3D printing is the most suitable method to manufacture the frame parts of powered ankle-foot prostheses but the compressive strength of the 3D-printed part needs to be ensured. According to the compression test standard ASTM D695, the effect of infill pattern and density, which is transferred to the mass of the standard specimen, on the compressive strength is investigated with a carbon fiber-reinforced nylon material. With the same infill pattern, specimens with more mass have a higher compressive strength. With the same mass, specimens with triangular fill have a higher compressive strength than those with rectangular and gyroid fills. Compared with specimens with a solid fill, specimens with a triangular fill can also provide more compressive strength in a unit mass. According to the results of standard specimens, following the requirement of strength and lightweight, 41% triangular fill is selected to manufacture the supporting part of a powered ankle-foot prosthesis. Under a compressive load of 1225 N, the strain of the assembly of the standard adaptor and the 3D-printed part is 1.32 ± 0.04%, which can meet the requirement of the design. This study can provide evidence for other 3D-printed applications with the requirement of compressive strength.

Three-dimensional (3D) knee angle measurement is one of the key measures in human gait analysis. Inertial sensor capable of measuring joint motion under unconstrained conditions is a practical tool for clinical evaluation and rehabilitation. An inertial measurement unit (IMU) consisting of accelerometer and gyroscope allows orientation measurement in 3D with an additional sensor (i.e., magnetometer). However, ferromagnetic interference negatively affects the performance of magnetometer and thus reduces measurement accuracy. In this study, a technique based on nonlinear autoregressive neural network with exogenous inputs (NARX) is presented to measure 3D segmental orientation during gait without the use of magnetometer. With IMUs attached to the thigh and shank, 3D knee angles in long-distance treadmill walking were computed and validated against an optical motion analysis system as the gold standard. Pseudo-integrator (PI) was also compared to the reference system for benchmarking. The learning capability of NARX was further assessed with the comparison of complementary filter (CF) to the reference system. The proposed NARX model was shown to outperform PI with biases between -3.5° and -0.2°, and root mean square errors between 4.5° and 2.5°. Results demonstrated the capability of NARX in providing accurate estimates of 3D knee joint angle while avoiding interference as encountered in systems incorporating magnetometer, suggesting that NARX is feasible to computing long-term ambulatory measurements of body segment orientation and 3D joint angles.

A systematic review of the mechanical design of powered ankle–foot prostheses developed from 2000 to 2019 was conducted through database and manual searches. A total of ten English and two Chinese databases were searched using the same keywords. Moreover, information on commercialized prostheses was collected through a manual search. A total of 8729 publications were obtained from the database search, and 83 supplementary publications and 49 online product introductions were accumulated through the manual search. A total of 91 powered ankle–foot prostheses were extracted from 159 publications and online information after exclusion. The mechanical design characteristics of the prostheses were described briefly and compared after they were categorized into 11 subclassifications. This review revealed that a considerable number of powered ankle–foot prostheses were developed in the last 20 years. The development of such prostheses was characterized by alternative modes, that is, from pneumatic or hydraulic drivers to motorized drivers and from rigid transmissions to elastic actuators. This review contributes to the comprehensive understanding of current designs, which can benefit the combination of the advantages of and redundancy avoidance in future powered ankle–foot prostheses.

One of the major challenges in developing powered lower limb prostheses is emulating the behavior of an intact lower limb with different walking speeds over diverse terrains. Numerous studies have been conducted on control algorithms in the field of rehabilitation robotics to achieve this overarching goal. Recent studies on powered prostheses have frequently used a hierarchical control scheme consisting of three control levels. Most control structures have at least one element of discrete transition properties that requires numerous sensors to improve classification accuracy, consequently increasing computational load and costs. In this study, we proposed a user-independent and free-mode method for eliminating the need to switch among different controllers. We constructed a database by using four OPAL wearable devices (Mobility Lab, APDM Inc., USA) for seven able-bodied subjects. We recorded the gait of each subject at three ambulation speeds during ground-level walking to train a nonlinear autoregressive network with an exogenous input recurrent neural network (NARX RNN) to estimate foot orientation (angular position) in the sagittal plane using shank angular velocity as external input. The trained NARX RNN estimated the foot orientation of all the subjects at different walking speeds over flat terrain with an average root-mean-square error (RMSE) of 2.1° ± 1.7°. The minimum correlation between the estimated and measured values was 86%. Moreover, a t-test showed that the error was normally distributed with a high certainty level (0.88 minimum p-value).

A powered transfemoral prosthesis is needed as people with transfemoral amputation show 60 percent extra metabolic cost when compared to people with no amputation. Recently, as illustrated in the literature, the most high-torque robotic knee prosthesis utilize harmonic reducers. Despite the advantage of high reduction ratio and efficiency, the harmonic drive cannot be back-driven. Therefore, the harmonic drive is not an optimal solution for prosthetic systems with direct and indirect contact with the environment. In this paper, we outline an initial design of robotic knee prosthesis. The proposed robotic knee prosthesis consists of BLDC motor, cycloidal gear with reduction ratio 13:1, four-bar mechanism, and timing belt transmission with 4:1 reduction ratio. To optimize the torque transmission and range of motion (RoM), a multiobjective optimization problem must be undertaken. The end-effector motion depends on each bar length in the four-bar mechanism. The four-bar mechanism was optimized using particle swarm optimization (PSO). To complete the optimization, a set of 50 steps was collected using wearable sensors. Then, the data of sagittal plan were processed to identify the target profile for PSO. The prototype’s computer-aided manufacturing (CAM) was completed using a MarkTwo 3D printer with carbon fiber composite. The overall design can achieve a maximum torque of 84 N.m. However, the current design lacks the elastic component (no spring is added on the actuator output), which is necessary for a functional prosthesis; this limitation will be addressed in future study.