A 31-year-old man underwent a knee-disarticulation amputation in 2009, approximately 36 hours after a motorcycle collision. During the amputation surgery, two nerve transfers were performed to prevent neuroma formation. 1 The severed sciatic nerve was separated into its tibial and common peroneal branches. Small nerve branches to the distal portions of the residual semitendinosus muscle and the long head of the biceps femoris muscle ( Figure 1 Figure 1 Natively Innervated and Surgically Reinnervated Residual Thigh Muscles.) were located and cut where the nerves entered their respective muscles. The tibial nerve branch was then sewn over the motor point on the semitendinosus, and the common peroneal nerve branch was sewn over the motor point on the long head of the biceps femoris, thus allowing the transferred nerves to reinnervate these hamstring muscles. This surgery is analogous to targeted muscle reinnervation (TMR) surgery that is performed as part of arm amputation to improve the control of motorized arm prostheses. 2 As expected from our experience with TMR, discrete contractions in reinnervated muscles developed after a few months. When the patient attempted dorsiflexion of his missing foot, a contraction could be seen and palpated in the distal semitendinosus. Similarly, contraction of the distal long head of the biceps femoris occurred when he attempted plantarflexion of his missing foot. 
 
 Thirteen mechanical sensors are included in the mechanical design of the prosthesis (a three-axis accelerometer, a three-axis gyroscope, and sensors for vertical load, knee and ankle position, torque, and velocity). EMG and mechanical-sensor data were processed with the use of a phase-dependent pattern-classification method before heel contact and before toe off. 7 A dynamic Bayesian network was used for classification. 8 The patient ambulated freely around the laboratory and completed 10 additional circuits, during which transitions between ambulation modes in the prosthesis were performed automatically under two conditions: with the use of both EMG and mechanical-sensor data to control the prosthesis and with the use of mechanical-sensor data alone to control it. 
 
 To investigate the effect of adding EMG control during various ambulation activities, the patient was first taught to walk with a robotic prosthesis that is designed to have a set of impedances (joint stiffnesses) for each ambulation mode, as described by our collaborators at Vanderbilt University. 5,6 The prosthetic leg, including the battery and embedded control system, has a mass of 4.7 kg. The knee and ankle joints are actuated in the sagittal plane with the use of two Maxon EC30 motors (Maxon Motor). The patient wore a socket with a supracondylar suspension system. During a 3-hour accommodation session, the prosthesis was configured so that the patient could ambulate safely and comfortably in each of the following modes: walking on level ground, walking up or down a ramp with a 10-degree slope, and ascending or descending a set of stairs with a reciprocal gait. In a separate session, the patient completed 20 repetitions of a circuit that included all ambulation modes, during which a researcher remotely transitioned the prosthesis between modes when the foot made contact with the ground (heel contact) and was lifted into the air (toe off), using a wireless key fob. 
 
 To evaluate the contribution of EMG signals from natively innervated and surgically reinnervated muscles to robotic prosthesis control, a pattern-recognition system was tested in a virtual environment with the patient in a seated position. 4 Ten bipolar surface electrodes were placed over eight natively innervated residual limb muscles (proximal biceps femoris, rectus femoris, vastus lateralis, vastus medialis, sartorius, gracilis, adductor magnus, and tensor fasciae latae) and the two reinnervated muscle segments. The patient performed the attempted movements described above as well as femoral rotation (in and out) and tibial rotation (in and out). The patient's ability to control the virtual device in real time (i.e., the percentage of completed motions in the virtual-environment test) with the use of a pattern-recognition system was compared with previously reported data on four persons who had undergone transfemoral amputation without TMR surgery (non-TMR amputees). 4 
 
 The patient gave written informed consent for participation in the institutional review board–approved experiments described here, which took place between January 2010 and October 2012. The quality of the EMG signals from the patient's residual limb was investigated with the use of high-density EMG, which has been used to interpret EMG signal quality in upper-limb amputees after TMR. 3 A cylindrical grid of 96 electrodes, with a center-to-center distance between adjacent electrodes of approximately 25 mm, was placed on the distal residual limb. EMG signals were collected with the use of a TMS Refa 128 measurement system (Twente Medical Systems International) as the patient attempted knee flexion, knee extension, ankle plantarflexion, and ankle dorsiflexion. Contractions were held for 5 seconds at medium intensity and were repeated 10 times. The root-mean-square EMG signal measured at each electrode for each contraction type was color-coded according to amplitude for qualitative interpretation. 
 
