Evaluation of a Voltametric E-Tongue Combined with Data Preprocessing for Fast and Effective Machine Learning-Based Classification of Tomato Purées by Cultivar.

The potential of a voltametric E-tongue coupled with a custom data pre-processing stage to improve the performance of machine learning techniques for rapid discrimination of tomato purées between cultivars of different economic value has been investigated. To this aim, a sensor array with screen-printed carbon electrodes modified with gold nanoparticles (GNP), copper nanoparticles (CNP) and bulk gold subsequently modified with poly(3,4-ethylenedioxythiophene) (PEDOT), was developed to acquire data to be transformed by a custom pre-processing pipeline and then processed by a set of commonly used classifiers. The GNP and CNP-modified electrodes, selected based on their sensitivity to soluble monosaccharides, demonstrated good ability in discriminating samples of different cultivars. Among the different data analysis methods tested, Linear Discriminant Analysis (LDA) proved to be particularly suitable, obtaining an average F1 score of 99.26%. The pre-processing stage was beneficial in reducing the number of input features, decreasing the computational cost, i.e., the number of computing operations to be performed, of the entire method and aiding future cost-efficient hardware implementation. These findings proved that coupling the multi-sensing platform featuring properly modified sensors with the custom pre-processing method developed and LDA provided an optimal tradeoff between analytical problem solving and reliable chemical information, as well as accuracy and computational complexity. These results can be preliminary to the design of hardware solutions that could be embedded into low-cost portable devices.

A Higher Kick Frequency Swimming Training Program Optimizes Swim-to-Cycle Transition in Triathlon.

ABSTRACT: Ambrosini, L, Presta, V, Vitale, M, Menegatti, E, Guarnieri, A, Bianchi, V, De Munari, I, Condello, G, and Gobbi, G. A higher kick frequency swimming training program optimizes swim-to-cycle transition in triathlon. J Strength Cond Res 38(5): 976-984, 2024-The purpose of this study was to evaluate the effect of an 8-week swimming training program on biomechanical and physiological responses during a swim-to-cycle simulation. Fifteen triathletes were randomly allocated to 3 groups: a 6-beat-kick group (K6), a 4-beat-kick group (K4), and a control group (CG). Biomechanical and physiological parameters were evaluated during a 400-m swim and a 10-minute cycle segment before (Pretraining) and after (Posttraining) the program. A lower stroke frequency ( p = 0.004) and a higher stroke length ( p = 0.002) was found in K6 compared with CG at Posttraining. A reduction in the K6 emerged between Pretraining and Posttraining during cycling for heart rate ( p = 0.005), V̇O 2 ( p = 0.014), and energy expenditure ( p = 0.008). A positive association emerged between swim kick index and cycling cadence in the K6 group. The improvement in stroke frequency and length observed in the K6 group could be explained as an improvement in swimming technique. Similarly, the reduction in energy expenditure during cycling at Posttraining for the K6 group suggests an improvement in the working economy. Triathlon coaches and athletes should consider the inclusion of high swim kick into their training programs to enhance swim and cycling performance, which can ultimately lead to an improvement in the swim-to-cycle transition and the overall triathlon performance.

Smart Immunosensors for Point-of-Care Serological Tests Aimed at Assessing Natural or Vaccine-Induced SARS-CoV-2 Immunity.

Innovative and highly performing smart voltammetric immunosensors for rapid and effective serological tests aimed at the determination of SARS-CoV-2 antibodies were developed and validated in human serum matrix. Two immunosensors were developed for the determination of immunoglobulins directed against either the nucleocapsid or the spike viral antigen proteins. The immunosensors were realized using disposable screen-printed electrodes modified with nanostructured materials for the immobilization of the antigens. Fast quantitative detection was achieved, with analysis duration being around 1 h. Signal readout was carried out through a smart, compact and battery-powered potentiostat, based on a Wi-Fi protocol and devised for the Internet of Things (IoT) paradigm. This device is used for the acquisition, storage and sharing of clinical data. Outstanding immunosensors' sensitivity, specificity and accuracy (100%) were assessed, according to the diagnostic guidelines for epidemiological data. The overall performance of the sensing devices, combined with the portability of the IoT-based device, enables their suitability as a high-throughput diagnostic tool. Both of the immunosensors were validated using clinical human serum specimens from SARS-CoV-2 infected patients, provided by IRCCS Ospedale San Raffaele.

Rapid Quantification of SARS-Cov-2 Spike Protein Enhanced with a Machine Learning Technique Integrated in a Smart and Portable Immunosensor.

