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The correlation between nutritional habits and gut health directly impacts the gut-brain axis via a complex and not yet fully disclosed communication network. Establishing a link between our food intake and specific physiological responses as well as a better knowledge of diseases and the gut microbiota involves solving a challenging puzzle of biochemical pathways. Our understanding is limited by the inaccessibility of the gastrointestinal region to routine non-invasive chemical analysis. Here, we move a step further toward the direct assessment of a protein fermentation product, i.e., ammonium ions, via Ion Selective Electrodes (ISEs) in gastrointestinal digesta samples. By modulating the digestible protein content of the diet regimes of two groups of pigs, we discriminate the level of protein fermentation with a straightforward quasi-in vivo detection method which does not require any sample preparation. Our results show more than a 2-fold increase in ammonium ion concentration (from 180 ppm to 400 ppm) in the proximal colon for a diet based on poorly digestible proteins compared with a diet based on easily digestible proteins. Our approach shows good correlation with a standard laboratory technique for the determination of NH4+, i.e., the Indophenol Blue Method. Our results show the direct sensing of a protein fermentation product in a real matrix and demonstrate the great potential of potentiometric sensors to assess ammonium concentration profiles along the gastrointestinal tract for diets varying in protein digestibility and fermentation.

Changes in respiratory rate have been found to be one of the early signs of health deterioration in patients. In remote environments where diagnostic tools and medical attention are scarce, such as deep space exploration, the monitoring of the respiratory signal becomes crucial to timely detect life-threatening conditions. Nowadays, this signal can be measured using wearable technology; however, the use of such technology is often hampered by the low quality of the recordings, which leads more often to wrong diagnosis and conclusions. Therefore, to apply these data in diagnosis analysis, it is important to determine which parts of the signal are of sufficient quality. In this context, this study aims to evaluate the performance of a signal quality assessment framework, where two machine learning algorithms (support vector machine–SVM, and convolutional neural network–CNN) were used. The models were pre-trained using data of patients suffering from chronic obstructive pulmonary disease. The generalization capability of the models was evaluated by testing them on data from a different patient population, presenting normal and pathological breathing. The new patients underwent bariatric surgery and performed a controlled breathing protocol, displaying six different breathing patterns. Data augmentation (DA) and transfer learning (TL) were used to increase the size of the training set and to optimize the models for the new dataset. The effect of the different breathing patterns on the performance of the classifiers was also studied. The SVM did not improve when using DA, however, when using TL, the performance improved significantly ( p < 0.05) compared to DA. The opposite effect was observed for CNN, where the biggest improvement was obtained using DA, while TL did not show a significant change. The models presented a low performance for shallow, slow and fast breathing patterns. These results suggest that it is possible to classify respiratory signals obtained with, SCOPUS: ar.j, info:eu-repo/semantics/published

Objective: Over the last two decades, radar-based contactless monitoring of vital signs (heartbeat and respiration rate) has raised increasing interest as an emerging and added value to health care. However, until now, the flaws caused by indoor multipath propagation formed a fundamental hurdle for the adoption of such technology in practical healthcare applications where reliability and robustness are crucial. Multipath reflections, originated from one person, combine with the direct signals and multipaths of other people and stationary objects, thus jeopardizing individual vital signs extraction and localization. This work focuses on tackling indoor multipath propagation. Methods: We describe a methodology, based on accurate models of the indoor multipaths and of the radar signals, that enables separating the undesired multipaths from desired signals of multiple individuals, removing a key obstacle to real-world contactless vital signs monitoring and localization. Results: We also demonstrated it by accurately measure individual heart rates, respiration rates, and absolute distances (range information) of paired volunteers in a challenging real-world office setting. Conclusion: The approach, validated using a frequency-modulated continuous wave (FMCW) radar, was shown to function in an indoor environment where radar signals are severely affected by multipath reflections. Significance: Practical applications arise for health care, assisted living, geriatric and quarantine medicine, rescue and security purposes., Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Circuits and Systems

Background and objective: Long-term electrocardiogram monitoring comes at the expense of signal quality. During unconstrained movements, the electrocardiogram is often corrupted by motion artefacts, which can lead to inaccurate physiological information. In this situation, automated quality assessment methods are useful to increase the reliability of the measurements. A generic machine learning pipeline that generates classification models for electrocardiogram quality assessment is presented in this article. The presented pipeline is tested on signals from varied acquisition sources, towards selecting segments that can be used for heart rate analysis in lifestyle applications. Methods: Electrocardiogram recordings from traditional, wearable and ubiquitous devices, are segmented in 10 s windows and manually labeled by experienced researchers into two quality classes. To capture the electrocardiogram dynamics, a comprehensive set of 43 features is extracted from each segment, based on the time-domain signal, its Fast Fourier Transform, the Autocorrelation function and the Stationary Wavelet Transform. To select the most relevant features for each acquisition source we employ both a customized hybrid approach and the state-of-the-art Neighborhood Component Analysis method and compare them. Support Vector Machines (SVM), Decision Trees, K-Nearest-Neighbors and supervised ensemble methods are tested as possible binary classifiers. Results: The results for the best performing models on traditional, wearable and ubiquitous electrocardiogram datasets are, respectively: balanced-accuracy: 89%, F1-score: 93% with the Fine Gaussian SVM model and 10 features; balanced-accuracy: 93%, F1-score: 93% with the Fine Gaussian SVM model and 11 features; balanced-accuracy: 95%, F1-score: 86%, with the Fine Gaussian SVM model and 8 features. Conclusions: According to the results, our generic pipeline can generate classification models tailored to individual acquisition sources, provi, Accepted author manuscript, Circuits and Systems

Background and objective: Long-term electrocardiogram monitoring comes at the expense of signal quality. During unconstrained movements, the electrocardiogram is often corrupted by motion artefacts, which can lead to inaccurate physiological information. In this situation, automated quality assessment methods are useful to increase the reliability of the measurements. A generic machine learning pipeline that generates classification models for electrocardiogram quality assessment is presented in this article. The presented pipeline is tested on signals from varied acquisition sources, towards selecting segments that can be used for heart rate analysis in lifestyle applications. Methods: Electrocardiogram recordings from traditional, wearable and ubiquitous devices, are segmented in 10 s windows and manually labeled by experienced researchers into two quality classes. To capture the electrocardiogram dynamics, a comprehensive set of 43 features is extracted from each segment, based on the time-domain signal, its Fast Fourier Transform, the Autocorrelation function and the Stationary Wavelet Transform. To select the most relevant features for each acquisition source we employ both a customized hybrid approach and the state-of-the-art Neighborhood Component Analysis method and compare them. Support Vector Machines (SVM), Decision Trees, K-Nearest-Neighbors and supervised ensemble methods are tested as possible binary classifiers. Results: The results for the best performing models on traditional, wearable and ubiquitous electrocardiogram datasets are, respectively: balanced-accuracy: 89%, F1-score: 93% with the Fine Gaussian SVM model and 10 features; balanced-accuracy: 93%, F1-score: 93% with the Fine Gaussian SVM model and 11 features; balanced-accuracy: 95%, F1-score: 86%, with the Fine Gaussian SVM model and 8 features. Conclusions: According to the results, our generic pipeline can generate classification models tailored to individual acquisition sources, provi, Accepted author manuscript, Signal Processing Systems