 When mechanical-sensor data only were used, the majority of errors caused relatively small perturbations that were noticeable to the patient. Critical errors — defined as errors resulting in perturbations that might cause the patient to fall — occurred during 2% of trials. No such critical errors occurred with the use of the TMR-enhanced control system. With the use of this TMR-enhanced combination of mechanical-sensor and EMG information, the patient was also able to walk safely outdoors and to climb and descend multiple flights of stairs with the robotic leg prosthesis ( Figure 3 Figure 3 Stair Ascent with Reciprocal Gait with the Use of the TMR-Enhanced Control System. and Video 2 ). 
 
 EMG data from the residual limb and mechanical-sensor data produced a unique stride pattern for each ambulation mode. The inclusion of EMG information increased the accuracy of the control system. With the use of mechanical-sensor data only, the overall real-time error rate (i.e., the rate of movement misclassification) across all ambulation modes was 12.9%. The real-time error rate decreased to 2.2% when EMG information from the natively innervated muscles was added. The error rate decreased further, to 1.8%, when the EMG information from reinnervated muscles was added, and the patient was able to ambulate robustly and transition seamlessly between ambulation modes with the use of this TMR-enhanced, intuitive control system. 
 
 The classification accuracy of the patient's attempted movements was 96.0% with a virtual system configured to control ankle plantarflexion and dorsiflexion and knee flexion and extension, and the accuracy was 92.0% with a system additionally configured to control tibial rotation and femoral rotation. Classification accuracies for these attempted movements were 91.0±4.7% and 86.8±3.0%, respectively, in non-TMR amputees. 4 This translates into an improvement in absolute accuracy of 5.0 percentage points and 5.2 percentage points, respectively — and, more important, into a 44% and 39% reduction in the error rate; these findings indicate that TMR improves real-time pattern-recognition control. The patient also completed virtual movements much faster than did the non-TMR amputees ( Figure 2E and 2F ) and could reliably reposition both a virtual avatar (i.e., a graphical representation of the joint) and a robotic knee-and-ankle prosthesis in real time ( Video 1 , available with the full text of this article at NEJM.org). 
 
 Discussion 
 
 To change the ambulation mode in commercially available motorized prosthetic knees, the user must press buttons on a key fob or perform a set of predefined, exaggerated motions.9,10 Furthermore, these devices must be manually repositioned when users are seated. The leg prosthesis used in this study showed control accuracy with mechanical sensors alone, albeit when controlling a limited number of ambulation modes, but several errors occurred that could put users at risk of falling. User safety is of paramount importance; thus, error rates must be very low, and prevention of even a few errors has clinical significance. 
 
 With the control strategy that we used, EMG signals from natively innervated muscles were decoded with a pattern-recognition algorithm and combined with data from sensors on the prosthesis to interpret the patient's intended movement. This resulted in a reduction in the error rate from 12.9% to 2.2%. We expected a reduction in the error rate with the use of EMG data from natively innervated muscles, because all ambulation modes required activation of knee muscles; however, we were surprised by the magnitude of the reduction. The error rate was further reduced, to 1.8%, with the use of the TMR-enhanced control system. 
 
 The errors that did occur affected the patient's stability to varying degrees. Each ambulation mode was set up with the use of an impedance-control paradigm to generate the knee and ankle torques.11 The control system predicted the patient's desired ambulation mode, which in turn instantaneously dictated the impedance settings. The degree of disturbance created by system error corresponded to the level of mismatch between the impedance settings for the actual activities and those for the predicted activities. Because the impedance settings for some ambulation modes were very similar, this resulted in a very forgiving control system: many errors, such as misclassifications between walking and ramp ascent, were not noticeable to the patient. 
 
 Moderate disturbances that the patient noticed but could tolerate occurred during misclassifications between walking on level ground and descending stairs or a ramp. Errors that caused moderate disturbances were reduced with the use of the TMR-enhanced system, as compared with the use of EMG signals from natively innervated muscles only. Thus, although the additional reduction in the error rate was small, the improvement was clinically relevant. Finally, errors during the transition from any ambulation mode to stair ascent caused large, critical disturbances that were difficult for the patient to recover from safely. This type of error did not occur when EMG data — either from natively innervated muscles only or from the TMR-enhanced muscle set — were added to the control system. 
 
