Stanford scientists have developed a generative learning framework paired with a compact wearable EMG device that extrapolates limited sensor inputs to reconstruct muscle activity equivalent to that captured by high-density sensor arrays.
Stanford researchers have developed an innovative wearable device that enhances mindfulness training by augmenting the user's real-time auditory environment.
Stanford researchers have developed the Large-scale Electrophysiology Amplification Platform (LEAP), a wireless, label-free optical system for monitoring the electrical activity of neurons and heart cells.
Stanford researchers have developed a networked audio system that enhances the experience of teleconferencing, and online performances, gaming, and gatherings.
Stanford researchers have patented a real-time auralization-reverberation system (CAVIAR - Chamber for Augmented Virtual and Interactive Audio Realities) for providing immersive and interactive audio environments.
Stanford researchers have developed a patented, wearable, haptic feedback device that provides position and velocity information on the limbs and torso by imparting rotational skin stretch.
Stanford researchers have developed an innovative brain-machine interface aimed at restoring communication for individuals with paralysis by translating their attempted speech into text.
Stanford researchers have developed a system that addresses a critical challenge in brain-computer interface (BCI) technology: the need for tedious and lengthy recalibration procedures that disrupt daily use.
Researchers in the Murmann Mixed Signal Group have developed a pipelined chip architecture with inverted residual and linear bottlenecks-based networks for energy efficient Machine Learning inference on edge devices.
Stanford researchers in Zhenan Bao's Group have developed a nanomesh sensor printed directly on the hand that uses an AI-trained model to detect multiple movement types from a single sensor.
Stanford inventors have developed an information theoretic, seizure detection algorithm for electroencephalography (EEG) towards improving diagnosis, management, and treatment of patients with epilepsy.