Threats. ACS Sens. 2017, 2, 553–561.
  72. Grancarić, A.M.; Jerković, I.; Koncar, V.; Cochrane, C.; Kelly, F.M.; Soulat, D.; Legrand, X. Conductive polymers for smart textile applications. J. Ind. Text. 2017, 48, 612–642.
  73. Li, L.; Fan, T.; Hu, R.; Liu, Y.; Lu, M. Surface micro-dissolution process for embedding carbon nanotubes on cotton fabric as a conductive textile. Cellulose 2017, 24, 1121–1128.
  74. Trovato, V.; Teblum, E.; Kostikov, Y.; Pedrana, A.; Re, V.; Nessim, G.D.; Rosace, G. Sol-gel approach to incorporate millimeter-long carbon nanotubes into fabrics for the development of electrical-conductive textiles. Mater. Chem. Phys. 2020, 240, 122218.
  75. Katragadda, R.B.; Xu, Y. A novel intelligent textile technology based on silicon flexible skins. Sens. Actuators A 2008, 143, 169–174.
  76. Khan, Y.; Thielens, A.; Muin, S.; Ting, J.; Baumbauer, C.; Arias, A.C. A New Frontier of Printed Electronics: Flexible Hybrid Electronics. Adv. Mater. 2020, 32, 1905279.
  77. Park, J.; Martinez, M.M.; Berlinger, M.H.; Ringrose, A.; Clifton, D.J.; McKinney, S.E.; Amit, G. Wristband health tracker. U.S. Patent 749002, 9 February 2016.
  78. Wu, W.; Haick, H. Materials and Wearable Devices for Autonomous Monitoring of Physiological Markers. Adv. Mater. 2018, 30, 1705024.
  79. Tamura, T.; Maeda, Y.; Sekine, M.; Yoshida, M. Wearable photoplethysmographic sensors—Past and present. Electronics 2014, 3, 282–302.
  80. Poh, M.Z.; Swenson, N.C.; Picard, R.W. Motion-tolerant magnetic earring sensor and wireless earpiece for wearable photoplethysmography. IEEE Trans. Inf. Technol. Biomed. 2010, 14, 786–794.
  81. Zhang, T.T.; Ser, W.; Daniel, G.Y.T.; Zhang, J.; Yu, J.; Chua, C.; Louis, I.M. Sound based heart rate monitoring for wearable systems. In Proceedings of the 2010 International Conference on Body Sensor Networks, Singapore, 7–9 June 2010; pp. 139–143.
  82. Lee, H.E.; Shin, J.H.; Park, J.H.; Hong, S.K.; Park, S.H.; Lee, S.H.; Lee, J.H.; Kang, I.S.; Lee, K.J. Micro Light-Emitting Diodes for Display and Flexible Biomedical Applications. Adv. Funct. Mater. 2019, 29, 1808075.
  83. Karlen, W.; Raman, S.; Ansermino, J.M.; Dumont, G.A. Multiparameter respiratory rate estimation from the photoplethysmogram. IEEE Trans. Bio-Med. Eng. 2013, 60, 1946–1953.
  84. Lee, J.; Kim, M.; Park, H.K.; Kim, I.Y. Motion Artifact Reduction in Wearable Photoplethysmography Based on Multi-Channel Sensors with Multiple Wavelengths. Sensors 2020, 20, 1493.
  85. Pellicer, A.; Bravo, M.C. Near-infrared spectroscopy: A methodology-focused review. Semin. Fetal. Neonatal. Med. 2011, 16, 42–49.
  86. Tremper, K.K. Pulse Oximetry. Chest 1989, 95, 713–715.
  87. Wolf, M.; Wolf, U.; Toronov, V.; Michalos, A.; Paunescu, L.A.; Choi, J.H.; Gratton, E. Different Time Evolution of Oxyhemoglobin and Deoxyhemoglobin Concentration Changes in the Visual and Motor Cortices during Functional Stimulation: A Near-Infrared Spectroscopy Study. Neuroimage 2002, 16, 704–712.
  88. Fallgatter, A.J.; Strik, W.K. Right frontal activation during the continuous performance test assessed with near-infrared spectroscopy in healthy subjects. Neurosci. Lett. 1997, 223, 89–92.
  89. Buzhan, P.; Dolgoshein, B.; Ilyin, A.; Kantserov, V.; Kaplin, V.; Karakash, A.; Pleshko, A.; Popova, E.; Smirnov, S.; Volkov, Y.U.; et al. The advanced study of silicon photomultiplier. In Advanced Technology and Particle Physics; Astroparticle, Particle, Space Physics, Radiation Interaction, Detectors and Medical Physics Applications; World Scientific: Singapore, 2002; Volume 1, pp. 717–728. ISBN 978-981-238-180-4.
