Estimation of muscular forces from SSA smoothed sEMG signals calibrated by inverse dynamics-based physiological static optimization

The estimation of muscular forces is useful in several areas such as biomedical or rehabilitation engineering. As muscular forces cannot be measured in vivo non-invasively they must be estimated by using indirect measurements such as surface electromyography (sEMG) signals or by means of inverse dynamic (ID) analyses. This paper proposes an approach to estimate muscular forces based on both of them. The main idea is to tune a gain matrix so as to compute muscular forces from sEMG signals. To do so, a curve fitting process based on least-squares is carried out. The input is the sEMG signal filtered using singular spectrum analysis technique. The output corresponds to the muscular force estimated by the ID analysis of the recorded task, a dumbbell weightlifting. Once the model parameters are tuned, it is possible to obtain an estimation of muscular forces based on sEMG signal. This procedure might be used to predict muscular forces in vivo outside the space limitations of the gait analysis laboratory.Postprint (published version

3D motion analysis applying intertial sensing

Small inertial sensors like accelerometers and gyroscopes are more and more used in ambulatory motion analysis (Busseral. 1998; Baten et al. 2000; Veltink et al. 2003). Typically, angular orientation of a body segment is determined by integrating the output from the angular rate sensors strapped on the segment

Application of multibody dynamics techniques to the analysis of human gait

La tesi que es presenta tracta l’estudi cinemàtic i dinàmic de la marxa humana mitjançant tècniques de dinàmica de sistemes multisòlid. Per a aquest propòsit, s’utilitzen dos models biomecànics: un model pla format per 11 segments i 14 graus de llibertat i un model tridimensional format per 18 segments i 57 graus de llibertat. La formulació dinàmica multisòlid ha estat desenvolupada en coordenades mixtes (naturals i relatives). La marxa de l’individu s’enregistra al laboratori utilitzant un sistema de captura del moviment mitjançant el qual s’obté la posició de cadascun dels 37 marcadors situats sobre el cos del subjecte. Les dades de posició es filtren utilitzant un algorisme basat en el singular spectrum analysis (SSA) i les coordenades naturals del model es calculen mitjançant relacions algebraiques entre les posicions dels marcadors. Posteriorment, un procés de consistència cinemàtica assegura les restriccions de sòlid rígid. El processament cinemàtic continua amb l’aproximació de les posicions mitjançant corbes B-spline d’on se n’obtenen, per derivació analítica, els valors de velocitat i acceleració. En una anàlisi dinàmica inversa de la marxa humana, s’acostumen a utilitzar com a dades d’entrada els paràmetres antropomètrics (geomètrics i inercials) dels segments, les dades cinemàtiques i les mesures de les plaques de força. En contraposició al que fan la majoria d’autors, en aquesta tesi, les mesures de les plaques de força no són utilitzades directament en l’anàlisi sinó que només s’usen per solucionar el problema del repartiment del torsor resultant de les forces de contacte durant la fase de doble suport. En aquesta fase, els dos peus es recolzen sobre el terra i les mesures cinemàtiques són insuficients per determinar el torsor en cada peu. El nou mètode de repartiment que es proposa (anomenat contact force plate sharing, CFP) és una de les aportacions de la tesi i destaca pel fet que permet determinar un conjunt de forces i moments dinàmicament consistents amb el model biomecànic, sense haver de modificar-ne les coordenades cinemàtiques ni afegir forces o moments residuals en algun dels segments. Encara dins l’àmbit de l’estudi dinàmic invers, s’ha analitzat la sensitivitat dels parells articulars a errors comesos en estimar els paràmetres antropomètrics, a errors que poden contenir les mesures de les plaques de força i a errors que es poden cometre en el processament cinemàtic de les mesures. L’estudi permet concloure que els resultats són molt sensibles als errors cinemàtics i a les forces mesurades per les plaques, sent els errors en els paràmetres antropomètrics menys influents. La tesi també presenta un nou model tridimensional de contacte peu-terra basat en el contacte esfera-pla i els seus paràmetres s’estimen mitjançant dos enfocaments diferents basats en tècniques d’optimització. El model s’utilitza com un mètode alternatiu per solucionar el problema del repartiment durant la fase de doble suport en dinàmica inversa, i també s’utilitza en simulacions de dinàmica directa per estimar les forces de contacte entre el model biomecànic i el seu entorn. En l’anàlisi dinàmica directa és necessària la implementació d’un controlador que està basat, en aquest cas, en el filtre de Kalman estès. Les contribucions més importants de la tesi, en el cas de l’anàlisi dinàmica inversa, es centren en el mètode CFP i en l’ús del model de contacte per solucionar el repartiment de forces de contacte en la fase de doble suport. Referent a l’anàlisi de la influència dels errors en les dades d’entrada del problema dinàmic invers, la modelització