Joint trajectory generator for powered orthosis based on gait modelling using PCA and FFT

Robotica ◽  
2017 ◽  
Vol 36 (3) ◽  
pp. 395-407 ◽  
Author(s):  
Nicholas B. Melo ◽  
Carlos E. T. Dórea ◽  
Pablo J. Alsina ◽  
Márcio V. Araújo
Keyword(s):  
Principal Component Analysis ◽  
Fourier Series ◽  
Energy Consumption ◽  
Lower Limb ◽  
Joint Angle ◽  
Principal Component ◽  
Orthotic Devices ◽  
Joint Angles ◽  
Anthropometric Features ◽  
Trajectory Generator

SUMMARYIn this work, we propose a method able to find user-oriented gait trajectories that can be used in powered lower limb orthosis applications. Most research related to active orthotic devices focuses on solving hardware issues. However, the problem of generating a set of joint trajectories that are user-oriented still persists. The proposed method uses principal component analysis to extract shared features from a gait dataset, taking into consideration gait-related variables such as joint angle information and the user's anthropometric features, used directly in an orthosis application. The trajectories of joint angles used by the model are represented by a given number of harmonics according to their respective Fourier series analyses. This representation allows better performance of the model, whose capability to generate gait information is validated through experiments using a real active orthotic device, analysing both joint motor energy consumption and user metabolic effort.

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 China’s Energy Consumption Based on Robust Principal Component Analysis and PSO-LSSVM Optimized by the Tabu Search Algorithm

Energies ◽  
2019 ◽  
Vol 12 (1) ◽  
pp. 196 ◽  
Author(s):  
Lihui Zhang ◽  
Riletu Ge ◽  
Jianxue Chai
Keyword(s):  
Principal Component Analysis ◽  
Energy Consumption ◽  
Tabu Search ◽  
Industrial Structure ◽  
Principal Component ◽  
Component Analysis ◽  
Support Vector ◽  
Forecasting Model ◽  
Robust Principal Component Analysis ◽  
Consumption Structure

China’s energy consumption issues are closely associated with global climate issues, and the scale of energy consumption, peak energy consumption, and consumption investment are all the focus of national attention. In order to forecast the amount of energy consumption of China accurately, this article selected GDP, population, industrial structure and energy consumption structure, energy intensity, total imports and exports, fixed asset investment, energy efficiency, urbanization, the level of consumption, and fixed investment in the energy industry as a preliminary set of factors; Secondly, we corrected the traditional principal component analysis (PCA) algorithm from the perspective of eliminating “bad points” and then judged a “bad spot” sample based on signal reconstruction ideas. Based on the above content, we put forward a robust principal component analysis (RPCA) algorithm and chose the first five principal components as main factors affecting energy consumption, including: GDP, population, industrial structure and energy consumption structure, urbanization; Then, we applied the Tabu search (TS) algorithm to the least square to support vector machine (LSSVM) optimized by the particle swarm optimization (PSO) algorithm to forecast China’s energy consumption. We collected data from 1996 to 2010 as a training set and from 2010 to 2016 as the test set. For easy comparison, the sample data was input into the LSSVM algorithm and the PSO-LSSVM algorithm at the same time. We used statistical indicators including goodness of fit determination coefficient (R2), the root means square error (RMSE), and the mean radial error (MRE) to compare the training results of the three forecasting models, which demonstrated that the proposed TS-PSO-LSSVM forecasting model had higher prediction accuracy, generalization ability, and higher training speed. Finally, the TS-PSO-LSSVM forecasting model was applied to forecast the energy consumption of China from 2017 to 2030. According to predictions, we found that China shows a gradual increase in energy consumption trends from 2017 to 2030 and will breakthrough 6000 million tons in 2030. However, the growth rate is gradually tightening and China’s energy consumption economy will transfer to a state of diminishing returns around 2026, which guides China to put more emphasis on the field of energy investment.

scholarly journals Joint Angle, Range of Motion, Force, and Moment Assessment: Responses of the Lower Limb to Ankle Plantarflexion and Dorsiflexion

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Ukadike Chris Ugbolue ◽  
Chloe Robson ◽  
Emma Donald ◽  
Kerry L. Speirs ◽  
Frédéric Dutheil ◽  
...  
Keyword(s):  
Range Of Motion ◽  
Lower Limb ◽  
Joint Angle ◽  
Joint Angles ◽  
Hip Joints ◽  
Exercise Interventions ◽  
Biomechanical Assessment ◽  
Lower Limb Exercise ◽  
Males And Females ◽  
Foot Position