Objective: Over the last two decades, radar-based contactless monitoring of vital signs (heartbeat and respiration rate) has raised increasing interest as an emerging and added value to health care. However, until now, the flaws caused by indoor multipath propagation formed a fundamental hurdle for the adoption of such technology in practical healthcare applications where reliability and robustness are crucial. Multipath reflections, originated from one person, combine with the direct signals and multipaths of other people and stationary objects, thus jeopardizing individual vital signs extraction and localization. This work focuses on tackling indoor multipath propagation. Methods: We describe a methodology, based on accurate models of the indoor multipaths and of the radar signals, that enables separating the undesired multipaths from desired signals of multiple individuals, removing a key obstacle to real-world contactless vital signs monitoring and localization. Results: We also demonstrated it by accurately measure individual heart rates, respiration rates, and absolute distances (range information) of paired volunteers in a challenging real-world office setting. Conclusion: The approach, validated using a frequency-modulated continuous wave (FMCW) radar, was shown to function in an indoor environment where radar signals are severely affected by multipath reflections. Significance: Practical applications arise for health care, assisted living, geriatric and quarantine medicine, rescue and security purposes., Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Signal Processing Systems

Vignetting in camera lenses is generally seen as something to avoid. For spectral cameras with thin-film interference filters, however, we argue that vignetting can be an advantage. When illuminated by focused light, the bandwidth of interference filters increases with the chief-ray angle, causing position-dependent smoothing of the spectra. We show that vignetting can be used to reduce smoothing and preserve important spectral features. Furthermore, we demonstrate that by adding additional vignetting to a lens, measurements can be made more consistent across the scene. This makes vignetting a useful parameter during spectral camera design.

Cardiovascular diseases (CVD) are often characterized by their multifactorial complexity. This makes remote monitoring and ambulatory cardiac rehabilitation (CR) therapy challenging. Current wearable multimodal devices enable remote monitoring. Machine learning (ML) and artificial intelligence (AI) can help in tackling multifaceted datasets. However, for clinical acceptance, easy interpretability of the AI models is crucial. The goal of the present study was to investigate whether a multi-parameter sensor could be used during a standardized activity test to interpret functional capacity in the longitudinal follow-up of CR patients. A total of 129 patients were followed for 3 months during CR using 6-min walking tests (6MWT) equipped with a wearable ECG and accelerometer device. Functional capacity was assessed based on 6MWT distance (6MWD). Linear and nonlinear interpretable models were explored to predict 6MWD. The t-distributed stochastic neighboring embedding (t-SNE) technique was exploited to embed and visualize high dimensional data. The performance of support vector machine (SVM) models, combining different features and using different kernel types, to predict functional capacity was evaluated. The SVM model, using chronotropic response and effort as input features, showed a mean absolute error of 42.8 m (±36.8 m). The 3D-maps derived using the t-SNE technique visualized the relationship between sensor-derived biomarkers and functional capacity, which enables tracking of the evolution of patients throughout the CR program. The current study showed that wearable monitoring combined with interpretable ML can objectively track clinical progression in a CR population. These results pave the road towards ambulatory CR.

Cardiac rehabilitation (CR) is a highly recommended secondary prevention measure for patients with diagnosed cardiovascular disease. Unfortunately, participation rates are low due to enrollment and adherence issues. As such, new CR delivery strategies are of interest, as to improve overall CR delivery. The goal of the study was to obtain a better understanding of the short-term progression of functional capacity throughout multidisciplinary CR, measured as the change in walking distance between baseline six-minute walking test (6MWT) and four consecutive follow-up tests. One-hundred-and-twenty-nine patients diagnosed with cardiovascular disease participated in the study, of which 89 patients who completed the whole study protocol were included in the statistical analysis. A one-way repeated measures ANOVA was conducted to determine whether there was a significant change in mean 6MWT distance (6MWD) throughout CR. A three-way-mixed ANOVA was performed to determine the influence of categorical variables on the progression in 6MWD between groups. Significant differences in mean 6MWD between consecutive measurements were observed. Two subgroups were identified based on the change in distance between baseline and end-of-study. Patients who increased most showed a linear progression. In the other group progression leveled off halfway through rehabilitation. Moreover, the improvement during the initial phase of CR seemed to be indicative for overall progression. The current study adds to the understanding of the short-term progression in exercise capacity of patients diagnosed with cardiovascular disease throughout a CR program. The results are not only of interest for CR in general, but could be particularly relevant in the setting of home-based CR., Microelectronics, Circuits and Systems

Cardiovascular diseases (CVD) are often characterized by their multifactorial complexity. This makes remote monitoring and ambulatory cardiac rehabilitation (CR) therapy challenging. Current wearable multimodal devices enable remote monitoring. Machine learning (ML) and artificial intelligence (AI) can help in tackling multifaceted datasets. However, for clinical acceptance, easy interpretability of the AI models is crucial. The goal of the present study was to investigate whether a multi-parameter sensor could be used during a standardized activity test to interpret functional capacity in the longitudinal follow-up of CR patients. A total of 129 patients were followed for 3 months during CR using 6-min walking tests (6MWT) equipped with a wearable ECG and accelerometer device. Functional capacity was assessed based on 6MWT distance (6MWD). Linear and nonlinear interpretable models were explored to predict 6MWD. The t-distributed stochastic neighboring embedding (t-SNE) technique was exploited to embed and visualize high dimensional data. The performance of support vector machine (SVM) models, combining different features and using different kernel types, to predict functional capacity was evaluated. The SVM model, using chronotropic response and effort as input features, showed a mean absolute error of 42.8 m (±36.8 m). The 3D-maps derived using the t-SNE technique visualized the relationship between sensor-derived biomarkers and functional capacity, which enables tracking of the evolution of patients throughout the CR program. The current study showed that wearable monitoring combined with interpretable ML can objectively track clinical progression in a CR population. These results pave the road towards ambulatory CR., Circuits and Systems