 The patient perceived that the TMR-enhanced system provided intuitive control during ambulation and non–weight-bearing activities. The reduced error rate enabled him to ambulate confidently and transition seamlessly among all modes with near-normal gait kinematics. The patient provided especially positive feedback when he ambulated freely outside the laboratory and entered adjacent buildings without difficulty, without using handicapped-accessible entrances. In the non–weight-bearing mode, he could reliably control both joints, which is useful for repositioning the prosthesis to increase comfort, dressing, and preparing for transfers into or out of chairs or vehicles. 
 
 Obtaining robust EMG-control information is difficult, because surface EMG signals are more variable and noisy than mechanical-sensor data. Some EMG channels, such as those from the rectus femoris, were contaminated by substantial motion artifact at heel contact, resulting in large-amplitude noise at the start of each stride. Real-time filtering techniques may be used to alleviate this noise; because it was a consistent part of the EMG patterns across trials, it did not adversely affect system accuracy. The overall patterns of EMG activity for each ambulation mode were distinct, and the low rates of classification errors indicate that the patterns of EMG activity were repeatable. 
 
 Although this study establishes the feasibility of using EMG signals to improve the control of robotic leg prostheses, several challenges remain in making the control system clinically viable. First, the system relies on the recording of high-quality EMG signals through electrodes, which must remain in full contact with the residual limb during walking, without becoming uncomfortable for the user. This is challenging, because movement of the residual limb with respect to the socket creates movement artifact in EMG signals and can cause chafing or pressure sores at electrode contact points after prolonged use. Second, improvements in the pattern-recognition classification algorithms and the mechanical sensor system are necessary. Finally, the robotic prosthetic leg must be made more reliable, quieter, smaller, and lighter to benefit larger numbers of amputees. ||||| Researchers say that 32-year-old Zac Vawter successfully controlled movements of a motorized artificial leg using only his own thoughts. Video: Rehab Institute of Chicago. 
 
 In an advance that could eventually improve the mobility of thousands of people living with amputations, researchers said a 32-year-old man successfully controlled movements of a motorized artificial leg using only his own thoughts. 
 
 Aided by sensors receiving impulses from nerves and muscles that once carried signals to his missing knee and ankle, the patient was able to climb and descend stairs and walk up and down inclines much as he could with a natural leg, based on directions that came from his brain. Importantly, he was able to flex the device's ankle, enabling a near-normal gait, something not possible with current prosthetics. 
 
 Photos View Slideshow Andrew A. Nelles for The Wall Street Journal Zac Vawter walked down stairs with his bionic leg. 
 
 "It's night and day" between the experimental bionic leg and the mechanical prosthetic limb he uses every day, said Zac Vawter, a software engineer from Yelm, Wash., who lost his right leg in a motorcycle accident four years ago. "Going upstairs with my normal prosthetic, my sound leg goes up first for every step," he added. "With this I go foot-over-foot up the stairs and down the stairs." 
 
 Researchers said Mr. Vawter is the first person to have been able to control such a prosthetic by brain signals alone. Current state-of-the-art devices involving both the knee and the ankle require pressing a remote-control button at, say, the bottom of a flight of stairs to rock and kick the leg back to make the step up, said Levi J. Hargrove, a researcher at the Center for Bionic Medicine, Rehabilitation Institute of Chicago. 
 
 For Mr. Vawter, Dr. Hargrove said, "it's all intuitive. He can walk up or descend stairs in stride." 
 
 Dr. Hargrove is lead author of a report on the technology, published Wednesday by the New England Journal of Medicine. 
 
 More than a million Americans are living with amputations, including some 1,600 soldiers who returned from wars in Iraq and Afghanistan during the past decade having lost at least one limb. The current project is supported by an $8 million grant from the U.S. Army as part of an effort to address life-limiting injuries in soldiers, said Col. John Scherer, head of clinical and rehabilitative medicine research at the army's Medical Research and Materiel Command in Fort Detrick, Md. A goal of the bionic-leg project is to enable young soldiers to "participate in life" and even return to active duty. 
 
 Researchers call the device bionic because of its ability to interact intelligently with a human. Despite associations of "bionic" technology with superhuman strength, the prosthetics "don't necessarily need to be strong," Dr. Hargrove said. "They need to be smart." 
 
