Impact of information technology supported serious leisure gardening on the wellbeing of older adults: The Turntable project.

OBJECTIVE: The study presented in this paper aimed to assess the effect of an Information Technology enabled community gardening program for older adults, developed by an international consortium. METHODS: We have executed a quantitative, pre- and post-test field trial with older adult volunteers to test the proposed programme in two European countries, Italy and Belgium (n=98). We used standardized and ad hoc questionnaires to measure changes in the volunteers' mental and psychological state during the trial. The statistical data analysis sought for differences in the pre- and post-test values of the key scores related to the perceived quality of life and benefits of gardening via paired-samples t-tests, and also tried to identify the important factors of significant changes via logistic regression. RESULTS: We found significant improvements in the perceived benefits of gardening and also in the scores computed from the WHO Quality of Life instruments, especially in the social sub-domains. The improvements were associated with the country, age, marital state and education of the volunteers. Higher age or being widow, divorced or single increased the odds of a significant improvement in the scores in more than one sub-domains. CONCLUSION: Though the two trial settings were different in some aspects, the observed significant improvements generally confirmed the positive effects of gardening concerning the perceived quality of life and benefits of gardening.

Automatic signal quality assessment of raw trans-abdominal biopotential recordings for non-invasive fetal electrocardiography.

Introduction: Wearable monitoring systems for non-invasive multi-channel fetal electrocardiography (fECG) can support fetal surveillance and diagnosis during pregnancy, thus enabling prompt treatment. In these embedded systems, power saving is the key to long-term monitoring. In this regard, the computational burden of signal processing methods implemented for the fECG extraction from the multi-channel trans-abdominal recordings plays a non-negligible role. In this work, a supervised machine-learning approach for the automatic selection of the most informative raw abdominal recordings in terms of fECG content, i.e., those potentially leading to good-quality, non-invasive fECG signals from a low number of channels, is presented and evaluated. Methods: For this purpose, several signal quality indexes from the scientific literature were adopted as features to train an ensemble tree classifier, which was asked to perform a binary classification between informative and non-informative abdominal channels. To reduce the dimensionality of the classification problem, and to improve the performance, a feature selection approach was also implemented for the identification of a subset of optimal features. 10336 5-s long signal segments derived from a real dataset of multi-channel trans-abdominal recordings acquired from 55 voluntary pregnant women between the 21st and the 27th week of gestation, with healthy fetuses, were adopted to train and test the classification approach in a stratified 10-time 10-fold cross-validation scheme. Abdominal recordings were firstly pre-processed and then labeled as informative or non-informative, according to the signal-to-noise ratio exhibited by the extracted fECG, thus producing a balanced dataset of bad and good quality abdominal channels. Results and Discussion: Classification performance revealed an accuracy above 86%, and more than 88% of those channels labeled as informative were correctly identified. Furthermore, by applying the proposed method to 50 annotated 24-channel recordings from the NInFEA dataset, a significant improvement was observed in fetal QRS detection when only the channels selected by the proposed approach were considered, compared with the use of all the available channels. As such, our findings support the hypothesis that performing a channel selection by looking directly at the raw abdominal signals, regardless of the fetal presentation, can produce a reliable measurement of fetal heart rate with a lower computational burden.

To be assertive or not to be assertive: That is the question! Students' reactions to sexual harassment in academia.

Introduction: In the literature, no integrated definition of sexual harassment (SH) occurs but there is clear unanimity about SH being offensive, humiliating, and intimidating behavior. Within academic settings, SH has severe negative effects on students' physical or emotional wellbeing as well as on their ability to succeed academically. Methods: The aim of this study was to investigate the relationship between sex, gender roles, and the ways to manage SH (assertive and nonassertive reactions) in university students. It was hypothesized that female students would report more nonassertive reactions compared to male students. In addition, following the Bem theory on gender roles and using the self-report tool by the same author, it is hypothesized that female and male students, who are classified as feminine, will report more nonassertive responses, whereas male and female students, who are classified as masculine, will report more assertive responses. Our hypothesis was tested with a sample of 1,415 university students (593 men, 41.9%, and 822 women, 58.1%) who completed a questionnaire approved by the local ethical review board for research from the end of January 2019 to the first half of February 2019. Results: Contrary to our hypothesis, results showed that women react more than men in both assertive and nonassertive modalities. In addition, our results confirmed the main effect of both sex and gender roles on students' assertive and nonassertive reactions to SH in academia. Conclusion: Educational programs about SH may prove useful in preventing its occurrence. Gender equality plans in academia can improve a nonsexist and safe environment for students. It is urgent to improve transparency and accountability of policies on the management of SH: academic institutions need to formulate a procedure to facilitate SH reporting, considering the sensitive balance of confidentiality and transparency issues. Support for the victims (social services, healthcare, legal representation, and advice concerning career/professional development) must be included.

