Wearable robots and powered exoskeletons may improve ambulation for millions of individuals with poor mobility. Powered exoskeletons primarily assist in the sagittal plane to improve walking efficiency and speed. However, individuals with poor mobility often have limited mediolateral balance, which requires torque generation in the frontal plane. Existing hip exoskeletons that assist in both the sagittal and frontal planes are too heavy and bulky for use in the real world. Here we present the kinematic model, mechatronic design, and benchtop and human testing of a powered hip exoskeleton with a unique parallel kinematic actuator. The exoskeleton is lightweight (5.3 kg), has a slim profile, and can generate 30 N·m and 20 N·m of torque during gait in the sagittal and frontal planes. The exoskeleton torque density is 5.7 N·m/kg-53% higher than previously possible with series kinematic design. Testing with five healthy subjects indicate that frontal plane torques applied during stance or swing can alter step width, while sagittal plane torque can assist with hip flexion and extension. A device with these characteristics may improve both gait economy and balance in the real world.

Powered lower-limb prostheses have the potential to improve amputee mobility by closely imitating the biomechanical function of the missing biological leg. To accomplish this goal, powered prostheses need controllers that can seamlessly adapt to the ambulation activity intended by the user. Most powered prosthesis control architectures address this issue by switching between specific controllers for each activity. This approach requires online classification of the intended ambulation activity. Unfortunately, any misclassification can cause the prosthesis to perform a different movement than the user expects, increasing the likelihood of falls and injuries. Therefore, classification approaches require near-perfect accuracy to be used safely in real life. In this paper, we propose a unified controller for powered knee prostheses which allows for walking, stair ascent, and stair descent without the need for explicit activity classification. Experiments with one individual with an above-knee amputation show that the proposed controller enables seamless transitions between activities. Moreover, transition between activities is possible while leading with either the sound-side or the prosthesis. A controller with these characteristics has the potential to improve amputee mobility.

Existing controllers for robotic powered prostheses regulate the prosthesis speed, timing, and energy generation using predefined position or torque trajectories. This approach enables climbing stairs step-over-step. However, it does not provide amputees with direct volitional control of the robotic prosthesis, a functionality necessary to restore full mobility to the user. Here we show that proportional electromyographic (EMG) control of the prosthesis knee torque enables volitional control of a powered knee prosthesis during stair climbing. The proposed EMG controller continuously regulates knee torque based on activation of the residual hamstrings, measured using a single EMG electrode located within the socket. The EMG signal is mapped to a desired knee flexion/extension torque based on the prosthesis knee position, the residual limb position, and the interaction with the ground. As a result, the proposed EMG controller enabled an above-knee amputee to climb stairs at different speeds, while carrying additional loads, and even backwards. By enabling direct, volitional control of powered robotic knee prostheses, the proposed EMG controller has the potential to improve amputee mobility in the real world.

Ambulation in everyday life requires walking at variable speeds, variable inclines, and variable terrains. Powered prostheses aim to provide this adaptability through control of the actuated joints. Some powered prosthesis controllers can adapt to discrete changes in speed and incline but require manual tuning to determine the control parameters, leading to poor clinical viability. Other data-driven controllers can continuously adapt to changes in speed and incline but do so by imposing the same non-amputee gait patterns for all amputee subjects, which does not consider subjective preferences and differing clinical needs of users. Here, we present a controller for powered knee and ankle prostheses that can continuously adapt to different walking speeds, inclines, and uneven terrains without enforcing a specific prosthesis position, impedance, or torque. A virtual biarticular muscle connection determines the knee flexion torque, which changes with both speed and slope. Adaptation to inclines and uneven terrains is based solely on the global shank orientation. Continuously variable damping allows for speed adaptation. Minimum-jerk programming defines the prosthesis swing trajectory at variable cadences. Experiments with one individual with an above-knee amputation suggest that the proposed controller can effectively adapt to different walking speeds, inclines, and rough terrains.