 Dr. Hargrove and his colleagues developed the electronics for the device, including a software algorithm that receives signals from electrodes attached to skin on what remains of the amputated leg and converts those signals to knee and ankle movement on the prosthetic. The electrodes get the signals from muscles attached to nerves in the residual leg, including nerves that before the amputation carried brain signals to the ankle. Those nerves were implanted in Mr. Vawter's hamstring muscle shortly after the accident. 
 
 "When Zac wants to try to move," Dr. Hargrove said, the brain sends signals down the spinal cord to muscle that hasn't been damaged. "We have electrodes that are listening to those signals. The algorithm decodes patterns "to figure out what he was thinking," translating the results into movement such as straightening the knee while sitting or flexing the ankle. 
 
 In experiments typically involving 700 to 1,000 steps, Dr. Hargrove said, minor errors such as scuffing the foot occurred in about 2% of steps with the signals coming from the brain. Mr. Vawter didn't experience any more-serious errors that could have resulted in a fall, he said. 
 
 The accomplishment "takes us closer to the point where we're going to have robust commercial products that use signals from a person's brain to let them walk," said Daniel Ferris, a professor in the school of kinesiology and department of biomedical engineering at the University of Michigan, who wasn't involved in the current research. 
 
 Mr. Vawter said one drawback is that the device isn't fit for running, which he can do with his regular prosthetic. Other needed improvements, Dr. Hargrove said, are making the 10-pound device quieter and smaller. 
 
 Write to Ron Winslow at ron.winslow@wsj.com 
 
 A version of this article appeared September 26, 2013, on page A3 in the U.S. edition of The Wall Street Journal, with the headline: Brain Power Enough to Control 'Bionic' Leg. ||||| The act of walking may not seem like a feat of agility, balance, strength and brainpower. But lose a leg, as Zac Vawter did after a motorcycle accident in 2009, and you will appreciate the myriad calculations that go into putting one foot in front of the other. 
 
 Taking on the challenge, a team of software and biomedical engineers, neuroscientists, surgeons and prosthetists has designed a prosthetic limb that can reproduce a full repertoire of ambulatory tricks by communicating seamlessly with Vawter's brain. 
 
 A report published Wednesday in the New England Journal of Medicine describes how the team fit Vawter with a prosthetic leg that has learned — with the help of a computer and some electrodes — to read his intentions from a bundle of nerves that end above his missing knee. 
 
 For the roughly 1 million Americans who have lost a leg or part of one due to injury or disease, Vawter and his robotic leg offer the hope that future prosthetics might return the feel of a natural gait, kicking a soccer ball or climbing into a car without hoisting an inert artificial limb into the vehicle. 
 
 Vawter's prosthetic is a marvel of 21st century engineering. But it is Vawter's ability to control the prosthetic with his thoughts that makes the latest case remarkable. If he wants his artificial toes to curl toward him, or his artificial ankle to shift so he can walk down a ramp, all he has to do is imagine such movements. 
 
 The work was done at the Rehabilitation Institute of Chicago under an $8-million grant from the Army. The armed forces hope to apply findings from such studies to the care of about 1,200 service personnel who have lost a lower limb in Iraq and Afghanistan. 
 
 "We want to restore full capabilities" to people who've lost a lower limb, said Levi J. Hargrove, lead author of the new report. "While we're focused and committed to developing this system for our wounded warriors, we're very much thinking of this other, much larger population that could benefit as well." 
 
 The report describes advances across a wide range of disciplines: in orthopedic and peripheral nerve surgery, neuroscience, and the application of pattern-recognition software to the field of prosthetics. 
 
 Weighing just over 10 pounds, the leg has two independent engines powering movement in the ankle and knee. And it bristles with sensors, including an accelerometer and gyroscope, each capable of detecting and measuring movement in three dimensions. 
 
 Most prosthetics in use today require the physical turn of a key to transition from one movement to another. But with the robotic leg, those transitions are effortless, Vawter said. 
 
 "With this leg, it just flows," said the 32-year-old software engineer, who spends most of his days using a typical prosthetic but travels to Chicago several times a year from his home in Yelm, Wash. "The control system is very intuitive. There isn't anything special I have to do to make it work right." 
 
 Before Vawter could strap on the bionic lower limb, engineers in Chicago had to "teach" the prosthetic how to read his motor intentions from tiny muscle contractions in his right thigh. 
 