Background: Cardiac rehabilitation (CR) is known for its beneficial effects on functional capacity and is a key component within current cardiovascular disease management strategies. In addition, a larger increase in functional capacity is accompanied by better clinical outcomes. However, not all patients respond in a similar way to CR. Therefore, a patient-tailored approach to CR could open up the possibility to achieve an optimal increase in functional capacity in every patient. Before treatment can be optimized, the differences in response of patients in terms of cardiac adaptation to exercise should first be understood. In addition, digital biomarkers to steer CR need to be identified. Objective: The aim of the study was to investigate the difference in cardiac response between patients characterized by a clear improvement in functional capacity and patients showing only a minor improvement following CR therapy. Methods: A total of 129 patients in CR performed a 6-minute walking test (6MWT) at baseline and during four consecutive short-term follow-up tests while being equipped with a wearable electrocardiogram (ECG) device. The 6MWTs were used to evaluate functional capacity. Patients were divided into high- and low-response groups, based on the improvement in functional capacity during the CR program. Commonly used heart rate parameters and cardiac digital biomarkers representative of the heart rate behavior during the 6MWT and their evolution over time were investigated. Results: All participating patients improved in functional capacity throughout the CR program (P<.001). The heart rate parameters, which are commonly used in practice, evolved differently for both groups throughout CR. The peak heart rate (HR peak) from patients in the high-response group increased significantly throughout CR, while no change was observed in the low-response group (F 4,92=8.321, P<.001). Similar results were obtained for the recovery heart rate (, Circuits and Systems

Vignetting in camera lenses is generally seen as something to avoid. For spectral cameras with thin-film interference filters, however, we argue that vignetting can be an advantage. When illuminated by focused light, the bandwidth of interference filters increases with the chief-ray angle, causing position-dependent smoothing of the spectra. We show that vignetting can be used to reduce smoothing and preserve important spectral features. Furthermore, we demonstrate that by adding additional vignetting to a lens, measurements can be made more consistent across the scene. This makes vignetting a useful parameter during spectral camera design.

Cardiovascular diseases (CVD) are often characterized by their multifactorial complexity. This makes remote monitoring and ambulatory cardiac rehabilitation (CR) therapy challenging. Current wearable multimodal devices enable remote monitoring. Machine learning (ML) and artificial intelligence (AI) can help in tackling multifaceted datasets. However, for clinical acceptance, easy interpretability of the AI models is crucial. The goal of the present study was to investigate whether a multi-parameter sensor could be used during a standardized activity test to interpret functional capacity in the longitudinal follow-up of CR patients. A total of 129 patients were followed for 3 months during CR using 6-min walking tests (6MWT) equipped with a wearable ECG and accelerometer device. Functional capacity was assessed based on 6MWT distance (6MWD). Linear and nonlinear interpretable models were explored to predict 6MWD. The t-distributed stochastic neighboring embedding (t-SNE) technique was exploited to embed and visualize high dimensional data. The performance of support vector machine (SVM) models, combining different features and using different kernel types, to predict functional capacity was evaluated. The SVM model, using chronotropic response and effort as input features, showed a mean absolute error of 42.8 m (±36.8 m). The 3D-maps derived using the t-SNE technique visualized the relationship between sensor-derived biomarkers and functional capacity, which enables tracking of the evolution of patients throughout the CR program. The current study showed that wearable monitoring combined with interpretable ML can objectively track clinical progression in a CR population. These results pave the road towards ambulatory CR.

Cardiac rehabilitation (CR) is a highly recommended secondary prevention measure for patients with diagnosed cardiovascular disease. Unfortunately, participation rates are low due to enrollment and adherence issues. As such, new CR delivery strategies are of interest, as to improve overall CR delivery. The goal of the study was to obtain a better understanding of the short-term progression of functional capacity throughout multidisciplinary CR, measured as the change in walking distance between baseline six-minute walking test (6MWT) and four consecutive follow-up tests. One-hundred-and-twenty-nine patients diagnosed with cardiovascular disease participated in the study, of which 89 patients who completed the whole study protocol were included in the statistical analysis. A one-way repeated measures ANOVA was conducted to determine whether there was a significant change in mean 6MWT distance (6MWD) throughout CR. A three-way-mixed ANOVA was performed to determine the influence of categorical variables on the progression in 6MWD between groups. Significant differences in mean 6MWD between consecutive measurements were observed. Two subgroups were identified based on the change in distance between baseline and end-of-study. Patients who increased most showed a linear progression. In the other group progression leveled off halfway through rehabilitation. Moreover, the improvement during the initial phase of CR seemed to be indicative for overall progression. The current study adds to the understanding of the short-term progression in exercise capacity of patients diagnosed with cardiovascular disease throughout a CR program. The results are not only of interest for CR in general, but could be particularly relevant in the setting of home-based CR., Microelectronics, Signal Processing Systems

Cardiovascular diseases (CVD) are often characterized by their multifactorial complexity. This makes remote monitoring and ambulatory cardiac rehabilitation (CR) therapy challenging. Current wearable multimodal devices enable remote monitoring. Machine learning (ML) and artificial intelligence (AI) can help in tackling multifaceted datasets. However, for clinical acceptance, easy interpretability of the AI models is crucial. The goal of the present study was to investigate whether a multi-parameter sensor could be used during a standardized activity test to interpret functional capacity in the longitudinal follow-up of CR patients. A total of 129 patients were followed for 3 months during CR using 6-min walking tests (6MWT) equipped with a wearable ECG and accelerometer device. Functional capacity was assessed based on 6MWT distance (6MWD). Linear and nonlinear interpretable models were explored to predict 6MWD. The t-distributed stochastic neighboring embedding (t-SNE) technique was exploited to embed and visualize high dimensional data. The performance of support vector machine (SVM) models, combining different features and using different kernel types, to predict functional capacity was evaluated. The SVM model, using chronotropic response and effort as input features, showed a mean absolute error of 42.8 m (±36.8 m). The 3D-maps derived using the t-SNE technique visualized the relationship between sensor-derived biomarkers and functional capacity, which enables tracking of the evolution of patients throughout the CR program. The current study showed that wearable monitoring combined with interpretable ML can objectively track clinical progression in a CR population. These results pave the road towards ambulatory CR., Signal Processing Systems

Background: Cardiac rehabilitation (CR) is known for its beneficial effects on functional capacity and is a key component within current cardiovascular disease management strategies. In addition, a larger increase in functional capacity is accompanied by better clinical outcomes. However, not all patients respond in a similar way to CR. Therefore, a patient-tailored approach to CR could open up the possibility to achieve an optimal increase in functional capacity in every patient. Before treatment can be optimized, the differences in response of patients in terms of cardiac adaptation to exercise should first be understood. In addition, digital biomarkers to steer CR need to be identified. Objective: The aim of the study was to investigate the difference in cardiac response between patients characterized by a clear improvement in functional capacity and patients showing only a minor improvement following CR therapy. Methods: A total of 129 patients in CR performed a 6-minute walking test (6MWT) at baseline and during four consecutive short-term follow-up tests while being equipped with a wearable electrocardiogram (ECG) device. The 6MWTs were used to evaluate functional capacity. Patients were divided into high- and low-response groups, based on the improvement in functional capacity during the CR program. Commonly used heart rate parameters and cardiac digital biomarkers representative of the heart rate behavior during the 6MWT and their evolution over time were investigated. Results: All participating patients improved in functional capacity throughout the CR program (P<.001). The heart rate parameters, which are commonly used in practice, evolved differently for both groups throughout CR. The peak heart rate (HR peak) from patients in the high-response group increased significantly throughout CR, while no change was observed in the low-response group (F 4,92=8.321, P<.001). Similar results were obtained for the recovery heart rate (, Signal Processing Systems

