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Article ; Online: The spatio-temporal architecture of everyday manual behavior.

Sili, Daniele / De Giorgi, Chiara / Pizzuti, Alessandra / Spezialetti, Matteo / de Pasquale, Francesco / Betti, Viviana

2023 Volume 13, Issue 1, Page(s) 9451

Abstract: In everyday activities, humans move alike to manipulate objects. Prior works suggest that hand movements are built by a limited set of basic building blocks consisting of a set of common postures. However, how the low dimensionality of hand movements ... ...

Abstract	In everyday activities, humans move alike to manipulate objects. Prior works suggest that hand movements are built by a limited set of basic building blocks consisting of a set of common postures. However, how the low dimensionality of hand movements supports the adaptability and flexibility of natural behavior is unknown. Through a sensorized glove, we collected kinematics data from thirty-six participants preparing and having breakfast in naturalistic conditions. By means of an unbiased analysis, we identified a repertoire of hand states. Then, we tracked their transitions over time. We found that manual behavior can be described in space through a complex organization of basic configurations. These, even in an unconstrained experiment, recurred across subjects. A specific temporal structure, highly consistent within the sample, seems to integrate such identified hand shapes to realize skilled movements. These findings suggest that the simplification of the motor commands unravels in the temporal dimension more than in the spatial one.
MeSH term(s)	Humans ; Psychomotor Performance ; Hand ; Movement ; Posture ; Biomechanical Phenomena
Language	English
Publishing date	2023-06-09
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2615211-3
ISSN	2045-2322 ; 2045-2322
ISSN (online)	2045-2322
ISSN	2045-2322
DOI	10.1038/s41598-023-36280-4
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Article ; Online: Encoding Manual Dexterity through Modulation of Intrinsic α Band Connectivity.

Maddaluno, Ottavia / Della Penna, Stefania / Pizzuti, Alessandra / Spezialetti, Matteo / Corbetta, Maurizio / de Pasquale, Francesco / Betti, Viviana

The Journal of neuroscience : the official journal of the Society for Neuroscience

2024 Volume 44, Issue 20

Abstract: The human hand possesses both consolidated motor skills and remarkable flexibility in adapting to ongoing task demands. However, the underlying mechanisms by which the brain balances stability and flexibility remain unknown. In the absence of external ... ...

Abstract	The human hand possesses both consolidated motor skills and remarkable flexibility in adapting to ongoing task demands. However, the underlying mechanisms by which the brain balances stability and flexibility remain unknown. In the absence of external input or behavior, spontaneous (intrinsic) brain connectivity is thought to represent a prior of stored memories. In this study, we investigated how manual dexterity modulates spontaneous functional connectivity in the motor cortex during hand movement. Using magnetoencephalography, in 47 human participants (both sexes), we examined connectivity modulations in the α and β frequency bands at rest and during two motor tasks (i.e., finger tapping or toe squeezing). The flexibility and stability of such modulations allowed us to identify two groups of participants with different levels of performance (high and low performers) on the nine-hole peg test, a test of manual dexterity. In the α band, participants with higher manual dexterity showed distributed decreases of connectivity, specifically in the motor cortex, increased segregation, and reduced nodal centrality. Participants with lower manual dexterity showed an opposite pattern. Notably, these patterns from the brain to behavior are mirrored by results from behavior to the brain. Indeed, when participants were divided using the median split of the dexterity score, we found the same connectivity patterns. In summary, this experiment shows that a long-term motor skill-manual dexterity-influences the way the motor systems respond during movements.
MeSH term(s)	Humans ; Male ; Female ; Adult ; Motor Cortex/physiology ; Motor Skills/physiology ; Young Adult ; Magnetoencephalography/methods ; Alpha Rhythm/physiology ; Hand/physiology ; Psychomotor Performance/physiology ; Movement/physiology ; Neural Pathways/physiology
Language	English
Publishing date	2024-05-15
Publishing country	United States
Document type	Journal Article
ZDB-ID	604637-x
ISSN	1529-2401 ; 0270-6474
ISSN (online)	1529-2401
ISSN	0270-6474
DOI	10.1523/JNEUROSCI.1766-23.2024
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Article ; Online: Emotion Recognition for Human-Robot Interaction: Recent Advances and Future Perspectives.

Spezialetti, Matteo / Placidi, Giuseppe / Rossi, Silvia

Frontiers in robotics and AI

2020 Volume 7, Page(s) 532279

Abstract: A fascinating challenge in the field of human-robot interaction is the possibility to endow robots with emotional intelligence in order to make the interaction more intuitive, genuine, and natural. To achieve this, a critical point is the capability of ... ...

