Open Access
Add to BibSonomy
Abstract
This work proposes a 3D-printed sensor based on fiber Bragg grating (FBG) technology for respiratory rate (RR) and heart rate (HR) monitoring. Each sensor is composed of a single FBG fully encapsulated into a 3D-printable Flexible, during the printing process. Sensors with different material thicknesses and infill densities were tested. The sensor with the best metrological properties was selected and preliminary assessed in terms of capability of monitoring RR and HR on three users. Preliminary results proved that the developed sensor can be a valuable easy-to-fabricate solution, with high reproducibility and high strain sensitivity to chest wall deformations due to breathing and heart beating.
© 2022 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement
1. Introduction
Fiber Bragg grating (FBG) is an optical technology widely used to produce sensors for medical applications [1]. FBG sensors have many advantages, such as high sensitivity, multiplexing capability, small size and weight, and electromagnetic interference immunity. Moreover, FBG sensors are highly safe, including in humid environments since there is no electrical current at the sensing point [1,2]. In the last decades, the use of FBG sensors for medical applications has experienced a rapid evolution: from surgical tools instrumentation [3–6] to wearable garments [7–10] and biosensors [11,12] development.
In biomedical scenarios, the FBG aforementioned advantages make them very appealing for wearables designed to monitor physiological parameters, without interfering with the user's daily life. Bearing this in mind, the main focus of this work is the development of wearable sensors based on a single FBG for monitoring two vital signs: the respiratory rate (RR) and heart rate (HR). Commonly, when integrated into wearables, FBGs work as strain sensors and must meet the requirements of reduced size and weight, comfort, and flexibility [13,14]. Once placed in contact with the body, the sensors must be able to suffer deformations according to the mechanical movements caused by the user chest wall and reflect them in its output signal [13,14].
Previous work has proposed FBGs to monitor vital signs, items and smart garments in the form of wearables [15,16]. In 2011, Silva et al. designed and fabricated a wearable system based on a single FBG sensor to monitor RR and HR [7]. The main innovation of this system was the structure in which the FBG sensor was embedded: a foil made of polyvinyl chloride (PVC). A year later, a paper was published proposing a sensor consisting of a FBG embedded in a pneumatic cushion placed between the backrest of a seat and the back of the monitored person [17]. Laboratory studies have shown that the sensor was able to monitor dynamic strains (µ) in the range of 50-124 µµ, caused by the respiration, and about 8.3 µµ due to the heartbeat.
Years later, in 2017, Lo Presti et al. proposed a smart textile based on an array of 12 FBGs, evaluating its feasibility of monitoring RR and HR in healthy subjects in two body positions (standing and supine) [18]. In the same year, Nedoma et al. published a paper focused on a novel FBG-based system suitable for the simultaneous monitoring of RR and HR during magnetic resonance imaging (MRI) examinations [19]. The developed sensor was encapsulated in a polydimethylsiloxane polymer (PDMS). Still in 2017, a novel non-invasive optical ballistocardiography technique that allowed simultaneous measurement of cardiac and respiratory activities was reported. The unique design of this device provided additional capabilities, such as monitoring nascent morphology of cardiac and respiratory activity, HR and HR variability (HRV) [20].
There are several techniques and materials exploited to encapsulate FBGs to measure vital signs, like embedding FBGs in resin or plastic composites (for example in PDMS [20,21], Dragon skin [13,22], Ecoflex [17] or PVC [7]). These materials are very flexible and confer to the FBGs good robustness, high adaptability to the skin, and good compliance with the chest movements. However, some uncontrolled factors which may occur during the manufacturing process, such as a non-uniform bonding strength at the fiber-polymer interface and the presence of some bubbles of air in the cured polymer matrixes, may affect the performance of the final system.
