Francisco Javier Alonso

A simple SSA-based de-noising technique to remove ECG interference in EMG signals

Abstract Electromyography (EMG) signals provide significant information of muscle activity that may be used, among others, to estimate the activation stages during a certain activity or to predict fatigue. Heart activity or electrocardiogram (ECG) is one of the main contamination sources, especially in trunk muscles. This paper proposes a novel method based on Singular Spectrum Analysis (SSA) and frequency analysis to separate both signals present in the raw data. The performance of the method has been compared in time and frequency domains with traditional high-pass filtering or novel techniques such as Complete Ensemble Empirical Mode Decomposition or Wavelets analysis. The results show that for both time and frequency domains the proposed approach outperforms the other methods. Thus, the proposed SSA approach is a valid method to remove the ECG artifact from the contaminated EMG signals without using an ECG reference signal.

An automatic SSA-based de-noising and smoothing technique for surface electromyography signals

Abstract The surface electromyography (sEMG) signal is a low amplitude signal that emanates from contracting muscles. It can be used directly to measure muscle activity (once noise has been removed) or it can be smoothed for some other application, e.g., orthoses or prostheses control. Here, an automatic heuristic procedure is presented which applies singular spectrum analysis (SSA) and cluster analysis to de-noise and smooth sEMG signals. SSA is a non-parametric technique that decomposes the original time series into a set of additive time series in which the noise present in the acquired signal can be easily identified. The proposed approach constitutes an alternative to the traditional smoothing procedures, such as moving average (MOVAG), root mean square (RMS), or low-pass Butterworth filtering that are used to extract the trend of the signal. To assess the quality of the method, the results of its application to a non-stationary sEMG signal are compared with those of other step-wise filtering and smoothing techniques.

A Computational Benchmark for 2D Gait Analysis Problems

The aim of this paper is to present a computational benchmark for gait analysis that has been developed in order to share real data captured in a biomechanics laboratory and the results of the inverse dynamic analysis. This work belongs to the library of computational multibody benchmark problems that the Technical Committee for Multibody Dynamics of the International Federation for the Promotion of Mechanism and Machine Science (IFToMM) is developing. The work presents the kinematic and dynamic study of human motion by means of multibody system dynamics techniques. The subject selected to perform the experiments walks on a walkway that encloses two force plates. The motion is captured by 12 optical cameras that acquire the position of 37 passive markers. The inverse dynamic analysis (IDA) is carried out using a 12-segment 2D model with 14 degrees of freedom. Displacement signals are filtered using an algorithm based on Singular Spectrum Analysis (SSA) and the natural coordinates of the model are calculated using algebraic relations among the marker positions. Afterwards, a procedure ensures the kinematic consistency and the data processing continues with the approximation of the position histories using B-spline curves. The velocity and acceleration values are then obtained by analytical derivation. The double support indeterminacy is solved using the Corrected Force Plate (CFP) sharing method. The IDA provides the joint drive torques that the musculoskeletal system generates during human locomotion from acquired kinematic data, foot-ground contact forces and estimated body segment parameters (BSP). All this information is available online in http://iftomm-multibody.org/benchmark. Therefore, it can be viewed by other researchers, which can submit their own results using the same input data and proposing new solutions.

HYBRID MODELING AND FRACTIONAL CONTROL OF A SCKAFO ORTHOSIS FOR GAIT ASSISTANCE

SCKAFO, stance-control knee-ankle-foot orthosis, is a type of orthosis that permits free knee motion during swing while resisting knee flexion during stance, supporting thereby the limb during weight bearing. This orthosis specially assists patients who have incomplete spinal cord injury and allows them to walk with the aid of canes or crutches, maintaining a proper gait. In this paper, based on the human walking biomechanics, the SCKAFO hybrid modeling is proposed, which consists of eight different stages whose evolution is given by means of four planar sensors on each foot. In the model, it is considered that the patients can move their hip but not their knee that will be controlled using a DC motor. Two fractional order controllers are designed, following decision based control techniques, to control the knee angle. Simulation results are given in order to demonstrate the efficiency of the system performance.