 At the institute's Center for Bionic Medicine, Vawter spent countless hours with his thigh wired up with electrodes, imagining making certain movements on command with his missing knee, ankle and foot. 
 
 Using pattern-recognition software, engineers discerned, distilled and digitized those recorded electrical signals to catalog an entire repertoire of movements. The prosthetic could thus be programmed to recognize the subtlest contraction of a muscle in Vawter's thigh as a specific motor command. 
 
 Given surgical practices still in wide use, the prospects for such a connection between a patient's prosthetic and his or her peripheral nerves are generally dim. In most amputations, the nerves in the thigh are left to languish or die. 
 
 Dr. Todd Kuiken, a neurosurgeon at the rehabilitation institute, pioneered a practice called "reinervation" of nerves severed by amputation, and Vawter's orthopedic surgeon at the University of Washington Medical Center was trained to conduct the delicate operation. Dr. Douglas Smith rewired the severed nerves to control some of the muscles in Vawter's thigh that would be used less frequently in the absence of his lower leg. 
 
 Within a few months of the amputation, those nerves had recovered from the shock of the injury and begun to regenerate and carry electrical impulses. When Vawter thought about flexing his right foot in a particular way, the rerouted nerve endings would consistently cause a distinctive contraction in his hamstring. When he pondered how he would position his foot on a stair step and ready it for the weight of his body, the muscle contraction would be elsewhere — but equally consistent. 
 
 Compared with prosthetics that were not able to "read" the intent of their wearers, the robotic leg programmed to follow Vawter's commands reduced the kinds of errors that cause unnatural movements, discomfort and falls by as much as 44%, according to the New England Journal of Medicine report. 
 
 Vawter said he had "fallen down a whole bunch of times" while wearing his everyday prosthetic, but not once while moving around on his bionic leg. 
 
 He said he could move a lot faster too — which would be helpful for keeping up with his 5-year-old son and 3-year-old daughter. But first, Vawter added, he needs to persuade Hargrove's team to let him wear it home. 
 
 melissa.healy@latimes.com ||||| Zac Vawter, a software engineer who lives in the Seattle area, already knew about advances in bionic technology when a motorcycle wreck led to the amputation of his right leg just above the knee in 2009. 
 
 As doctors at Harbor View Medical Center in Seattle battled for three days to try to save his leg, Vawter asked about the method that uses the mind to move a prosthetic limb. The technology had previously been used only in arms. 
 
 Four years and an $8 million grant from the U.S. Army’s Telemedicine and Advanced Technology Research Center later, Vawter is considered the “test pilot” of the bionic leg that can tackle slopes, stairs and in-chair movement markedly better than existing devices. A team of researchers led by Levi Hargrove from the Rehabilitation Institute of Chicago’s Center for Bionic Medicine reported their results with the novel prosthetic in the New England Journal of Medicine. 
 
 “In my mind, it’s still the same thing in terms of moving my ankle down or up, or extending my leg forward or back,” Vawter said in a telephone interview. “It’s just walk like I would normally walk. It’s not special training or buttons or tricks. That’s a big piece of what I think is groundbreaking and phenomenal about this work.” 
 
 Additional refinements are needed to make the thought-controlled bionic leg commercially viable, Hargrove said in a telephone interview. Vawter is allowed to use the machine only a week at a time during visits every few months to the clinic in Chicago. Freedom Innovations LLC, a closely held company based in Irvine, California, is working on making the motorized machine smaller, quieter and more robust. 
 
 Prosthetic Evolution 
 
 It’s been an evolutionary process. Most prosthetic legs work like a walking stick with springs, giving the patient something on which to balance. The next step up, robotic prosthetics, are further advanced with remote controls and embedded sensors that measure how much weight they must bear, the knee position and the way a person is turning, like mobile phones determine orientation. The thought-controlled device goes further, harnessing nerves that formerly regulated the leg’s movement to maneuver the prosthetic leg. 
 
 The new leg allows Vawter to seamlessly transition between walking and standing, with the biggest difference showing up when he is climbing stairs. With a standard prosthetic leg, Vawter always steps up first with his healthy left leg, then pulls the right leg along. With the thought-controlled leg, he is able to walk foot-over-foot, he said. Someone watching him climb wouldn’t know he had a prosthesis based on his gait, Hargrove said, though they may hear the motor whirring. 
 
