Awarded NIH R01 to carry out research combining biomechanics, machine learning, exoskeletons & smart clothing
This collaborative research brings together expertise in biomechanics (Zelik), wearable robotics (Goldfarb) and machine learning (Volgyesi). The objective of this research is to address core scientific challenges related to sensing, actuation and control of cyber-physically assistive clothing (CPAC), for the purpose of reducing societal incidence of low back pain, by preventing lumbar (spine) overloading and overuse injuries. Low back pain is targeted because it is one of the leading causes of physical disability and missed work. High and/or repetitive forces on lumbar muscles and discs can occur during daily tasks, and are known to be major risk factors that can lead to back pain and injury. The long-term vision is to create smart clothing that can monitor lumbar loading, train safe movement patterns, and directly assist wearers to reduce the musculoskeletal forces that cause pain and injury. This proposed transformation of clothing is similar to how wristwatches have transformed from timepieces into health monitors; however, CPAC is even more exciting because it combines the form-factor of clothing with the assistance benefits of an exoskeleton to reduce biological tissue loading for a broad range of individuals, occupations and tasks.