Deep Spatial-Temporal Model for rehabilitation gait: optimal trajectory generation for knee joint of lower-limb exoskeleton

2017 ◽  
Vol 37 (3) ◽  
pp. 369-378 ◽  
Author(s):  
Du-Xin Liu ◽  
Xinyu Wu ◽  
Wenbin Du ◽  
Can Wang ◽  
Chunjie Chen ◽  
...  
Keyword(s):  
Knee Joint ◽  
Lower Limb ◽  
Short Term Memory ◽  
Gait Pattern ◽  
Short Term ◽  
Term Memory ◽  
Content Type ◽  
Lower Limb Exoskeleton ◽  
Temporal Model ◽  
Long Short Term Memory

Purpose The purpose of this paper is to model and predict suitable gait trajectories of lower-limb exoskeleton for wearer during rehabilitation walking. Lower-limb exoskeleton is widely used for assisting walk in rehabilitation field. One key problem for exoskeleton control is to model and predict suitable gait trajectories for wearer. Design/methodology/approach In this paper, the authors propose a Deep Spatial-Temporal Model (DSTM) for generating knee joint trajectory of lower-limb exoskeleton, which first leverages Long-Short Term Memory framework to learn the inherent spatial-temporal correlations of gait features. Findings With DSTM, the pathological knee joint trajectories can be predicted based on subject’s other joints. The energy expenditure is adopted for verifying the effectiveness of new recovery gait pattern by monitoring dynamic heart rate. The experimental results demonstrate that the subjects have less energy expenditure in new recovery gait pattern than in others’ normal gait patterns, which also means the new recovery gait is more suitable for subject. Originality/value Long-Short Term Memory framework is first used for modeling rehabilitation gait, and the deep spatial–temporal relationships between joints of gait data can obtained successfully.

scholarly journals Continuous Estimation of Knee Joint Angle Based on Surface Electromyography Using a Long Short-Term Memory Neural Network and Time-Advanced Feature

Sensors ◽  
2020 ◽  
Vol 20 (17) ◽  
pp. 4966
Author(s):  
Xunju Ma ◽  
Yali Liu ◽  
Qiuzhi Song ◽  
Can Wang
Keyword(s):  
Neural Network ◽  
Knee Joint ◽  
Surface Electromyography ◽  
Joint Angle ◽  
Short Term Memory ◽  
Mean Square ◽  
Short Term ◽  
Term Memory ◽  
Knee Joint Angle ◽  
Long Short Term Memory

Continuous joint angle estimation based on a surface electromyography (sEMG) signal can be used to improve the man-machine coordination performance of the exoskeleton. In this study, we proposed a time-advanced feature and utilized long short-term memory (LSTM) with a root mean square (RMS) feature and its time-advanced feature (RMSTAF; collectively referred to as RRTAF) of sEMG to estimate the knee joint angle. To evaluate the effect of joint angle estimation, we used root mean square error (RMSE) and cross-correlation coefficient ρ between the estimated angle and actual angle. We also compared three methods (i.e., LSTM using RMS, BPNN (back propagation neural network) using RRTAF, and BPNN using RMS) with LSTM using RRTAF to highlight its good performance. Five healthy subjects participated in the experiment and their eight muscle (i.e., rectus femoris (RF), biceps femoris (BF), semitendinosus (ST), gracilis (GC), semimembranosus (SM), sartorius (SR), medial gastrocnemius (MG), and tibialis anterior (TA)) sEMG signals were taken as algorithm inputs. Moreover, the knee joint angles were used as target values. The experimental results showed that, compared with LSTM using RMS, BPNN using RRTAF, and BPNN using RMS, the average RMSE values of LSTM using RRTAF were respectively reduced by 8.57%, 46.62%, and 68.69%, whereas the average ρ values were respectively increased by 0.31%, 4.15%, and 18.35%. The results demonstrated that LSTM using RRTAF, which contained the time-advanced feature, had better performance for estimating the knee joint motion.