 ‘Dramatic Improvement’ 
 
 “It’s still a prosthetic, but it’s in between the leg I wear every day and prior to amputation,” said Vawter, who can’t yet jump to the rim of a basketball net with the robotic leg, as he could before the amputation. “It’s a dramatic improvement over my current prosthetic, but there is still a long way to go.” 
 
 The rate of errors, including the risk of falls, was shaved to just 1.8 percent with the new device, down from 12.9 percent with the standard robotic leg prosthesis. 
 
 The new device may be available within three to five years for the 1 million Americans with leg amputations, Hargrove said. The approach may benefit the 1,200 soldiers injured while serving in the U.S. military, many of whom are young and want to continue active lives. It may also help older people who want to remain at home, particularly those who have trouble standing and caring for themselves because of amputations, he said. 
 
 The researchers started with an advanced motorized knee and ankle prosthesis developed at Vanderbilt University. Their goal was to improve the “steering” of the device, using only the mind. 
 
 Natural Signaling 
 
 The first thing was to recreate the natural signaling process used to move, which was disconnected when the leg was severed. The signal in the brain that moves through the spinal cord, down the peripheral nerves and into the muscles remains intact until the spot of the amputation, Hargrove said. 
 
 The researchers “rewired” Vawter, redirecting two of the critical severed nerves into his hamstring, the muscle at the back of the leg. When he thinks about moving his knee or ankle, those nerves still fire, releasing a tiny burst of electricity. 
 
 Sensors taped on to the legs capture the signals. That data is added to a pattern-detection computer system that takes information from the robotic leg to predict the patient’s intended movement. While the researchers expected the additional information to make the leg operate more smoothly, the magnitude of the benefit was unexpected, they wrote in the New England Journal. Many errors weren’t even noticeable. 
 
 The researchers measured nine muscles in the leg and analyzed the activities that were most important for regular function, Hargrove said. When Vawter performs any of those activities, the computer program predicts what he is doing. 
 
 Reinnervation Expert 
 
 Vawter became the test pilot of the device through his surgeon, Douglas Smith. A contributor on the paper, Smith is an expert on the use of targeted muscle reinnervation, when the nerves are repurposed to improve the control of a motorized arm prosthesis. He performed Vawter’s amputation, and identified him as a good candidate for helping develop the thought-controlled leg prosthesis. 
 
 The researchers adjusted the leg and the computer systems based on Vawter’s feedback. It has gone through major revisions of the hardware and many little changes to the software, said Vawter, who works at Engineered Software Inc. in Lacey, Washington. 
 
 Current prosthetic legs cost a few thousand dollars, with robotic devices as much as $100,000, Hargrove said. There is no price target yet for the thought-controlled bionic leg, he said. 
 
 “The value it will provide to the people who use it will be enormous,” Hargrove said. “We feel we’ve been able to eliminate the vast majority of safety critical errors. You can never predict how they will use these devices in their own lives, but we are making fantastic progress.” 
 
 To contact the reporter on this story: Michelle Fay Cortez in Minneapolis at mcortez@bloomberg.net 
 
 To contact the editor responsible for this story: Reg Gale at rgale5@bloomberg.net
– Zac Vawter imagines movements that his leg mimics just like everyone else. When going up an incline, he visualizes his ankle moving as needed, and it does. But there's something remarkable about that moment: It's actually "a marvel of 21st century engineering," reports the Los Angeles Times. Vawter, 32, is the "test pilot" for a new bionic leg—the first of its kind to communicate with the brain (though the technology has been used with arm prostheses). It uses sensors to harness "reinnervated" nerves; they're essentially nerves that, rather than being allowed to die, are surgically "rewired" to control his right thigh muscles; the bionic leg is then programmed to read the contractions of those muscles. The comparison to his other prosthesis is "night and day," Vawter tells the Wall Street Journal. The error rate (that includes things like the risk of falling) is shaved, from 12.9% with a robotic leg, down to just 1.8%, Bloomberg reports via a New England Journal of Medicine report. Weighing about 10 pounds, the bionic leg is the work of the Rehabilitation Institute of Chicago, which saw an $8 million boost from the US Army. The latter hopes the leg can one day benefit some 1,200 Iraq and Afghanistan vets who could use it. But there's much broader appeal: About 1 million Americans are without lower limbs and the device could be available to them in three to five years, Bloomberg reports. "The value it will provide to the people who use it will be enormous," the study's lead author said. "We are making fantastic progress."