Powered prostheses can enable individuals with above-knee amputations to ascend stairs step-over-step. To accomplish this task, available stair ascent controllers impose a pre-defined joint impedance behavior or follow a pre-programmed position trajectory. These control approaches have proved successful in the laboratory. However, they are not robust to changes in stair height or cadence, which is essential for real-world ambulation. Here we present an adaptive stair ascent controller that enables individuals with above-knee amputations to climb stairs of varying stair heights at their preferred cadence and with their preferred gait pattern. We found that modulating the prosthesis knee and ankle position as a function of the user's thigh in swing provides toe clearance for varying stair heights. In stance, modulating the torque-angle relationship as a function of the prosthesis knee position at foot contact provides sufficient torque assistance for climbing stairs of different heights. Furthermore, the proposed controller enables individuals to climb stairs at their preferred cadence and gait pattern, such as step-by-step, step-over-step, and two-steps. The proposed adaptive stair controller may improve the robustness of powered prostheses to environmental and human variance, enabling powered prostheses to more easily move from the lab to the real-world.

We present a novel powered knee prosthesis with an active variable transmission (AVT) that adapts motor torque and speed output based on the requirements of different ambulation activities. The AVT works in combination with a spring/damper system to allow for both active and passive operation modes. Preliminary tests performed by an able-bodied subject wearing a bypass orthosis show that the proposed knee can support walking on level ground in passive mode, as well as ambulating on stairs with a reciprocal gait pattern in active mode.

Powered ankle-foot prostheses must provide high power and torque while satisfying stringent size and weight requirements. Previous works have focused on improving the prosthesis torque/weight ratio with novel actuation systems such as series and parallel elastic actuators, clutchable leverages, and pneumatic artificial muscles. In this paper, we propose an alternative design approach to minimize the prosthesis size and weight, while improving torque generation and electrical efficiency, based on a polycentric kinematic chain. The proposed approach is implemented on a novel powered polycentric ankle prosthesis (p2Ankle). Kinematics and mechatronic design are presented together with bench-top testing. Preliminary validation in standing and walking is conducted with an able-bodied user using a bypass orthosis.

Robotic prostheses can improve walking ability in persons with transfemoral amputations by closely matching kinetics and kinematics of the intact leg during walking. However, achieving this goal requires the prosthesis to adapt to walking speed, a function that no powered transfemoral prosthesis has yet achieved. In this paper, we present, and perform initial testing on a new control framework that allows biologically accurate leg function at varying walking speeds, without the need for tuning. The proposed framework comprises two novel controllers that rely on quasi-stiffness modulation in stance phase and minimum jerk trajectory in swing phase. Preliminary testing was conducted in an able-bodied subject using a bypass adapter to walk on a robotic prosthesis at five different walking speeds (from 0.62 to 1.16 m/s). Experimental results demonstrated the ability of the proposed controller to approximate intact leg function at different walking speeds.

Recent powered (or robotic) prosthetic legs independently control different joints and time periods of the gait cycle, resulting in control parameters and switching rules that can be difficult to tune by clinicians. This challenge might be addressed by a unifying control model used by recent bipedal robots, in which virtual constraints define joint patterns as functions of a monotonic variable that continuously represents the gait cycle phase. In the first application of virtual constraints to amputee locomotion, this paper derives exact and approximate control laws for a partial feedback linearization to enforce virtual constraints on a prosthetic leg. We then encode a human-inspired invariance property called effective shape into virtual constraints for the stance period. After simulating the robustness of the partial feedback linearization to clinically meaningful conditions, we experimentally implement this control strategy on a powered transfemoral leg. We report the results of three amputee subjects walking overground and at variable cadences on a treadmill, demonstrating the clinical viability of this novel control approach.

The field of wearable robotics is gaining momentum thanks to its potential application in rehabilitation engineering, assistive robotics, and power augmentation. These devices are designed to be used in direct contact with the user to aid with movement or increase the power of specific skeletal joints. The design of the so-called physical human-robot interface is critical, since it determines not only the efficacy of the robot but the kinematic compatibility of the device with the human skeleton and the degree of adaptation to different anthropometries as well. Failing to deal with these problems causes misalignments between the robot and the user joint. Axes misalignment leads to the impossibility of controlling the torque effectively transmitted to the user joint and causes undesired loading forces on articulations and soft tissues. In this paper, we propose a general analytical method for the design of exoskeletons able to assist human joints without being subjected to misalignment effects. This method is based on a kinetostatic analysis of a coupled mechanism (robot-human skeleton) and can be applied in the design of self-aligning mechanisms. The method is exemplified in the design of an assistive robotic chain for a two-degree-of-freedom (DOF) human articulation.