Long-term heart rate (HR) monitoring by wrist-worn photoplethysmograph (PPG) sensors enables the assessment of health conditions during daily life with high user comfort. However, PPG signals are vulnerable to motion artifacts (MAs), which significantly affect the accuracy of estimated physiological parameters such as HR. This paper proposes a novel modular algorithm framework for MA removal based on different wavelengths for wrist-worn PPG sensors. The framework uses a green PPG signal for HR monitoring and an infrared PPG signal as the motion reference. The proposed framework includes four main steps: motion detection, motion removal using continuous wavelet transform, approximate HR estimation and signal reconstruction. The proposed algorithm is evaluated against an electrocardiogram (ECG) in terms of HR error for a dataset of 6 healthy subjects performing 21 types of motion. The proposed MA removal method reduced the average error in HR estimation from 4.3, 3.0 and 3.8 bpm to 0.6, 1.0 and 2.1 bpm in periodic, random, and continuous non-periodic motion situations, respectively.

Advancements in wireless sensor network technologies have enabled the proliferation of miniaturized body-worn sensors, capable of long-term pervasive biomedical signal monitoring. Remote cardiovascular monitoring has been one of the beneficiaries of this development, resulting in non-invasive, photoplethysmography (PPG) sensors being used in ambulatory settings. Wrist-worn PPG, although a popular alternative to electrocardiogram (ECG), suffers from motion artifacts inherent in daily life. Hence, in this paper, we present a novel deep learning framework (CorNET) to efficiently estimate heart rate (HR) information and perform biometric identification (BId) using only wrist-worn, single-channel PPG signal collected in ambulant environment. We have formulated a completely personalized data-driven approach, using a four-layer deep neural network. Two convolution neural network layers are used in conjunction with two long short-term memory layers, followed by a dense output layer for modelling the temporal sequence inherent within the pulsatile signal representative of cardiac activity. The final dense layer is customized with respect to the application, functioning as: a) regression layer - having a single neuron to predict HR; b) classification layer - two neurons which identifies a subject among a group. The proposed network was evaluated on the TROIKA dataset having 22 PPG records collected during various physical activities. We achieve a mean absolute error of 1.47±3.37 BPM for HR estimation and an average accuracy of 96% for BId on 20 subjects. CorNET was further evaluated successfully in an ambulant use-case scenario with custom sensors for two subjects.

Advancements in wireless sensor network technologies have enabled the proliferation of miniaturized body-worn sensors, capable of long-term pervasive biomedical signal monitoring. Remote cardiovascular monitoring has been one of the beneficiaries of this development, resulting in non-invasive, photoplethysmography (PPG) sensors being used in ambulatory settings. Wrist-worn PPG, although a popular alternative to electrocardiogram (ECG), suffers from motion artifacts inherent in daily life. Hence, in this paper, we present a novel deep learning framework (CorNET) to efficiently estimate heart rate (HR) information and perform biometric identification (BId) using only wrist-worn, single-channel PPG signal collected in ambulant environment. We have formulated a completely personalized data-driven approach, using a four-layer deep neural network. Two convolution neural network layers are used in conjunction with two long short-term memory layers, followed by a dense output layer for modelling the temporal sequence inherent within the pulsatile signal representative of cardiac activity. The final dense layer is customized with respect to the application, functioning as: a) regression layer - having a single neuron to predict HR; b) classification layer - two neurons which identifies a subject among a group. The proposed network was evaluated on the TROIKA dataset having 22 PPG records collected during various physical activities. We achieve a mean absolute error of 1.47±3.37 BPM for HR estimation and an average accuracy of 96% for BId on 20 subjects. CorNET was further evaluated successfully in an ambulant use-case scenario with custom sensors for two subjects.

Advancements in wireless sensor network technologies have enabled the proliferation of miniaturized body-worn sensors, capable of long-term pervasive biomedical signal monitoring. Remote cardiovascular monitoring has been one of the beneficiaries of this development, resulting in non-invasive, photoplethysmography (PPG) sensors being used in ambulatory settings. Wrist-worn PPG, although a popular alternative to electrocardiogram (ECG), suffers from motion artifacts inherent in daily life. Hence, in this paper, we present a novel deep learning framework (CorNET) to efficiently estimate heart rate (HR) information and perform biometric identification (BId) using only wrist-worn, single-channel PPG signal collected in ambulant environment. We have formulated a completely personalized data-driven approach, using a four-layer deep neural network. Two convolution neural network layers are used in conjunction with two long short-term memory layers, followed by a dense output layer for modelling the temporal sequence inherent within the pulsatile signal representative of cardiac activity. The final dense layer is customized with respect to the application, functioning as: a) regression layer - having a single neuron to predict HR; b) classification layer - two neurons which identifies a subject among a group. The proposed network was evaluated on the TROIKA dataset having 22 PPG records collected during various physical activities. We achieve a mean absolute error of 1.47±3.37 BPM for HR estimation and an average accuracy of 96% for BId on 20 subjects. CorNET was further evaluated successfully in an ambulant use-case scenario with custom sensors for two subjects.

Long-term heart rate (HR) monitoring by wrist-worn photoplethysmograph (PPG) sensors enables the assessment of health conditions during daily life with high user comfort. However, PPG signals are vulnerable to motion artifacts (MAs), which significantly affect the accuracy of estimated physiological parameters such as HR. This paper proposes a novel modular algorithm framework for MA removal based on different wavelengths for wrist-worn PPG sensors. The framework uses a green PPG signal for HR monitoring and an infrared PPG signal as the motion reference. The proposed framework includes four main steps: motion detection, motion removal using continuous wavelet transform, approximate HR estimation and signal reconstruction. The proposed algorithm is evaluated against an electrocardiogram (ECG) in terms of HR error for a dataset of 6 healthy subjects performing 21 types of motion. The proposed MA removal method reduced the average error in HR estimation from 4.3, 3.0 and 3.8 bpm to 0.6, 1.0 and 2.1 bpm in periodic, random, and continuous non-periodic motion situations, respectively.