Identifying the knee joint angular position under neuromuscular electrical stimulation via long short-term memory neural networks

2020 ◽  
Vol 36 (4) ◽  
pp. 511-526
Author(s):  
Héber H. Arcolezi ◽  
Willian R. B. M. Nunes ◽  
Selene Cerna ◽  
Rafael A. de Araujo ◽  
Marcelo Augusto Assunção Sanches ◽  
...  
Keyword(s):  
Neural Networks ◽  
Electrical Stimulation ◽  
Knee Joint ◽  
Short Term Memory ◽  
Angular Position ◽  
Neuromuscular Electrical Stimulation ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory

scholarly journals Artificial Neural Networks in Motion Analysis—Applications of Unsupervised and Heuristic Feature Selection Techniques

Sensors ◽  
2020 ◽  
Vol 20 (16) ◽  
pp. 4581
Author(s):  
Marion Mundt ◽  
Arnd Koeppe ◽  
Franz Bamer ◽  
Sina David ◽  
Bernd Markert
Keyword(s):  
Neural Networks ◽  
Principal Component Analysis ◽  
Motion Analysis ◽  
Lower Limb ◽  
Short Term Memory ◽  
Principal Component ◽  
Joint Angles ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory

The use of machine learning to estimate joint angles from inertial sensors is a promising approach to in-field motion analysis. In this context, the simplification of the measurements by using a small number of sensors is of great interest. Neural networks have the opportunity to estimate joint angles from a sparse dataset, which enables the reduction of sensors necessary for the determination of all three-dimensional lower limb joint angles. Additionally, the dimensions of the problem can be simplified using principal component analysis. Training a long short-term memory neural network on the prediction of 3D lower limb joint angles based on inertial data showed that three sensors placed on the pelvis and both shanks are sufficient. The application of principal component analysis to the data of five sensors did not reveal improved results. The use of longer motion sequences compared to time-normalised gait cycles seems to be advantageous for the prediction accuracy, which bridges the gap to real-time applications of long short-term memory neural networks in the future.

scholarly journals Prediction of gait trajectories based on the Long Short Term Memory neural networks

PLoS ONE ◽  
2021 ◽  
Vol 16 (8) ◽  
pp. e0255597
Author(s):  
Abdelrahman Zaroug ◽  
Alessandro Garofolini ◽  
Daniel T. H. Lai ◽  
Kurt Mudie ◽  
Rezaul Begg
Keyword(s):  
Lower Limb ◽  
Short Term Memory ◽  
Series Data ◽  
Short Term ◽  
Term Memory ◽  
Wide Range ◽  
Limb Kinematics ◽  
Long Short Term Memory ◽  
Balance Loss ◽  
Lower Limb Kinematics

The forecasting of lower limb trajectories can improve the operation of assistive devices and minimise the risk of tripping and balance loss. The aim of this work was to examine four Long Short Term Memory (LSTM) neural network architectures (Vanilla, Stacked, Bidirectional and Autoencoders) in predicting the future trajectories of lower limb kinematics, i.e. Angular Velocity (AV) and Linear Acceleration (LA). Kinematics data of foot, shank and thigh (LA and AV) were collected from 13 male and 3 female participants (28 ± 4 years old, 1.72 ± 0.07 m in height, 66 ± 10 kg in mass) who walked for 10 minutes at preferred walking speed (4.34 ± 0.43 km.h-1) and at an imposed speed (5km.h-1, 15.4% ± 7.6% faster) on a 0% gradient treadmill. The sliding window technique was adopted for training and testing the LSTM models with total kinematics time-series data of 10,500 strides. Results based on leave-one-out cross validation, suggested that the LSTM autoencoders is the top predictor of the lower limb kinematics trajectories (i.e. up to 0.1s). The normalised mean squared error was evaluated on trajectory predictions at each time-step and it obtained 2.82–5.31% for the LSTM autoencoders. The ability to predict future lower limb motions may have a wide range of applications including the design and control of bionics allowing improved human-machine interface and mitigating the risk of falls and balance loss.

Forecasting container throughput with long short-term memory networks

2019 ◽  
Vol 120 (3) ◽  
pp. 425-441 ◽  
Author(s):  
Sonali Shankar ◽  
P. Vigneswara Ilavarasan ◽  
Sushil Punia ◽  
Surya Prakash Singh
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Short Term Memory ◽  
Moving Average ◽  
Short Term ◽  
Error Matrix ◽  
Term Memory ◽  
Content Type ◽  
Forecasting Performance ◽  
Long Short Term Memory

