Over the past decade, interest in advancing photonic systems for bioapplications has been steadily growing, and various key factors have driven this trend [...]

The transition toward Healthcare Industry 5.0 requires personalized, intelligent, and privacy-preserving solutions. Additionally, the proliferation of home monitoring in the current Healthcare 4.0 framework has been embracing the challenge for invasiveness/privacy issues brought by home monitoring sensors and devices, potentially forsaking the conceived human-centric attributes of Smart Healthcare Industry 5.0. At the intersection of Healthcare Industry 5.0 and secured home monitoring, we propose a multi-service non-invasive solution for distributed home monitoring. A new infrastructure based on Fiber Bragg Grating (FBG) accelerometers is designed, realizing non-invasive/high-privacy distributed home monitoring by solely monitoring floor vibrations. To achieve home monitoring based on the low-dimensional data collected by FBG accelerometers, a multi-class hierarchical support vector machinebased algorithm (H-SVM) is proposed, which achieves simultaneous multi-service classification with FBG noise/interference mitigation. Moreover, an enhanced time difference of arrival (TDoA)-based algorithm (E-TDoA) is proposed for real-time localization of users, with the enhanced accuracy specifically based on FBG-collected data. Experimental results show an overall accuracy of 93.62% for multi-service classification, with a low processing time of 4.1 ms, meanwhile comprising a localization accuracy of 0.715 m (sufficient for home monitoring multi-service delivery).

Advancements in fiber optic sensor technology and telecommunications have paved the way for innovative health monitoring systems. However, previous works in this area have often been limited by the lack of comprehensive datasets, hindering the development of accurate and robust solutions. This paper addresses this gap by presenting a novel dataset and approach to gait-based user identification using a set of four optical Fiber Bragg Grating (FBG) sensor-based accelerometers, integrated into smart home environments. By computing features such as entropy, mean, standard deviation, kurtosis, and skewness from raw signals, and employing three unsupervised machine learning models-K-means, DB-scan, and Gaussian Mixture Model (GMM)-we achieve high accuracy in distinguishing individuals. Our dataset results for each sensor are as follows: for Sensor 1, K-means with a Silhouette Score of 0.3038 and Davies-Bouldin Index of 1.1238; for Sensor 2, K-means with a Silhouette Score of 0.3543 and Davies-Bouldin Index of 0.9659; for Sensor 3, DB-scan with a Silhouette Score of 0.2650 and Davies-Bouldin Index of 0.6855; and for Sensor 4, K-means with a Silhouette Score of 0.3949 and Davies-Bouldin Index of 1.0438. This approach not only enhances user identification but also facilitates personalized healthcare applications and unobtrusive monitoring.

This study addresses the growing need for noninvasive, secure, and efficient biometric identification methods in Internet of Things (IoT) applications, where traditional biometric systems often face challenges due to privacy concerns, environmental constraints, and practical limitations. To tackle these issues, we introduce a novel biometric identification system that leverages custom-built multiplexed Fiber Bragg Grating (FBG) accelerometers and bispectral feature extraction. By applying bispectral analysis to the acquired gait signals, we extract robust and discriminative features for individual identification. Unsupervised clustering algorithms, namely K-means and DBSCAN, were employed to categorize individuals based on these features, successfully identifying ten distinct clusters corresponding to ten participants and demonstrating the system's effectiveness. The K-means model achieved a Davies-Bouldin Index of 0.79 and a Silhouette Score of 0.45, while DBSCAN yielded a Davies-Bouldin Index of 0.87 and a Silhouette Score of 0.36. Given the proprietary nature of the data and the custom-built FBG accelerometers used in this study, direct comparisons to state-of-the-art methods are not available. However, these results underscore the potential of our unique approach and technology to advance biometric identification, providing a promising non-invasive, scalable, and discreet solution with broad applicability in IoT environments.

The optical Vernier effect (OVE) has been widely exploited as a method to enhance the sensitivity of interferometric optical fiber sensors. This is usually achieved by cascading or paralleling two interferometers, one as sensing element, and other as reference. Here it is explored the use of a single interferometer, which detects the changed signal, and a virtual reference spectrum to be superimposed with the sensing optical signal, realizing the OVE. Open Fabry-Perot interferometer (FPI) cavities with different lengths (Lsens from 118 to 539 mu m) are characterized to refractive index (RI) variations, using glucose solutions with varying concentrations. One of the sensors with the highest sensitivity (L-sens= 292 mu m) is chosen for further exploration of the OVE. Using the optical spectrum obtained when this sensor was immersed in water, virtual reference optical spectra corresponding to FPIs with different lengths (Lref from 320 to 390 mu m) are simulated, using, in this case, the OriginPro software. Once the most suitable conditions are chosen, the behavior of the spectral overlap is evaluated, and the sensing magnification resulting from the OVE application is determined. A sensitivity of (20562 +/- 1461) nm/RIU is achieved, which corresponds to a magnification factor, M = 14.1. The use of a virtual reference signal in the OVE is a simpler and more economical approach, with the possibility to adjust the magnified sensitivity.