There is limited research on the biomechanical assessment of the lower limb joints in relation to dynamic movements that occur at the hip, knee, and ankle joints when performing dorsiflexion (DF) and plantarflexion (PF) among males and females. This study investigated the differences in joint angles (including range of motion (ROM)) and forces (including moments) between the left and right limbs at the ankle, knee, and hip joints during dynamic DF and PF movements in both males and females. Using a general linear model employing multivariate analysis in relation to the joint angle, ROM, force, and moment datasets, the results revealed significant main effects for gender, sidedness, phases, and foot position with respect to joint angles. Weak correlations were observed between measured biomechanical variables. These results provide insightful information for clinicians and biomechanists that relate to lower limb exercise interventions and modelling efficacy standpoints.

scholarly journals Principal components of hand kinematics and neurophysiological signals in motor cortex during reach to grasp movements

2014 ◽  
Vol 112 (8) ◽  
pp. 1857-1870 ◽  
Author(s):  
Mohsen Mollazadeh ◽  
Vikram Aggarwal ◽  
Nitish V. Thakor ◽  
Marc H. Schieber
Keyword(s):  
Motor Cortex ◽  
Joint Angle ◽  
Principal Component ◽  
Field Potentials ◽  
Joint Angles ◽  
Reach To Grasp ◽  
Lower Accuracy ◽  
Neurophysiological Data ◽  
Kinematic Synergies ◽  
Cross Correlations

A few kinematic synergies identified by principal component analysis (PCA) account for most of the variance in the coordinated joint rotations of the fingers and wrist used for a wide variety of hand movements. To examine the possibility that motor cortex might control the hand through such synergies, we collected simultaneous kinematic and neurophysiological data from monkeys performing a reach-to-grasp task. We used PCA, jPCA and isomap to extract kinematic synergies from 18 joint angles in the fingers and wrist and analyzed the relationships of both single-unit and multiunit spike recordings, as well as local field potentials (LFPs), to these synergies. For most spike recordings, the maximal absolute cross-correlations of firing rates were somewhat stronger with an individual joint angle than with any principal component (PC), any jPC or any isomap dimension. In decoding analyses, where spikes and LFP power in the 100- to 170-Hz band each provided better decoding than other LFP-based signals, the first PC was decoded as well as the best decoded joint angle. But the remaining PCs and jPCs were predicted with lower accuracy than individual joint angles. Although PCs, jPCs or isomap dimensions might provide a more parsimonious description of kinematics, our findings indicate that the kinematic synergies identified with these techniques are not represented in motor cortex more strongly than the original joint angles. We suggest that the motor cortex might act to sculpt the synergies generated by subcortical centers, superimposing an ability to individuate finger movements and adapt the hand to grasp a wide variety of objects.

PRINCIPAL COMPONENT ANALYSIS OF DYNAMICAL DATA IN STRETCH SHORTENING CYLE OF HUMAN LOWER LIMB

2012 ◽  
Vol 45 ◽  
pp. S635 ◽  
Author(s):  
Carlos Rodrigues ◽  
João Cardoso ◽  
Alberto Carvalho ◽  
Carlos Carvalho ◽  
Velhote Correia ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Lower Limb ◽  
Principal Component ◽  
Component Analysis

scholarly journals A Mechanical Sensor Designed for Dynamic Joint Angle Measurement

2017 ◽  
Vol 2017 ◽  
pp. 1-12 ◽  
Author(s):  
Congo Tak-Shing Ching ◽  
Su-Yu Liao ◽  
Teng-Yun Cheng ◽  
Chih-Hsiu Cheng ◽  
Tai-Ping Sun ◽  
...  
Keyword(s):  
Gait Analysis ◽  
Linear Correlation ◽  
Lower Limb ◽  
Joint Angle ◽  
Intraclass Correlation ◽  
Reliability And Validity ◽  
Angle Measurement ◽  
Joint Angles ◽  
Rehabilitation Programs ◽  
Analysis System

Background. The measurement of the functional range of motion (FROM) of lower limb joints is an essential parameter for gait analysis especially in evaluating rehabilitation programs.Aim. To develop a simple, reliable, and affordable mechanical goniometer (MGR) for gait analysis, with six-degree freedom to dynamically assess lower limb joint angles.Design. Randomized control trials, in which a new MGR was developed for the measurements of FROM of lower limb joints.Setting. Reliability of the designed MGR was evaluated and validated by a motion analysis system (MAS).Population. Thirty healthy subjects participated in this study.Methods. Reliability and validity of the new MGR were tested by intraclass correlation coefficient (ICC), Bland-Altman plots, and linear correlation analysis.Results. The MGR has good inter- and intrarater reliability and validity withICC≥0.93(for both). Moreover, measurements made by MGR and MAS were comparable and repeatable with each other, as confirmed by Bland-Altman plots. Furthermore, a very high degree of linear correlation (R≥0.92for all joint angle measurements) was found between the lower limb joint angles measured by MGR and MAS.Conclusion. A simple, reliable, and affordable MGR has been designed and developed to aid clinical assessment and treatment evaluation of gait disorders.

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