Estimation of the temporal derivatives of a noisy position signal is a ubiquitous problem in industrial and robotics engineering. Here, we propose a new approach to get velocity and acceleration estimates of cyclical/periodic signals near to steady-state regime, by using adaptive oscillators. Our method combines the advantages of introducing no delay, and filtering out the high-frequency noise. We expect this method to be useful in control applications requiring undelayed but smooth estimates of velocity and acceleration (e.g., velocity control and inverse dynamics) of quasi-periodic tasks (e.g., active vibration compensation, robot locomotion, and lower-limb movement assistance).

This paper presents the design and experimental testing of the robotic elbow exoskeleton NEUROBOTICS Elbow Exoskeleton (NEUROExos). The design of NEUROExos focused on three solutions that enable its use for poststroke physical rehabilitation. First, double-shelled links allow an ergonomic physical human-robot interface and, consequently, a comfortable interaction. Second, a four-degree-of-freedom passive mechanism, embedded in the link, allows the user's elbow and robot axes to be constantly aligned during movement. The robot axis can passively rotate on the frontal and horizontal planes 30° and 40°, respectively, and translate on the horizontal plane 30 mm. Finally, a variable impedance antagonistic actuation system allows NEUROExos to be controlled with two alternative strategies: independent control of the joint position and stiffness, for robot-in-charge rehabilitation mode, and near-zero impedance torque control, for patient-in-charge rehabilitation mode. In robot-in-charge mode, the passive joint stiffness can be changed in the range of 24-56 N·m/rad. In patient-in-charge mode, NEUROExos output impedance ranges from 1 N·m/rad, for 0.3 Hz motion, to 10 N·m/rad, for 3.2 Hz motion.

Inferring temporal derivatives (like velocity and acceleration) from a noisy position signal is a well-known challenge in control engineering, due to the intrinsic trade-off between noise filtering and estimation bandwidth. To tackle this problem, in this paper we propose a new approach specifically designed for periodic movements. This approach uses an adaptive oscillator as fundamental building block. It is a tool capable of synchronizing to a periodic input while learning its features (frequency, amplitude, ...) in dedicated state variables. Since the oscillator's input and output are perfectly synchronized during steady-state regime, a non-delayed estimate of the input temporal derivatives can be obtained simply by deriving the output analytical form. Pending a (quasi-)periodic input signal, these temporal derivatives are thus synchronized with the actual kinematics, while the signal bandwidth can be arbitrarily tuned by the intrinsic dynamics of the oscillator. We further validate this approach by developing an impedance-based strategy for assisting human walking in the LOPES lower-limb exoskeleton. Preliminary results with a single participant give rise to three main conclusions. First, our method indeed provides velocity and acceleration estimates of the participant's joint kinematics which are smoother and less delayed with respect to the actual kinematics than using a standard Kalman filter. Second, closing the human-robot loop with a high-gain impedance field depending on the acceleration is not possible with a Kalman filter approach, due to unstable dynamics. In contrast, our approach tolerates high gains (up to 70% of the nominal walking torque), showing its intrinsic stability. Finally, no clear benefit of the acceleration-dependent field with respect to a simpler position-dependent field is visible regarding the reduction of metabolic cost. This last result illustrates the challenge of designing sound assistive strategies for complex tasks like walking.

This paper introduces NEUROExos, an elbow powered exoskeleton for rehabilitation. The NEUROExos is provided with three novel characteristics which address the major problems arising in rehabilitation robotics. A double shell link structure allows for a comfortable human-robot interaction, while a 4-DOF passive mechanism gives a perfect kinematic compatibility with the user. Moreover, NEUROExos is powered by a variable impedance antagonistic actuator, which provides the exoskeleton with a software-controllable passive compliance. We present the main characteristics of the exoskeleton, with a focus on the actuation and control of the platform. Additionally, results on a healthy subject show the relevance of this design during a prototypical rehabilitation task.