Researchers in the Arbabian Lab have developed a system that uses a combination of radio frequency (RF) electromagnetic and ultrasound (US) waves to detect, localize, and identify multiple battery-free tags.
Stanford researchers have developed a portable hybrid frame-event based near eye gaze tracking system that has a superior speed while using a lower data bandwidth. They demonstrated real time results for gaze-tracking.
The Bronte-Stewart lab has designed an algorithm for calculating neural activity burst duration to better manage closed loop deep brain stimulation in patients with Parkinson's disease.
Stanford researchers have created a portable, wearable device for long-term nystagmus tracking to better diagnose episodic vertigo. Current methods utilize head goggles in video nystagmography to monitor eye movement while the patient is in a clinical setting.
Radiation therapy is a common option in diseases like breast cancer, but can also cause significant damage to the skin. Permanent scarring and fibrosis can result, with both aesthetic and functional consequences for cancer patients.
Stanford researchers have developed CheXpert which can reduce noise and identify several pathologies on x-rays with very high accuracy via machine learning. CheXpert can read photos of x-rays from a mobile phone and is robust to noise.
Stanford researchers have developed a crowdsourced framework for real-time robotic teleoperation with six degrees of freedom. Through smartphone controllers, RoboTurk enables large human workforces to remotely operate the robots without the need for prior training.
Stanford researchers at the Bao Research Group have patented a body area sensor network (bodyNET) that can be used to monitor human physiological signals for next-generation personalized healthcare.
Stanford researchers have developed a compact, low-cost complete sensor solution (sensor plus reader) which can interpret fully-passive sensors through a simple handheld external reader. The readout mechanism can take measurements independent of the readout distance (i.e.
Stanford researchers at the Bao Research Group have developed a second-generation stretchable multi-sensor tag technology for detecting physiological signals.
Stanford researchers at the Liao and Xing Labs have developed and tested a machine learning algorithm for augmented detection of bladder cancer. Machine learning has the potential to enhance medical decision making in cancer detection and image analysis.
Coronary artery bypass grafting (CABG) surgery is performed on nearly half a million patients with multivessel or diffuse coronary artery disease each year in the United States.