Kaspect-Health is a project to design The biomedical cloud for hospital gait laboratories, with hopes of turbocharging the analytics performed on knee, shoulder, and ankle experimental data.
Understanding muscle control defines our ability to treat injuries with joint surgery, combat progressive neurological diseases such as Parkinson’s Disease, and guide stroke rehabilitation. This is important, not only for our understanding of motor networks in healthy individuals, but also for our understanding of disease and potential neuro-rehabilitative targets[1]. No prior models fully incorporate the neuralmuscular combinations across all muscles, joint torques, tendon stretch values, and motorneuron activations—in addition to leveraging every available gait analysis signal (each capable of generating terabytes per second). This gap in the research is becoming more and more apparent; data are becoming larger, and by simplifying analyses, labs leave data on the table.
Major hospitals that perform gait analysis have Masters and PhD level personnel trained in kinesiology to manage and interpret the huge data pipeline for all patient tests. Surgeons use these tests to decide whether and how to operate. These labs need a Big Data platform to test their neuromechanical hypotheses, but lack the resources and technical knowhow capable of implementing it on site.
“Exploring the nature of muscle redundancy via subjectspecific and generic musculoskeletal models" Journal of Biomechanics, 2015; ValeroCuevas FJ, Cohn BA, Yngvason HF, Lawrence EL