To overcome these issues, recently, fused deposition modeling (FDM) has been proposed as a promising method for fabricating various components, including sensors [23]. The FDM technique allows to develop a sensing element very quickly and with high printing precision [23,24]. It is establishing therefore a good alternative to the most common polymer encapsulation methods. The FDM technique has been mainly used in the fabrication of sensors for civil engineering [25–27]. Few papers proposed this technique in the fabrication of sensors for medical applications [28,29], but none of them for monitoring respiration and cardiac activity.
The paper aims at evaluating if the FDM method can be an innovative technique to build a FBG-based wearable system able to monitor the users’ vital signs in real time, namely RR and HR. This sensing element is composed of an optical fiber with a single FBG sensor into 3D-printed material (i.e., the Flexible). The optical fiber with FBG was fully embedded in the material during the printing process. To optimize the strain sensitivity of the wearable system, the influence of three infills percentages and two different thicknesses on the sensor response was investigated. Then, the most sensitive sensor was worn and tested on three different users during normal breathing and apnea.
This paper is organized as follows: the first section presents the introduction to the work, the second section explains the working principle, followed by the development of the sensor (section 3), section 4 presents the tests performed on users, and finally the discussion and conclusion follows (section 5).
2. Working principle
A FBG is a periodic perturbation of the refractive index along the fiber core. The modulation of the refractive index generates a grating, which reflects a narrowband of wavelengths centered at the Bragg wavelength (λB), when the Bragg condition is met:
where λB is the reflected Bragg wavelength, neff is the effective refractive index of the optical fiber core and $\Lambda $ is the grating period, which corresponds to the periodic modulation of the refractive index.The FBGs can be affected by changes in strain (Δl) and/or temperature (ΔT). Consequently, λB varies (ΔλB), according to the following equation:
The first term is related to the strain induced wavelength shift, and the second one to the thermal effect on the same parameter. Sl and ST represent the FBG sensitivity coefficients to strain and temperature, respectively.
When the FBG is embedded into a 3D printed polymer matrix, Sl and ST are largely influenced by the elastic and thermal properties of the material, much thicker than the silica fiber [27]. The elastic properties will influence the response to strain, however the T contribution may be ignored, because the parameters to be measured have considerably higher dynamic behavior than temperature variations, which are time dependent.
The developed sensor is intended to be worn on the chest to measure RR and HR. Therefore, it should be able to measure the displacement occurring on the chest during respiration (from 4 to 12 mm) and heart beating (from 0.2 to 0.5 mm) [13]. These chest deformations stretch the surface of the 3D-printed polymer, being transferred up to the sensor, and leading to a change in the FBG output (ΔλB) as depicted in Fig. 1.
3. 3D-printed sensor development: design, fabrication, and metrological characterization
3.1 Design and production
The sensor developed for measuring RR and HR values consists of an elastic material (Flexible, Fish box mini model, Avistron, Bergheim, Germany) printed by a 3D printer (Ultimaker 3D Extended, Ultimaker, Utrecht, Netherlands) and a single optical fiber with a single FBG (Fig. 2). The FBGs were inscribed into photosensitive optical fiber (GF1, Thorlabs, New Jersey, United States of America) for a length around 5 mm, using a pulsed Q-switched Nd: YAG laser system (LS-2137U, LOTIS TII, Minsk, Belarus), emitting at the fourth harmonic (266 nm). The FBGs were recorded through the phase mask technique, employing a laser pump energy of 25 J, a repetition rate of 10 Hz, and an exposure time of 1 min, approximately.
As the aim of the present study was to develop a FBG-based fully printed 3D sensor with high robustness and able to detect chest wall deformations due to respiration and the heart beating pumping, an elastic material was chosen, due to its stretchability without tearing.
The fabrication process consists of the following steps (Fig. 2):
- 1. 3D drawing of the sensor (Step 1);
- 2. Sensor printing on a 3D printer with the Flexible filament (Step 2);
- 3. When the 3D-printed part is halfway through its printing, the process is paused, and the optical fiber containing the FBG is placed into the corresponding groove (Step 3);
- 4. The optical fiber is tensioned and fixed with cyanoacrylate glue on each side of the FBG, after which, it was given a resting time of 10 s, for the glue to cure (Step 4);
- 5. Printing is resumed (Step 5).