  90. Finocchiaro, P.; Pappalardo, A.; Cosentino, L.; Belluso, M.; Billotta, S.; Bonanno, G.; Carbone, B.; Condorelli, G.; Di Mauro, S.; Fallica, G.; et al. Characterization of a novel 100-channel silicon photomultiplier—Part II: Charge and time. IEEE Trans. Electron. Dev. 2008, 55, 2765–2773.
  91. Pagano, R.; Corso, D.; Lombardo, S.; Valvo, G.; Sanfilippo, D.N.; Fallica, G.; Libertino, S. Dark current in silicon photomultiplier pixels: Data and model. IEEE Trans. Electron. Dev. 2012, 59, 2410–2416.
  92. Santangelo, M.F.; Sciuto, E.L.; Busacca, A.C.; Petralia, S.; Conoci, S.; Libertino, S. Si-photomultipliers for bio-sensing applications. IEEE J. Sel. Top. Quantum Electron. 2016, 22, 6900307.
  93. Santangelo, M.F.; Libertino, S.; Turner, A.; Filippini, D.; Mak, W. Integrating printed microfluidics with silicon photomultipliers for miniaturised and highly sensitive ATP bioluminescence detection. Biosens. Bioelectron. 2018, 99, 464–470.
  94. Sciuto, E.L.; Coniglio, M.A.; Corso, D.; Van der Meer, J.R.; Acerbi, F.; Gola, A.; Libertino, S. Biosensor in monitoring water quality and safety: An example of miniaturizable whole-cell based sensor for Hg2+ optical detection in water. Water 2019, 11, 1986.
  95. Petralia, S.; Cosentino, T.; Sinatra, F.; Favetta, M.; Fiorenza, P.; Bongiorno, C.; Sciuto, L.C.; Conoci, S.; Libertino, S. Silicon Nitride Surfaces as Active Substrate for Electrical DNA Biosensors. Sens. Actuators B 2017, 252, 492–502.
  96. Maira, G.; Mazzillo, M.; Libertino, S.; Fallica, G.; Lombardo, S. Crucial aspects for the use of silicon photomultiplier devices in Continuous Wave functional Near Infrared Spectroscopy. Biomed. Opt. Express 2018, 9, 4679–4688.
  97. De Luca, G.; Bonaccorsi, P.; Trovato, V.; Mancuso, A.; Papalia, T.; Pistone, A.; Casaletto, M.P.; Mezzi, A.; Brunetti, B.; Minuti, L.; et al. Tripodal tris-disulfides as capping agents for a controlled mixed functionalization of gold nanoparticles. New J. Chem. 2018, 42, 16436–16440.
  98. Pagano, R.; Libertino, S.; Sanfilippo, D.; Fallica, G.; Lombardo, S. Improvement of Sensitivity in Continuous Wave Near Infra-Red Spectroscopy Systems by using Silicon Photo-Multipliers. Biomed. Opt. Express 2016, 7, 1183–1192.
  99. Kaneta, T.; Alahmad, W.; Varanusupakul, P. Microfluidic paper-based analytical devices with instrument-free detection and miniaturized portable detectors. Appl. Spectrosc. Rev. 2019, 54, 117–141.
  100. Xu, J.; Tao, X.; Liu, X.; Yang, L. Wearable Eye Patch Biosensor for Noninvasive and Simultaneous Detection of Multiple Biomarkers in Human Tears. Anal. Chem. 2022, 94, 8659–8667.
  101. de Castro, L.F.; de Freitas, S.V.; Duarte, L.C.; de Souza, J.A.C.; Paixão, T.R.L.C.; Coltro, W.K.T. Salivary diagnostics on paper microfluidic devices and their use as wearable sensors for glucose monitoring. Anal. Bioanal. Chem. 2019, 411, 4919–4928.
  102. Koh, A.; Kang, D.; Xue, Y.; Lee, S.; Pielak, R.M.; Kim, J.; Hwang, T.; Min, S.; Banks, A.; Bastien, P.; et al. A soft, wearable microfluidic device for the capture, storage, and colorimetric sensing of sweat. Sci. Transl. Med. 2016, 8, 366ra165.
  103. Ardalan, S.; Hosseinifard, M.; Vosough, M.; Golmohammadi, H. Towards smart personalized perspiration analysis: An IoT-integrated cellulose-based microfluidic wearable patch for smartphone fluorimetric multi-sensing of sweat biomarkers. Biosens. Bioelectron. 2020, 168, 112450.
  104. Zheng, Y.; Panatdasirisuk, W.; Liu, J.; Tong, A.; Xiang, Y.; Yang, S. Patterned, Wearable UV Indicators from Electrospun Photochromic Fibers and Yarns. Adv. Mater. Technol. 2020, 5, 2000564.
  105. Liu, Y.; Li, H.; Feng, Q.; Su, H.; Li, D.; Shang, Y.; Chen, H.; Li, B.; Dong, H. A Three-Dimensional-Printed Recyclable, Flexible, and Wearable Device for Visualized UV, Temperature, and Sweat pH Sensing. ACS Omega 2022, 7, 9834–9845.
