Continuous Prediction of Joint Angular Positions and Moments: A Potential Control Strategy for Active Knee-Ankle Prostheses

Dey, Sharmita; Yoshida, Takashi; Foerster, Robert H.; Ernst, Michael; Schmalz, Thomas; Schilling, Arndt F.

doi:10.1109/tmrb.2020.3011841

Cited by 10 publications

(6 citation statements)

References 27 publications

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“…The study focused on phase prediction and control of the stance proportion in a gait cycle, not allowing to parameterize both the stance and swing behaviors. Dey et al [15] also demonstrated a successful continuous prediction of joint angular positions and timing. Their approach was desired to perform an active knee-ankle prosthesis control strategy by recognizing movement intention for powered-leg prosthesis users.…”

Section: Introductionmentioning

confidence: 97%

Knee Angle Generation with Walking Speed Adaptation Ability for a Powered Transfemoral Prosthetic Leg Prototype

Pranata

Nguyen

et al. 2023

Inventions

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This paper presents a microcontroller-based solution for generating real-time normal walking knee angle of a powered transfemoral prosthetic leg prototype. The proposed control algorithm was used to determine the prosthetic knee angle by utilizing seven hip angle movement features generated from only the inertia measurement unit (IMU) deployed on the prosthetic socket on the thigh of the same side. Then, a proportional–integral–derivative (PID) controller is developed to control the motor to reach the desired knee angle in real time. Furthermore, a novel parallel four-bar linkage-based master–slave validation framework combining a motion capture system was introduced to evaluate the performance of the knee angle generation on a speed-adjustable treadmill with able-bodied subjects. In the framework evaluation, 3 different walking speeds were applied to the treadmill to validate different speed adaptation capabilities of the prosthetic leg control system, precisely 50 cm/s, 60 cm/s, and 70 cm/s. Through the proposed 4-bar linkage framework, the prosthesis’s movement can simulate able-bodied subjects well with maximum RMSE never exceeding 0.27° in the swing flexion phase, 4.4° to 5.8° in the stance phase, and 1.953° to 13.466° in the swing extension phase. The treadmill results showed that the prosthetic leg is able to perform a normal walking gait following different walking speeds of the subject. Finally, a corridor walking experiment with a bypass adapter was successfully performed to examine the feasibility of real prosthetic walking situations.

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Section: Introductionmentioning

confidence: 97%

Knee Angle Generation with Walking Speed Adaptation Ability for a Powered Transfemoral Prosthetic Leg Prototype

Pranata

Nguyen

et al. 2023

Inventions

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“…For example, sensors placed on the foot and shank can adequately measure ankle kinematics (r 2 of 0.97 for ankle angle) [20]. Sensors are often positioned proximal and distal to the neighboring joint of interest (e.g., thigh-shank for knee kinematics and thigh-waist for hip kinematics) [20,21]. For example, Molinaro et al (2020) estimated sagittal plane hip torque using data from rotary encoders mounted on the sagittal plane of the hip and bilateral thigh IMUs (3D accelerometer and gyroscope) [21].…”

Section: Introductionmentioning

confidence: 99%

“…For example, Molinaro et al (2020) estimated sagittal plane hip torque using data from rotary encoders mounted on the sagittal plane of the hip and bilateral thigh IMUs (3D accelerometer and gyroscope) [21]. Additionally, Dey et al (2020) used thigh kinematics (angles, angular velocity, and angular acceleration) as inputs to a Random Forest regression to estimate joint angles and moments at the ankle and knee [20].…”

Section: Introductionmentioning

confidence: 99%

“…Along with sensor count and type, precise sensor location for extracting relevant physiological signals is essential for effective HMIP [ 19 ]. For example, sensors placed on the foot and shank can adequately measure ankle kinematics (r 2 of 0.97 for ankle angle) [ 20 ]. Sensors are often positioned proximal and distal to the neighboring joint of interest (e.g., thigh–shank for knee kinematics and thigh–waist for hip kinematics) [ 20 , 21 ].…”

Section: Introductionmentioning

confidence: 99%

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The Effect of Sensor Feature Inputs on Joint Angle Prediction across Simple Movements

Hollinger,

Schall,

Chen

et al. 2024

Sensors

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The use of wearable sensors, such as inertial measurement units (IMUs), and machine learning for human intent recognition in health-related areas has grown considerably. However, there is limited research exploring how IMU quantity and placement affect human movement intent prediction (HMIP) at the joint level. The objective of this study was to analyze various combinations of IMU input signals to maximize the machine learning prediction accuracy for multiple simple movements. We trained a Random Forest algorithm to predict future joint angles across these movements using various sensor features. We hypothesized that joint angle prediction accuracy would increase with the addition of IMUs attached to adjacent body segments and that non-adjacent IMUs would not increase the prediction accuracy. The results indicated that the addition of adjacent IMUs to current joint angle inputs did not significantly increase the prediction accuracy (RMSE of 1.92° vs. 3.32° at the ankle, 8.78° vs. 12.54° at the knee, and 5.48° vs. 9.67° at the hip). Additionally, including non-adjacent IMUs did not increase the prediction accuracy (RMSE of 5.35° vs. 5.55° at the ankle, 20.29° vs. 20.71° at the knee, and 14.86° vs. 13.55° at the hip). These results demonstrated how future joint angle prediction during simple movements did not improve with the addition of IMUs alongside current joint angle inputs.

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“…In addition, RFG can achieve excellent prediction performance using a smaller dataset compared to the other machine learning methods. 27 Based on the above advantages of the CorrCA and RFG, we propose a new estimation strategy: CorrCA-RFG. Firstly, a single set of linear projections are yielded from multiple subjects' sEMG signals using CorrCA.…”

Section: Introductionmentioning

confidence: 99%

Robust motion estimation with user-independent sEMG features extracted by correlated components analysis

Zhang

Lin

You

et al. 2022

Measurement and Control

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Motion estimation from surface electromyogram (sEMG) signals has been studied extensively over the past decades. Nevertheless, it is challenging for novel subjects to adapt to a trained estimation model since sEMG signals inherently contain user-dependent features that interfere with the estimation model and reduce the estimation accuracy. To achieve accurate motion estimation, a strategy of correlated components analysis-based random forest regressor (CorrCA-RFG) was proposed. The proposed CorrCA-RFG firstly uses CorrCA to extract user-independent features related to motion among multiple subjects, and obtain the projection vectors from sEMG data to the motion-dependent feature space. Then, the RFG is trained by the user-independent sEMG features and establishes the estimation model. To validate the effectiveness of the proposed CorrCA-RFG, this strategy was tested on a public dataset and an experimental study and compared to three methods, namely random forest regressor (RFG), canonical components analysis-based random forest regressor (CCA-RFG), and a convolutional neural network (CNN). For both cases, the estimation performance of the CorrCA-RFG outperformed the other three methods. These results demonstrate that the proposed CorrCA-RFG enables robust motion estimation by extracting user-independent sEMG features.

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Continuous Prediction of Joint Angular Positions and Moments: A Potential Control Strategy for Active Knee-Ankle Prostheses

Cited by 10 publications

References 27 publications

Knee Angle Generation with Walking Speed Adaptation Ability for a Powered Transfemoral Prosthetic Leg Prototype

Knee Angle Generation with Walking Speed Adaptation Ability for a Powered Transfemoral Prosthetic Leg Prototype

The Effect of Sensor Feature Inputs on Joint Angle Prediction across Simple Movements

Robust motion estimation with user-independent sEMG features extracted by correlated components analysis

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