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Detection of eye contact with deep neural networks is as accurate as human experts.

Nature communications
Eye contact is among the most primary means of social communication used by humans. Quantification of eye contact is valuable as a part of the analysis of social roles and communication skills, and for clinical screening. Estimating a subject's looki...

A goal-driven modular neural network predicts parietofrontal neural dynamics during grasping.

Proceedings of the National Academy of Sciences of the United States of America
One of the primary ways we interact with the world is using our hands. In macaques, the circuit spanning the anterior intraparietal area, the hand area of the ventral premotor cortex, and the primary motor cortex is necessary for transforming visual ...

Training confounder-free deep learning models for medical applications.

Nature communications
The presence of confounding effects (or biases) is one of the most critical challenges in using deep learning to advance discovery in medical imaging studies. Confounders affect the relationship between input data (e.g., brain MRIs) and output variab...

Restoration of sensory information via bionic hands.

Nature biomedical engineering
Individuals who have lost the use of their hands because of amputation or spinal cord injury can use prosthetic hands to restore their independence. A dexterous prosthesis requires the acquisition of control signals that drive the movements of the ro...

Hier R-CNN: Instance-Level Human Parts Detection and A New Benchmark.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Detecting human parts at instance-level is an essential prerequisite for the analysis of human keypoints, actions, and attributes. Nonetheless, there is a lack of a large-scale, rich-annotated dataset for human parts detection. We fill in the gap by ...

An sEMG-Controlled 3D Game for Rehabilitation Therapies: Real-Time Time Hand Gesture Recognition Using Deep Learning Techniques.

Sensors (Basel, Switzerland)
In recent years the advances in Artificial Intelligence (AI) have been seen to play an important role in human well-being, in particular enabling novel forms of human-computer interaction for people with a disability. In this paper, we propose a sEMG...

Ultra-sensitive and resilient compliant strain gauges for soft machines.

Nature
Soft machines are a promising design paradigm for human-centric devices and systems required to interact gently with their environment. To enable soft machines to respond intelligently to their surroundings, compliant sensory feedback mechanisms are ...

Analysis of Visuo Motor Control between Dominant Hand and Non-Dominant Hand for Effective Human-Robot Collaboration.

Sensors (Basel, Switzerland)
The human-in-the-loop technology requires studies on sensory-motor characteristics of each hand for an effective human-robot collaboration. This study aims to investigate the differences in visuomotor control between the dominant (DH) and non-dominan...

Sensor Fusion of Motion-Based Sign Language Interpretation with Deep Learning.

Sensors (Basel, Switzerland)
Sign language was designed to allow hearing-impaired people to interact with others. Nonetheless, knowledge of sign language is uncommon in society, which leads to a communication barrier with the hearing-impaired community. Many studies of sign lang...

A Multimodal Intention Detection Sensor Suite for Shared Autonomy of Upper-Limb Robotic Prostheses.

Sensors (Basel, Switzerland)
Neurorobotic augmentation (e.g., robotic assist) is now in regular use to support individuals suffering from impaired motor functions. A major unresolved challenge, however, is the excessive cognitive load necessary for the human-machine interface (H...