Generalizing Upper Limb Force Modeling With Transfer Learning: A Multimodal Approach Using EMG and IMU for New Users and Conditions.

In the field of EMG-based force modeling, the ability to generalize models across individuals could play a significant role in its adoption across a range of applications, including assistive devices, robotic and rehabilitation devices. However, current studies have predominately focused on intra-subject modeling, largely neglecting the burden of end-user data acquisition. In this work, we propose the use of transfer learning (TL) to generalize force modeling to a new user by first establishing a baseline model trained using other users' data, and then adapting to the end-user using a small amount of new data (only 10% , 20% , and 40% of the new user data).

Tutorial. Surface electromyogram (sEMG) amplitude estimation: Best practices.

This tutorial intends to provide insight, instructions and "best practices" for those who are novices-including clinicians, engineers and non-engineers-in extracting electromyogram (EMG) amplitude from the bipolar surface EMG (sEMG) signal of voluntary contractions. A brief discussion of sEMG amplitude extraction from high density sEMG (HDsEMG) arrays and feature extraction from electrically elicited contractions is also provided. This tutorial attempts to present its main concepts in a straightforward manner that is accessible to novices in the field not possessing a wide range of technical background (if any) in this area.

Home-based upper limb stroke rehabilitation mechatronics: challenges and opportunities.

Interest in home-based stroke rehabilitation mechatronics, which includes both robots and sensor mechanisms, has increased over the past 12 years. The COVID-19 pandemic has exacerbated the existing lack of access to rehabilitation for stroke survivors post-discharge. Home-based stroke rehabilitation devices could improve access to rehabilitation for stroke survivors, but the home environment presents unique challenges compared to clinics.

Considerations for at-home upper-limb rehabilitation technology following stroke: Perspectives of stroke survivors and therapists.

Introduction: This study investigated the needs of stroke survivors and therapists, and how they may contrast, for the design of robots for at-home post stroke rehabilitation therapy, in the Ontario, Canada, context.

Methods: Individual interviews were conducted with stroke survivors ( = 10) and therapists ( = 6). The transcripts were coded using thematic analysis inspired by the WHO International Classification of Functioning, Disability, and Health.

Deep Multi-Scale Fusion of Convolutional Neural Networks for EMG-Based Movement Estimation.

EMG-based motion estimation is required for applications such as myoelectric control, where the simultaneous estimation of kinematic information, namely joint angle and velocity, is challenging and critical. We propose a novel method for accurately modelling the generated joint angle and velocity simultaneously under isotonic, isokinetic (quasi-dynamic), and fully dynamic conditions. Our solution uses two streams of CNN, called TS-CNN to learn informative features from raw EMG data using different scales and estimate the generated motion during elbow flexion and extension.

Convolutional Neural Network Approach for Elbow Torque Estimation during Quasi-dynamic and Dynamic Contractions.

Accurate torque estimation during dynamic conditions is challenging, yet an important problem for many applications such as robotics, prosthesis control, and clinical diagnostics. Our objective is to accurately estimate the torque generated at the elbow during flexion and extension, under quasi-dynamic and dynamic conditions. High-density surface electromyogram (HD-EMG) signals, acquired from the long head and short head of biceps brachii, brachioradialis, and triceps brachii of five participants are used to estimate the torque generated at the elbow, using a convolutional neural network (CNN).

Automated Channel Selection in High-Density sEMG for Improved Force Estimation.

Accurate and real-time estimation of force from surface electromyogram (EMG) signals enables a variety of applications. We developed and validated new approaches for selecting subsets of high-density (HD) EMG channels for improved and lower-dimensionality force estimation. First, a large dataset was recorded from a number of participants performing isometric contractions in different postures, while simultaneously recording HD-EMG channels and ground-truth force.

An Investigation of Dimensionality Reduction Techniques for EMG-based Force Estimation.