The 3D drawing of the piece was designed with several grooves, so that the optical fiber and glue have space to be placed, without interrupting the printing of the posterior layers (Fig. 3). After defining all the details of the design, in order to successfully place the FBG, two more factors responsible for improving the performance of the sensor were investigated: the thickness of the 3D-printed sensor and the infilling density of the print [30]. In details, two thicknesses (2 and 3 mm), and three infills (20, 60 and 100%) were tested (Fig. 3 b). Therefore, six different sensors were produced (Table 1).
Fig. 3. Schematics of the sensor: (a) cut in the middle of the 3D drawing to show the grooves inside the sensor; (b) infills photographs; (c) representation of all sensor components and dimensions; and (d) sensor photography.
Table 1. Characteristics of the six sensors produced
3.2 Metrological characterization
The experimental setup to analyze the sensors response to strain was composed by a waveform generator (33220A, Agilent Technologies, Santa Clara, California) to produce sinusoidal waves that fed a Z/Tilt Piezoelectric Flexure Stage (P287.70, Physik Instrumente, Karlsruhe, Germany), via a high-voltage piezoelectric amplifier (E-508.00, Physik Instrumente, Karlsruhe, Germany). This specific actuator was chosen due to its movement range being close to the chest movements during breathing and heart beats, presenting a maximum displacement of 700 µm. The sensors’ responses were characterized by pressing the sensor with the actuator, causing 150 µm (displacement amplitude) sinusoidal movements in the cardiac frequency range (from 0.5 to 10.0 Hz), and of 450 µm, in the respiratory frequency range (from 0.1 to 0.3 Hz). The study of the higher frequencies (and small amplitude motion, 150 µm) was done sequentially for the frequencies of 0.5, 1, 2, 4, 6, 8, and 10 Hz. For lower frequencies (and greater range of motion, 450 µm), the frequencies studied were 0.1, 0.15, 0.2, and 0.3 Hz. The signal frequency sweeps were made in loop, firstly by increasing the frequency and then decreasing it, until the value of the first frequency applied.
After testing, the 3 mm thick sensors did not show the necessary sensitivity to amplitudes related with cardiac activity. Therefore, no graph of sensors A, B and C will be presented. The same did not happen for the sensors with 2 mm, so the Fig. 4 a) and b) show the Bragg wavelength shift signals obtained from each sensor (D, E and F) for 150 and 450 µm displacement amplitudes, respectively.
Fig. 4. Sensors D, E and F sensitivity test results to: (a) 150 µm; and (b) 450 µm amplitude movements.
For the 150 µm signal, which is representative of cardiac activity, sensor F with ≈ 0.024 nm of Bragg wavelength shift was the one that achieved a signal with greater amplitude, i.e., greater sensitivity. To the 450 µm signal, representative of respiration, sensors D and F showed a similar Bragg wavelength shift amplitude (≈ 0.090 nm). Considering the data from sensor F acquired at the lower frequency (0.1 Hz) for the greater amplitude movements (450 µm), a sensitivity of 0.190 ± 0.001 pm/µm was reached. On the other hand, as expected, for the higher frequency (10 Hz), acquired at lower amplitude movements (150 µm), a slightly lower sensitivity was attained (0.120 ± 0.004 pm/µm). These results supported that the sensor with the best sensitivity to both parameters was sensor F and therefore it was chosen to be used in the tests with users.
Since the measurements performed using this sensor are expected to be very dynamic compared to the variation in body temperature, this variation can be removed from the signal by digital filtering. Therefore, the thermal characterization has been neglected.
4. Tests on users
The preliminary assessment of the optical sensor (OS) feasibility to monitor RR and HR was evaluated on three users with the respective anthropometric characteristics presented in Table 2.