Simultaneous measurement of Electrocardiogram (ECG) and bio-impedance (BioZ) via disposable health patches is desired for patients suffering from chronic cardiovascular and respiratory diseases. However, a sensing IC must consume ultra-low power under a sub-volt supply to comply with miniaturized and disposable batteries. This work presents a 0.6 V analog frontend (AFE) IC consisting of an instrumentation amplifier (IA), a current source (CS) and a SAR ADC. The AFE can measure ECG and BioZ simultaneously with a single IA by employing an orthogonal chopping scheme. To ensure the IA can tolerate up to 300mVpp DC electrode offset and 400mV pp common-mode (CM) interference, a DC-servo loop (DSL) combined with a common-mode feedforward (CMFF) loop is employed. A buffer-assisted scheme boosts the IA's input impedance by 7x to 140MΩ at 10Hz. To improve the BioZ sensitivity, the CG utilizes dynamic element matching to reduce the 1/f noise of the output current, leading to 35mΩ/√Hz BioZ sensitivity down to 1Hz. The ADC shows a 9.7b ENOB when sampled at 20ksps. The total power consumption of the AFE is 3.8μW., Bio-Electronics

Heart-rate estimation is a fundamental feature of modern wearable devices. In this paper we propose a machine learning technique to estimate heart-rate from electrocardiogram (ECG) data collected using wearable devices. The novelty of our approach lies in (1) encoding spatio-temporal properties of ECG signals directly into spike train and using this to excite recurrently connected spiking neurons in a Liquid State Machine computation model; (2) a novel learning algorithm; and (3) an intelligently designed unsupervised readout based on Fuzzy c-Means clustering of spike responses from a subset of neurons (Liquid states), selected using particle swarm optimization. Our approach differs from existing works by learning directly from ECG signals (allowing personalization), without requiring costly data annotations. Additionally, our approach can be easily implemented on state-of-the-art spiking-based neuromorphic systems, offering high accuracy, yet significantly low energy footprint, leading to an extended battery-life of wearable devices. We validated our approach with CARLsim, a GPU accelerated spiking neural network simulator modeling Izhikevich spiking neurons with Spike Timing Dependent Plasticity (STDP) and homeostatic scaling. A range of subjects is considered from in-house clinical trials and public ECG databases. Results show high accuracy and low energy footprint in heart-rate estimation across subjects with and without cardiac irregularities, signifying the strong potential of this approach to be integrated in future wearable devices.

In this paper, we introduce a data driven iterative low pass filtering technique, the Empirical Iterative Algorithm (EIA) for Galvanic Skin Response (GSR) signal preprocessing. This algorithm is inspired on Empirical Mode Decomposition (EMD), with performance enhancements provided by applying Midpoint-based Empirical Decomposition (MED), and removing the sifting process in order to make it computational inexpensive while maintaining effectiveness towards removal of high frequency artefacts. Based on GSR signals recorded at the wrist we present an algorithm benchmark, with results from EIA being compared with a smoothing technique based on moving average filter - commonly used to pre-process GSR signals. The comparison is established on data from 20 subjects, collected while performing 33 different randomized activities with right hand, left hand and both hands, respectively. In average, the proposed algorithm enhances the signal quality by 51%, while the traditional moving average filter reaches 16% enhancement. Also, it performs 136 times faster than the EMD in terms of average computational time. As a show case, using the GSR signal from one subject, we inspect the impact of applying our algorithm on GSR features with psychophysiological relevance. Comparison with no preprocessing and moving average filtering shows the ability of our algorithm to retain relevant low frequency information

Rapid advances in semiconductor fabrication technology have enabled the proliferation of miniaturized body-worn sensors capable of long term pervasive biomedical signal monitoring. In this paper, we present a novel deep learning-based framework (BiometricNET) on biometric identification using data collected from wrist-worn Photoplethysmography (PPG) signals in ambulatory environments. We have formulated a completely personalized data-driven approach, using a four-layer deep neural network - employing two convolution neural network (CNN) layers in conjunction with two long short-term memory (LSTM) layers, followed by a dense output layer for modelling the temporal sequence inherent within the pulsatile signal representative of cardiac activity. The proposed network configuration was evaluated on the TROIKA dataset collected from 12 subjects involved in physical activity, achieved an average five-fold cross-validation accuracy of 96%.

Heart-rate estimation is a fundamental feature of modern wearable devices. In this paper we propose a machine learning technique to estimate heart-rate from electrocardiogram (ECG) data collected using wearable devices. The novelty of our approach lies in (1) encoding spatio-temporal properties of ECG signals directly into spike train and using this to excite recurrently connected spiking neurons in a Liquid State Machine computation model; (2) a novel learning algorithm; and (3) an intelligently designed unsupervised readout based on Fuzzy c-Means clustering of spike responses from a subset of neurons (Liquid states), selected using particle swarm optimization. Our approach differs from existing works by learning directly from ECG signals (allowing personalization), without requiring costly data annotations. Additionally, our approach can be easily implemented on state-of-the-art spiking-based neuromorphic systems, offering high accuracy, yet significantly low energy footprint, leading to an extended battery-life of wearable devices. We validated our approach with CARLsim, a GPU accelerated spiking neural network simulator modeling Izhikevich spiking neurons with Spike Timing Dependent Plasticity (STDP) and homeostatic scaling. A range of subjects is considered from in-house clinical trials and public ECG databases. Results show high accuracy and low energy footprint in heart-rate estimation across subjects with and without cardiac irregularities, signifying the strong potential of this approach to be integrated in future wearable devices.

© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting /republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works, This paper presents a 1.2 V 36 µW reconfigurable analog front-end (R-AFE) as a general-purpose low-cost IC for multiple-mode biomedical signals acquisition. The R-AFE efficiently reuses a reconfigurable preamplifier, a current generator (CG), and a mixed signal processing unit, having an area of 1.1 mm2 per R-AFE while supporting five acquisition modes to record different forms of cardiovascular and respiratory signals. The R-AFE can interface with voltage-, current-, impedance-, and light-sensors and hence can measure electrocardiography (ECG), bio-impedance (BioZ), photoplethysmogram (PPG), galvanic skin response (GSR), and general-purpose analog signals. Thanks to the chopper preamplifier and the low-noise CG utilizing dynamic element matching, the R-AFE mitigates 1/f noise from both the preamplifier and the CG for improved measurement sensitivity. The IC achieves competitive performance compared to the state-of-the-art dedicated readout ICs of ECG, BioZ, GSR, and PPG, but with approximately 1.4×-5.3× smaller chip area per channel., Peer Reviewed, Postprint (author's final draft)