An IoT-WiFi smart and portable electrochemical immunosensor for the quantification of SARS-CoV-2 spike protein was developed with integrated machine learning features. The immunoenzymatic sensor is based on the immobilization of monoclonal antibodies directed at the SARS-CoV-2 S1 subunit on Screen-Printed Electrodes functionalized with gold nanoparticles. The analytical protocol involves a single-step sample incubation. Immunosensor performance was validated in a viral transfer medium which is commonly used for the desorption of nasopharyngeal swabs. Remarkable specificity of the response was demonstrated by testing H1N1 Hemagglutinin from swine-origin influenza A virus and Spike Protein S1 from Middle East respiratory syndrome coronavirus. Machine learning was successfully used for data processing and analysis. Different support vector machine classifiers were evaluated, proving that algorithms affect the classifier accuracy. The test accuracy of the best classification model in terms of true positive/true negative sample classification was 97.3%. In addition, the ML algorithm can be easily integrated into cloud-based portable Wi-Fi devices. Finally, the immunosensor was successfully tested using a third generation replicating incompetent lentiviral vector pseudotyped with SARS-CoV-2 spike glycoprotein, thus proving the applicability of the immunosensor to whole virus detection.

A Self-Calibrating IoT Portable Electrochemical Immunosensor for Serum Human Epididymis Protein 4 as a Tumor Biomarker for Ovarian Cancer.

Nowadays, analytical techniques are moving towards the development of smart biosensing strategies for the point-of-care accurate screening of disease biomarkers, such as human epididymis protein 4 (HE4), a recently discovered serum marker for early ovarian cancer diagnosis. In this context, the present work represents the first implementation of a competitive enzyme-labelled magneto-immunoassay exploiting a homemade IoT Wi-Fi cloud-based portable potentiostat for differential pulse voltammetry readout. The electrochemical device was specifically designed to be capable of autonomous calibration and data processing, switching between calibration, and measurement modes: in particular, firstly, a baseline estimation algorithm is applied for correct peak computation, then calibration function is built by interpolating data with a four-parameter logistic function. The calibration function parameters are stored on the cloud for inverse prediction to determine the concentration of unknown samples. Interpolation function calibration and concentration evaluation are performed directly on-board, thus reducing the power consumption. The analytical device was validated in human serum, demonstrating good sensing performance for analysis of HE4 with detection and quantitation limits in human serum of 3.5 and 29.2 pM, respectively, reaching the sensitivity that is required for diagnostic purposes, with high potential for applications as portable and smart diagnostic tool for point-of-care testing.

A Model-Based Design Floating-Point Accumulator. Case of Study: FPGA Implementation of a Support Vector Machine Kernel Function.

Recent research in wearable sensors have led to the development of an advanced platform capable of embedding complex algorithms such as machine learning algorithms, which are known to usually be resource-demanding. To address the need for high computational power, one solution is to design custom hardware platforms dedicated to the specific application by exploiting, for example, Field Programmable Gate Array (FPGA). Recently, model-based techniques and automatic code generation have been introduced in FPGA design. In this paper, a new model-based floating-point accumulation circuit is presented. The architecture is based on the state-of-the-art delayed buffering algorithm. This circuit was conceived to be exploited in order to compute the kernel function of a support vector machine. The implementation of the proposed model was carried out in Simulink, and simulation results showed that it had better performance in terms of speed and occupied area when compared to other solutions. To better evaluate its figure, a practical case of a polynomial kernel function was considered. Simulink and VHDL post-implementation timing simulations and measurements on FPGA confirmed the good results of the stand-alone accumulator.

Automatic diet monitoring: a review of computer vision and wearable sensor-based methods.

Food intake and eating habits have a significant impact on people's health. Widespread diseases, such as diabetes and obesity, are directly related to eating habits. Therefore, monitoring diet can be a substantial base for developing methods and services to promote healthy lifestyle and improve personal and national health economy. Studies have demonstrated that manual reporting of food intake is inaccurate and often impractical. Thus, several methods have been proposed to automate the process. This article reviews the most relevant and recent researches on automatic diet monitoring, discussing their strengths and weaknesses. In particular, the article reviews two approaches to this problem, accounting for most of the work in the area. The first approach is based on image analysis and aims at extracting information about food content automatically from food images. The second one relies on wearable sensors and has the detection of eating behaviours as its main goal.

Plug&Play Brain-Computer Interfaces for effective Active and Assisted Living control.

Brain-Computer Interfaces (BCI) rely on the interpretation of brain activity to provide people with disabilities with an alternative/augmentative interaction path. In light of this, BCI could be considered as enabling technology in many fields, including Active and Assisted Living (AAL) systems control. Interaction barriers could be removed indeed, enabling user with severe motor impairments to gain control over a wide range of AAL features. In this paper, a cost-effective BCI solution, targeted (but not limited) to AAL system control is presented. A custom hardware module is briefly reviewed, while signal processing techniques are covered in more depth. Steady-state visual evoked potentials (SSVEP) are exploited in this work as operating BCI protocol. In contrast with most common SSVEP-BCI approaches, we propose the definition of a prediction confidence indicator, which is shown to improve overall classification accuracy. The confidence indicator is derived without any subject-specific approach and is stable across users: it can thus be defined once and then shared between different persons. This allows some kind of Plug&Play interaction. Furthermore, by modelling rest/idle periods with the confidence indicator, it is possible to detect active control periods and separate them from "background activity": this is capital for real-time, self-paced operation. Finally, the indicator also allows to dynamically choose the most appropriate observation window length, improving system's responsiveness and user's comfort. Good results are achieved under such operating conditions, achieving, for instance, a false positive rate of 0.16 min-1, which outperform current literature findings.