Purpose Better forecasting always leads to better management and planning of the operations. The container throughput data are complex and often have multiple seasonality. This makes it difficult to forecast accurately. The purpose of this paper is to forecast container throughput using deep learning methods and benchmark its performance over other traditional time-series methods. Design/methodology/approach In this study, long short-term memory (LSTM) networks are implemented to forecast container throughput. The container throughput data of the Port of Singapore are used for empirical analysis. The forecasting performance of the LSTM model is compared with seven different time-series forecasting methods, namely, autoregressive integrated moving average (ARIMA), simple exponential smoothing, Holt–Winter’s, error-trend-seasonality, trigonometric regressors (TBATS), neural network (NN) and ARIMA + NN. The relative error matrix is used to analyze the performance of the different models with respect to bias, accuracy and uncertainty. Findings The results showed that LSTM outperformed all other benchmark methods. From a statistical perspective, the Diebold–Mariano test is also conducted to further substantiate better forecasting performance of LSTM over other counterpart methods. Originality/value The proposed study is a contribution to the literature on the container throughput forecasting and adds value to the supply chain theory of forecasting. Second, this study explained the architecture of the deep-learning-based LSTM method and discussed in detail the steps to implement it.

Pattern-based dual learning for point-of-interest (POI) recommendation

2020 ◽  
Vol 120 (10) ◽  
pp. 1901-1921
Author(s):  
Tipajin Thaipisutikul ◽  
Yi-Cheng Chen
Keyword(s):  
Recommendation System ◽  
Short Term Memory ◽  
Real Life ◽  
Experimental Results ◽  
Short Term ◽  
Term Memory ◽  
Content Type ◽  
Point Of Interest ◽  
Poi Recommendation ◽  
Long Short Term Memory

PurposeTourism spot or point-of-interest (POI) recommendation has become a common service in people's daily life. The purpose of this paper is to model users' check-in history in order to predict a set of locations that a user may soon visit.Design/methodology/approachThe authors proposed a novel learning-based method, the pattern-based dual learning POI recommendation system as a solution to consider users' interests and the uniformity of popular POI patterns when making recommendations. Differing from traditional long short-term memory (LSTM), a new users’ regularity–POIs’ popularity patterns long short-term memory (UP-LSTM) model was developed to concurrently combine the behaviors of a specific user and common users.FindingsThe authors introduced the concept of dual learning for POI recommendation. Several performance evaluations were conducted on real-life mobility data sets to demonstrate the effectiveness and practicability of POI recommendations. The metrics such as hit rate, precision, recall and F-measure were used to measure the capability of ranking and precise prediction of the proposed model over all baselines. The experimental results indicated that the proposed UP-LSTM model consistently outperformed the state-of-the-art models in all metrics by a large margin.Originality/valueThis study contributes to the existing literature by incorporating a novel pattern–based technique to analyze how the popularity of POIs affects the next move of a particular user. Also, the authors have proposed an effective fusing scheme to boost the prediction performance in the proposed UP-LSTM model. The experimental results and discussions indicate that the combination of the user's regularity and the POIs’ popularity patterns in PDLRec could significantly enhance the performance of POI recommendation.

scholarly journals A long short-term memory neural network model for knee joint acceleration estimation using mechanomyography signals

2020 ◽  
Vol 17 (6) ◽  
pp. 172988142096870
Author(s):  
Chenlei Xie ◽  
Daqing Wang ◽  
Haifeng Wu ◽  
Lifu Gao
Keyword(s):  
Neural Network ◽  
Knee Joint ◽  
Network Model ◽  
Neural Network Model ◽  
Short Term Memory ◽  
Human Motion ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory ◽  
Power Assistance

With the growth of the number of elderly and disabled with motor dysfunction, the demand for assisted exercise is increasing. Wearable power assistance robots are developed to provide athletic ability of limbs for the elderly or the disabled who have weakened limbs to better self-care ability. Existing wearable power-assisted robots generally use surface electromyography (sEMG) to obtain effective human motion intentions. Due to the characteristics of sEMG signals, it is limited in many applications. To solve the above problems, we design a long short-term memory (LSTM) neural network model based on human mechanomyography (MMG) signals to estimate the motion acceleration of knee joint. The acceleration can be further calculated by the torque required for movement control of the wearable power assistance robots for the lower limb. We detect MMG signals on the clothed thigh, extract features of the MMG signals, and then, use principal component analysis to reduce the features’ dimensions. Finally, the dimension-reduced features are inputted into the LSTM neural network model in time series for estimating the acceleration. The experimental results show that the average correlation coefficient ( R) is 94.48 ± 1.91% for the estimation of acceleration in the process of continuously performing under approximately π/4 rad/s. This approach can be applied in the practical applications of wearable field.