The level of protein aggregates can be used as an indicator of some diseases, including neurodegenerative disorders such as Alzheimer's disease, and may be closely related to the efficacy of anti-cancer treatments. Moreover, strict control over protein aggregate levels is essential in pharmaceutical manufacturing to prevent potential side effects in the human body. In this study, we present a low-cost solution to evaluate protein aggregate levels in liquid samples using a smartphone-based fluorescence detection device. Initially, protein aggregate levels were assessed in bovine serum albumin samples. Then, this analysis was conducted using total protein extracts from breast cancer cells. The results obtained with the device were consistent with commercial spectrometer measurements, resulting in mean absolute errors of 0.098 for bovine serum albumin samples and 0.152 for the proteins extracted from cancer cells, corresponding to differences of 9.8 % and 15.2 % between the commercial spectrometer and the developed device in the considered scales.

Pipelines are structures with great relevance in different industrial sectors and are essential for the proper functioning of the logistics that support today’s society. Due to their characteristics, locations, and continuous operation, allied with the huge network of pipelines across the world, they require specialized labor, maintenance, and adequate sensing systems to access their proper operation and detect any damage they may suffer throughout their service life. In this work, a fiber Bragg grating (FBG)-based optical fiber accelerometer (OFA), which was designed and calibrated to operate through wavelength and optical power variations using different interrogation setups, was fixed together with a pair of FBG arrays along a 1020 carbon steel pipeline section with the objective of monitoring the pipeline natural frequency (fn_pipeline) to indirectly evaluate the detection and evolution of corrosion when this structure was buried in sand. Here, corrosion was induced in a small area of the pipeline for 164 days, and the OFA was able to detect a maximum fn_pipeline variation of 3.8 Hz in that period. On the other hand, the attached FBGs showed a limited performance once they could successfully operate when the pipeline was unburied, but presented operational limitations when the pipeline was buried in sand. This was due to the inability of the structure to vibrate long enough under these conditions and obtained data from these sensors were insufficient to obtain the fn_pipeline.

In this paper, it is proposed an indoor localization system, relying on multiplexed fiber Bragg grating (FBG)-based accelerometers, targeting the activity levels monitoring of elders living alone. From the proposed consumer sensing system, floor vibration patterns generated by walking with high precision and reliability, were obtained. An indoor localization algorithm is also newly proposed, specifically customized to the localization system formed by the mesh of FBG-based accelerometers. Experimental setup is conducted, and an average root mean square (RMS) error of 0.285 m and a variance of 0.019 m2 were obtained, revealing that the proposed localization methodology delivers a reliable localization estimative, considering that typical foot size is about 0.200 m or greater. Compared with the conventional indoor localization technologies, this presents high accuracy, high reliability, and high privacy. Therefore, the proposed FBG-based localization system is a non-invasive consumer Internet-of-Things (CIoT)-based solution for smart healthcare, which provides low-complexity and high-privacy data for indoor localization.

Understanding human movement patterns and evaluating a range of medical disorders depend heavily on the analysis of gait. In this study, we propose a new method for gait analysis, based on multiplexed fiber Bragg grating (FBG) accelerometers. Our work expands the capabilities of FBG-based accelerometers by extracting gait features through the analysis of output signals. In contrast to traditional wearable sensors, our solution offers scalability and discreet monitoring while integrating smoothly into the current infrastructure. Step duration, cadence, peak acceleration, and gait symmetry are among the critical gait metrics that we calculate using MATLAB-based methods to preprocess the accelerometer data. Experiments show that our method is a good fit for precisely capturing gait dynamics. The study revealed significant differences among individuals (p<0.05) in gait parameters based on height and age groups, indicating variations in step time, and normalized cadence. Our findings have important ramifications for biometric identification, rehabilitation, and healthcare applications.

