Online Adaptive and LSTM-Based Trajectory Generation of Lower Limb Exoskeletons for Stroke Rehabilitation

Liang, Feng-Yan; Zhong, Chun-Hao; Zhao, Xuan; Castro, D.; Chen, Bing; Gao, Fei; Liao, Wei-Hsin

doi:10.1109/robio.2018.8664778

Cited by 20 publications

(12 citation statements)

References 11 publications

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“…Liu et al [14] used a deep spatio-temporal model based on long short-term memory (LSTM) networks for advanced prediction of knee angle trajectories from measurements of other joint angles of both legs for controlling a powered exoskeleton. Liang et al [15] used motion data from wearable sensors to learn a synergy between upper and lower limb trajectories using LSTM networks and predict the reference hip and knee angle trajectories for stroke patients. Mundt et al [16] used feed-forward neural networks and LSTM networks to predict lower limb joint angles and moments from inertial data simulated from optical motion tracking data.…”

Section: Related Workmentioning

confidence: 99%

“…However, this method is not feasible for powered prosthesis control for transtibial and transfemoral amputees. Moreover, most of the existing studies (with a few exceptions like [14], [15]) have not verified the performance of such algorithms for the real-time prediction of gait trajectories. Similarly, many of these studies were performed under constrained locomotion protocols, which do not complement the natural walking conditions of humans.…”

Section: Related Workmentioning

confidence: 99%

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A Function Approximator Model for Robust Online Foot Angle Trajectory Prediction Using a Single IMU Sensor: Implication for Controlling Active Prosthetic Feet

Dey

Schilling²

2023

IEEE Trans. Ind. Inf.

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Lower limb dysfunction hinders the locomotive activity in individuals and compels them to use assistive devices such as prostheses or orthoses to restore the missing locomotive functions. Passive assistive devices have limitations in replacing the lost functionality. On the other hand, active devices could restore more natural locomotion using motorized joints. To make proper use of these embedded motors, a control architecture is required to translate the motion intent of the user into the intended prosthesis joint trajectories. Here, we propose a temporal convolution-based online foot angle trajectory prediction network (FATP-N) that predicts the foot angular positions during unconstrained walking from the shank angular position measured using a single wearable motion tracker sensor. The data acquisition experiments were designed to reflect natural gait with turns, varying inclines, speeds, and mixed cadences. The trained models were prepared for real-time prediction by compressing them to reduce the storage and time complexity. The validations were performed on different motion conditions, including those that have not been used for model training, to verify the robustness of the model. The proposed FATP-N achieved high accuracy and could generate predictions in real-time. The results indicate that the proposed approach offers a possible architecture for real-time control of powered intelligent prostheses.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

A Function Approximator Model for Robust Online Foot Angle Trajectory Prediction Using a Single IMU Sensor: Implication for Controlling Active Prosthetic Feet

Dey

Schilling²

2023

IEEE Trans. Ind. Inf.

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“…To have a fair comparison, we also employ PCA-based linear regression to perform simulations following the steps in [19,43] based on the above-mentioned protocols. Note that we can finally obtain the linear equations (i.e., the interlimb synergy) for each subject's data between one side's hip or knee angle and the contralateral motion data.…”

Section: Interlimb Synergy Modelingmentioning

confidence: 99%

Gait Synergy Analysis and Modeling on Amputees and Stroke Patients for Lower Limb Assistive Devices

Liang

Gao

Cao

et al. 2022

Sensors

Self Cite

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The concept of synergy has drawn attention and been applied to lower limb assistive devices such as exoskeletons and prostheses for improving human–machine interaction. A better understanding of the influence of gait kinematics on synergies and a better synergy-modeling method are important for device design and improvement. To this end, gait data from healthy, amputee, and stroke subjects were collected. First, continuous relative phase (CRP) was used to quantify their synergies and explore the influence of kinematics. Second, long short-term memory (LSTM) and principal component analysis (PCA) were adopted to model interlimb synergy and intralimb synergy, respectively. The results indicate that the limited hip and knee range of motions (RoMs) in stroke patients and amputees significantly influence their synergies in different ways. In interlimb synergy modeling, LSTM (RMSE: 0.798° (hip) and 1.963° (knee)) has lower errors than PCA (RMSE: 5.050° (hip) and 10.353° (knee)), which is frequently used in the literature. Further, in intralimb synergy modeling, LSTM (RMSE: 3.894°) enables better synergy modeling than PCA (RMSE: 10.312°). In conclusion, stroke patients and amputees perform different compensatory mechanisms to adapt to new interlimb and intralimb synergies different from healthy people. LSTM has better synergy modeling and shows a promise for generating trajectories in line with the wearer’s motion for lower limb assistive devices.

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“…Lin et al developed a convolutional neural network (CNN)-based model for predicting BCI rehabilitation outcomes [ 15 ]. Liang et al used the long short-term memory (LSTM) neural network for generating motor trajectories of the lower-extremity exoskeleton for stroke rehabilitation [ 16 ] and a graph embedding-based model, Ego-CNN, for identifying key graph structures during MI [ 17 ]. However, BCI systems using DL methods require large amounts of EEG data for training models, which results in a bottleneck in therapy.…”

Section: Introductionmentioning

confidence: 99%

A Novel Deep Learning Method Based on an Overlapping Time Window Strategy for Brain–Computer Interface-Based Stroke Rehabilitation

Cao

Wu²,

Chen³

et al. 2022

Brain Sciences

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Globally, stroke is a leading cause of death and disability. The classification of motor intentions using brain activity is an important task in the rehabilitation of stroke patients using brain–computer interfaces (BCIs). This paper presents a new method for model training in EEG-based BCI rehabilitation by using overlapping time windows. For this aim, three different models, a convolutional neural network (CNN), graph isomorphism network (GIN), and long short-term memory (LSTM), are used for performing the classification task of motor attempt (MA). We conducted several experiments with different time window lengths, and the results showed that the deep learning approach based on overlapping time windows achieved improvements in classification accuracy, with the LSTM combined vote-counting strategy (VS) having achieved the highest average classification accuracy of 90.3% when the window size was 70. The results verified that the overlapping time window strategy is useful for increasing the efficiency of BCI rehabilitation.

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Online Adaptive and LSTM-Based Trajectory Generation of Lower Limb Exoskeletons for Stroke Rehabilitation

Cited by 20 publications

References 11 publications

A Function Approximator Model for Robust Online Foot Angle Trajectory Prediction Using a Single IMU Sensor: Implication for Controlling Active Prosthetic Feet

A Function Approximator Model for Robust Online Foot Angle Trajectory Prediction Using a Single IMU Sensor: Implication for Controlling Active Prosthetic Feet

Gait Synergy Analysis and Modeling on Amputees and Stroke Patients for Lower Limb Assistive Devices

A Novel Deep Learning Method Based on an Overlapping Time Window Strategy for Brain–Computer Interface-Based Stroke Rehabilitation

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