Impact of pulsed-wave-Doppler velocity-envelope tracing techniques on classification of complete fetal cardiac cycles.

Fetal echocardiography is an operator-dependent examination technique requiring a high level of expertise. Pulsed-wave Doppler (PWD) is often used as a reference for the mechanical activity of the heart, from which several quantitative parameters can be extracted. These aspects suggest the development of software tools that can reliably identify complete and clinically meaningful fetal cardiac cycles that can enable their automatic measurement. Several scientific works have addressed the tracing of the PWD velocity envelope. In this work, we assess the different steps involved in the signal processing chains that enable PWD envelope tracing. We apply a supervised classifier trained on envelopes traced by different signal processing chains for distinguishing complete and measurable PWD heartbeats from incomplete or malformed ones, which makes it possible to determine the impact of each of the different processing steps on the detection accuracy. In this study, we collected 43 images and labeled 174,319 PWD segments from 25 pregnant women volunteers. By considering seven envelope tracing techniques and the 23 different processing steps involved in their implementation, the results of our study reveal that, compared to the steps investigated in most other works, those that achieve binarisation and envelope extraction are significantly more important (p < 0.05). The best approaches among those studied enabled greater than 98% accuracy on our large manually annotated dataset.

A non-invasive multimodal foetal ECG-Doppler dataset for antenatal cardiology research.

Non-invasive foetal electrocardiography (fECG) continues to be an open topic for research. The development of standard algorithms for the extraction of the fECG from the maternal electrophysiological interference is limited by the lack of publicly available reference datasets that could be used to benchmark different algorithms while providing a ground truth for foetal heart activity when an invasive scalp lead is unavailable. In this work, we present the Non-Invasive Multimodal Foetal ECG-Doppler Dataset for Antenatal Cardiology Research (NInFEA), the first open-access multimodal early-pregnancy dataset in the field that features simultaneous non-invasive electrophysiological recordings and foetal pulsed-wave Doppler (PWD). The dataset is mainly conceived for researchers working on fECG signal processing algorithms. The dataset includes 60 entries from 39 pregnant women, between the 21st and 27th week of gestation. Each dataset entry comprises 27 electrophysiological channels (2048 Hz, 22 bits), a maternal respiration signal, synchronised foetal trans-abdominal PWD and clinical annotations provided by expert clinicians during signal acquisition. MATLAB snippets for data processing are also provided.

Systematic analysis of wavelet denoising methods for neural signal processing.

Objective.Among the different approaches for denoising neural signals, wavelet-based methods are widely used due to their ability to reduce in-band noise. All wavelet denoising algorithms have a common structure, but their effectiveness strongly depends on several implementation choices, including the mother wavelet, the decomposition level, the threshold definition, and the way it is applied (i.e. the thresholding). In this work, we investigated these factors to quantitatively assess their effects on neural signals in terms of noise reduction and morphology preservation, which are important when spike sorting is required downstream.Approach.Based on the spectral characteristics of the neural signal, according to the sampling rate of the signals, we considered two possible decomposition levels and identified the best-performing mother wavelet. Then, we compared different threshold estimation and thresholding methods and, for the best ones, we also evaluated their effect on clearing the approximation coefficients. The assessments were performed on synthetic signals that had been corrupted by different types of noise and on a murine peripheral nervous system dataset, both of which were sampled at about 16 kHz. The results were statistically analysed in terms of their Pearson's correlation coefficients, root-mean-square errors, and signal-to-noise ratios.Main results.As expected, the wavelet implementation choices greatly influenced the processing performance. Overall, the Haar wavelet with a five-level decomposition, hard thresholding method, and the threshold proposed by Hanet al(2007) achieved the best outcomes. Based on the adopted performance metrics, wavelet denoising with these parametrizations outperformed conventional 300-3000 Hz linear bandpass filtering.Significance.These results can be used to guide the reasoned and accurate selection of wavelet denoising implementation choices in the context of neural signal processing, particularly when spike-morphology preservation is required.

