Multi-Joint Leg Moment Estimation During Walking Using Thigh or Shank Angles.

To reinstate human-like locomotion by using robotic prosthetics, orthotics or exoskeletons, a main challenge is how to coordinate the motion of these devices with that of the biological limbs. One approach to overcome this challenge is to identify firstly the relationships that exist between the kinematics and kinetics of the lower extremity joints and limbs. In this work we aimed to continuously estimate sagittal plane ankle, knee and hip moments using shank or thigh angles. For this purpose, neural network and wavelets were used in a nonlinear auto-regressive model with exogenous inputs. This approach circumvented the need for switching rules or intermediate parameters. To assess the performance of the estimator, four case studies were developed. First, thigh angles (inputs) were used to estimate hip moments (outputs). Second, thigh angles were used to estimate knee moments. Third, ankle moments were estimated using thigh angles, and in the fourth case study, ankle moments were estimated using shank angles. Three different databases involving 106 subjects at different walking speeds were used to evaluate estimation quality. The testing procedure involved both inter-subject and intra-subject evaluations. The best estimation performance was observed when ankle moments were estimated from shank angles. The weakest estimation performance was observed when knee moments were estimated using thigh angles at 0.5 m/s. For this case, the estimation quality was much better at 1.5 m/s. Average RMS errors were 0.13- 0.15, 0.10- 0.13, and 0.09- 0.12 [Nm/kg] for hip, knee and ankle moments, respectively. Average mean absolute errors MAEs were 0.10- 0.11, 0.07- 0.10, and 0.06- 0.08 [Nm/kg] for hip, knee and ankle moments, respectively. Average correlation coefficients were 0.90- 0.98 and 0.98- 0.99 for hip and ankle moment estimations. The value for knee was comparable only at high speed (0.96 for 1.5 m/s), while it was less accurate at slow speed (0.71 for 0.5 m/s). In general, for all of the joints, the estimation accuracy was comparable with that of other studies, although one source of input was employed (either shank or thigh angle).

Estimation of knee and ankle angles during walking using thigh and shank angles.

Estimation of joints' trajectories is commonly used in human gait analysis, and in the development of motion planners and high-level controllers for prosthetics, orthotics, exoskeletons and humanoids. Human locomotion is the result of the cooperation between leg joints and limbs. This suggests the existence of underlying relationships between them which lead to a harmonic gait. In this study we aimed to estimate knee and ankle trajectories using thigh and shank angles. To do so, an estimation approach was developed that continuously mapped the inputs to the outputs, which did not require switching rules, speed estimation, gait percent identification or look-up tables. The estimation algorithm was based on a nonlinear auto-regressive model with exogenous inputs. The method was then combined with wavelets theory, and then the two were used in a neural network. To evaluate the estimation performance, three scenarios were developed which used only one source of inputs (i.e., only shank angles or only thigh angles). First, knee anglesθk(outputs) were estimated using thigh anglesθth(inputs). Second, ankle anglesθa(outputs) were estimated using thigh anglesθsh(inputs), and third, the ankle angles were estimated using shank angles (inputs). The proposed approach was investigated for 22 subjects at different walking speeds and the leave-one-subject-out procedure was used for training and testing the estimation algorithm. Average root mean square errors were 3.9°-5.3° and 2.1°-2.3° for knee and ankle angles, respectively. Average mean absolute errors (MAEs) MAEs were 3.2°-4° and 1.7°-1.8°, and average correlation coefficientsρccwere 0.95-0.98 and 0.94-0.96 for knee and ankle angles, respectively. The limitations and strengths of the proposed approach are discussed in detail and the results are compared with several studies.

A Support Vector Regression Approach for Continuous Prediction of Ankle Angle and Moment During Walking: An Implication for Developing a Control Strategy for Active Ankle Prostheses.

Lower limb amputations impair normal locomotion. This calls for the use of prosthetic devices to restore the lost or disabled functionality. Most of the commercially available prostheses offer only passive assistance with limited capacity. On the other hand, active prostheses may better restore movement, by supporting missing muscle function with additional motor power. The control algorithms of such embedded motors must understand the users locomotive intention to produce the required locomotion similar to that of an able-bodied individual. For individuals with transtibial amputation, the control algorithm should produce the desired locomotion by controlling an active ankle joint to generate appropriate ankle angle and ankle moment. In this paper, a strategy is proposed for the continuous estimation of ankle angle and ankle moment during walking using a support vector regression approach. Experimentally obtained hip and knee joint motion data were provided as the inputs to the support vector regression model. It is shown that, for level ground walking at self-selected speed, the proposed method could predict the ankle angle and moment with high accuracy (mean R2 value of 0.98 for ankle angle and 0.97 for ankle moment).

Synergy-Based Gaussian Process Estimation of Ankle Angle and Torque: Conceptualization for High Level Controlling of Active Robotic Foot Prostheses/Orthoses.

Human gait is the result of a complex and fascinating cooperation between different joints and segments in the lower extremity. This study aims at investigating the existence of this cooperation or the so-called synergy between the shank motion and the ankle motion. One potential use of this synergy is to develop the high level controllers for active foot prostheses/orthoses. The central point in this paper is to develop a high level controller that is able to continuously map shank kinematics (inputs) to ankle angles and torques (outputs). At the same time, it does not require speed determination, gait percent identification, switching rules, and look-up tables. Furthermore, having those targets in mind, an important part of this study is to determine which input type is required to achieve such targets. This should be fulfilled through using minimum number of inputs. To do this, the Gaussian process (GP) regression has been used to estimate the ankle angles and torques for 11 subjects at three walking speeds (0.5, 1, and 1.5 m/s) based on the shank angular velocity and angle. The results show that it is possible to estimate ankle motion based on the shank motion. It was found that the estimation achieved less quality with only shank angular velocity or angle, whereas the aggregated angular velocity and angle resulted in much higher output estimation quality. In addition, the estimation quality was acceptable for the speeds that there was a training procedure before and when it was tested for the untrained speeds, the estimation quality was not as acceptable as before. The pros and cons of the proposed method are investigated at different scenarios.

Does it pay to have a damper in a powered ankle prosthesis? A power-energy perspective.

In this paper we investigated on peak power (PP) and energy (ER) requirements for different active ankle actuation concepts that can have both elasticity and damping characteristics. A lower PP or ER requirement is an important issue because it will lead to a smaller motor or battery. In addition to spring, these actuation concepts are assumed to have (passive) damper in series (series elastic-damper actuator SEDA) or parallel (parallel elastic-damper actuator PEDA) to the motor. For SEA (series elastic actuator), SEDA and PEDA, we calculated the required minimum motor PP and ER in different human gaits: normal level walking, ascending and descending the stairs. We found that for level walking and ascending the stairs, the SEA concept, and for descending, the SEDA, were the favorable concepts to reduce required minimum PP and ER in comparison to a DD (direct drive) concept. In SEDA concept, the minimum PP could be reduced to half of what SEA would require. Nevertheless, it was found that spring was always required, however damper showed 'task specific' advantages. As a result, if a simple design perspective is in mind, from PP-ER viewpoint, SEA could be the best compromise to be used for different above-mentioned gaits. For SEDA or PEDA concepts, a controllable damper should be used. In addition, our results show that it is beneficial to select spring stiffness in SEA, based on level walking gait. The PP and ER requirements would increase very slightly for stairs ascending, and to some extent (10.5%) for descending as a consequence of this selection. In contrast, stiffness selection based on stair ascending or descending, increases the PP requirements of level walking more noticeably (17-24%).