Over the last few years, thousands of soldiers and an even greater number of civilians have suffered traumatic injuries due to blast exposure, largely attributed to improvised explosive devices in terrorist and insurgent activities. The use of body armor is allowing soldiers to survive blasts that would otherwise be fatal due to systemic damage. Emerging evidence suggests that exposure to a blast can produce neurologic consequences in the brain but much remains unknown. To elucidate the current scientific basis for understanding blast-induced traumatic brain injury (bTBI), the NIH convened a workshop in April 2008. A multidisciplinary group of neuroscientists, engineers, and clinicians were invited to share insights on bTBI, specifically pertaining to: physics of blast explosions, acute clinical observations and treatments, preclinical and computational models, and lessons from the international community on civilian exposures. This report provides an overview of the state of scientific knowledge of bTBI, drawing from the published literature, as well as presentations, discussions, and recommendations from the workshop. One of the major recommendations from the workshop was the need to characterize the effects of blast exposure on clinical neuropathology. Clearer understanding of the human neuropathology would enable validation of preclinical and computational models, which are attempting to simulate blast wave interactions with the central nervous system. Furthermore, the civilian experience with bTBI suggests that polytrauma models incorporating both brain and lung injuries may be more relevant to the study of civilian countermeasures than considering models with a neurologic focus alone.

Surface electromyography (sEMG) has been the predominant method for sensing electrical activity for a number of applications involving muscle-computer interfaces, including myoelectric control of prostheses and rehabilitation robots. Ultrasound imaging for sensing mechanical deformation of functional muscle compartments can overcome several limitations of sEMG, including the inability to differentiate between deep contiguous muscle compartments, low signal-to-noise ratio, and lack of a robust graded signal. The objective of this study was to evaluate the feasibility of real-time graded control using a computationally efficient method to differentiate between complex hand motions based on ultrasound imaging of forearm muscles. Dynamic ultrasound images of the forearm muscles were obtained from six able-bodied volunteers and analyzed to map muscle activity based on the deformation of the contracting muscles during different hand motions. Each participant performed 15 different hand motions, including digit flexion, different grips (i.e., power grasp and pinch grip), and grips in combination with wrist pronation. During the training phase, we generated a database of activity patterns corresponding to different hand motions for each participant. During the testing phase, novel activity patterns were classified using a nearest neighbor classification algorithm based on that database. The average classification accuracy was 91%. Real-time image-based control of a virtual hand showed an average classification accuracy of 92%. Our results demonstrate the feasibility of using ultrasound imaging as a robust muscle-computer interface. Potential clinical applications include control of multiarticulated prosthetic hands, stroke rehabilitation, and fundamental investigations of motor control and biomechanics.