In this paper, extracted features in time and frequency domain, from high-density surface electromyogram (HD-sEMG) signals acquired from the long head and short head of biceps brachii, and brachioradialis during isometric elbow flexion are used to estimate force induced at the wrist using an artificial neural network (ANN). Different hidden layer sizes were considered to investigate its effect on the model accuracy. Also, we applied two dimensionality reduction techniques, principal component analysis (PCA) and t-distributed stochastic neighbor embedding (t-SNE), on the feature set and investigated their effects on force estimation accuracy.

PCA-Based Channel Selection in High-Density EMG for Improving Force Estimation.

In this paper, a method for selecting channels to improve estimated force using fast orthogonal search (FOS) has been proposed. Surface electromyogram (sEMG) signals acquired from linear surface electrode arrays, placed on the long head and short head of biceps brachii, and brachioradialis during isometric contractions are used to estimate force induced at the wrist using the FOS algorithm. The method utilizes principle component analysis (PCA) in the frequency domain to select the channels with the highest contribution to the first principal component (PC).

Non-invasive detection of low-level muscle fatigue using surface EMG with wavelet decomposition.

Median frequency (MDF) is widely used for detection and tracking of muscle fatigue using surface electromyography (sEMG). However, MDF does not behave consistently or accurately distinguish fatigued from non-fatigued states. In this paper, we study the concept of low-level fatigue and propose increasing average ratio (IAR) and trigger pattern index (TPI) based on discrete wavelet transform (DWT) for distinguishing low-level muscle fatigue.

The role of electromechanical delay in modelling the EMG-force relationship during quasi-dynamic contractions of the upper-limb.

There is a discontinuity in published electromechanical delays (EMD) in upper-limb muscles and the state-of-the-art in modelling end-point force from electromyographic signals collected from one or more muscles. Published values are typically in the range of 10 to 30ms, depending on the nature of the contraction. In published literature where the EMG-force relationship is modelled, generally a delay of 100ms or more is induced during linear enveloping to match the EMD.

Force Modelling of Upper Limb Biomechanics Using Ensemble Fast Orthogonal Search on High-Density Electromyography.

An important quality of upper limb force estimation is the repeatability and worst-case performance of the estimator. The following paper proposes a methodology using an ensemble learning technique coupled with the fast orthogonal search (FOS) algorithm to reliably predict varying isometric contractions of the right arm. This method leverages the rapid and precise modelling offered by FOS combined with a univariate outlier detection algorithm to dynamically combine the output of numerous FOS models.

Enhanced dynamic EMG-force estimation through calibration and PCI modeling.

To accurately estimate muscle forces using electromyogram (EMG) signals, precise EMG amplitude estimation, and a modeling scheme capable of coping with the nonlinearities and dynamics of the EMG-force relationship are needed. In this work, angle-based EMG amplitude calibration and parallel cascade identification (PCI) modeling are combined for EMG-based force estimation in dynamic contractions, including concentric and eccentric contractions of the biceps brachii and triceps brachii muscles. Angle-based calibration has been shown to improve surface EMG (SEMG) based force estimation during isometric contractions through minimization of the effects of joint angle related factors, and PCI modeling captures both the nonlinear and dynamic properties of the process.

Shouldering the load: A review of Joan Stevenson's work on occupational lifting and design evaluation of load carriage equipment.

Objective: In this paper, Dr. Joan Stevenson's work on assessment of the effects of lifting, supporting and transporting loads is reviewed. A defining attribute of this work is the use of objective, biomechanical measures as the basis from which a fuller understanding of all factors affecting worker performance can be obtained, and how such performance should be measured and evaluated.

Enhanced multi-site EMG-force estimation using contact pressure.

A modification method based on integrated contact pressure and surface electromyogram (SEMG) recordings over the biceps brachii muscle is presented. Multi-site sEMGs are modified by pressure signals recorded at the same locations for isometric contractions. The resulting pressure times SEMG signals are significantly more correlated to the force induced at the wrist (FW), yielding SEMG-force models with superior performance in force estimation.

Surface EMG force modeling with joint angle based calibration.

In this paper, a calibration method to compensate for changes in SEMG amplitude with joint angle is introduced. Calibration factors were derived from constant amplitude surface electromyogram (SEMG) recordings from the biceps brachii (during elbow flexion) and the triceps brachii (during elbow extension) across seven elbow joint angles. SEMG data were then recorded from the elbow flexors (biceps brachii and brachioradialis) and extensors (triceps brachii) during isometric, constant force flexion and extension contractions at the same joint angles.