Abstract	A fascinating challenge in the field of human-robot interaction is the possibility to endow robots with emotional intelligence in order to make the interaction more intuitive, genuine, and natural. To achieve this, a critical point is the capability of the robot to infer and interpret human emotions. Emotion recognition has been widely explored in the broader fields of human-machine interaction and affective computing. Here, we report recent advances in emotion recognition, with particular regard to the human-robot interaction context. Our aim is to review the state of the art of currently adopted emotional models, interaction modalities, and classification strategies and offer our point of view on future developments and critical issues. We focus on facial expressions, body poses and kinematics, voice, brain activity, and peripheral physiological responses, also providing a list of available datasets containing data from these modalities.
Language	English
Publishing date	2020-12-21
Publishing country	Switzerland
Document type	Journal Article ; Review
ZDB-ID	2781824-X
ISSN	2296-9144 ; 2296-9144
ISSN (online)	2296-9144
ISSN	2296-9144
DOI	10.3389/frobt.2020.532279
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Book ; Online: On the $k$-Hamming and $k$-Edit Distances

Epifanio, Chiara / Forlizzi, Luca / Marzi, Francesca / Mignosi, Filippo / Placidi, Giuseppe / Spezialetti, Matteo

2023

Abstract: In this paper we consider the weighted $k$-Hamming and $k$-Edit distances, that are natural generalizations of the classical Hamming and Edit distances. As main results of this paper we prove that for any $k\geq 2$ the DECIS-$k$-Hamming problem is $\ ... ...

Abstract	In this paper we consider the weighted $k$-Hamming and $k$-Edit distances, that are natural generalizations of the classical Hamming and Edit distances. As main results of this paper we prove that for any $k\geq 2$ the DECIS-$k$-Hamming problem is $\mathbb{P}$-SPACE-complete and the DECIS-$k$-Edit problem is NEXPTIME-complete. Comment: Submitted
Keywords	Computer Science - Computational Complexity ; Computer Science - Computation and Language ; Computer Science - Data Structures and Algorithms
Publishing date	2023-06-15
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Measurements by A LEAP-Based Virtual Glove for the Hand Rehabilitation.

Placidi, Giuseppe / Cinque, Luigi / Polsinelli, Matteo / Spezialetti, Matteo

Sensors (Basel, Switzerland)

2018 Volume 18, Issue 3

Abstract: Hand rehabilitation is fundamental after stroke or surgery. Traditional rehabilitation requires a therapist and implies high costs, stress for the patient, and subjective evaluation of the therapy effectiveness. Alternative approaches, based on ... ...

Abstract	Hand rehabilitation is fundamental after stroke or surgery. Traditional rehabilitation requires a therapist and implies high costs, stress for the patient, and subjective evaluation of the therapy effectiveness. Alternative approaches, based on mechanical and tracking-based gloves, can be really effective when used in virtual reality (VR) environments. Mechanical devices are often expensive, cumbersome, patient specific and hand specific, while tracking-based devices are not affected by these limitations but, especially if based on a single tracking sensor, could suffer from occlusions. In this paper, the implementation of a multi-sensors approach, the Virtual Glove (VG), based on the simultaneous use of two orthogonal LEAP motion controllers, is described. The VG is calibrated and static positioning measurements are compared with those collected with an accurate spatial positioning system. The positioning error is lower than 6 mm in a cylindrical region of interest of radius 10 cm and height 21 cm. Real-time hand tracking measurements are also performed, analysed and reported. Hand tracking measurements show that VG operated in real-time (60 fps), reduced occlusions, and managed two LEAP sensors correctly, without any temporal and spatial discontinuity when skipping from one sensor to the other. A video demonstrating the good performance of VG is also collected and presented in the Supplementary Materials. Results are promising but further work must be done to allow the calculation of the forces exerted by each finger when constrained by mechanical tools (e.g., peg-boards) and for reducing occlusions when grasping these tools. Although the VG is proposed for rehabilitation purposes, it could also be used for tele-operation of tools and robots, and for other VR applications.
MeSH term(s)	Gloves, Protective ; Hand ; Hand Strength ; Humans ; Stroke Rehabilitation ; User-Computer Interface ; Virtual Reality
Language	English
Publishing date	2018-03-10
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2052857-7
ISSN	1424-8220 ; 1424-8220
ISSN (online)	1424-8220
ISSN	1424-8220
DOI	10.3390/s18030834
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Book ; Online: Entropy and Compression

Aragona, Riccardo / Marzi, Francesca / Mignosi, Filippo / Spezialetti, Matteo

A simple proof of an inequality of Khinchin-Ornstein-Shields

2019

Abstract: This paper concerns the folklore statement that ``entropy is a lower bound for compression''. More precisely we derive from the entropy theorem a simple proof of a pointwise inequality firstly stated by Ornstein and Shields and which is the almost-sure ... ...