In this paper, we introduce a data driven iterative low pass filtering technique, the Empirical Iterative Algorithm (EIA) for Galvanic Skin Response (GSR) signal preprocessing. This algorithm is inspired on Empirical Mode Decomposition (EMD), with performance enhancements provided by applying Midpoint-based Empirical Decomposition (MED), and removing the sifting process in order to make it computational inexpensive while maintaining effectiveness towards removal of high frequency artefacts. Based on GSR signals recorded at the wrist we present an algorithm benchmark, with results from EIA being compared with a smoothing technique based on moving average filter - commonly used to pre-process GSR signals. The comparison is established on data from 20 subjects, collected while performing 33 different randomized activities with right hand, left hand and both hands, respectively. In average, the proposed algorithm enhances the signal quality by 51%, while the traditional moving average filter reaches 16% enhancement. Also, it performs 136 times faster than the EMD in terms of average computational time. As a show case, using the GSR signal from one subject, we inspect the impact of applying our algorithm on GSR features with psychophysiological relevance. Comparison with no preprocessing and moving average filtering shows the ability of our algorithm to retain relevant low frequency information

In this paper, we introduce a data driven iterative low pass filtering technique, the Empirical Iterative Algorithm (EIA) for Galvanic Skin Response (GSR) signal preprocessing. This algorithm is inspired on Empirical Mode Decomposition (EMD), with performance enhancements provided by applying Midpoint-based Empirical Decomposition (MED), and removing the sifting process in order to make it computational inexpensive while maintaining effectiveness towards removal of high frequency artefacts. Based on GSR signals recorded at the wrist we present an algorithm benchmark, with results from EIA being compared with a smoothing technique based on moving average filter - commonly used to pre-process GSR signals. The comparison is established on data from 20 subjects, collected while performing 33 different randomized activities with right hand, left hand and both hands, respectively. In average, the proposed algorithm enhances the signal quality by 51%, while the traditional moving average filter reaches 16% enhancement. Also, it performs 136 times faster than the EMD in terms of average computational time. As a show case, using the GSR signal from one subject, we inspect the impact of applying our algorithm on GSR features with psychophysiological relevance. Comparison with no preprocessing and moving average filtering shows the ability of our algorithm to retain relevant low frequency information

Rapid advances in semiconductor fabrication technology have enabled the proliferation of miniaturized body-worn sensors capable of long term pervasive biomedical signal monitoring. In this paper, we present a novel deep learning-based framework (BiometricNET) on biometric identification using data collected from wrist-worn Photoplethysmography (PPG) signals in ambulatory environments. We have formulated a completely personalized data-driven approach, using a four-layer deep neural network - employing two convolution neural network (CNN) layers in conjunction with two long short-term memory (LSTM) layers, followed by a dense output layer for modelling the temporal sequence inherent within the pulsatile signal representative of cardiac activity. The proposed network configuration was evaluated on the TROIKA dataset collected from 12 subjects involved in physical activity, achieved an average five-fold cross-validation accuracy of 96%.

Rapid advances in semiconductor fabrication technology have enabled the proliferation of miniaturized body-worn sensors capable of long term pervasive biomedical signal monitoring. In this paper, we present a novel deep learning-based framework (BiometricNET) on biometric identification using data collected from wrist-worn Photoplethysmography (PPG) signals in ambulatory environments. We have formulated a completely personalized data-driven approach, using a four-layer deep neural network - employing two convolution neural network (CNN) layers in conjunction with two long short-term memory (LSTM) layers, followed by a dense output layer for modelling the temporal sequence inherent within the pulsatile signal representative of cardiac activity. The proposed network configuration was evaluated on the TROIKA dataset collected from 12 subjects involved in physical activity, achieved an average five-fold cross-validation accuracy of 96%.

Active electrodes (AEs), i.e., electrodes with built-in readout circuitry, are increasingly being implemented in wearable healthcare and lifestyle applications due to AEs' robustness to environmental interference. An AE locally amplifies and buffers μV-level EEG signals before driving any cabling. The low output impedance of an AE mitigates cable motion artifacts, thus enabling the use of high-impedance dry electrodes for greater user comfort. However, developing a wearable EEG system, with medical grade signal quality on noise, electrode offset tolerance, common-mode rejection ratio, input impedance, and power dissipation, remains a challenging task. This paper reviews state-of-the-art bio-amplifier architectures and low-power analog circuits design techniques intended for wearable EEG acquisition, with a special focus on an AE system interfaced with dry electrodes., Accepted Author Manuscript, Microelectronics

Heart-rate estimation is a fundamental feature of modern wearable devices. In this paper we propose a machine intelligent approach for heart-rate estimation from electrocardiogram (ECG) data collected using wearable devices. The novelty of our approach lies in (1) encoding spatio-temporal properties of ECG signals directly into spike train and using this to excite recurrently connected spiking neurons in a Liquid State Machine computation model; (2) a novel learning algorithm; and (3) an intelligently designed unsupervised readout based on Fuzzy c-Means clustering of spike responses from a subset of neurons (Liquid states), selected using particle swarm optimization. Our approach differs from existing works by learning directly from ECG signals (allowing personalization), without requiring costly data annotations. Additionally, our approach can be easily implemented on state-of-the-art spiking-based neuromorphic systems, offering high accuracy, yet significantly low energy footprint, leading to an extended battery life of wearable devices. We validated our approach with CARLsim, a GPU accelerated spiking neural network simulator modeling Izhikevich spiking neurons with Spike Timing Dependent Plasticity (STDP) and homeostatic scaling. A range of subjects are considered from in-house clinical trials and public ECG databases. Results show high accuracy and low energy footprint in heart-rate estimation across subjects with and without cardiac irregularities, signifying the strong potential of this approach to be integrated in future wearable devices., Comment: 51 pages, 12 figures, 6 tables, 95 references. Under submission at Elsevier Neural Networks

This paper presents a digital active electrode (DAE) system for multi-parameter biopotential signal acquisition in portable and wearable devices. It is built around an IC that performs analog signal processing and digitization with the help of on-chip instrumentation amplifiers, a 12 bit ADC and a digital interface. Via a standard bus, up to 16 digital active electrodes (15-channels) can be connected to a commercially available microcontroller, thus significantly reducing system complexity and cost. In addition, the DAE utilizes an innovative functionally DC-coupled amplifier to preserve input DC signal, while still achieving state-of-the-art performance: 60 nV/sqrt(Hz) input-referred noise and 350 mV electrode-offset tolerance. A common-mode feedforward scheme improves the CMRR of an AE pair from 40 dB to maximum 102 dB., Electronic Instrumentation