EMG-force modeling using parallel cascade identification.

Measuring force production in muscles is important for many applications such as gait analysis, medical rehabilitation, and human-machine interaction. Substantial research has focused on finding signal processing and modeling techniques which give accurate estimates of muscle force from the surface-recorded electromyogram (EMG). The proposed methods often do not capture both the nonlinearities and dynamic components of the EMG-force relation.

Wavelet frequency-temporal relative phase pattern analysis for intermuscular synchronization of dynamic surface EMG signals.

Cross-correlation is often used as the primary technique to compare two biological signals. Cross-correlation is an effective means to measure the synchronization of two signals assuming the relative phases of all frequencies are distributed linearly, that is, a group delay. The group delay assumption imposes an unfavorable restriction on signals with varying relative phase correlation at different frequencies.

Joint angle-based EMG amplitude calibration.

A calibration method is proposed to compensate for the changes in the surface electromyogram (SEMG) amplitude level of the biceps brachii at different joint angles due to the movement of the muscle bulk under the EMG electrodes for a constant force level. To this end, an experiment was designed, and SEMG and force measurements were collected from 5 subjects. The fast orthogonal search (FOS) method was used to find a mapping between SEMG from the biceps and force recorded at the wrist.

Dynamic modeling of EMG-force relationship using parallel cascade identification.

Parallel cascade identification (PCI) is used as a dynamic estimation tool to map surface electromyography recordings from upper-arm muscles to the elbow-induced force at the wrist. PCI mapping is composed of parallel connection of a cascade of linear dynamic and nonlinear static blocks. Experimental comparison between PCI and previously published orthogonalization scheme has shown superior force prediction by PCI.

Optimal electrode configurations for finger movement classification using EMG.

The myoelectric signal has played a major role in the development of prosthesis control technology. A myoelectric classification system has the ability to determine a prosthesis user's intent based solely on his or her muscle activity, thereby allowing for more intuitive prosthetic control. Much work has been done on the recognition of upper arm and gross hand movement tasks, but it was not until accuracy levels approached 100% [3] that more attention was given to specific finger movements.

Contraction-based variations in upper limb EMG-force models under isometric conditions.

In this work, a previously developed model, which maps joint kinematic data and estimated muscle activation levels to net elbow joint torque, is trained with 4 groups of datasets in order to improve force estimation accuracy and gain insight into muscle behaviour. The training datasets are defined such that surface electromyogram (EMG) and force data are grouped within individual trials, across trials, within force levels and across force levels, and model performance is assessed. Average evaluation error ranged between 5% and 15%, with the lowest error observed for models trained with datasets grouped within separate force levels.

Use of the fast orthogonal search method to estimate optimal joint angle for upper limb Hill-muscle models.

An important aspect of accurate representation of human movement is the ability to account for differences between individuals. The following paper proposes a methodology using Hill-based candidate functions in the fast orthogonal search (FOS) method to predict translational force at the wrist from flexion and extension torque at the elbow. Within this force-prediction framework, it is possible to implicitly estimate subject-specific physiological parameters of Hill-based models of upper arm muscles.

Fast orthogonal search method to estimate upper arm Hill-based muscle model parameters.

We propose a methodology to estimate subject-specific physiological parameters of Hill-based models of upper arm muscles. The methodology uses Hill-type candidate functions in the Fast Orthogonal Search (FOS) method to predict force at the wrist during elbow flexion and extension. To this end, surface EMG data from three muscles of the upper arm were recorded from 5 subjects as they performed isometric contractions at different elbow joint angles.

Lower limb muscle synergies during gait in humans.

Muscle synergies were extracted using non-negative matrix factorization from muscle activation patterns in six lower limb muscles during normal gait. Greater than 70% of the variance in the muscle activations was described by four extracted synergies and greater than 90% was described by five synergies. Considering the timing of synergy activation, the extracted synergies appear to be related to functional divisions of the gait cycle.