estadística dels errors conjuntament amb la pertorbació conjunta de més d’un paràmetre antropomètric a la vegada (mantenint constant l’alçada i el pes de la persona) és també una novetat. Per altra banda, el model de contacte presentat és també una contribució original. En l’estat de l’art actual no es troben models que usin dades reals capturades al laboratori i que a la vegada s’utilitzin per solucionar el problema de repartiment en el doble suport i per simular el contacte peu-terra en una anàlisi dinàmica directa. Finalment, el fet de desenvolupar un model que s’utilitzi tant per a l’anàlisi dinàmica directa com inversa és també una de les aportacions d’aquesta tesi. Tot i que les dues anàlisis, per separat, són temes de recerca comuns en l’àmbit de la Biomecànica, es troben a faltar estudis que comprovin la validesa dels resultats que se n’obtenen. En aquesta tesi, els resultats de la dinàmica inversa s’han utilitzat com a dades d’entrada de l’anàlisi dinàmica directa, el resultat de la qual (el moviment) ha pogut ser comparat amb el que s’obté de la captura del laboratori (entrada de la dinàmica inversa). D’aquesta manera, el cercle es tanca i es pot verificar la validesa tant dels models com dels resultats obtinguts.This thesis presents the kinematic and dynamic study of human motion by means of multibody system dynamics techniques. For this purpose, two biomechanical models are used: a 2D model formed by 11 segments with 14 degrees of freedom, and a 3D model that consists of 18 segments with 57 degrees of freedom. The movement of the subject is recorded in the laboratory using a motion capture system that provides the position along time of 37 markers attached on the body of the subject. Position data are filtered using an algorithm based on singular spectrum analysis (SSA) and the natural coordinates of the model are calculated using algebraic relations between the marker positions. Afterwards, a kinematic procedure ensures the kinematic consistency and the data processing continues with the approximation of the position histories using B-spline curves and obtaining, by analytical derivation, the velocity and acceleration values. This information is used as input of an inverse dynamic analysis. Differing to most published works, in this thesis the force plates measurements are not used directly as inputs of the analysis. When both feet contact the ground, kinematic measurements are insufficient to determine the individual wrench at each foot. One of the contributions of the thesis is a new strategy that is proposed to solve the this indeterminacy (called corrected force plate sharing, CFP) based on force plates data. Using this method, a set of two contact wrenches dynamically consistent with the movement are obtained with no need neither to add residual wrenches nor to modify the original motion. Also in the IDA field, the sensitivity of the joint torques to errors in the anthropometric parameters, in the force plate measurements and to errors committed during the kinematic data processing is studied. The analysis shows that the results are very sensitive to errors in force measurements and in the kinematic processing, being the errors in the body segment parameters less influential. A new 3D foot-ground contact model is presented and its parameters are estimated using optimization techniques. The model is used as an alternative method to solve the mentioned sharing problem during the double support phase and it is also used, in a forward dynamic analysis, to estimate the contact forces between the biomechanical model and its environment. The forward dynamic simulation requires the implementation of a controller that is based, in this case, on the extended Kalman filter. The most important contributions of the thesis in IDA are focused on the CFP sharing method and regarding the analysis of the influence of errors in input data on the inverse dynamics results, the statistical modelling of the uncertainties together with the perturbation of more than one parameter at same time (remaining height and weight as a constant parameters) is also new in the literature. Moreover, the presented foot-ground contact model is also original. In the current state of the art, there are no models that use real data captured in the laboratory to solve the contact wrench sharing problem during the double support phase. Furthermore, there are few studies simulating the foot-ground interaction in a forward dynamic analysis using a continuous foot-ground contact model. Finally, developing a model that is used for both forward and inverse dynamic analysis is a relevant aspect of the methodology used. Although the two approaches separately are common research topics in the field of biomechanics, a small number of studies prove the validity of the obtained results. In this thesis, the results of the inverse dynamics are used as input data for the forward dynamic analysis, and the results of the latter (the motion) have been compared with the motion capture in the laboratory (input of the inverse dynamics analysis). Thus, the circle has been closed which allows us to validate the accuracy of both the models and the obtained results