Annotated real and synthetic datasets for non-invasive foetal electrocardiography post-processing benchmarking.

Non-invasive foetal electrocardiography (fECG) can be obtained at different gestational ages by means of surface electrodes applied on the maternal abdomen. The signal-to-noise ratio (SNR) of the fECG is usually low, due to the small size of the foetal heart, the foetal-maternal compartment, the maternal physiological interferences and the instrumental noise. Even after powerful fECG extraction algorithms, a post-processing step could be required to improve the SNR of the fECG signal. In order to support the researchers in the field, this work presents an annotated dataset of real and synthetic signals, which was used for the study "Wavelet Denoising as a Post-Processing Enhancement Method for Non-Invasive Foetal Electrocardiography" [1]. Specifically, 21 15 s-long fECG, dual-channel signals obtained by multi-reference adaptive filtering from real electrophysiological recordings were included. The annotation of the foetal R peaks by an expert cardiologist was also provided. Recordings were performed on 17 voluntary pregnant women between the 21st and the 27th week of gestation. The raw recordings were also included for the researchers interested in applying a different fECG extraction algorithm. Moreover, 40 10 s-long synthetic non-invasive fECG were provided, simulating the electrode placement of one of the abdominal leads used for the real dataset. The annotation of the foetal R peaks was also provided, as generated by the FECGSYN tool used for the signals' creation. Clean fECG signals were also included for the computation of indexes of signal morphology preservation. All the signals are sampled at 2048 Hz. The data provided in this work can be used as a benchmark for fECG post-processing techniques but can also be used as raw signals for researchers interested in foetal QRS detection algorithms and fECG extraction methods.

A Novel Tool for Non-Invasive Fetal Electrocardiography Research: the NInFEA Dataset.

In this work, a novel open-source dataset for noninvasive fetal electrocardiography research is presented. It is composed of 60 high-quality electrophysiological recordings acquired between the 21st and the 27th weeks of gestation. For each acquisition, whose average duration is 30.5 s, 24 unipolar abdominal leads and three bipolar thoracic leads were included, along with a maternal respiration signal collected by a thoracic resistive belt. The chosen electrodes positioning map allows reproducing up to ten setups presented in the scientific literature. Each biopotential recording was acquired synchronously with the corresponding fetal cardiac pulsed-wave Doppler (PWD) signal, to provide complete information about the fetal cardiac cycle, both from the electrical and mechanical point of view.This is the first dataset allowing the non-invasive fetal ECG analysis even in early pregnancies with a ground truth about the fetal heart activity, given by the PWD signal. For this reason, it can be used to assess fetal ECG extraction algorithms requiring multiple channels, eventually including maternal references. This dataset is being released on Physionet by the end of June 2020 and will be continuously improved in the framework of the Non-Invasive Fetal ECG Analysis (NInFEA) project of the University of Cagliari (Italy).

Wavelet denoising as a post-processing enhancement method for non-invasive foetal electrocardiography.

BACKGROUND AND OBJECTIVE: The detection of a clean and undistorted foetal electrocardiogram (fECG) from non-invasive abdominal recordings is an open research issue. Several physiological and instrumental noise sources hamper this process, even after that powerful fECG extraction algorithms have been used. Wavelet denoising is widely used for the improvement of the SNR in biomedical signal processing. This work aims to systematically assess conventional and unconventional wavelet denoising approaches for the post-processing of fECG signals by providing evidence of their effectiveness in improving fECG SNR while preserving the morphology of the signal of interest. METHODS: The stationary wavelet transform (SWT) and the stationary wavelet packet transform (SWPT) were considered, due to their different granularity in the sub-band decomposition of the signal. Three thresholds from the literature, either conventional (Minimax and Universal) and unconventional, were selected. To this aim, the unconventional one was adapted for the first time to SWPT by trying different approaches. The decomposition depth was studied in relation to the characteristics of the fECG signal. Synthetic and real datasets, publicly available for benchmarking and research, were used for quantitative analysis in terms of noise reduction, foetal QRS detection performance and preservation of fECG morphology. RESULTS: The adoption of wavelet denoising approaches generally improved the SNR. Interestingly, the SWT methods outperformed the SWPT ones in morphology preservation (p<0.04) and SNR (p<0.0003), despite their coarser granularity in the sub-band analysis. Remarkably, the Han et al. threshold, adopted for the first time for fECG processing, provided the best quality improvement (p<0.003). CONCLUSIONS: The findings of our systematic analysis suggest that particular care must be taken when selecting and using wavelet denoising for non-invasive fECG signal post-processing. In particular, despite the general noise reduction capability, signal morphology can be significantly altered on the basis of the parameterization of the wavelet methods. Remarkably, the adoption of a finer sub-band decomposition provided by the wavelet packet was not able to improve the quality of the processing.