This paper presents a digital active electrode (DAE) system for multi-parameter biopotential signal acquisition in portable and wearable devices. It is built around an IC that performs analog signal processing and digitization with the help of on-chip instrumentation amplifiers, a 12 bit ADC and a digital interface. Via a standard bus, up to 16 digital active electrodes (15-channels) can be connected to a commercially available microcontroller, thus significantly reducing system complexity and cost. In addition, the DAE utilizes an innovative functionally DC-coupled amplifier to preserve input DC signal, while still achieving state-of-the-art performance: 60 nV/sqrt(Hz) input-referred noise and 350 mV electrode-offset tolerance. A common-mode feedforward scheme improves the CMRR of an AE pair from 40 dB to maximum 102 dB., Electronic Instrumentation

Conventional gel electrodes are widely used for biopotential measurements, despite important drawbacks such as skin irritation, long set-up time and uncomfortable removal. Recently introduced dry electrodes with rigid metal pins overcome most of these problems; however, their rigidity causes discomfort and pain. This paper presents dry electrodes offering high user comfort, since they are fabricated from EPDM rubber containing various additives for optimum conductivity, flexibility and ease of fabrication. The electrode impedance is measured on phantoms and human skin. After optimization of the polymer composition, the skin-electrode impedance is only ~10 times larger than that of gel electrodes. Therefore, these electrodes are directly capable of recording strong biopotential signals such as ECG while for low-amplitude signals such as EEG, the electrodes need to be coupled with an active circuit. EEG recordings using active polymer electrodes connected to a clinical EEG system show very promising results: alpha waves can be clearly observed when subjects close their eyes, and correlation and coherence analyses reveal high similarity between dry and gel electrode signals. Moreover, all subjects reported that our polymer electrodes did not cause discomfort. Hence, the polymer-based dry electrodes are promising alternatives to either rigid dry electrodes or conventional gel electrodes.

Conventional gel electrodes are widely used for biopotential measurements, despite important drawbacks such as skin irritation, long set-up time and uncomfortable removal. Recently introduced dry electrodes with rigid metal pins overcome most of these problems; however, their rigidity causes discomfort and pain. This paper presents dry electrodes offering high user comfort, since they are fabricated from EPDM rubber containing various additives for optimum conductivity, flexibility and ease of fabrication. The electrode impedance is measured on phantoms and human skin. After optimization of the polymer composition, the skin-electrode impedance is only ~10 times larger than that of gel electrodes. Therefore, these electrodes are directly capable of recording strong biopotential signals such as ECG while for low-amplitude signals such as EEG, the electrodes need to be coupled with an active circuit. EEG recordings using active polymer electrodes connected to a clinical EEG system show very promising results: alpha waves can be clearly observed when subjects close their eyes, and correlation and coherence analyses reveal high similarity between dry and gel electrode signals. Moreover, all subjects reported that our polymer electrodes did not cause discomfort. Hence, the polymer-based dry electrodes are promising alternatives to either rigid dry electrodes or conventional gel electrodes.

Conventional gel electrodes are widely used for biopotential measurements, despite important drawbacks such as skin irritation, long set-up time and uncomfortable removal. Recently introduced dry electrodes with rigid metal pins overcome most of these problems; however, their rigidity causes discomfort and pain. This paper presents dry electrodes offering high user comfort, since they are fabricated from EPDM rubber containing various additives for optimum conductivity, flexibility and ease of fabrication. The electrode impedance is measured on phantoms and human skin. After optimization of the polymer composition, the skin-electrode impedance is only ~10 times larger than that of gel electrodes. Therefore, these electrodes are directly capable of recording strong biopotential signals such as ECG while for low-amplitude signals such as EEG, the electrodes need to be coupled with an active circuit. EEG recordings using active polymer electrodes connected to a clinical EEG system show very promising results: alpha waves can be clearly observed when subjects close their eyes, and correlation and coherence analyses reveal high similarity between dry and gel electrode signals. Moreover, all subjects reported that our polymer electrodes did not cause discomfort. Hence, the polymer-based dry electrodes are promising alternatives to either rigid dry electrodes or conventional gel electrodes.

This paper presents an active electrode system for gel-free biopotential EEG signal acquisition. The system consists of front-end chopper amplifiers and a back-end common-mode feedback (CMFB) circuit. The front-end AC-coupled chopper amplifier employs input impedance boosting and digitally-assisted offset trimming. The former increases the input impedance of the active electrode to 2 G at 1 Hz and the latter limits the chopping induced output ripple and residual offset to 2 mV and 20 mV respectively. Thanks to chopper stabilization, the active electrode achieves 0.8 μVrms (0.5-100 Hz) input referred noise. The use of a back-end CMFB circuit further improves the CMRR of the active electrode readout to 82 dB at 50 Hz. Both front-end and back-end circuits are implemented in a 0.18 μm CMOS process and the total current consumption of an 8-channel readout system is 88 μA from 1.8 V supply. EEG measurements using the proposed active electrode system demonstrate its benefits compared to passive electrode systems, namely reduced sensitivity to cable motion artifacts and mains interference., Accepted Author Manuscript, Electronic Instrumentation

Within the currently rising trend of heterogeneous microsystem integration and packaging, capillary self-assembly emerges as an innovative technique to enhance, complement and eventually replace pick-and-place assembly. Vast literature and experimental data support such claim. Still, the technique needs to overcome some important limitations in order to fully express its potential and earn wide industrial recognition. In this paper, we review and illustrate what are in our opinion the challenges ahead for making part-to-substrate capillary self-assembly reliable andseriously competitive with long-established assembly techniques. After setting self-assembly methods in the context of microsystemassembly and integration technologies, we focus on the standard embodiment of capillary self-assembly, and we describe in details the main, often novel technological steps required for its effective and reproducible performance. This preludes to an outline of what are presently, in our view, the major failure modes affecting the overall yield of the capillary self-assembly technique. Consequently, we propose solutions to face and overcome these challenges, which need to be met to foster the success of this technique., info:eu-repo/semantics/published

We report on the fabrication of aluminum gallium nitride (AlGaN) Schottky diodes for extreme ultraviolet (EUV) detection. AlGaN layers were grown on silicon wafers by molecular beam epitaxy with the conventional and inverted Schottky structure, where the undoped, active layer was grown before or after the n-doped layer, respectively. Different current mechanisms were observed in the two structures. The inverted Schottky diode was designed for the optimized backside sensitivity in the hybrid imagers. A cut-off wavelength of 280 nm was observed with three orders of magnitude intrinsic rejection ratio of the visible radiation. Furthermore, the inverted structure was characterized using a EUV source based on helium discharge and an open electrode design was used to improve the sensitivity. The characteristic He I and He II emission lines were observed at the wavelengths of 58.4 nm and 30.4 nm, respectively, proving the feasibility of using the inverted layer stack for EUV detection