Table 2. Anthropometric characteristics of the users
The BioHarness (BH) device (ZEPHYR performance systems, Medtronic, Colorado, United States of America) was used as a reference for both the RR and HR. It records the respiration waveform at 25 samples/s and the ECG at 250 samples/s. As shown in Fig. 5, each user was asked to place the two systems – the BH elastic band around the chest and the 3D-printed sensor under the BH on the left side of the chest. The output of the developed sensor was collected by an optical interrogation system composed by a spectrometer (I MON 512E, Ibsen photonics, Farum, Denmark), a circulator (6015-3, Thorlabs, New Jersey, United States of America) and an optical source (AS4500, BA Technology, Shanghai, China) with an acquisition rate of 1000 Hz. Each user was invited to lie down on a physiotherapy bed to perform three tests of two types of breathing: 30 s of apnea (Ap) followed by 90 s of normal breathing (NB). The data gathered by the optical sensor during the test of each user are shown in Fig. 6.
Fig. 5. Schematic diagram of the experimental arrangement for the vital signals acquisition during the tests on users.
In order to obtain the RR and HR from the recorded signals, different signal processing steps were applied to evidence each signal component aimed to be detected (respiratory and heart beat signals). For RR estimation, due to be a high amplitude movement of the chest and therefore presenting a high signal-to-noise ratio, only a smooth filter was applied. For the HR monitoring, since the vibrations of the heart have a higher frequency and much lower amplitude than the respiratory movements, knowing that the normal HR is between 50 to 120 bpm, a bandpass frequency (0.8 Hz - 2.0 Hz) was applied [13] to reveal the heart beat signal. Both RR and HR were calculated as the number of maximum peaks over time windows of 30 s. Afterward, these values were converted into rpm and bpm, respectively by multiplying the number of maximum peaks per 2.
For cardiac and respiratory activities, a maximum will correspond to one heartbeat and one breath, respectively. Figure 7 shows the comparison between the signals of the two sensors during the tests to detect the vital signs (respiratory and heart signals) of the three users.
Fig. 7. Comparison between the signals from the two sensors during the tests performed by each user.
The RR and HR results comparison, obtained using both sensors, is shown in Fig. 8 and Fig. 9, respectively.
Fig. 8. Comparison between the RR obtained from the two sensors during the tests performed by each user.
Fig. 9. Comparison between the cardiac signals from the two sensors during the tests performed by each user.
Figure 8 shows the RR in time slots of 30 seconds, for normal breathing. The average RR of users 1-3 was 10, 14, and 13 rpm, respectively (values obtained by the two sensors). It can be seen that the signals from the two sensors completely match for all the tests of the three users.
Figure 9 shows heart rate throughout the test, with users 1-3 having mean HRs of 67, 85, and 91 bpm, respectively (values obtained by the reference sensor). It can be verified that the HR registered by the two devices is very similar. Also, the maximum difference between rates of two sensors occurred mainly in the range time corresponding to the first change from Ap to NB (see Fig. 9, range time 30-60 s for users 1 and 2).
The developed sensor results were compared with the reference (BH using paired difference, paired absolute difference and percent difference). Using the BH as the standard, the paired difference was assessed by calculating the difference between both techniques in each time range, being calculated as (HRBH-HROS). Absolute values were also considered, in order to better assess the magnitude of difference without considering direction. Percent differences were calculated as [(HRBH – HROS)/HRBH]×100. Results are presented and compared with two commercial HR monitors in Table 3.
Table 3. Heart rate (bpm and %) differences between BH and OS and comparison of the results with commercial HR monitors that also used ECG as reference.
5. Discussion and conclusions
In this work, a 3D printed sensor produced with Flexible, with an embedded FBG was developed to monitor two vital signs, namely HR and RR. Six different configurations were tested for this sensor, varying two thicknesses and three infill percentages. To define the best sensor for the target application, all sensors were tested for frequencies between 0.1 and 10.0 Hz, and two different movement amplitudes, 150 and 450 µm. The sensor that showed higher sensitivity for both types of signals was the device with the least thickness and less filling, which was referred to as the sensor F throughout the paper.