Automatic detection of complete and measurable cardiac cycles in antenatal pulsed-wave Doppler signals.

BACKGROUND AND OBJECTIVE: Pulsed-wave Doppler (PWD) echocardiography is the primary tool for antenatal cardiological diagnosis. Based on it, different measurements and validated reference parameters can be extracted. The automatic detection of complete and measurable cardiac cycles would represent a useful tool for the quality assessment of the PWD trace and the automated analysis of long traces. METHODS: This work proposes and compares three different algorithms for this purpose, based on the preliminary extraction of the PWD velocity spectrum envelopes: template matching, supervised classification over a reduced set of relevant waveshape features, and supervised classification over the whole waveshape potentially representing a cardiac cycle. A custom dataset comprising 43 fetal cardiac PWD traces (174,319 signal segments) acquired on an apical five-chamber window was developed and used for the assessment of the different algorithms. RESULTS: The adoption of a supervised classifier trained with the samples representing the upper and lower envelopes of the PWD, with additional features extracted from the image, achieved significantly better results (p < 0.0001) than the other algorithms, with an average accuracy of 98% ± 1% when using an SVM classifier and a leave-one-subject-out cross-validation. Further, the robustness of the results with respect to the classifier model was proved. CONCLUSIONS: The results reveal excellent detection performance, suggesting that the proposed approach can be adopted for the automatic analysis of long PWD traces or embedded in ultrasound machines as a first step for the extraction of measurements and reference clinical parameters.

Morphological Neural Computation Restores Discrimination of Naturalistic Textures in Trans-radial Amputees.

Humans rely on their sense of touch to interact with the environment. Thus, restoring lost tactile sensory capabilities in amputees would advance their quality of life. In particular, texture discrimination is an important component for the interaction with the environment, but its restoration in amputees has been so far limited to simplified gratings. Here we show that naturalistic textures can be discriminated by trans-radial amputees using intraneural peripheral stimulation and tactile sensors located close to the outer layer of the artificial skin. These sensors exploit the morphological neural computation (MNC) approach, i.e., the embodiment of neural computational functions into the physical structure of the device, encoding normal and shear stress to guarantee a faithful neural temporal representation of stimulus spatial structure. Two trans-radial amputees successfully discriminated naturalistic textures via the MNC-based tactile feedback. The results also allowed to shed light on the relevance of spike temporal encoding in the mechanisms used to discriminate naturalistic textures. Our findings pave the way to the development of more natural bionic limbs.

Design and Usability Assessment of a Multi-Device SOA-Based Telecare Framework for the Elderly.

Telemonitoring is a branch of telehealth that aims at remotely monitoring vital signs, which is important for chronically ill patients and the elderly living alone. The available standalone devices and applications for the self-monitoring of health parameters largely suffer from interoperability problems; meanwhile, telemonitoring medical devices are expensive, self-contained, and are not integrated into user-friendly technological platforms for the end user. This paper presents the technical aspects and usability assessment of the telemonitoring features of the HEREiAM platform, which supports heterogeneous information technology systems. By exploiting a service-oriented architecture, the measured parameters collected by off-the-shelf Bluetooth medical devices are sent as XML documents to a private cloud that implements an interoperable health service infrastructure, which is compliant with the most recent healthcare standards and security protocols. This Android-based system is designed to be accessible both via TV and portable devices, and includes other utilities designed to support the elderly living alone. Four usability assessment sessions with quality-validated questionnaires were performed to accurately understand the ease of use, usefulness, acceptance, and quality of the proposed system. The results reveal that our system achieved very high usability scores even at its first use, and the scores did not significantly change over time during a field trial that lasted for four months, reinforcing the idea of an intuitive design. At the end of such a trial, the user-experience questionnaire achieved excellent scores in all aspects with respect to the benchmark. Good results were also reported by general practitioners who assessed the quality of their remote interfaces for telemonitoring.

