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Tommaso D'Alessio

dblp:72/3233 · DBLP profile ↗
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3ranked-venue papers
0as first author
0since 2021 · last 2011
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1Systems, architecture and hardware · 1Graphics, computer vision, multimedia, augmented reality and games · 1Applied, interdisciplinary, general and emerging computing · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Human-computer interaction and pervasive computing
1 paper
Accessibility and assistive technology · 50% Human-robot interaction · 25% Wearable and physiological sensing · 25%

Topics — the 4 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Accessibility and assistive technology
assistive technology
0.112011
A soft wearable robot for tremor assessment and suppression · ICRA 2011
Wearable and physiological sensing
brain-computer interface
0.112011
A soft wearable robot for tremor assessment and suppression · ICRA 2011
Accessibility and assistive technology › assistive technology
tremor suppression
0.112011
A soft wearable robot for tremor assessment and suppression · ICRA 2011
Human-robot interaction
wearable robot
0.112011
A soft wearable robot for tremor assessment and suppression · ICRA 2011

Methods — techniques the papers use, named apart from their topics

functional electrical stimulation · 0.1brain neural computer interface · 0.1
YearPublicationVenuePosition
2011 A soft wearable robot for tremor assessment and suppression
abstract
Tremor constitutes the most common motor disorder, and poses a functional problem to a large number of patients. Despite of the considerable experience in tremor management, current treatment based on drugs or surgery does not attain an effective attenuation in 25 % of patients, motivating the need for research in new therapeutic alternatives. In this context, this paper presents the concept design, development, and preliminary validation of a soft wearable robot for tremor assessment and suppression. The TREMOR neurorobot comprises a Brain Neural Computer Interface that monitors the whole neuromusculoskeletal system, aiming at characterizing both voluntary movement and tremor, and a Functional Electrical Stimulation system that compensates for tremulous movements without impeding the user perform functional tasks. First results demonstrate the performance of the TREMOR neurorobot as a novel means of assessing and attenuating pathological tremors.
Juan Alvaro Gallego, Eduardo Rocon de Lima, Jose Luis Ibanez, Jakob Lund Dideriksen, A. D. Koutsou, Rita Paradiso, Mirjana B. Popovic, José M. Belda-Lois, Francesco Gianfelici, Dario Farina, Dejan B. Popovic, M. Manto, Tommaso D'Alessio, José Luis Pons Rovira
ICRA13
2009 Markerless Human Motion Analysis in Gauss-Laguerre Transform Domain: An Application to Sit-To-Stand in Young and Elderly People
abstract
A markerless computer vision technique specifically designed to track natural elements on the human body surface is presented. The method implements the estimate of translation, rotation, and scaling by means of a maximum likelihood approach carried out in the Gauss-Laguerre transform domain. The approach is particularly suitable for human movement analysis in clinical contexts, where kinematics is at present performed by means of marker-based systems. Specific drawbacks of these latter systems, such as the burden of time for marker placement and the intrinsic intrusive nature, would be removed by the proposed method. Experimental results in terms of tracking performance are obtained by analyzing video sequences capturing the execution of the sit-to-stand task in two groups of young and elderly volunteers. The results are compared with clinical studies that used marker-based systems, and are particularly encouraging for a future extension of the approach to other motor tasks and to predict scores obtained from the physical performance batteries that are widely and regularly used by clinicians and physical therapists.
Michela Goffredo, Maurizio Schmid, Silvia Conforto, Marco Carli, Alessandro Neri 0001, Tommaso D'Alessio
IEEE Trans. Inf. Technol. Biomed.6
1999 Optimal estimation of power spectral density by means of a time-varying autoregressive approach
Silvia Conforto, Tommaso D'Alessio
Signal Process.2