Bayesian and neural inference on lstm-based object recognition from tactile and kinesthetic information
F Pastor, J García-González… - IEEE Robotics and …, 2020 - ieeexplore.ieee.org
Recent advances in the field of intelligent robotic manipulation pursue providing robotic
hands with touch sensitivity. Haptic perception encompasses the sensing modalities …
hands with touch sensitivity. Haptic perception encompasses the sensing modalities …
Effective recognition of human lower limb jump locomotion phases based on multi-sensor information fusion and machine learning
Jump locomotion is the basic movement of human. However, no thorough research on the
recognition of jump sub-phases has been carried so far. This paper aims to use multi-sensor …
recognition of jump sub-phases has been carried so far. This paper aims to use multi-sensor …
Disentangled adversarial autoencoder for subject-invariant physiological feature extraction
Recent developments in biosignal processing have enabled users to exploit their
physiological status for manipulating devices in a reliable and safe manner. One major …
physiological status for manipulating devices in a reliable and safe manner. One major …
A kinematic and EMG dataset of online adjustment of reach-to-grasp movements to visual perturbations
Control of reach-to-grasp movements for deft and robust interactions with objects requires
rapid sensorimotor updating that enables online adjustments to changing external goals (eg …
rapid sensorimotor updating that enables online adjustments to changing external goals (eg …
[HTML][HTML] Multimodal fusion of emg and vision for human grasp intent inference in prosthetic hand control
Objective: For transradial amputees, robotic prosthetic hands promise to regain the
capability to perform daily living activities. Current control methods based on physiological …
capability to perform daily living activities. Current control methods based on physiological …
Universal physiological representation learning with soft-disentangled rateless autoencoders
Human computer interaction (HCI) involves a multidisciplinary fusion of technologies,
through which the control of external devices could be achieved by monitoring physiological …
through which the control of external devices could be achieved by monitoring physiological …
HANDdata–first-person dataset including proximity and kinematics measurements from reach-to-grasp actions
HANDdata is a dataset designed to provide hand kinematics and proximity vision data
during reach to grasp actions of non-virtual objects, specifically tailored for autonomous …
during reach to grasp actions of non-virtual objects, specifically tailored for autonomous …
Enhancing visionless object recognition on grasp using ontology: the OntOGrasp framework
The understanding of the external characteristics of objects that need to be grasped is
crucial for enhancing the dexterity of a robotic hand. Utilizing ontology-based knowledge …
crucial for enhancing the dexterity of a robotic hand. Utilizing ontology-based knowledge …
Classifications of dynamic EMG in hand gesture and unsupervised grasp motion segmentation
The electromyography (EMG) signals have been widely utilized in human–robot interaction
for extracting user hand/arm motion instructions. A major challenge of the online interaction …
for extracting user hand/arm motion instructions. A major challenge of the online interaction …
Realtime hand landmark tracking to aid development of a prosthetic arm for reach and grasp motions
A prosthetic device, also known as a prosthesis, can help with rehabilitation when an arm or
other limbs is severed or lost. The upper-limb prosthesis aids restoration of motor skills …
other limbs is severed or lost. The upper-limb prosthesis aids restoration of motor skills …