Systematic analysis of single- and multi-reference adaptive filters for non-invasive fetal electrocardiography.

Non-invasive fetal electrocardiography (ECG) has been a research challenge for the past few decades. Due to instrumental noise and the spectral overlap of the maternal ECG signal, the signal-to-noise ratio for fetal ECG is very low. Various techniques have been proposed for cancelling the maternal ECG signal and extracting the fetal QRS complex from non-invasive abdominal recordings. Of these, adaptive filters enable satisfactory extraction when there is only a limited number of signal channels available, but the extraction quality is strongly dependent on the electrode placement. In this work, we systematically analyze this issue by comparing single- and multi-reference implementations of QRD-recursive least square (RLS) adaptive filters and evaluating their performances on real and simulated data in terms of the signal-to-interference ratio (SIR), maternal ECG attenuation, and fetal-QRS-complex detection accuracy. Beyond demonstrating the expected superior performance of the multi-reference version (p < 0.05) with respect to all metrics, except the QRS detection accuracy on synthetic data, we also analyze in detail the effectiveness of this technique with different lead orientations with respect to the correct interpretation of the adopted quality indexes. The results reveal that the single-reference approach, which is preferred when only the fetal heart rate is of interest, cannot produce a signal that has acceptable fetal QRS detection accuracy, regardless of the reference lead selection.

Comparison of Single- and Multi-reference QRD-RLS adaptive filter for non-invasive fetal electrocardiography.

Non-invasive fetal electrocardiography (ECG) would allow accessing very relevant information on fetal cardiac function, especially for arrhythmias. However, the signal-to-noise ratio is significantly low, since fetal ECG is embedded in instrumental noise and spectrally overlapping maternal electrophysiological interferences. Among the different techniques proposed in the scientific literature, some variants of adaptive filters have been proposed for maternal ECG cancellation and fetal QRS complex enhancement. Such techniques encompass approaches using one or more reference signals, which is an important aspect for the development of accurate and unobtrusive monitoring systems.In this work, this aspect is systematically analyzed by comparing single- and multi-reference implementations of the QRD-RLS adaptive filter, and by challenging them in the fetal ECG enhancement on three abdominal leads differently oriented in space. The performance is assessed on real data in terms of signal-to-interference ratio, detection of fetal QRS complexes and maternal ECG attenuation. Multi-reference implementation reveals its superiority, whereas the single-reference implementation suffers from the electrodes positioning and cannot be trustily used even for the fetal heart rate only on the adopted dataset.

Six-Month Assessment of a Hand Prosthesis with Intraneural Tactile Feedback.

OBJECTIVE: Hand amputation is a highly disabling event, which significantly affects quality of life. An effective hand replacement can be achieved if the user, in addition to motor functions, is provided with the sensations that are naturally perceived while grasping and moving. Intraneural peripheral electrodes have shown promising results toward the restoration of the sense of touch. However, the long-term usability and clinical relevance of intraneural sensory feedback have not yet been clearly demonstrated. METHODS: To this aim, we performed a 6-month clinical study with 3 transradial amputees who received implants of transverse intrafascicular multichannel electrodes (TIMEs) in their median and ulnar nerves. After calibration, electrical stimulation was delivered through the TIMEs connected to artificial sensors in the digits of a prosthesis to generate sensory feedback, which was then used by the subjects while performing different grasping tasks. RESULTS: All subjects, notwithstanding their important clinical differences, reported stimulation-induced sensations from the phantom hand for the whole duration of the trial. They also successfully integrated the sensory feedback into their motor control strategies while performing experimental tests simulating tasks of real life (with and without the support of vision). Finally, they reported a decrement of their phantom limb pain and a general improvement in mood state. INTERPRETATION: The promising results achieved with all subjects show the feasibility of the use of intraneural stimulation in clinical settings. ANN NEUROL 2019;85:137-154.