A plug&play Brain Computer Interface solution for AAL systems.

We present a complete BCI-enabled (Brain Computer Interface) solution for Ambient Assisted Living system control. BCI are alternative, augmentative communication means capable of exploiting just the brain waveforms to infer intent, thus potentially posing as a technological bridge capable of overcoming limitations in the usual neuromuscular pathways. The module was completely developed in a customized way, encompassing hardware and software components. We demonstrate the effectiveness of the approach on a practical control scenario in which the user can issue 4 different commands, at his own pace and will, in real-time. No initial calibration is necessary, in line with the aimed plug&play approach. Results are very promising, especially in false positives rejection, well improving over literature.

An Identification Procedure for Behavioral Analysis in a Multi-User environment.

As the average age of the EU population increases, ICT solutions are going to play a key role in order to find answers to the new challenges the demographic change is carrying on. At the University of Parma an AAL (Ambient Assisted Living) system named CARDEA has been developed during the last 10 years. Within CARDEA, behavioral analysis is carried out, based on environmental sensors. If multiple users live in the same environment, however, data coming from sensors need to be properly tagged: in this paper, a simple technique for such tagging is proposed, which exploits the same wireless transmission used for transmitting data, thus not requiring additional hardware components and avoiding more complex and expensive (radio)localization techniques. Preliminary results are shown, featuring a satisfactory accuracy.

Self-tuning behavioral analysis in AAL "FOOD" project pilot environments.

Behavioral analysis, based on unobtrusive monitoring through environmental sensors, is expected to increase health awareness of AAL systems. In this paper, techniques for assessing behavioral quantitative features are discussed, suitable for detecting behavioral anomalies in an unsupervised fashion, i.e., with no need of defining target reference behaviors and of tuning user-specific threshold parameters. Such technique is being exploited for analyzing data coming from a set of European pilot sites, in the framework of the EU/AAL-JP project "FOOD", specifically focused at kitchen activity. Simple results are illustrated, suitable for proof-of-concept validation.

Action tagging in a multi-user indoor environment for behavioural analysis purposes.

EU population is getting older, so that ICT-based solutions are expected to provide support in the challenges implied by the demographic change. At the University of Parma an AAL (Ambient Assisted Living) system, named CARDEA, has been developed. In this paper a new feature of the system is introduced, in which environmental and personal (i.e., wearable) sensors coexist, providing an accurate picture of the user's activity and needs. Environmental devices may greatly help in performing activity recognition and behavioral analysis tasks. However, in a multi-user environment, this implies the need of attributing environmental sensors outcome to a specific user, i.e., identifying the user when he performs a task detected by an environmental device. We implemented such an "action tagging" feature, based on information fusion, within the CARDEA environment, as an inexpensive, alternative solution to the problematic issue of indoor locationing.

SSVEP-based BCI: A "Plug & play" approach.

Brain-Computer Interface (BCI) can provide users with an alternative/augmentative interaction path, based on the interpretation of their brain activity. Steady State Visual Evoked Potentials (SSVEP) paradigm has many appealing features, aiming at implementing BCI-enabled communication-control applications. In this paper, we present a complete signal processing chain for a self-paced, SSVEP-based BCI. The proposed approach mostly focuses at reducing the user effort in dealing with BCI, featuring no need of user-specific calibration or training. In this paper, the classification algorithm is introduced and first validated on offline waveforms, aiming at improving classification accuracy and minimizing the false positive rate. Then, implementation of an online, self-paced SSVEP BCI is illustrated. The scheme refers to a four-way choice and exploits discrimination between intentional control states and nocontrol ones. Good performance is achieved, both in terms of true positive rate (>94%), as well as low false positive rate (0.26 min(-1)), even in experiments carried out outside lab-controlled conditions.

Light on! Real world evaluation of a P300-based brain-computer interface (BCI) for environment control in a smart home.

Brain-computer interface (BCI) systems aim to enable interaction with other people and the environment without muscular activation by the exploitation of changes in brain signals due to the execution of cognitive tasks. In this context, the visual P300 potential appears suited to control smart homes through BCI spellers. The aim of this work is to evaluate whether the widely used character-speller is more sustainable than an icon-based one, designed to operate smart home environment or to communicate moods and needs. Nine subjects with neurodegenerative diseases and no BCI experience used both speller types in a real smart home environment. User experience during BCI tasks was evaluated recording concurrent physiological signals. Usability was assessed for each speller type immediately after use. Classification accuracy was lower for the icon-speller, which was also more attention demanding. However, in subjective evaluations, the effect of a real feedback partially counterbalanced the difficulty in BCI use. PRACTITIONER SUMMARY: Since inclusive BCIs require to consider interface sustainability, we evaluated different ergonomic aspects of the interaction of disabled users with a character-speller (goal: word spelling) and an icon-speller (goal: operating a real smart home). We found the first one as more sustainable in terms of accuracy and cognitive effort.