Evaluation of Motion/Force Transmission Between Passive/Active Orthosis and Subject Through Forward Dynamic Analysis

Forward dynamic analysis of the acquired gait of subjects assisted by either passive or active knee-ankle-foot orthoses and crutches is used to evaluate the motion and force transmission between orthosis and subject depending on the connecting stiffness. Unlike inverse dynamic analysis, this approach allows to consider the subject’s limbs and the assistive devices as different entities, so that their relative behavior may be studied. The quality of motion transmission and the intensity of interface forces are evaluated for a range of connecting stiffness values, so that those providing the best trade-off between both aspects can be identified.

An Automatic Filtering Procedure for Processing Biomechanical Kinematic Signals

In biomechanics studies it is necessary to obtain acceleration of certain parts of the body in order to perform dynamical analysis. The motion capture systems introduce systematic measurement errors that appear in the form of high-frequency noise in recorded displacement signals. The noise is dramatically amplified when differentiating displacements to obtain velocities and accelerations. To avoid this phenomenon it is necessary to smooth the displacement signal prior to differentiation. The use of Singular Spectrum Analysis (SSA) is presented in this paper as an alternative to traditional digital filtering methods. SSA decomposes original time series into a number of additive time series each of which can be easily identified as being part of the modulated signal, or as being part of the random noise. An automatic filtering procedure based in SSA is presented in this work. The procedure is applied to two signals to demonstrate its performance.

Effects of Introducing Fractional Dynamics in Hill's Model for Muscle Contraction

Abstract Muscles can be conceived as distributed electro-mechanical-chemical systems that can be described by a set of coupled partial differential equations. Because of the difficulty of solving this kind of equations, these systems are usually approximated by a set of lumped elements leading to a set of coupled ordinary differential equations instead. Hills model is the most popular of such models having four basic elements that describe the behavior of the muscle: contractile, damping, series elastic, and parallel elastic elements. The aim of this paper is to study the effects of introducing fractional dynamics into the Hills model in order to characterize unhealthy muscles in spinal cord injured (SCI) subjects for control purposes. By doing so, more general dynamic behaviors can be obtained but keeping the simplicity of the lumped parameter models for control applications.

An Integrated Differentiation-Projection Approach for the kinematic Data Consistency of biomechanical Systems

Several sources of error corrupt the results obtained in the kinematic and dynamic analysis of biomechanical systems and reduce its usefulness. The main source of error is the inaccuracy of velocities and accelerations derived from experimentally measured displacements of markers placed on the skin of joints. This error is mainly due to the amplification of high-frequency low-amplitude noise introduced by the motion capture system when the raw displacement signals are differentiated. Another source of error is the skin motion artifact, that produces violations of the kinematic constraint equations of the multibody system. An integrated differentiation-projection approach to ensure the kinematic data consistency in the context of the analysis of biomechanical systems is presented. The raw data differentiation problem is solved by applying a smoothing-differentiation technique based on the Newmark integration scheme. Several benchmark kinematic signals that include computer generated data of a four-bar mechanism were processed using the differentiation-projection method to study its performance.

Calibration and Validation of a Skeletal Multibody Model for Leg-Orthosis Contact Force Estimation

Estimation of contact forces between lower limb and orthosis during gait is useful to prevent skin issues in subjects wearing this type of assistive devices. While inverse-dynamics based gait analysis of multibody models is difficult to apply due to the limited accuracy of motion capture systems, a forward-dynamics based analysis in which leg and orthosis are considered as independent entities is shown to provide acceptable results. Contact model parameters are calibrated through comparison of measured and calculated bending torque at the orthosis location where a load cell is installed, and the attained correlation allows to validate the model.

Design of semi-rigid wearable devices based on skin strain analysis

Nowadays, both usability and comfort play a key role in the development of medical and wearable products. When designing any device that is in contact with the human body, the mechanical behaviour of the embraced soft tissue must be known. The unavoidable displacement of the soft tissue during motion may lead to discomfort adn, thus, the withdrawal of the wearable product. This work presents a new methodology to design and test a wearable device based on the measurement of the dynamic skin strain field. Furthermore, from this field, the anatomical lines with minimum strain (Lines of non extension, LoNEs) are calculated to design the structural parts of the wearable device. Whith this new criteria, the resulting product is not only optimized to reduce the friction in skin-device interface, but fully personalized to the patients morphology and motion. The methodology is applied to the design of an ankle-foot wearable orthosis for subjects with ankle dorsiflexors muscles weakness due to nervous system disorders. The results confirm that the use of LoNEs may benefit the design of products with a high interaction with the skin.