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 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).

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.

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).

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.

Peer-offloading of ultra-large-scale mobile edge computing enabled small cell (ULS-MEC-SC) will be highly demanded with optimal quality of service (QoS), and cost efficiency. Modeling and analysis of inter-MEC-SC topology has been foreseen to gain dominant influence to ULS-MEC-SC peer-offloading. Although complex network shows its strength revealing topological information, efforts are still lacked for topological analysis of ULS-MEC-SC. Therefore, in this paper, for the first time, we model and analyze inter-MEC-SC topology for ULS-MEC-SC peer-offloading, by considering complex network. We also propose systemic translation between graph theory based indicators and network-layer performance, for correlating graph theory based topological analysis with service-centric performance in ULS-MEC-SC peer-offloading. Comprehensive scenarios of ULS-MEC-SC peer-offloading are designed, comparing and analyzing performance of complex network modeling, which is referable of revealing unique strengths of typical complex networks (random graph, small world, and scale free network), regarding operation cost, latency, and convergence speed. Therefore, this paper paves the way to complex network based topological modeling, determination, and analysis of future ULS-MEC-SC peer-offloading.

In this paper, we present an optical fiber based architecture for non-invasive home monitoring of elder citizens. The approach is based on a network of optical fiber sensors distributed along the space/room to be monitored. The sensing mechanism is based on optical fiber Bragg grating (FBG) sensors, produced by the phase mask method and integrated within an accelerometer structure. This type of sensing solution has high sensitivity, allied with an extra resilience. Here we present the proposed architecture, the evaluation of different parameters that influence the accelerometer feedback, and the theoretical approach for indoor localization using this type of sensing mechanism. One advantage of the proposed solution is that it does not depend on wearables, which are considered burden for elders.

In this paper, we present an optical fiber based approach to accurately evaluate the respiratory capacity of individuals through spirometry. The proposed system converts the air flow rate into strain variations in an optical fiber, which will modulate the spectral response of a Fabry-Perot interferometer (FPI) micro-cavity, created in the fiber core. Simulation studies were performed to verify the proposed theoretical approach. Additionally, preliminary results concerning the study of the system's reliability are also presented, which confirm the suitability of using FPI sensors for the evaluation of spirometry parameters. The simulation and implementation results showcase the accuracy of the measurements performed by the proposed solution, confirming its appropriateness to be used as a smart low-cost eHealth spirometer. This is the first time such FPI approach is used with this purpose.

This paper proposes a device-to-device communication based small cell (SC) deployment scheme in Smart Grid, named as JODI-D2D, to achieve high QoS, high reliability, and high energy efficiency. SC deployment is formulated as a joint optimization of data rate maximization and interference minimization, serving the distributed smart grid user equipment (SGUE), while reducing the computation latency. The SC powering strategy is determined with the k-means clustering, which considers the features of SGUEs, endowed by both power and communication layers. Simulation results show that the proposed JODI-D2D achieves high QoS with improved data rate performance, sufficient computation latency and maximized energy efficiency for SG applications.

Breast cancer (BC) treatments are often aggressive for the patients, causing severe impairments to their physical condition. Physical supervised exercise has proven to be beneficial for the patients' physical and emotional health recovery. In this work, we present an eHealth solution based on optical fiber sensors (OFSs), to monitor the physical activity of BC patients, during their exercises. Such solution will allow the patients to exercise from the comfort of their home, with the continuous supervision of the physiotherapist, and with a possibility to connect remotely with other patients. The use case, presented in this work, is focused on the design of optical fiber based eHealth enablers to monitor the exercises prescribed in the recovery of the handgrip force, often affected by the treatments that BC patients are subjected to.

With the latest advances in electronics and networking, eHealth has become a reality and is enhancing the life quality of people, by enabling them to live healthier and a more fulfilling life. On the other hand, the continuous population’s aging is pushing the limits on our health system, increasing the need for eHealth solutions, especially those enabling continuous monitoring of senior citizens and patients. Towards such goal, this paper presents an innovative eHealth system for ankle angular movement monitoring for physical rehabilitation. The proposed solution has a non- invasive design, due to its small size and ease of use; hence, it provides users with full autonomy to live a fully active life. The system is composed of optical fiber Bragg grating (FBG) sensors, used for the angular movement monitoring. The proposed system was tested in a healthy volunteer, and the obtained results show the accuracy of the proposed system, by displaying the expected behavior of normal healthy ankles. The system can be used in continuous ankle monitoring during routine daily activity in rehabilitating patients or elder citizens’ scenarios, or can even be integrated into an exoskeleton, along with other sensors, for complete monitoring of all movements of the subject.

The advances and fast spread of mobile devices and technologies, we witness today, have extended its advantages over medical and health practice supported by mobile devices, giving rise to the growing research of Internet of Things (IoT), especially the e-Health field. The features provided by mobile technologies revealed to be of major importance when we consider the continuous aging of population and the consequent increase of its debilities. In addition to the increase of lifetime span of population, also the increase of health risks and their locomotive impairments increases, requiring a close monitoring and continuous evaluation. Such monitoring should be as noninvasive as possible, in order not to compromise the mobility and the day-to-day activities of citizens. Therefore, we present the development of a noninvasive optical fiber sensor (OFS) architecture adaptable to a shoe sole for plantar pressure remote monitoring, which is suitable to be integrated in an IoT e-Health solution to monitor the well-being of individuals. This paper explores the production of the OFS multiplexed network (using fiber Bragg gratings) to monitor the foot plantar pressure distribution during gait (walking movement). From the acquired gait data, it is possible to infer health conditions of the patient's foot and spine posture. To guarantee the patients mobility, the proposed system consists of an OFS network integrated with a wireless transceiver to enable efficient ubiquitous monitoring of patients. This paper shows the calibration and measurement results, which reflect the accuracy of the proposed system, under normal walking in controlled area.

The continuous aging of the population has increased health risks, mostly translated in mobility impairments. New therapeutic options have come to light in the past years, with the rise of e-health architectures. The use of optical fiber based sensors has been considered as a possible e-health enabling technology, due to their advantages compared to electronic based sensors. In this paper, we present an e-health optical fiber based solution to monitor the knee angular flexion/extension during gait. The presented design is an accurate non-invasive solution, using kinesio tape and optical fiber Bragg grating sensors, in order not to limit the movement of the individuals. The paper shows the accuracy of the acquired measurements during gait using the proposed designed sensor.

The continuous aging of the population has increased health risks, leading to the need for close monitoring of elder citizens and patients. Fortunately, the field of e-Health has been experiencing great progress, although it still faces challenges, since it needs to be applied without compromising the mobility or lifestyle of monitored patients; i.e. non-invasive. Addressing such challenges, we present a non-invasive energy- efficient insole optical fiber sensor architecture for plantar pressure monitoring. The paper shows the design and implementation of a non-invasive "in-sole" optical fiber sensor network (using Fiber Bragg Gratings), which is able to monitor health conditions by observing plantar pressure distribution in the foot. The system integrates the optical fiber sensor network with an energy efficient wireless transceiver, in order to allow continuous monitoring of monitored patients, during their uninterrupted daily routine. The paper presents the calibration and laboratory measurements of the sensing network. The recorded measurements show the suitability and high accuracy of the proposed system in monitoring the plantar pressure distribution during walking movement (i.e. gait).