There is a growing tendency for people to spend more and more hours of their day sitting. This position leads to an increase in the postural problems in the population. For workers who have to spend long hours at work, in a seated position, investment should be directed towards the development of equipment that improves their working conditions, such as smart instrumented chairs with warnings of changes in position. With this work we propose a standard office chair instrumentation approach, designed to allow monitoring physiological parameters such as: posture of the seated person (user); body temperature; and respiratory frequency. The system also allows monitoring environmental parameters such as: temperature; relative humidity; atmospheric concentration of carbon dioxide (CO2); noise; and light levels. The chair enables the interaction with control equipment to adjust the comfort level, advising the need for rest time, repositioning notifications, and the real time visualization of data, using applications for Windows and Android. The system was tested by six users and evaluated in the detection of six different postures for each user, while sitting on the chair, presenting an 100% accuracy on the posture detection and a maximum of 18% error on the physiological parameters sensing. Experimental results show the adequate functionality of the instrumented chair, which could contribute to the prevention of pathologies associated with improper posture and the improvement of work productivity.

According to the World Health Organization, there are about 2.3 million new annual breast cancer (BC) cases. Knowing that after surgery BC survivors (BCSs) frequently report a decrease on their muscle activity and a reduction on their fine motor skills, it is fundamental that the rehabilitation process starts right after surgery, focusing on the upper limb function recovery. One of the faculties commonly affected during the treatment, is the handgrip capacity. Its rehabilitation process requires a close monitoring of the typically prescribed exercises, which is most often done based on qualitative observation, provided only during the physical therapy sessions. The capacity to quantitatively evaluate the rehabilitation process, both from home or in a clinic scenario, represents an added value to the rehabilitation process for the medical staff and for the BCS itself. Targeting the improvement of the BCS rehabilitation process, it is proposed a low-cost plastic optical fiber (POF) sensor for handgrip force monitoring during the performance of exercises designed for its rehabilitation. The tool designed presented a sensitivity of (-7 ± 1) m V/N, demonstrating a good performance during the execution of different handspring exercises and the capacity to evaluate the maximum effort from different subjects, for the same exercise.

Hand movements can be severely affected due to certain medical conditions such as stroke, or as a side effect from the treatments for other diseases such as breast cancer. Here, we propose a solution based on optical fiber Bragg gratings (FBGs) for the monitoring of rehabilitation exercises to recover the hand fine skills, the fingers strength and the precision of movements. The developed sensor was initially calibrated to force, and a value of 18.16 pm/N was obtained for its sensitivity. The sensor was tested for handgrip exercises of precision, oppositional, and lateral pinches, and the results obtained are in accordance with literature, which proved the sensors potential for monitoring this type of exercises during physical therapies. This solution can be further integrated with the Internet of Things (IoT) health systems for a remote digital health solution, as a facilitator for the physical therapies to be performed remotely from the patient's home.

Facing the increasing aging of the world population, it is important to develop new non-invasive systems that do not require any action from the user, allowing their monitoring, with privacy guarantee. This will contribute to diminish the burdens associated with the elderly people care. In this work it is presented a system based on optical fiber Fabry-Perot interferometer (FPI) accelerometers, placed on the floor of an indoor environment, to recognize human falls. Both the sensor production method and the acquisition system are cheaper than commercial ones, available for this purpose. In addition, the proposed system comprises real time data acquisition and processing, representing a promising low-cost solution for the elders indoor monitoring by external entities, by enabling its implementation in apps that alerts the caregivers.

Detecting and monitoring protein aggregates is important to evaluate disease progression, particularly in neurodegenerative disorders such as Parkinson's and Alzheimer's. Apart from the evaluation of disease progression, the detection of protein aggregates is used during the manufacturing process of some pharmaceutical formulations because it is extremely important to monitor the levels of protein aggregates given the potential immunogenic responses they can induce in the human body. The systems yet developed to detect these biological entities are often complex, expensive, and, in some cases, require specialized personnel to handle them. Thus, the application of such devices becomes difficult in resource-limited settings. Here we propose a simpler low-cost alternative - a smartphone-based fluorescence detection device - for the detection of protein aggregates. The results obtained with the developed system were consistent with measurements made with a commercial spectrometer, therefore proving the suitability of the proposed device for this application.

