Andrea Guidi

Bio: Andrea Guidi is an academic researcher from University of Pisa. The author has contributed to research in topics: Mood & Mood swing. The author has an hindex of 9, co-authored 20 publications receiving 360 citations.

A synergy-based hand control is encoded in human motor cortical areas

Abstract: How the human brain controls hand movements to carry out different tasks is still debated. The concept of synergy has been proposed to indicate functional modules that may simplify the control of hand postures by simultaneously recruiting sets of muscles and joints. However, whether and to what extent synergic hand postures are encoded as such at a cortical level remains unknown. Here, we combined kinematic, electromyography, and brain activity measures obtained by functional magnetic resonance imaging while subjects performed a variety of movements towards virtual objects. Hand postural information, encoded through kinematic synergies, were represented in cortical areas devoted to hand motor control and successfully discriminated individual grasping movements, significantly outperforming alternative somatotopic or muscle-based models. Importantly, hand postural synergies were predicted by neural activation patterns within primary motor cortex. These findings support a novel cortical organization for hand movement control and open potential applications for brain-computer interfaces and neuroprostheses.

Speech analysis for mood state characterization in bipolar patients

Abstract: Bipolar disorders are characterized by an unpredictable behavior, resulting in depressive, hypomanic or manic episodes alternating with euthymic states. A multi-parametric approach can be followed to estimate mood states by integrating information coming from different physiological signals and from the analysis of voice. In this work we propose an algorithm to estimate speech features from running speech with the aim of characterizing the mood state in bipolar patients. This algorithm is based on an automatic segmentation of speech signals to detect voiced segments, and on a spectral matching approach to estimate pitch and pitch changes. In particular average pitch, jitter and pitch standard deviation within each voiced segment, are estimated. The performances of the algorithm are evaluated on a speech database, which includes an electroglottographic signal. A preliminary analysis on subjects affected by bipolar disorders is performed and results are discussed.

A wearable system for the evaluation of the human-horse interaction: A preliminary study

Abstract: This study reports on a preliminary estimation of the human-horse interaction through the analysis of the heart rate variability (HRV) in both human and animal by using the dynamic time warping (DTW) algorithm. Here, we present a wearable system for HRV monitoring in horses. Specifically, we first present a validation of a wearable electrocardiographic (ECG) monitoring system for horses in terms of comfort and robustness, then we introduce a preliminary objective estimation of the human-horse interaction. The performance of the proposed wearable system for horses was compared with a standard system in terms of movement artifact (MA) percentage. Seven healthy horses were monitored without any movement constraints. As a result, the lower amount of MA% of the wearable system suggests that it could be profitably used for reliable measurement of physiological parameters related to the autonomic nervous system (ANS) activity in horses, such as the HRV. Human-horse interaction estimation was achieved through the analysis of their HRV time series. Specifically, DTW was applied to estimate dynamic coupling between human and horse in a group of fourteen human subjects and one horse. Moreover, a support vector machine (SVM) classifier was able to recognize the three classes of interaction with an accuracy greater than 78%. Preliminary significant results showed the discrimination of three distinct real human-animal interaction levels. These results open the measurement and characterization of the already empirically-proven relationship between human and horse.

Smartphone Application for the Analysis of Prosodic Features in Running Speech with a Focus on Bipolar Disorders: System Performance Evaluation and Case Study

Abstract: Bipolar disorder is one of the most common mood disorders characterized by large and invalidating mood swings. Several projects focus on the development of decision support systems that monitor and advise patients, as well as clinicians. Voice monitoring and speech signal analysis can be exploited to reach this goal. In this study, an Android application was designed for analyzing running speech using a smartphone device. The application can record audio samples and estimate speech fundamental frequency, F0, and its changes. F0-related features are estimated locally on the smartphone, with some advantages with respect to remote processing approaches in terms of privacy protection and reduced upload costs. The raw features can be sent to a central server and further processed. The quality of the audio recordings, algorithm reliability and performance of the overall system were evaluated in terms of voiced segment detection and features estimation. The results demonstrate that mean F0 from each voiced segment can be reliably estimated, thus describing prosodic features across the speech sample. Instead, features related to F0 variability within each voiced segment performed poorly. A case study performed on a bipolar patient is presented.

A Novel Algorithm for Movement Artifact Removal in ECG Signals Acquired from Wearable Systems Applied to Horses.