Abstract	This paper concerns the folklore statement that ``entropy is a lower bound for compression''. More precisely we derive from the entropy theorem a simple proof of a pointwise inequality firstly stated by Ornstein and Shields and which is the almost-sure version of an average inequality firstly stated by Khinchin in 1953. We further give an elementary proof of original Khinchin inequality that can be used as an exercise for Information Theory students and we conclude by giving historical and technical notes of such inequality. Comment: Compared to version 1, in version 2 we added a simpler proof than the one given by Shields of a more general theorem (Theorem 4, pg. 7) presented by Ornstein and Shields. Consequently we also modified the title of the paper. In version 3 we have reordered the sections of the paper, simplified the proof of Theorem 4 (now Theorem 3) and significantly reduced the proof of Theorem 3 (now Theorem 4)
Keywords	Computer Science - Information Theory ; 94A15 ; 94A17
Subject code	514
Publishing date	2019-07-10
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: A Classification Algorithm for Electroencephalography Signals by Self-Induced Emotional Stimuli.

Iacoviello, Daniela / Petracca, Andrea / Spezialetti, Matteo / Placidi, Giuseppe

IEEE transactions on cybernetics

2016 Volume 46, Issue 12, Page(s) 3171–3180

Abstract: The aim of this paper is to propose a real-time classification algorithm for the low-amplitude electroencephalography (EEG) signals, such as those produced by remembering an unpleasant odor, to drive a brain-computer interface. The peculiarity of these ... ...

Abstract	The aim of this paper is to propose a real-time classification algorithm for the low-amplitude electroencephalography (EEG) signals, such as those produced by remembering an unpleasant odor, to drive a brain-computer interface. The peculiarity of these EEG signals is that they require ad hoc signals preprocessing by wavelet decomposition, and the definition of a set of features able to characterize the signals and to discriminate among different conditions. The proposed method is completely parameterized, aiming at a multiclass classification and it might be considered in the framework of machine learning. It is a two stages algorithm. The first stage is offline and it is devoted to the determination of a suitable set of features and to the training of a classifier. The second stage, the real-time one, is to test the proposed method on new data. In order to avoid redundancy in the set of features, the principal components analysis is adapted to the specific EEG signal characteristics and it is applied; the classification is performed through the support vector machine. Experimental tests on ten subjects, demonstrating the good performance of the algorithm in terms of both accuracy and efficiency, are also reported and discussed.
Language	English
Publishing date	2016-12
Publishing country	United States
Document type	Journal Article
ISSN	2168-2275
ISSN (online)	2168-2275
DOI	10.1109/TCYB.2015.2498974
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Article ; Online: A Modular Framework for EEG Web Based Binary Brain Computer Interfaces to Recover Communication Abilities in Impaired People.

Placidi, Giuseppe / Petracca, Andrea / Spezialetti, Matteo / Iacoviello, Daniela

Journal of medical systems

2016 Volume 40, Issue 1, Page(s) 34

Abstract: A Brain Computer Interface (BCI) allows communication for impaired people unable to express their intention with common channels. Electroencephalography (EEG) represents an effective tool to allow the implementation of a BCI. The present paper describes ... ...

Abstract	A Brain Computer Interface (BCI) allows communication for impaired people unable to express their intention with common channels. Electroencephalography (EEG) represents an effective tool to allow the implementation of a BCI. The present paper describes a modular framework for the implementation of the graphic interface for binary BCIs based on the selection of symbols in a table. The proposed system is also designed to reduce the time required for writing text. This is made by including a motivational tool, necessary to improve the quality of the collected signals, and by containing a predictive module based on the frequency of occurrence of letters in a language, and of words in a dictionary. The proposed framework is described in a top-down approach through its modules: signal acquisition, analysis, classification, communication, visualization, and predictive engine. The framework, being modular, can be easily modified to personalize the graphic interface to the needs of the subject who has to use the BCI and it can be integrated with different classification strategies, communication paradigms, and dictionaries/languages. The implementation of a scenario and some experimental results on healthy subjects are also reported and discussed: the modules of the proposed scenario can be used as a starting point for further developments, and application on severely disabled people under the guide of specialized personnel.
MeSH term(s)	Brain-Computer Interfaces ; Disabled Persons ; Electroencephalography/instrumentation ; Humans ; Internet
Language	English
Publishing date	2016-01
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	423488-1
ISSN	1573-689X ; 0148-5598
ISSN (online)	1573-689X
ISSN	0148-5598
DOI	10.1007/s10916-015-0402-4
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Article ; Online: A real-time classification algorithm for EEG-based BCI driven by self-induced emotions.