Performance evaluation for tool wear prediction based on Bi-directional, Encoder–Decoder and Hybrid Long Short-Term Memory models

2022 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Satish Kumar ◽  
Tushar Kolekar ◽  
Ketan Kotecha ◽  
Shruti Patil ◽  
Arunkumar Bongale
Keyword(s):  
Feature Selection ◽  
Tool Wear ◽  
Hybrid Model ◽  
Short Term Memory ◽  
Short Term ◽  
Wear Prediction ◽  
Term Memory ◽  
Content Type ◽  
Tool Wear Prediction ◽  
Long Short Term Memory

Purpose Excessive tool wear is responsible for damage or breakage of the tool, workpiece, or machining center. Thus, it is crucial to examine tool conditions during the machining process to improve its useful functional life and the surface quality of the final product. AI-based tool wear prediction techniques have proven to be effective in estimating the Remaining Useful Life (RUL) of the cutting tool. However, the model prediction needs improvement in terms of accuracy.Design/methodology/approachThis paper represents a methodology of fusing a feature selection technique along with state-of-the-art deep learning models. The authors have used NASA milling data sets along with vibration signals for tool wear prediction and performance analysis in 15 different fault scenarios. Multiple steps are used for the feature selection and ranking. Different Long Short-Term Memory (LSTM) approaches are used to improve the overall prediction accuracy of the model for tool wear prediction. LSTM models' performance is evaluated using R-square, Mean Absolute Error (MAE), Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) parameters.FindingsThe R-square accuracy of the hybrid model is consistently high and has low MAE, MAPE and RMSE values. The average R-square score values for LSTM, Bidirection, Encoder–Decoder and Hybrid LSTM are 80.43, 84.74, 94.20 and 97.85%, respectively, and corresponding average MAPE values are 23.46, 22.200, 9.5739 and 6.2124%. The hybrid model shows high accuracy as compared to the remaining LSTM models.Originality/value The low variance, Spearman Correlation Coefficient and Random Forest Regression methods are used to select the most significant feature vectors for training the miscellaneous LSTM model versions and highlight the best approach. The selected features pass to different LSTM models like Bidirectional, Encoder–Decoder and Hybrid LSTM for tool wear prediction. The Hybrid LSTM approach shows a significant improvement in tool wear prediction.

scholarly journals Comparison of Decision Tree and Long Short-Term Memory Approaches for Automated Foot Strike Detection in Lower Extremity Amputee Populations

Sensors ◽  
2021 ◽  
Vol 21 (21) ◽  
pp. 6974
Author(s):  
Pascale Juneau ◽  
Natalie Baddour ◽  
Helena Burger ◽  
Andrej Bavec ◽  
Edward D. Lemaire
Keyword(s):  
Lower Limb ◽  
Short Term Memory ◽  
Walk Test ◽  
Short Term ◽  
Term Memory ◽  
Foot Strike ◽  
Novel Method ◽  
Long Short Term Memory ◽  
Lower Limb Amputees ◽  
Hidden Nodes

Foot strike detection is important when evaluating a person’s gait characteristics. Accelerometer and gyroscope signals from smartphones have been used to train artificial intelligence (AI) models for automated foot strike detection in able-bodied and elderly populations. However, there is limited research on foot strike detection in lower limb amputees, who have a more variable and asymmetric gait. A novel method for automated foot strike detection in lower limb amputees was developed using raw accelerometer and gyroscope signals collected from a smartphone positioned at the posterior pelvis. Raw signals were used to train a decision tree model and long short-term memory (LSTM) model for automated foot strike detection. These models were developed using retrospective data (n = 72) collected with the TOHRC Walk Test app during a 6-min walk test (6MWT). An Android smartphone was placed on a posterior belt for each participant during the 6MWT to collect accelerometer and gyroscope signals at 50 Hz. The best model for foot strike identification was the LSTM with 100 hidden nodes in the LSTM layer, 50 hidden nodes in the dense layer, and a batch size of 64 (99.0% accuracy, 86.4% sensitivity, 99.4% specificity, and 83.7% precision). This research created a novel method for automated foot strike identification in lower extremity amputee populations that is equivalent to manual labelling and accessible for clinical use. Automated foot strike detection is required for stride analysis and to enable other AI applications, such as fall detection.

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