Abstract: This study reports on a novel method to detect and reduce the contribution of movement artifact (MA) in electrocardiogram (ECG) recordings gathered from horses in free movement conditions. We propose a model that integrates cardiovascular and movement information to estimate the MA contribution. Specifically, ECG and physical activity are continuously acquired from seven horses through a wearable system. Such a system employs completely integrated textile electrodes to monitor ECG and is also equipped with a triaxial accelerometer for movement monitoring. In the literature, the most used technique to remove movement artifacts, when noise bandwidth overlaps the primary source bandwidth, is the adaptive filter. In this study we propose a new algorithm, hereinafter called Stationary Wavelet Movement Artifact Reduction (SWMAR), where the Stationary Wavelet Transform (SWT) decomposition algorithm is employed to identify and remove movement artifacts from ECG signals in horses. A comparative analysis with the Normalized Least Mean Square Adaptive Filter technique (NLMSAF) is performed as well. Results achieved on seven hours of recordings showed a reduction greater than 40% of MA percentage (between before- and after- the application of the proposed algorithm). Moreover, the comparative analysis with the NLMSAF, applied to the same ECG recordings, showed a greater reduction of MA percentage in favour of SWMAR with a statistical significant difference (p–value < 0.0.5).

Speech emotion recognition using deep 1D & 2D CNN LSTM networks

Abstract: We aimed at learning deep emotion features to recognize speech emotion Two convolutional neural network and long short-term memory (CNN LSTM) networks, one 1D CNN LSTM network and one 2D CNN LSTM network, were constructed to learn local and global emotion-related features from speech and log-mel spectrogram respectively The two networks have the similar architecture, both consisting of four local feature learning blocks (LFLBs) and one long short-term memory (LSTM) layer LFLB, which mainly contains one convolutional layer and one max-pooling layer, is built for learning local correlations along with extracting hierarchical correlations LSTM layer is adopted to learn long-term dependencies from the learned local features The designed networks, combinations of the convolutional neural network (CNN) and LSTM, can take advantage of the strengths of both networks and overcome the shortcomings of them, and are evaluated on two benchmark databases The experimental results show that the designed networks achieve excellent performance on the task of recognizing speech emotion, especially the 2D CNN LSTM network outperforms the traditional approaches, Deep Belief Network (DBN) and CNN on the selected databases The 2D CNN LSTM network achieves recognition accuracies of 9533% and 9589% on Berlin EmoDB of speaker-dependent and speaker-independent experiments respectively, which compare favourably to the accuracy of 916% and 929% obtained by traditional approaches; and also yields recognition accuracies of 8916% and 5214% on IEMOCAP database of speaker-dependent and speaker-independent experiments, which are much higher than the accuracy of 7378% and 4002% obtained by DBN and CNN

Toward a new generation of smart skins

Abstract: Rapid advances in soft electronics, microfabrication technologies, miniaturization and electronic skins are facilitating the development of wearable sensor devices that are highly conformable and intimately associated with human skin. These devices—referred to as ‘smart skins’—offer new opportunities in the research study of human biology, in physiological tracking for fitness and wellness applications, and in the examination and treatment of medical conditions. Over the past 12 months, electronic skins have been developed that are self-healing, intrinsically stretchable, designed into an artificial afferent nerve, and even self-powered. Greater collaboration between engineers, biologists, informaticians and clinicians will be required for smart skins to realize their full potential and attain wide adoption in a diverse range of real-world settings.

Representational models: A common framework for understanding encoding, pattern-component, and representational-similarity analysis.

Abstract: Representational models specify how activity patterns in populations of neurons (or, more generally, in multivariate brain-activity measurements) relate to sensory stimuli, motor responses, or cognitive processes. In an experimental context, representational models can be defined as hypotheses about the distribution of activity profiles across experimental conditions. Currently, three different methods are being used to test such hypotheses: encoding analysis, pattern component modeling (PCM), and representational similarity analysis (RSA). Here we develop a common mathematical framework for understanding the relationship of these three methods, which share one core commonality: all three evaluate the second moment of the distribution of activity profiles, which determines the representational geometry, and thus how well any feature can be decoded from population activity. Using simulated data for three different experimental designs, we compare the power of the methods to adjudicate between competing representational models. PCM implements a likelihood-ratio test and therefore provides the most powerful test if its assumptions hold. However, the other two approaches-when conducted appropriately-can perform similarly. In encoding analysis, the linear model needs to be appropriately regularized, which effectively imposes a prior on the activity profiles. With such a prior, an encoding model specifies a well-defined distribution of activity profiles. In RSA, the unequal variances and statistical dependencies of the dissimilarity estimates need to be taken into account to reach near-optimal power in inference. The three methods render different aspects of the information explicit (e.g. single-response tuning in encoding analysis and population-response representational dissimilarity in RSA) and have specific advantages in terms of computational demands, ease of use, and extensibility. The three methods are properly construed as complementary components of a single data-analytical toolkit for understanding neural representations on the basis of multivariate brain-activity data.

Medical Instrumentation Application And Design

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Learning from Data

Abstract: This chapter contains sections titled: Learning Machine Statistical Learning Theory Types of Learning Methods Common Learning Tasks Model Estimation Review Questions and Problems References for further study