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UID:5B40257A-843A-4F9C-A59F-3CF51656B359
DTSTART;TZID=America/New_York:20250207T140000
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DESCRIPTION:Abstract: Human-machine interfaces (HMIs) have not only become 
 popular technologies but have become the hope of many individuals for rest
 oring their lost limb function. Any HMI has two important intrinsic design
  components—(i) decode the human commands and (ii)controlling the machin
 e to convert that command into action. Decades of research went into makin
 g the interface between the human and the machine seamless but were unable
  to effectively address the inherent challenges\, namely\, complexity\, ad
 aptability and variability. To overcome the above challenges\, it is criti
 cal to computationally understand and quantitatively characterize the huma
 n sensorimotor control. Emerging areas in HMIs critically depend on the ab
 ility to build bioinspired models\, experimentally validate them and utili
 ze them in adaptive and intuitive control. The human hand with high dimens
 ionality encompasses the three inherent challenges and may serve as an ide
 al validation paradigm. How central nervous system (CNS)controls this high
  dimensional human hand effortlessly is still an unsolved mystery. To addr
 ess this high dimensional control problem\, many bioinspired motor control
  models have been proposed\, one of which is based on synergies. According
  to this model\, instead of controlling individual motor units\, CNS simpl
 ifies the control using coordinated control of groups of motor units calle
 d synergies. However\, there are several unanswered questions today— Whe
 re are synergies present in CNS? What is their role in motor control and m
 otor learning? By combining the concepts of human motor control\, computat
 ional neuroscience\, machine learning and validation with noninvasive huma
 n experiments\, can we answer these fundamental questions? The goal of thi
 s research is to develop efficient\, seamless and near-natural human-machi
 ne interfaces based on biomimetically inspired models.\n\nCo-sponsored by:
  Mechanical and Nuclear Engineering\, VCU\n\nSpeaker(s): Ramana\n\nRoom: E
 3229\, Bldg: East Engineering Building\, 401 W Main Street\, Mechanical an
 d Nuclear Engineer\, Richmond\, Virginia\, United States\, 23284
LOCATION:Room: E3229\, Bldg: East Engineering Building\, 401 W Main Street\
 , Mechanical and Nuclear Engineer\, Richmond\, Virginia\, United States\, 
 23284
ORGANIZER:rhadimani@vcu.edu
SEQUENCE:15
SUMMARY:Synergy-based brain-machine interfaces in human-robot interaction
URL;VALUE=URI:https://events.vtools.ieee.org/m/467271
X-ALT-DESC:Description: <br /><p class="Default" style="text-align: justify
 \;"><span style="color: windowtext\;"><span style="mso-spacerun: yes\;">&n
 bsp\;</span></span><strong><span style="font-size: 11.5pt\; color: windowt
 ext\;">Abstract: </span></strong><span style="font-size: 11.5pt\; color: w
 indowtext\;">Human-machine interfaces (HMIs) have not only become popular 
 technologies but have become the hope of many individuals for restoring th
 eir lost limb function. Any HMI has two important intrinsic design compone
 nts&mdash\;(i) decode the human commands and (ii)controlling the machine t
 o convert that command into action. Decades of research went into making t
 he interface between the human and the machine seamless but were unable to
  effectively address the inherent challenges\, namely\, complexity\, adapt
 ability and variability. To overcome the above challenges\, it is critical
  to computationally understand and quantitatively characterize the human s
 ensorimotor control. Emerging areas in HMIs critically depend on the abili
 ty to build bioinspired models\, experimentally validate them and utilize 
 them in adaptive and intuitive control. The human hand with high dimension
 ality encompasses the three inherent challenges and may serve as an ideal 
 validation paradigm. How central nervous system (CNS)controls this high di
 mensional human hand effortlessly is still an unsolved mystery. To address
  this high dimensional control problem\, many bioinspired motor control mo
 dels have been proposed\, one of which is based on synergies. According to
  this model\, instead of controlling individual motor units\, CNS simplifi
 es the control using coordinated control of groups of motor units called s
 ynergies. However\, there are several unanswered questions today&mdash\; W
 here are synergies present in CNS? What is their role in motor control and
  motor learning? By combining the concepts of human motor control\, comput
 ational neuroscience\, machine learning and validation with noninvasive hu
 man experiments\, can we answer these fundamental questions? The goal of t
 his research is to develop efficient\, seamless and near-natural human-mac
 hine interfaces based on biomimetically inspired models.</span></p>
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