Towards Online Estimation of Human Joint Muscular Torque with a Lower Limb Exoskeleton Robot

Extended Application of Inertial Measurement Units in Biomechanics: From Activity Recognition to Force Estimation

Abnormal posture or movement is generally the indicator of musculoskeletal injuries or diseases. Mechanical forces dominate the injury and recovery processes of musculoskeletal tissue. Using kinematic data collected from wearable sensors (notably IMUs) as input, activity recognition and musculoskeletal force (typically represented by ground reaction force, joint force/torque, and muscle activity/force) estimation approaches based on machine learning models have demonstrated their superior accuracy. The purpose of the present study is to summarize recent achievements in the application of IMUs in biomechanics, with an emphasis on activity recognition and mechanical force estimation. The methodology adopted in such applications, including data pre-processing, noise suppression, classification models, force/torque estimation models, and the corresponding application effects, are reviewed. The extent of the applications of IMUs in daily activity assessment, posture assessment, disease diagnosis, rehabilitation, and exoskeleton control strategy development are illustrated and discussed. More importantly, the technical feasibility and application opportunities of musculoskeletal force prediction using IMU-based wearable devices are indicated and highlighted. With the development and application of novel adaptive networks and deep learning models, the accurate estimation of musculoskeletal forces can become a research field worthy of further attention.

Human-Robot Interaction Torque Estimation Methods for a Lower Limb Rehabilitation Robotic System with Uncertainties

Lower limb rehabilitation robot (LLRR) users, to successfully conduct isotonic exercises, require real-time feedback on the torque they exert on the robot to meet the goal of the treatment. Still, direct torque measuring is expensive, and indirect encoder-based estimation strategies, such as inverse dynamics (ID) and Nonlinear Disturbance Observers (NDO), are sensitive to Body Segment Inertial Parameters (BSIPs) uncertainties. We envision a way to minimize such parametric uncertainties. This paper proposes two human–robot interaction torque estimation methods: the Identified ID-based method (IID) and the Identified NDO-based method (INDO). Evaluating in simulation the proposal to apply, in each rehabilitation session, a sequential two-phase method: (1) An initial calibration phase will use an online parameter estimation to reduce sensitivity to BSIPs uncertainties. (2) The torque estimation phase uses the estimated parameters to obtain a better result. We conducted simulations under signal-to-noise ratio (SNR) = 40 dB and 20% BSIPs uncertainties. In addition, we compared the effectiveness with two of the best methods reported in the literature via simulation. Both proposed methods obtained the best Coefficient of Correlation, Mean Absolute Error, and Root Mean Squared Error compared to the benchmarks. Moreover, the IID and INDO fulfilled more than 72.2% and 88.9% of the requirements, respectively. In contrast, both methods reported in the literature only accomplish 27.8% and 33.3% of the requirements when using simulations under noise and BSIPs uncertainties. Therefore, this paper extends two methods reported in the literature and copes with BSIPs uncertainties without using additional sensors.

Inertial Motion Capture-Based Wearable Systems for Estimation of Joint Kinetics: A Systematic Review

In biomechanics, joint kinetics has an important role in evaluating the mechanical load of the joint and understanding its motor function. Although an optical motion capture (OMC) system has mainly been used to evaluate joint kinetics in combination with force plates, inertial motion capture (IMC) systems have recently been emerging in joint kinetic analysis due to their wearability and ubiquitous measurement capability. In this regard, numerous studies have been conducted to estimate joint kinetics using IMC-based wearable systems. However, these have not been comprehensively addressed yet. Thus, the aim of this review is to explore the methodology of the current studies on estimating joint kinetic variables by means of an IMC system. From a systematic search of the literature, 48 studies were selected. This paper summarizes the content of the selected literature in terms of the (i) study characteristics, (ii) methodologies, and (iii) study results. The estimation methods of the selected studies are categorized into two types: the inverse dynamics-based method and the machine learning-based method. While these two methods presented different characteristics in estimating the kinetic variables, it was demonstrated in the literature that both methods could be applied with good performance for the kinetic analysis of joints in different daily activities.