  106. Ran, J.; Xu, R.; Xia, R.; Cheng, D.; Yao, J.; Bi, S.; Cai, G.; Wang, X. Carbon nanotube/polyurethane core–sheath nanocomposite fibers for wearable strain sensors and electro-thermochromic textiles. Smart Mater. Struct. 2021, 30, 075022.
  107. He, Y.; Li, W.; Han, N.; Wang, J.; Zhang, X. Facile flexible reversible thermochromic membranes based on micro/nanoencapsulated phase change materials for wearable temperature sensor. Appl. Energy 2019, 247, 615–629.
  108. Kim, D.-H.; Bae, J.; Lee, J.; Ahn, J.; Hwang, W.-T.; Ko, J.; Kim, I.-D. Porous Nanofiber Membrane: Rational Platform for Highly Sensitive Thermochromic Sensor. Adv. Funct. Mater. 2022, 32, 2200463.
  109. Kim, D.S.; Lee, Y.H.; Kim, J.W.; Lee, H.; Jung, G.; Ha, J.S. A stretchable array of high-performance electrochromic devices for displaying skin-attached multi-sensor signals. Chem. Eng. J. 2022, 429, 132289.
  110. Kim, M.; Jung, I.D.; Kim, Y.; Yun, J.; Gao, C.; Lee, H.-W.; Lee, S.W. An electrochromic alarm system for smart contact lenses. Sens. Actuators B Chem. 2020, 322, 128601.
  111. Chaudhary, A.; Ghosh, T.; Pathak, D.K.; Kandpal, S.; Tanwar, M.; Rani, C.; Kumar, R. Prussian blue-based inorganic flexible electrochromism glucose sensor. IET Nanodielectrics 2021, 4, 165–170.
  112. Trovato, V.; Mezzi, A.; Brucale, M.; Rosace, G.; Rosaria Plutino, M. Alizarin-functionalized organic-inorganic silane coatings for the development of wearable textile sensors. J. Colloid Interface Sci. 2022, 617, 463–477.
  113. Lee, J.; Jun, H.; Kubota, Y.; Kim, T. Synthesis of red fluorescent dye with acid gas sensitive optical properties and fabrication of a washable and wearable textile sensor. Text. Res. J. 2021, 91, 2036–2052.
  114. Hu, J.; Liu, Y.; Zhang, X.; Han, H.; Li, Z.; Han, T. Fabricating a mechanochromic AIE luminogen into a wearable sensor for volatile organic compound (VOC) detection. Dye. Pigment. 2021, 192, 109393.
  115. Xu, H.; Zhang, M.K.; Lu, Y.F.; Li, J.J.; Ge, S.J.; Gu, Z.Z. Dual-Mode Wearable Strain Sensor Based on Graphene/Colloidal Crystal Films for Simultaneously Detection of Subtle and Large Human Motions. Adv. Mater. Technol. 2020, 5, 1901056.
  116. Khan, M.R.R.; Oh, S.; Choi, G.; Lee, H.S. Highly sensitive, fast and wide dynamic range lactate sensor containing solvatochromic sensing membrane by combining the capacitance-to-phase conversion technique. Sens. Actuators B Chem. 2020, 309, 127783.
  117. Zhang, Q.; Wang, X.; Decker, V.; Meyerhoff, M.E. Plasticizer-Free Thin-Film Sodium-Selective Optodes Inkjet-Printed on Transparent Plastic for Sweat Analysis. ACS Appl. Mater. Interfaces 2020, 12, 25616–25624.
  118. El-Naggar, M.E.; Abu Ali, O.A.; Saleh, D.I.; Abu-Saied, M.A.; Khattab, T.A. Preparation of green and sustainable colorimetric cotton assay using natural anthocyanins for sweat sensing. Int. J. Biol. Macromol. 2021, 190, 894–903.
  119. Reda, A.; El-Safty, S.A.; Selim, M.M.; Shenashen, M.A. Optical glucose biosensor built-in disposable strips and wearable electronic devices. Biosens. Bioelectron. 2021, 185, 113237.
  120. Lei, Y.; Zhao, W.; Zhang, Y.; Jiang, Q.; He, J.-H.; Baeumner, A.J.; Wolfbeis, O.S.; Wang, Z.L.; Salama, K.N.; Alshareef, H.N. A MXene-Based Wearable Biosensor System for High-Performance In Vitro Perspiration Analysis. Small 2019, 15, 1901190.
More
© Text is available under the terms and conditions of the Creative Commons Attribution (CC BY) license; additional terms may apply. By using this site, you agree to the Terms and Conditions and Privacy Policy.
This entry is offline, you can click here to edit this entry!
Video Production Service
Feedback