Research on robotic exoskeletons has rapidly expanded over the previous decade. Advances in robotic hardware and energy supplies have enabled viable prototypes for human testing. This review paper describes current lower limb robotic exoskeletons, with specific regard to common trends in the field. The preponderance of published literature lacks rigorous quantitative evaluations of exoskeleton performance, making it difficult to determine the disadvantages and drawbacks of many of the devices. We analyzed common approaches in exoskeleton design and the convergence, or lack thereof, with certain technologies. We focused on actuators, sensors, energy sources, materials, and control strategies. One of the largest hurdles to be overcome in exoskeleton research is the user interface and control. More intuitive and flexible user interfaces are needed to increase the success of robotic exoskeletons. In the last section, we discuss promising future solutions to the major hurdles in exoskeleton control. A number of emerging technologies could deliver substantial advantages to existing and future exoskeleton designs. We conclude with a listing of the advantages and disadvantages of the emerging technologies and discuss possible futures for the field.

Since the early 2000s, researchers have been trying to develop lower-limb exoskeletons that augment human mobility by reducing the metabolic cost of walking and running versus without a device. In 2013, researchers finally broke this 'metabolic cost barrier'. We analyzed the literature through December 2019, and identified 23 studies that demonstrate exoskeleton designs that improved human walking and running economy beyond capable without a device. Here, we reviewed these studies and highlighted key innovations and techniques that enabled these devices to surpass the metabolic cost barrier and steadily improve user walking and running economy from 2013 to nearly 2020. These studies include, physiologically-informed targeting of lower-limb joints; use of off-board actuators to rapidly prototype exoskeleton controllers; mechatronic designs of both active and passive systems; and a renewed focus on human-exoskeleton interface design. Lastly, we highlight emerging trends that we anticipate will further augment wearable-device performance and pose the next grand challenges facing exoskeleton technology for augmenting human mobility.

Myoelectric pattern recognition systems for prosthesis control are often studied in controlled laboratory settings, but obstacles remain to be addressed before they are clinically viable. One important obstacle is the difficulty of maintaining system usability with socket misalignment. Misalignment inevitably occurs during prosthesis donning and doffing, producing a shift in electrode contact locations. We investigated how the size of the electrode detection surface and placement of electrode poles (electrode orientation) affected system robustness with electrode shift. Electrodes oriented parallel to muscle fibers outperformed electrodes oriented perpendicular to muscle fibers in both shift and no-shift conditions (p<0.01). Another finding was the significant difference (p<0.01) in performance for the direction of electrode shift. Shifts perpendicular to the muscle fibers reduced classification accuracy and real-time controllability much more than shifts parallel to the muscle fibers. Increasing the size of the electrode detection surface was found to help reduce classification accuracy sensitivity to electrode shifts in a direction perpendicular to the muscle fibers but did not improve the real-time controllability of the pattern recognition system. One clinically important result was that a combination of longitudinal and transverse electrodes yielded high controllability with and without electrode shift using only four physical electrode pole locations.

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.

The clinical application of robotic technology to powered prosthetic knees and ankles is limited by the lack of a robust control strategy. We found that the use of electromyographic (EMG) signals from natively innervated and surgically reinnervated residual thigh muscles in a patient who had undergone knee amputation improved control of a robotic leg prosthesis. EMG signals were decoded with a pattern-recognition algorithm and combined with data from sensors on the prosthesis to interpret the patient's intended movements. This provided robust and intuitive control of ambulation--with seamless transitions between walking on level ground, stairs, and ramps--and of the ability to reposition the leg while the patient was seated.

Lower limb prostheses that can generate net positive mechanical work may restore more ambulation modes to amputees. However, configuration of these devices imposes an additional burden on clinicians relative to conventional prostheses; devices for transfemoral amputees that require configuration of both a knee and an ankle joint are especially challenging. In this paper, we present an approach to configuring such powered devices. We developed modified intrinsic control strategies—which mimic the behavior of biological joints, depend on instantaneous loads within the prosthesis, or set impedance based on values from previous states, as well as a set of starting configuration parameters. We developed tables that include a list of desired clinical gait kinematics and the parameter modifications necessary to alter them. Our approach was implemented for a powered knee and ankle prosthesis in five ambulation modes (level-ground walking, ramp ascent/descent, and stair ascent/descent). The strategies and set of starting configuration parameters were developed using data from three individuals with unilateral transfemoral amputations who had previous experience using the device; this approach was then tested on three novice unilateral transfemoral amputees. Only 17% of the total number of parameters (i.e., 24 of the 140) had to be independently adjusted for each novice user to achieve all five ambulation modes and the initial accommodation period (i.e., time to configure the device for all modes) was reduced by 56%, to 5 hours or less. This approach and subsequent reduction in configuration time may help translate powered prostheses into a viable clinical option where amputees can more quickly appreciate the benefits such devices can provide.

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Pattern recognition of myoelectric signals for prosthesis control has been extensively studied in research settings and is close to clinical implementation. These systems are capable of intuitively controlling the next generation of dexterous prosthetic hands. However, pattern recognition systems perform poorly in the presence of electrode shift, defined as movement of surface electrodes with respect to the underlying muscles. This work focused on investigating the optimal interelectrode distance, channel configuration, and EMG feature sets for myoelectric pattern recognition in the presence of electrode shift. Increasing interelectrode distance from 2 cm to 4 cm improved pattern recognition system performance in terms of classification error and controllability (p<0.01). Additionally, for a constant number of channels, an electrode configuration that included electrodes oriented both longitudinally and perpendicularly with respect to muscle fibers improved robustness in the presence of electrode shift (p<0.05). We investigated the effect of the number of recording channels with and without electrode shift and found that four to six channels were sufficient for pattern recognition control. Finally, we investigated different feature sets for pattern recognition control using a LDA classifier and found that an autoregressive set significantly (p<0.01) reduced sensitivity to electrode shift compared to a traditional time-domain feature set.