Led by Bijan Najafi, Ph.D., iCAMP is an interdisciplinary research and development collaboration that engages creative teams from vascular surgery, orthopedics, podiatry, nursing, geriatrics, and engineering at Baylor College of Medicine.
iCAMP aims to improve stability, healing and mobility, worldwide. This is especially important for patients suffering from diabetes, which affects more than one in four older adults in the United States. We have been developing the first of their kind "smart" wearable technologies that have shown enormous promise in identifying and preventing limb and life threatening gangrene as well as providing early-warnings to prevent life-threatening falls and fractures in older adults. These "game changing" technologies are actually "game-based" in that they allow the wearers to improve their stability and mobility through novel video-game-like therapies. In addition to our local research initiatives, we partner with a host of businesses, analysts, and research teams worldwide to foster advances in the field of motion performance.
Our unique expertise is the translation of wearable and smart technologies - from jewelry to socks - for more accurate movement assessment of patients in their natural environment where they are the most comfortable and active. Our goal is to better understand how people move through and interact with their environment. In this way, we believe we may be able to fundamentally change the way we objectively measure quality of life for people across disciplines.
Using body worn sensors (or wearable technologies) along with ancillary technologies such as virtual reality, thermal imaging, and artificial intelligence, our team employs smart signal processing to identify physical activity patterns, spatio-temporal parameters of gait, balance, and three dimensional joint kinematics and kinetics. Our team has developed and validated several novel metrics to define disease state, assess motor learning, assess motor-cognitive decline, and determine biomechanical variabilities by extracting most relevant information from human motion.