Correction: An automated system for the objective evaluation of human gustatory sensitivity using tongue biopotential recordings.

[This corrects the article DOI: 10.1371/journal.pone.0177246.].

Automatic Recognition of Complete Atrioventricular Activity in Fetal Pulsed-Wave Doppler Signals.

Echocardiography is the gold standard for antenatal cardiological assessment. However, the adoption of this technique is challenging, since it is intrinsically operator-dependent and because of the different confounding factors related to the fetal heart size, the fetal movements and the ultrasound artifacts. Among the different options, fetal echocardiography is widely used, concurring to an early diagnosis of several cardiac pathologies. In this work, a neural network-based algorithm targeted at the identification of the most important features of Doppler fetal echocardiography videos is presented and evaluated on real signals. Compared to other approaches, the proposed algorithm works on a couple of ID signals, representing the pulse-wave Doppler envelope extracted from the video, thus preserving a Iightweight approach. For the validation, a small dataset was created, including recordings from five voluntary pregnant women 21st to 27th gestational week), for a total of 20 records, 10 seconds each. The dataset was annotated by an expert cardiologist in order to identify the epochs of the signal where a complete readable cardiac cycle could be identified. The performance of the method was evaluated through a 5-fold cross-validation. An average accuracy up to 88% was obtained, confirming the validity of the proposed approach and paving the way to future improvements of the technique.

Functional estimation of bony segment lengths using magneto-inertial sensing: Application to the humerus.

Inertial sensor technology has assumed an increasingly important role in the field of human motion analysis. However, the reliability of the kinematic estimates could still be critical for specific applications in the field of functional evaluation and motor rehabilitation. Within this context, the definition of subject-specific multi-body kinematic models is crucial since it affects the accuracy and repeatability of movement reconstruction. A key step for kinematic model calibration is the determination of bony segment lengths. This study proposes a functional approach for the in vivo estimation of the humerus length using a single magneto-inertial measurement unit (MIMU) positioned on the right distal posterior forearm. The humerus length was estimated as the distance between the shoulder elevation axis and the elbow flexion-extension axis. The calibration exercise involved five shoulder elevations in the sagittal plane with the elbow completely extended and five elbow flexion-extensions with the upper arm rigidly aligned to the trunk. Validation of the method was conducted on five healthy subjects using the humerus length computed from magnetic resonance imaging as the gold standard. The method showed mean absolute errors of 12 ± 9 mm, which were in the estimate of the humerus length. When using magneto-inertial technology, the proposed functional method represents a promising alternative to the regressive methods or manual measurements for performing kinematic model calibrations. Although the proposed methodology was validated for the estimation of the humerus length, the same approach can be potentially extended to other body segments.

Exploiting All Programmable SoCs in Neural Signal Analysis: A Closed-Loop Control for Large-Scale CMOS Multielectrode Arrays.

Microelectrode array (MEA) systems with up to several thousands of recording electrodes and electrical or optical stimulation capabilities are commercially available or described in the literature. By exploiting their submillisecond and micrometric temporal and spatial resolutions to record bioelectrical signals, such emerging MEA systems are increasingly used in neuroscience to study the complex dynamics of neuronal networks and brain circuits. However, they typically lack the capability of implementing real-time feedback between the detection of neuronal spiking events and stimulation, thus restricting large-scale neural interfacing to open-loop conditions. In order to exploit the potential of such large-scale recording systems and stimulation, we designed and validated a fully reconfigurable FPGA-based processing system for closed-loop multichannel control. By adopting a Xilinx Zynq-all-programmable system on chip that integrates reconfigurable logic and a dual-core ARM-based processor on the same device, the proposed platform permits low-latency preprocessing (filtering and detection) of spikes acquired simultaneously from several thousands of electrode sites. To demonstrate the proposed platform, we tested its performances through ex vivo experiments on the mice retina using a state-of-the-art planar high-density MEA that samples 4096 electrodes at 18 kHz and record light-evoked spikes from several thousands of retinal ganglion cells simultaneously. Results demonstrate that the platform is able to provide a total latency from whole-array data acquisition to stimulus generation below 2 ms. This opens the opportunity to design closed-loop experiments on neural systems and biomedical applications using emerging generations of planar or implantable large-scale MEA systems.