Considering that fall accidents are one of the leading causes of non-natural death of elders, it is crucial to design and to implement home’ fall detection systems. Current home monitoring systems are targeting this challenge, pursuing non-invasive, low latency, and simplified fall detection algorithms. Therefore, in this paper, edge-enabled non-wearable and non-invasive fall detection system is proposed. Concretely, outperforming the conventional invasive/privacy-sensitive fall detection technologies, the proposed system comprises four photonic-based accelerometers solely relying on the fiber Bragg grating (FBG) technology, which monitor the vibrations induced by the body impact in the platform by the Bragg wavelength shifts. A newly-developed support vector machine-based multi-class fall detection algorithm is proposed, based on the data collected by the accelerometers. Moreover, feasibility analysis of the proposed fall detection algorithm also reveals the possibility of fall prediction, given the slipping as the pre-falling phenomenon. Experimental results showcase that the proposed fall detection algorithm achieves overall accuracy up to 96.5%, with average processing time achieved as 21.3 ms, indicating the sufficiency to provide high quality of experience (QoE) fall detection services. Besides, fall prediction based on the pre-falling case study of slipping is discussed, revealing that fall can be predicted~197.5 ms beforehand, which is sufficient for further fall prevention (e.g., airbag).

Optical fiber sensors have great potential for application in civil construction, especially in the field of structural health monitoring. However, one of the barriers to the diffusion of these sensors is allied to the fact that their implementation is quite expensive, mainly due to the high price of commercial optical interrogators. Therefore, the present work seeks to develop a low-cost optical interrogation system. For this, the present study proposes the development of this system based on the use of tunable filters and edge filters in the monitoring of low and high frequencies, and consequent digital processing of the collected signals through the find_picks algorithm and the Fourier transform. As a result, it was possible to observe the adequate functioning of the tunable filters, with the validation of the system using a FBG, and a previous simulation for the edge filter system with frequencies superior to 100 kHz.

In this work, a solution to monitor users' activity within an indoor scenario is proposed. It is based on non-wearable and non-invasive sensing, and it is specially fitted for the elders' home monitoring. The localization of a person is estimated through an optical sensing network that detects the floor vibration produced by the footstep when walking. Optical fiber Bragg sensors are integrated within high sensitive accelerometers to detect such vibration. Three similar accelerometers were developed from which sensitivities of 269 pm/G, 225 pm/G, and 209 pm/G were found. Allied to vibration detection, an algorithm is employed to retrieve one's position from the data. In preliminary localization tests, the system has demonstrated an accuracy under 5 cm over a 3.2 m(2) detection area, proving itself to be a promising solution for the targeted application.

The detection of protein aggregates and its concentration, plays a relevant role in several fronts, namely at the detection of neurodegenerative diseases such as Alzheimer's, and the assessment of cellular stress linked to cell death. Evaluation of protein aggregation is an essential step of quality control in all stages of biopharmaceutical synthesis and transport, in order to guarantee the activity and desired immunogenicity of their products. The detection of protein aggregates often requires the use of expensive and complex techniques. Here we propose a fast and easy implementation solution, based on an optical fibre Fabry-Perot Interferometer (FPI) end tip resonator. The solution proposed revealed to be effective on the detection of protein aggregates, providing similar results as the ones obtained using a commercial spectrometer for fluorescence detection.

People spend more and more time sitting and this habit has been shown to cause spine pathologies. Thus, the scientific community and the industry have become interested in instrumented chairs development to detect incorrect user postures. In this work we present the development and implementation of invisibles and non-intrusive plastic optical fiber sensor cells to monitor the posture and evaluate the ergonomic behavior of a seated person. The low-cost plastic optical fiber (POF) based sensing devices developed in this work, were implemented in an office chair, to evaluate the workers posture throughout their work day. In addition to the sensors, Android and PC software applications were developed, to provide real time feedback and alerts to the user whenever an inadequate posture is detected, or the seating position is the same for a long time. The proposed approach was evaluated in a study involving six users, and results show that it can detect the user's position with 96.6% accuracy.

Three-dimensional fused deposition modelling (FMD) is revolutionizing the production of new custom parts. It has been applied to create, for instance, protection cases and solid supports for all kinds of sensing structures, in a cost- and time-effective way. This paper uses the 3D printing technique to embed an optical fiber sensor, specifically a fiber Bragg grating (FBG) sensor, into a flexible polymer during the printing process, resulting in a compact and robust sensing structure. This FBG cell is intended to be tested in monitoring the heart rate (HR) from different parts of the body. This study preliminary assessed the capability of monitoring HR from the wrist (at the level of the radial artery) and the chest. Results showed promising performance, with a root-mean-square deviation of 5.6 beats per minute.