Gait Verification using Knee Acceleration Signals

A novel gait recognition method for biometric applications is proposed. The approach has the following distinct features. First, gait patterns are determined via knee acceleration signals, circumventing difficulties associated with conventional vision-based gait recognition methods. Second, an automatic procedure to extract gait features from acceleration signals is developed that employs a multiple-template classification method. Consequently, the proposed approach can adjust the sensitivity and specificity of the gait recognition system with great flexibility. Experimental results from 35 subjects demonstrate the potential of the approach for successful recognition. By setting sensitivity to be 0.95 and 0.90, the resulting specificity ranges from 1 to 0.783 and 1.00 to 0.945, respectively

Wearable inertial sensor system towards daily human kinematic gait analysis: benchmarking analysis to MVN BIOMECH

This paper presents a cost- and time-effective wearable inertial sensor system, the InertialLAB. It includes gyroscopes and accelerometers for the real-time monitoring of 3D-angular velocity and 3D-acceleration of up to six lower limbs and trunk segment and sagittal joint angle up to six joints. InertialLAB followed an open architecture with a low computational load to be executed by wearable processing units up to 200 Hz for fostering kinematic gait data to third-party systems, advancing similar commercial systems. For joint angle estimation, we developed a trigonometric method based on the segments’ orientation previously computed by fusion-based methods. The validation covered healthy gait patterns in varying speed and terrain (flat, ramp, and stairs) and including turns, extending the experiments approached in the literature. The benchmarking analysis to MVN BIOMECH reported that InertialLAB provides more reliable measures in stairs than in flat terrain and ramp. The joint angle time-series of InertialLAB showed good waveform similarity (>0.898) with MVN BIOMECH, resulting in high reliability and excellent validity. User-independent neural network regression models successfully minimized the drift errors observed in InertialLAB’s joint angles (NRMSE < 0.092). Further, users ranked InertialLAB as good in terms of usability. InertialLAB shows promise for daily kinematic gait analysis and real-time kinematic feedback for wearable third-party systems.This work has been supported in part by the Fundação para a Ciência e Tecnologia (FCT) with the Reference Scholarship under Grant SFRH/BD/108309/2015 and SFRH/BD/147878/2019, by the FEDER Funds through the Programa Operacional Regional do Norte and national funds from FCT with the project SmartOs under Grant NORTE-01-0145-FEDER-030386, and through the COMPETE 2020—Programa Operacional Competitividade e Internacionalização (POCI)—with the Reference Project under Grant POCI-01-0145-FEDER-006941

Motion capture as a modern technology for analysing ergometer rowing

The paper presents a purpose-built laboratory stand consisting of a Vicon motion capture system with reference video cameras, wireless EMG system, Concept 2 Indoor Rower ergometer, wireless heart rate monitor and the Nexus software. A pilot study of people who exercise on the ergometer helped to create a proper configuration of all the components of the laboratory. Moreover, a procedure for carrying out research was developed, which consists of several steps divided into 4 stages: preparation of the motion acquisition system; preparation of the participant; familiarising participants with the technique of rowing, recording their movements and acquiring other measurement signals. Preliminary analysis of the results obtained from heterogeneous signals from various devices showed that all the components of the research stand are mutually compatible and the received signals do not interfere with one another

A Logistic Regression Model for Biomechanical Risk Classification in Lifting Tasks

Lifting is one of the most potentially harmful activities for work-related musculoskeletal disorders (WMSDs), due to exposure to biomechanical risk. Risk assessment for work activities that involve lifting loads can be performed through the NIOSH (National Institute of Occupational Safety and Health) method, and specifically the Revised NIOSH Lifting Equation (RNLE). Aim of this work is to explore the feasibility of a logistic regression model fed with time and frequency domains features extracted from signals acquired through one inertial measurement unit (IMU) to classify risk classes associated with lifting activities according to the RNLE. Furthermore, an attempt was made to evaluate which are the most discriminating features relating to the risk classes, and to understand which inertial signals and which axis were the most representative. In a simplified scenario, where only two RNLE variables were altered during lifting tasks performed by 14 healthy adults, inertial signals (linear acceleration and angular velocity) acquired using one IMU placed on the subject's sternum during repeated rhythmic lifting tasks were automatically segmented to extract several features in the time and frequency domains. The logistic regression model fed with significant features showed good results to discriminate "risk" and "no risk" NIOSH classes with an accuracy, sensitivity and specificity equal to 82.8%, 84.8% and 80.9%, respectively. This preliminary work indicated that a logistic regression model-fed with specific inertial features extracted by signals acquired using a single IMU sensor placed on the sternum-is able to discriminate risk classes according to the RNLE in a simplified context, and therefore could be a valid tool to assess the biomechanical risk in an automatic way also in more complex conditions (e.g., real working scenarios)