Adaptive Adjustment Strategy for Walking Characteristics of Single-Legged Exoskeleton Robots

In order to achieve the normal walking of hemiplegic patients, this paper proposes a single-legged exoskeleton robot according to the bionics principle, and presents an adaptive adjustment strategy for walking characteristics. The least square regression analysis is used to fit the angle data of healthy leg joints by cubic polynomials, and then the parametric design of the fitted curve is carried out to obtain the influence of the user’s stride frequency and stride length on the joint angle, so that the gait of the exoskeleton can be adjusted in real time according to the stride length and stride frequency of the healthy leg to realize normal walking. In order to verify the effectiveness of the adaptive adjustment strategy proposed in this paper, the angle of leg joints under normal gait is collected in advance. In addition, an adult male is chosen as the subject to walk on the horizontal ground wearing the single-legged exoskeleton as the experiment. The experimental results show that the designed exoskeleton is reasonable, and the adaptive adjustment strategy proposed in this paper can make the exoskeleton adapt well and follow the gait of healthy legs to achieve a more natural walking state.

Optimizing Calibration Procedure to Train a Regression-Based Prediction Model of Actively Generated Lumbar Muscle Moments for Exoskeleton Control

The risk of low-back pain in manual material handling could potentially be reduced by back-support exoskeletons. Preferably, the level of exoskeleton support relates to the required muscular effort, and therefore should be proportional to the moment generated by trunk muscle activities. To this end, a regression-based prediction model of this moment could be implemented in exoskeleton control. Such a model must be calibrated to each user according to subject-specific musculoskeletal properties and lifting technique variability through several calibration tasks. Given that an extensive calibration limits the practical feasibility of implementing this approach in the workspace, we aimed to optimize the calibration for obtaining appropriate predictive accuracy during work-related tasks, i.e., symmetric lifting from the ground, box stacking, lifting from a shelf, and pulling/pushing. The root-mean-square error (RMSE) of prediction for the extensive calibration was 21.9 nm (9% of peak moment) and increased up to 35.0 nm for limited calibrations. The results suggest that a set of three optimally selected calibration trials suffice to approach the extensive calibration accuracy. An optimal calibration set should cover each extreme of the relevant lifting characteristics, i.e., mass lifted, lifting technique, and lifting velocity. The RMSEs for the optimal calibration sets were below 24.8 nm (10% of peak moment), and not substantially different than that of the extensive calibration.

Outcome measures frequently used to assess muscle strength in patients with myotonic dystrophy type 1: a systematic review

Measurement of muscle strength is fundamental for the management of patients with myotonic dystrophy type 1 (DM1). Nevertheless, guidance on this topic is somewhat limited due to heterogeneous outcome measures used. This systematic literature review aimed to summarize the most frequent outcome measures to assess muscle strength in patients with DM1. We searched on Pubmed, Web of Science and Embase databases. Observational studies using measures of muscle strength assessment in adult patients with DM1 were included. From a total of 80 included studies, 24 measured cardiac, 45 skeletal and 23 respiratory muscle strength. The most common method and outcome measures used to assess cardiac muscle strength were echocardiography and ejection fraction, for skeletal muscle strength were quantitative muscle test, manual muscle test and maximum isometric torque and medical research council and for respiratory muscle strength were manometry and maximal inspiratory and expiratory pressure. We successfully gathered the more consensual methods and measures to evaluate muscle strength in future clinical studies, particularly to test muscle strength response to treatments in patients with DM1. Future consensus on a set of measures to evaluate muscle strength (core outcome set), is important for these patients.

