Building Reference Models and Motion Tracking with Maya and MatchMover

Smart gloves have been under development during the last 40 years to support human-computer interaction based on hand and finger movement. Despite the many devoted efforts and the multiple advances in related areas, these devices have not become mainstream yet. Nevertheless, during recent years, new devices with improved features have appeared, being used for research purposes too. This paper provides a review of current commercial smart gloves focusing on three main capabilities: (i) hand and finger pose estimation and motion tracking, (ii) kinesthetic feedback, and (iii) tactile feedback. For the first capability, a detailed reference model of the hand and finger basic movements (known as degrees of freedom) is proposed. Based on the PRISMA guidelines for systematic reviews for the period 2015–2021, 24 commercial smart gloves have been identified, while many others have been discarded because they did not meet the inclusion criteria: currently active commercial and fully portable smart gloves providing some of the three main capabilities for the whole hand. The paper reviews the technologies involved, main applications and it discusses about the current state of development. Reference models to support end users and researchers comparing and selecting the most appropriate devices are identified as a key need.

Download Full-text

Physiologic Studies Provide New Perspectives on Early Speech Development

Perspectives on Speech Science and Orofacial Disorders ◽

10.1044/ssod20.2.29 ◽

2010 ◽

Vol 20 (2) ◽

pp. 29-36

Author(s):

Erin M. Wilson ◽

Ignatius S. B. Nip

Keyword(s):

Motor Control ◽

Motor Development ◽

Motion Tracking ◽

Speech Development ◽

Speech Motor Control ◽

Therapeutic Approaches ◽

Facial Motion ◽

Disordered Speech ◽

Speech Motor ◽

Physiologic Data

Abstract Although certain speech development milestones are readily observable, the developmental course of speech motor control is largely unknown. However, recent advances in facial motion tracking systems have been used to investigate articulator movements in children and the findings from these studies are being used to further our understanding of the physiologic basis of typical and disordered speech development. Physiologic work has revealed that the emergence of speech is highly dependent on the lack of flexibility in the early oromotor system. It also has been determined that the progression of speech motor development is non-linear, a finding that has motivated researchers to investigate how variables such as oromotor control, cognition, and linguistic factors affect speech development in the form of catalysts and constraints. Physiologic data are also being used to determine if non-speech oromotor behaviors play a role in the development of speech. This improved understanding of the physiology underlying speech, as well as the factors influencing its progression, helps inform our understanding of speech motor control in children with disordered speech and provide a framework for theory-driven therapeutic approaches to treatment.

Download Full-text

Assessment of area strain, twist, torsion and rotation in fetal echocardiography using three-dimensional (3D) Wall Motion Tracking Technology

Ultraschall in der Medizin - European Journal of Ultrasound ◽

10.1055/s-0033-1355076 ◽

2013 ◽

Vol 34 (S 01) ◽

Author(s):

C Enzensberger ◽

A Tenzer ◽

A Doelle ◽

R Axt-Fliedner

Keyword(s):

Motion Tracking ◽

Wall Motion ◽

Fetal Echocardiography ◽

Three Dimensional ◽

Tracking Technology ◽

Area Strain

Download Full-text

Einfluss der Bildrate auf die Strainanalyse bei zweidimensionalem Wall Motion Tracking in der fetalen Echokardiografie

10.1055/s-0037-1607007 ◽

2017 ◽

Author(s):

C Enzensberger ◽

L Rostock ◽

M Götte ◽

A Wolter ◽

J Herrmann ◽

...

Keyword(s):

Motion Tracking ◽

Wall Motion

Download Full-text

AutoLinker: Automatic Fragment Linking with Deep Conditional Transformer Neural Networks

10.26434/chemrxiv.12271508.v2 ◽

2020 ◽

Author(s):

Yuyao Yang ◽

Shuangjia Zheng ◽

Shimin Su ◽

Jun Xu ◽

Hongming Chen

Keyword(s):

Neural Networks ◽

Drug Discovery ◽

Drug Design ◽

State Of The Art ◽

The State ◽

Generative Model ◽

Lead Optimization ◽

Scaffold Hopping ◽

Reference Models ◽

Lead Generation

Fragment based drug design represents a promising drug discovery paradigm complimentary to the traditional HTS based lead generation strategy. How to link fragment structures to increase compound affinity is remaining a challenge task in this paradigm. Hereby a novel deep generative model (AutoLinker) for linking fragments is developed with the potential for applying in the fragment-based lead generation scenario. The state-of-the-art transformer architecture was employed to learn the linker grammar and generate novel linker. Our results show that, given starting fragments and user customized linker constraints, our AutoLinker model can design abundant drug-like molecules fulfilling these constraints and its performance was superior to other reference models. Moreover, several examples were showcased that AutoLinker can be useful tools for carrying out drug design tasks such as fragment linking, lead optimization and scaffold hopping.

Download Full-text