We report on the fabrication of aluminum gallium nitride (AlGaN) Schottky diodes for extreme ultraviolet (EUV) detection. AlGaN layers were grown on silicon wafers by molecular beam epitaxy with the conventional and inverted Schottky structure, where the undoped, active layer was grown before or after the n-doped layer, respectively. Different current mechanisms were observed in the two structures. The inverted Schottky diode was designed for the optimized backside sensitivity in the hybrid imagers. A cut-off wavelength of 280 nm was observed with three orders of magnitude intrinsic rejection ratio of the visible radiation. Furthermore, the inverted structure was characterized using a EUV source based on helium discharge and an open electrode design was used to improve the sensitivity. The characteristic He I and He II emission lines were observed at the wavelengths of 58.4 nm and 30.4 nm, respectively, proving the feasibility of using the inverted layer stack for EUV detection

Within the currently rising trend of heterogeneous microsystem integration and packaging, capillary self-assembly emerges as an innovative technique to enhance, complement and eventually replace pick-and-place assembly. Vast literature and experimental data support such claim. Still, the technique needs to overcome some important limitations in order to fully express its potential and earn wide industrial recognition. In this paper, we review and illustrate what are in our opinion the challenges ahead for making part-to-substrate capillary self-assembly reliable andseriously competitive with long-established assembly techniques. After setting self-assembly methods in the context of microsystemassembly and integration technologies, we focus on the standard embodiment of capillary self-assembly, and we describe in details the main, often novel technological steps required for its effective and reproducible performance. This preludes to an outline of what are presently, in our view, the major failure modes affecting the overall yield of the capillary self-assembly technique. Consequently, we propose solutions to face and overcome these challenges, which need to be met to foster the success of this technique., info:eu-repo/semantics/published

Polymer photonic crystal fibres combine two relatively recent developments in fibre technology. On the one hand, polymer optical fibre has very different physical and chemical properties to silica. In particular, polymer fibre has a much smaller Young's modulus than silica, can survive higher strains, is amenable to organic chemical processing and, depending on the constituent polymer, may absorb water. All of these features can be utilised to extend the range of applications of optical fibre sensors. On the other hand, the photonic crystal - or microstructured - geometry also offers advantages: flexibility in the fibre design including control of the dispersion properties of core and cladding modes, the possibility of introducing minute quantities of analyte directly into the electric field of the guided light and enhanced pressure sensitivity. When brought together these two technologies provide interesting possibilities for fibre sensors, particularly when combined with fibre Bragg or long period gratings. This paper discusses the features of polymer photonic crystal fibre relevant to sensing and provides examples of the applications demonstrated to date.

In traditional electrical sensing applications, multiplexing and interconnecting the different sensing elements is a major challenge. Recently, many optical alternatives have been investigated including optical fiber sensors of which the sensing elements consist of fiber Bragg gratings. Different sensing points can be integrated in one optical fiber solving the interconnection problem and avoiding any electromagnetical interference (EMI). Many new sensing applications also require flexible or stretchable sensing foils which can be attached to or wrapped around irregularly shaped objects such as robot fingers and car bumpers or which can even be applied in biomedical applications where a sensor is fixed on a human body. The use of these optical sensors however always implies the use of a light-source, detectors and electronic circuitry to be coupled and integrated with these sensors. The coupling of these fibers with these light sources and detectors is a critical packaging problem and as it is well-known the costs for packaging, especially with optoelectronic components and fiber alignment issues are huge. The end goal of this embedded sensor is to create a flexible optical sensor integrated with (opto)electronic modules and control circuitry. To obtain this flexibility, one can embed the optical sensors and the driving optoelectronics in a stretchable polymer host material. In this article different embedding techniques for optical fiber sensors are described and characterized. Initial tests based on standard manufacturing processes such as molding and laser structuring are reported as well as a more advanced embedding technique based on soft lithography processing.

In this paper, we report on the strain and pressure testing of highly flexible skins embedded with Bragg grating sensors recorded in either silica or polymer optical fibre. The photonic skins, with a size of 10cm x 10cm and thickness of 1mm, were fabricated by embedding the polymer fibre or silica fibre containing Bragg gratings in Sylgard 184 from Dow Corning. Pressure sensing was studied using a cylindrical metal post placed on an array of points across the skin. The polymer fibre grating exhibits approximately 10 times the pressure sensitivity of the silica fibre and responds to the post even when it is placed a few centimetres away from the sensing fibre. Although the intrinsic strain sensitivities of gratings in the two fibre types are very similar, when embedded in the skin the polymer grating displayed a strain sensitivity approximately 45 times greater than the silica device, which also suffered from considerable hysteresis. The polymer grating displayed a near linear response over wavelength shifts of 9nm for 1% strain. The difference in behaviour we attribute to the much greater Young's modulus of the silica fibre (70 GPa) compared to the polymer fibre (3 GPa).

In traditional electrical sensing applications, multiplexing and interconnecting the different sensing elements is a major challenge. Recently, many optical alternatives have been investigated including optical fiber sensors of which the sensing elements consist of fiber Bragg gratings. Different sensing points can be integrated in one optical fiber solving the interconnection problem and avoiding any electromagnetical interference (EMI). Many new sensing applications also require flexible or stretchable sensing foils which can be attached to or wrapped around irregularly shaped objects such as robot fingers and car bumpers or which can even be applied in biomedical applications where a sensor is fixed on a human body. The use of these optical sensors however always implies the use of a light-source, detectors and electronic circuitry to be coupled and integrated with these sensors. The coupling of these fibers with these light sources and detectors is a critical packaging problem and as it is well-known the costs for packaging, especially with optoelectronic components and fiber alignment issues are huge. The end goal of this embedded sensor is to create a flexible optical sensor integrated with (opto)electronic modules and control circuitry. To obtain this flexibility, one can embed the optical sensors and the driving optoelectronics in a stretchable polymer host material. In this article different embedding techniques for optical fiber sensors are described and characterized. Initial tests based on standard manufacturing processes such as molding and laser structuring are reported as well as a more advanced embedding technique based on soft lithography processing.

Polymer photonic crystal fibres combine two relatively recent developments in fibre technology. On the one hand, polymer optical fibre has very different physical and chemical properties to silica. In particular, polymer fibre has a much smaller Young's modulus than silica, can survive higher strains, is amenable to organic chemical processing and, depending on the constituent polymer, may absorb water. All of these features can be utilised to extend the range of applications of optical fibre sensors. On the other hand, the photonic crystal - or microstructured - geometry also offers advantages: flexibility in the fibre design including control of the dispersion properties of core and cladding modes, the possibility of introducing minute quantities of analyte directly into the electric field of the guided light and enhanced pressure sensitivity. When brought together these two technologies provide interesting possibilities for fibre sensors, particularly when combined with fibre Bragg or long period gratings. This paper discusses the features of polymer photonic crystal fibre relevant to sensing and provides examples of the applications demonstrated to date.