Since this is a sensor for monitoring very dynamic parameters, the optical fiber sensors calibration is normally performed for very low frequency parameters, such as, temperature, pressure and relative humidity, was neglected [13].
Applicability tests were performed with three users, to preliminarily evaluate the system’s ability to detect chest wall excursions related to respiratory and cardiac activities. An electronic commercial sensor was used as a reference to compare the obtained signals. The results showed that the proposed system is a wearable solution, and it is able to estimate the users’ RR and HR during normal breathing and apnea. Although the displacements caused by the heartbeat are smaller than those induced by the respiration, the values obtained for the HR only differ 0.8 ± 5.9 bpm, which is in line with other commercial wearable devices.
Due to the reliability of the results presented, the inherent advantages of using optical fibers over electronic sensors, and the simple, inexpensive and high reproducibility of the sensor, the proposed solution constitutes a promising method to replace existing electronic technology for detecting heart and respiration rates.
In the future, it would be of added value to develop a portable and low-cost, stand-alone interrogation system to install on elastic band, capable of being powered by a battery.
Funding
FCT/MCTES and FCT/MEC (UIDB/50008/2020-UIDP/50008/2020, UIDB/50025/2020-UIDP/50025/2020, FEDER-PT2020, UID/EEA/50008/2019); Scientific Employment Stimulus (CEECINST/00026/2018); European Regional Development Fund (POR LISBOA 2020, POR CENTRO 2020); Regional Operational Programme of Centre (CENTRO-01-0247-FEDER-072082); Cátia Tavares and Cátia Leitão (PD/BD/142787/2018, CEECIND/00154/2020).
Acknowledgments
This work is funded by FCT/MCTES and FCT/MEC through national funds and when applicable co-funded EU funds under the projects UIDB/50025/2020-UIDP/50025/2020, UIDB/50008/2020-UIDP/50008/2020, and the Scientific Employment Stimulus—Institutional Call—reference CEECINST/00026/2018. This work is also supported by the European Regional Development Fund (FEDER), through the Regional Operational Programme of Lisbon (POR LISBOA 2020) and the Regional Operational Programme of Centre (CENTRO 2020) of the Portugal 2020 framework [Project Safe-Home with Nr. 072082 (CENTRO-01-0247-FEDER-072082)]. Cátia Tavares and Cátia Leitão are grateful to FCT for the grant PD/BD/142787/2018, and the research contract CEECIND/00154/2020, respectively. M. Fátima Domingues and Nélia Alberto acknowledge the scientific action REACT and PREDICT, funded by FCT/MEC through national funds and when applicable co-funded by FEDER – PT2020 partnership agreement under the project UID/EEA/50008/2019.
Disclosures
The authors declare no conflict of interest.
Data availability
Data underlying the results presented in this paper are not publicly available at this time but may be obtained from the authors upon reasonable request.
References
1. D. Lo Presti, C. Massaroni, C. Leitão, M. F. Domingues, M. Sypabekova, D. Barrera, I. Floris, L. Massari, C. Oddo, S. Sales, I. Iordachita, D. Tosi, and E. Schena, “Fiber Bragg gratings for medical applications and future challenges: a review,,” IEEE Access 8, 156863–156888 (2020). [CrossRef]
2. D. Tosi, S. Poeggel, I. Iordachita, and E. Schen, “Fiber optic sensors for biomedical applications,” in Opto-Mechanical Fiber Optic Sensors (Heinemann, 2018), 301–333.
