Stanford engineers have developed a wearable, real-time activity monitor that estimates metabolic energy expenditure with substantially lower error than current methods such as smartwatches.
Stanford researchers have developed a new machine learning method for extracting gait parameters, such as cadence, step length, peak knee flexion, and Gait Deviation Index (GDI), from a single video.