BackgroundMany soldiers are expected to carry heavy loads over extended distances, often resulting in physical and mental fatigue. In this study, the design and testing of an autonomous leg exoskeleton is presented. The aim of the device is to reduce the energetic cost of loaded walking. In addition, we present the Augmentation Factor, a general framework of exoskeletal performance that unifies our results with the varying abilities of previously developed exoskeletons.MethodsWe developed an autonomous battery powered exoskeleton that is capable of providing substantial levels of positive mechanical power to the ankle during the push-off region of stance phase. We measured the metabolic energy consumption of seven subjects walking on a level treadmill at 1.5 m/s, while wearing a 23 kg vest.ResultsDuring the push-off portion of the stance phase, the exoskeleton applied positive mechanical power with an average across the gait cycle equal to 23 ± 2 W (11.5 W per ankle). Use of the autonomous leg exoskeleton significantly reduced the metabolic cost of walking by 36 ± 12 W, which was an improvement of 8 ± 3% (p = 0.025) relative to the control condition of not wearing the exoskeleton.ConclusionsIn the design of leg exoskeletons, the results of this study highlight the importance of minimizing exoskeletal power dissipation and added limb mass, while providing substantial positive power during the walking gait cycle.

Advanced upper-limb prostheses capable of actuating multiple degrees of freedom (DOF) are now commercially available. Pattern recognition algorithms that use surface electromyography (EMG) signals show great promise as multi-DOF controllers. Unfortunately, current pattern recognition systems are limited to activate only one degree of freedom at a time. This study introduces a novel classifier based on Bayesian theory to provide classification of simultaneous movements. This approach and two other classification strategies for simultaneous movements were evaluated using non-amputee and amputee subjects classifying up to three DOFs, where any two DOFs could be classified simultaneously. Similar results were found for non-amputee and amputee subjects. The new approach, based on a set of conditional parallel classifiers was the most promising with errors significantly less (p<0.05) than a single LDA classifier or a parallel approach. For 3-DOF classification, the conditional parallel approach had error rates of 6.6% on discrete and 10.9% on combined motions, while the single LDA had error rates of 9.4% on discrete and 14.1% on combined motions. The low error rates demonstrated suggest than pattern recognition techniques on surface EMG can be extended to identify simultaneous movements, which could provide more life-like motions for amputees compared to exclusively classifying sequential movements.

Human joint impedance is the dynamic relationship between the differential change in the position of a perturbed joint and the corresponding response torque; it is a fundamental property that governs how humans interact with their environments. It is critical to characterize ankle impedance during the stance phase of walking to elucidate how ankle impedance is regulated during locomotion, as well as provide the foundation for future development of natural, biomimetic powered prostheses and their control systems. In this study, ankle impedance was estimated using a model consisting of stiffness, damping and inertia. Ankle torque was well described by the model, accounting for 98 ± 1.2% of the variance. When averaged across subjects, the stiffness component of impedance was found to increase linearly from 1.5 Nm/rad/kg to 6.5 Nm/rad/kg between 20% and 70% of stance phase. The damping component was found to be statistically greater than zero only for the estimate at 70% of stance phase, with a value of 0.03 Nms/rad/kg. The slope of the ankle’s torque-angle curve—known as the quasi-stiffness—was not statistically different from the ankle stiffness values, and showed remarkable similarity. Finally, using the estimated impedance, the specifications for a biomimetic powered ankle prosthesis were introduced that would accurately emulate human ankle impedance during locomotion.

Currently, the mobility of above-knee amputees is limited by the lack of available prostheses that can efficiently replicate biologically accurate movements. In this study, a powered knee prosthesis was designed utilizing a novel mechanism, known as a clutchable series-elastic actuator (CSEA).The CSEA includes a low-power clutch in parallel with an electric motor within a traditional series-elastic actuator. The stiffness of the series elasticity was tuned to match the elastically conservative region of the knee’s torque-angle relationship during the stance phase of locomotion. During this region, the clutch was used to efficiently store energy in the series elasticity. The fully autonomous knee prosthesis design utilized a brushless electric motor, ballscrew transmission and cable drive, as well as commercial electrical components. The knee was lighter than the eighth percentile and shorter than the first percentile male shank segment. The CSEA Knee was tested in a unilateral above-knee amputee walking at 1.3 m/s. During walking, the CSEA Knee provided biomechanically accurate torque-angle behavior, agreeing within 17% of the net work and 27% of the stance flexion angle produced by the biological knee. In addition, the process of locomotion reduced the net electrical energy consumption of the CSEA Knee. The knee’s motor generated 1.8 J/stride, and the net energy consumption was 3.6 J/stride, an order of magnitude less energy than previously published powered knee prostheses.

BackgroundPassive exoskeletons that assist with human locomotion are often lightweight and compact, but are unable to provide net mechanical power to the exoskeletal wearer. In contrast, powered exoskeletons often provide biologically appropriate levels of mechanical power, but the size and mass of their actuator/power source designs often lead to heavy and unwieldy devices. In this study, we extend the design and evaluation of a lightweight and powerful autonomous exoskeleton evaluated for loaded walking in (J Neuroeng Rehab 11:80, 2014) to the case of unloaded walking conditions.FindingsThe metabolic energy consumption of seven study participants (85 ± 12 kg body mass) was measured while walking on a level treadmill at 1.4 m/s. Testing conditions included not wearing the exoskeleton and wearing the exoskeleton, in both powered and unpowered modes. When averaged across the gait cycle, the autonomous exoskeleton applied a mean positive mechanical power of 26 ± 1 W (13 W per ankle) with 2.12 kg of added exoskeletal foot-shank mass (1.06 kg per leg). Use of the leg exoskeleton significantly reduced the metabolic cost of walking by 35 ± 13 W, which was an improvement of 10 ± 3% (p = 0.023) relative to the control condition of not wearing the exoskeleton.ConclusionsThe results of this study highlight the advantages of developing lightweight and powerful exoskeletons that can comfortably assist the body during walking.Electronic supplementary materialThe online version of this article (doi:10.1186/1743-0003-11-151) contains supplementary material, which is available to authorized users.