3. X. He, J. Handa, P. Gehlbach, R. Taylor, and I. Iordachita, “A submillimetric 3-DOF force sensing instrument with integrated fiber Bragg grating for retinal microsurgery,” IEEE Trans. Biomed. Eng. 61(2), 522–534 (2014). [CrossRef]
4. C. Kim and C. Lee, “Development of a 6-DoF FBG force-moment sensor for a haptic interface with minimally invasive robotic surgery,” J. Mech. Sci. Technol. 30(8), 3705–3712 (2016). [CrossRef]
5. C. Lv, S. Wang, and C. Shi, “A high-precision and miniature fiber Bragg grating-based force sensor for tissue palpation during minimally invasive surgery,” Ann. Biomed. Eng. 48(2), 669–681 (2020). [CrossRef]
6. R. Xue, B. Ren, J. Huang, Z. Yan, and Z. Du, “Design and evaluation of FBG-based tension sensor in laparoscope surgical robots,” Sensors 18(7), 2067 (2018). [CrossRef]
7. A. F. Silva, A. F. Gonçalves, P. Mendes, and J. Correia, “FBG sensing glove for monitoring hand posture,” IEEE Sensors J. 11(10), 2442–2448 (2011). [CrossRef]
8. S. Ambastha, S. Umesh, S. Dabir, and S. Asokan, “Comparison of force required for lumbar puncture with different gauges of spinal needle using fiber Bragg grating force device,” IEEE Sensors J. 18(19), 8028–8033 (2018). [CrossRef]
9. C. Tavares, M. F. Domingues, A. Frizera-Neto, T. Leite, C. Leitão, N. Alberto, C. Marques, A. Radwan, E. Rocon, and P. André, “Gait shear and plantar pressure monitoring: A non-invasive OFS based solution for e-Health architectures,” Sensors 18(5), 1334 (2018). [CrossRef]
10. M. F. Domingues, C. Tavares, A. Nepomuceno, N. Alberto, P. André, H. R. Chi, and A. Radwan, “Non-invasive wearable optical sensors for full gait analysis in e-Health architectures,” IEEE Wirel. Commun. 28(3), 28–35 (2021). [CrossRef]
11. M. Loy, E. Hassan, M. Lobry, F. Liu, C. Caucheteur, R. Wattiez, M. DeRosa, W. Willmore, and J. Albert, “Rapid detection of circulating breast cancer cells using a multiresonant optical fiber aptasensor with plasmonic amplification,” ACS Sens. 5(2), 454–463 (2020). [CrossRef]
12. C. Leitão, S. O. Pereira, N. Alberto, M. Lobry, M. Loyez, F. M. Costa, J. L. Pinto, C. Caucheteur, and C. Marques, “Cortisol in-fiber ultrasensitive plasmonic immunosensing,” IEEE Sens. J. 21(3), 3028 (2020). [CrossRef] .
13. D. Lo Presti, C. Massaroni, J. D’Abbraccio, L. Massari, M. Caponero, U. Longo, D. Formica, C. Oddo, and E. Schena, “Wearable system based on flexible FBG for respiratory and cardiac monitoring,” IEEE Sens. J. 19(17), 7391–7398 (2019). [CrossRef]
14. I. Aizhan, A. Beisenova, D. Tosi, and C. Molardi, “Fiber-optic based smart textiles for real-time monitoring of breathing rate,” Sensors 20(12), 3408 (2020). [CrossRef]
15. C. Mayoral, J. Gutiérrez, J. Pérez, M. Treviño, I. Velasco, P. Cruz, R. Rosas, L. Carrillo, J. Ríos, E. Apreza, and R. Laguna, “Fiber optic sensors for vital signs monitoring. A Review of its practicality in the health field,” Biosensors 11(2), 58 (2021). [CrossRef]
16. C. Massaroni, M. Zaltieri, D. Lo Presti, A. Nicolò, D. Tosi, and E. Schena, “Fiber Bragg grating sensors for cardiorespiratory monitoring: A review,” IEEE Sens. J. 21(13), 14069–14080 (2021). [CrossRef]
17. L. Dziuda, F. W. Skibniewski, M. Krej, and J. Lewandowski, “Monitoring respiration and cardiac activity using fiber Bragg grating-based sensor,” IEEE Trans. Biomed. Eng. 59(7), 1934–1942 (2012). [CrossRef]
18. D. Lo Presti, C. Massaroni, D. Formica, P. Saccomandi, F. Giurazza, M. A. Caponero, and E. Schena, “Smart textile based on 12 fiber Bragg gratings array for vital signs monitoring,” IEEE Sensors J. 17(18), 6037–6043 (2017). [CrossRef]
19. J. Nedoma, M. Fajkus, R. Martinek, and V. Vasinek, “Non-invasive fiber-optic biomedical sensor for basic vital sign monitoring,” Adv. Electr. Electron. Eng. 15, 336–342 (2017). [CrossRef] .