Iacoviello, Daniela / Petracca, Andrea / Spezialetti, Matteo / Placidi, Giuseppe

Computer methods and programs in biomedicine

2015 Volume 122, Issue 3, Page(s) 293–303

Abstract: Background and objective: The aim of this paper is to provide an efficient, parametric, general, and completely automatic real time classification method of electroencephalography (EEG) signals obtained from self-induced emotions. The particular ... ...

Abstract	Background and objective: The aim of this paper is to provide an efficient, parametric, general, and completely automatic real time classification method of electroencephalography (EEG) signals obtained from self-induced emotions. The particular characteristics of the considered low-amplitude signals (a self-induced emotion produces a signal whose amplitude is about 15% of a really experienced emotion) require exploring and adapting strategies like the Wavelet Transform, the Principal Component Analysis (PCA) and the Support Vector Machine (SVM) for signal processing, analysis and classification. Moreover, the method is thought to be used in a multi-emotions based Brain Computer Interface (BCI) and, for this reason, an ad hoc shrewdness is assumed. Method: The peculiarity of the brain activation requires ad-hoc signal processing by wavelet decomposition, and the definition of a set of features for signal characterization in order to discriminate different self-induced emotions. The proposed method is a two stages algorithm, completely parameterized, aiming at a multi-class classification and may be considered in the framework of machine learning. The first stage, the calibration, is off-line and is devoted at the signal processing, the determination of the features and at the training of a classifier. The second stage, the real-time one, is the test on new data. The PCA theory is applied to avoid redundancy in the set of features whereas the classification of the selected features, and therefore of the signals, is obtained by the SVM. Results: Some experimental tests have been conducted on EEG signals proposing a binary BCI, based on the self-induced disgust produced by remembering an unpleasant odor. Since in literature it has been shown that this emotion mainly involves the right hemisphere and in particular the T8 channel, the classification procedure is tested by using just T8, though the average accuracy is calculated and reported also for the whole set of the measured channels. Conclusions: The obtained classification results are encouraging with percentage of success that is, in the average for the whole set of the examined subjects, above 90%. An ongoing work is the application of the proposed procedure to map a large set of emotions with EEG and to establish the EEG headset with the minimal number of channels to allow the recognition of a significant range of emotions both in the field of affective computing and in the development of auxiliary communication tools for subjects affected by severe disabilities.
MeSH term(s)	Adult ; Algorithms ; Brain-Computer Interfaces/statistics & numerical data ; Computer Systems ; Electroencephalography ; Emotions/physiology ; Humans ; Male ; Principal Component Analysis
Language	English
Publishing date	2015-12
Publishing country	Ireland
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	632564-6
ISSN	1872-7565 ; 0169-2607
ISSN (online)	1872-7565
ISSN	0169-2607
DOI	10.1016/j.cmpb.2015.08.011
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Article ; Online: Design of an efficient framework for fast prototyping of customized human-computer interfaces and virtual environments for rehabilitation.

Avola, Danilo / Spezialetti, Matteo / Placidi, Giuseppe

Computer methods and programs in biomedicine

2013 Volume 110, Issue 3, Page(s) 490–502

Abstract: Rehabilitation is often required after stroke, surgery, or degenerative diseases. It has to be specific for each patient and can be easily calibrated if assisted by human-computer interfaces and virtual reality. Recognition and tracking of different ... ...

Abstract	Rehabilitation is often required after stroke, surgery, or degenerative diseases. It has to be specific for each patient and can be easily calibrated if assisted by human-computer interfaces and virtual reality. Recognition and tracking of different human body landmarks represent the basic features for the design of the next generation of human-computer interfaces. The most advanced systems for capturing human gestures are focused on vision-based techniques which, on the one hand, may require compromises from real-time and spatial precision and, on the other hand, ensure natural interaction experience. The integration of vision-based interfaces with thematic virtual environments encourages the development of novel applications and services regarding rehabilitation activities. The algorithmic processes involved during gesture recognition activity, as well as the characteristics of the virtual environments, can be developed with different levels of accuracy. This paper describes the architectural aspects of a framework supporting real-time vision-based gesture recognition and virtual environments for fast prototyping of customized exercises for rehabilitation purposes. The goal is to provide the therapist with a tool for fast implementation and modification of specific rehabilitation exercises for specific patients, during functional recovery. Pilot examples of designed applications and preliminary system evaluation are reported and discussed.
MeSH term(s)	Algorithms ; Gestures ; Humans ; Pilot Projects ; Recovery of Function ; Rehabilitation/methods ; Rehabilitation/statistics & numerical data ; Software ; User-Computer Interface ; Vision, Ocular
Language	English
Publishing date	2013-06
Publishing country	Ireland
Document type	Journal Article
ZDB-ID	632564-6
ISSN	1872-7565 ; 0169-2607
ISSN (online)	1872-7565
ISSN	0169-2607
DOI	10.1016/j.cmpb.2013.01.009
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