20. K. Chethana, A. S. Prasad, A. S. Omkar, and S. Asokan, “Fiber Bragg grating sensor based device for simultaneous measurement of respiratory and cardiac activities,” J. Biophotonics. 10(2), 278–285 (2017). [CrossRef]
21. M. Fajkus, J. Nedoma, R. Martinek, V. Vasinek, and H. Nazeran, “A non-invasive multichannel hybrid fiber-optic sensor system for vital sign monitoring,” Sensors 17(12), 111 (2017). [CrossRef]
22. D. Lo Presti, F. Santucci, C. Massaroni, D. Formica, R. Setola, and E. Schena, “A multi-point heart rate monitoring using a soft wearable system based on fiber optic technology,” Sci. Rep. 11(1), 21162 (2021). [CrossRef]
23. S. B. Kesner and R. D. Howe, “Design principles for rapid prototyping forces sensors using 3-D printing,” IEEE/ASME Trans. Mechatronics 16(5), 866–870 (2011). [CrossRef]
24. T. M. Llewellyn-Jones, B. W. Drinkwater, and S. R. Trask, “3D printed components with ultrasonically arranged microscale structure,” Smart Mater. Struct. 25(2), 02LT01 (2016). [CrossRef]
25. C. Hong, Y. Zhang, Z. Lu, and Z. Yin, “A FBG tilt sensor fabricated using 3D printing technique for monitoring ground movement,” IEEE Sens. J. 19(15), 6392–6399 (2019). [CrossRef]
26. C. Hong, Y. Zhang, D. Su, and Z. Yin, “Development of a FBG based hoop-strain sensor using 3D printing method,” IEEE Access 7, 107154–107160 (2019). [CrossRef]
27. G. Teng, T. Zhang, Y. Li, and X. Qiao, “Highly sensitive FBG seismometer with a 3D-printed hexagonal configuration,” J. Lightwave. Technol. 38(16), 4588–4595 (2020). [CrossRef]
28. A. Leal-Junior, C. Marques, M. Ribeiro, M. Pontes, and A. Frizera, “FBG-embedded 3-D printed ABS sensing pads: the impact of infill density on sensitivity and dynamic range in force sensors,” IEEE Sens. J. 18(20), 8381–8388 (2018). [CrossRef]
29. Z. Hao, K. Cook, J. Canning, H. Chen, and C. Martelli, “3D printed smart orthotic insoles: monitoring a person's gait step by step,” IEEE Sens. Lett. 4(1), 1–4 (2020). [CrossRef]
30. C. Lubombo and M. A. Huneault, “Effect of infill patterns on the mechanical performance of lightweight 3D-printed cellular PLA parts,” Mater. Today Commun. 17, 214–228 (2018). [CrossRef]
31. M. Etiwy, Z. Akhrass, L. Gillinov, A. Alashi, R. Wang, G. Blackburn, S. M. Gillinov, D. Phelan, A. M. Gillinov, P. L. Houghtaling, H. Javadikasgari, and M. Y. Desai, “Accuracy of wearable heart rate monitors in cardiac rehabilitation,” Cardiovasc. Diagn. Ther. 9(3), 262–271 (2019). [CrossRef]