A Neural Network Estimation of Ankle Torques From Electromyography and Accelerometry

Estimations of human joint torques can provide clinically valuable information to inform patient care, plan therapy, and assess the design of wearable robotic devices. Predicting joint torques into the future can also be useful for anticipatory robot control design. In this work, we present a method of mapping joint torque estimates and sequences of torque predictions from motion capture and ground reaction forces to wearable sensor data using several modern types of neural networks. We use dense feedforward, convolutional, neural ordinary differential equation, and long short-term memory neural networks to learn the mapping for ankle plantarflexion and dorsiflexion torque during standing, walking, running, and sprinting, and consider both single-point torque estimation, as well as the prediction of a sequence of future torques. Our results show that long short-term memory neural networks, which consider incoming data sequentially, outperform dense feedforward, neural ordinary differential equation networks, and convolutional neural networks. Predictions of future ankle torques up to 0.4 s ahead also showed strong positive correlations with the actual torques. The proposed method relies on learning from a motion capture dataset, but once the model is built, the method uses wearable sensors that enable torque estimation without the motion capture data.

Review of control strategies for lower-limb exoskeletons to assist gait

Background

Many lower-limb exoskeletons have been developed to assist gait, exhibiting a large range of control methods. The goal of this paper is to review and classify these control strategies, that determine how these devices interact with the user.

Methods

In addition to covering the recent publications on the control of lower-limb exoskeletons for gait assistance, an effort has been made to review the controllers independently of the hardware and implementation aspects. The common 3-level structure (high, middle, and low levels) is first used to separate the continuous behavior (mid-level) from the implementation of position/torque control (low-level) and the detection of the terrain or user’s intention (high-level). Within these levels, different approaches (functional units) have been identified and combined to describe each considered controller.

Results

291 references have been considered and sorted by the proposed classification. The methods identified in the high-level are manual user input, brain interfaces, or automatic mode detection based on the terrain or user’s movements. In the mid-level, the synchronization is most often based on manual triggers by the user, discrete events (followed by state machines or time-based progression), or continuous estimations using state variables. The desired action is determined based on position/torque profiles, model-based calculations, or other custom functions of the sensory signals. In the low-level, position or torque controllers are used to carry out the desired actions. In addition to a more detailed description of these methods, the variants of implementation within each one are also compared and discussed in the paper.

Conclusions

By listing and comparing the features of the reviewed controllers, this work can help in understanding the numerous techniques found in the literature. The main identified trends are the use of pre-defined trajectories for full-mobilization and event-triggered (or adaptive-frequency-oscillator-synchronized) torque profiles for partial assistance. More recently, advanced methods to adapt the position/torque profiles online and automatically detect terrains or locomotion modes have become more common, but these are largely still limited to laboratory settings. An analysis of the possible underlying reasons of the identified trends is also carried out and opportunities for further studies are discussed.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12984-021-00906-3.

Exoskeleton Active Walking Assistance Control Framework Based on Frequency Adaptive Dynamics Movement Primitives

This paper introduces a novel exoskeleton active walking assistance control framework based on frequency adaptive dynamics movement primitives (FADMPs). The FADMPs proposed in this paper is an online learning and prediction algorithm which is able to online estimate the fundamental frequency of human joint trajectory, learn the shape of joint trajectory and predict the future joint trajectory during walking. The proposed active walking assistance control framework based on FADMPs is a model-based controller which relies on the human joint torque estimation. The assistance torque provided by exoskeleton is estimated by human lower limb inverse dynamics model which is sensitive to the noise in the joint motion trajectory. To estimate a smooth joint torque profile, the joint motion trajectory must be filtered first by a lowpass filter. However, lowpass filter will introduce an inevitable phase delay in the filtered trajectory. Both simulations and experiments in this paper show that the phase delay has a significant effect on the performance of exoskeleton active assistance. The active assistant control framework based on FADMPs aims at improving the performance of active assistance control by compensating the phase delay. Both simulations and experiments on active walking assistance control show that the performance of active assistance control can be further improved when the phase delay in the filtered trajectory is compensated by FADMPs.

Lower limb rehabilitation robotics: The current understanding and technology

BACKGROUND

With the increasing rate of ambulatory disabilities and rise in the elderly population, advance methods to deliver the rehabilitation and assistive services to patients have become important. Lower limb robotic therapeutic and assistive aids have been found to improve the rehabilitation outcome.

OBJECTIVE

The article aims to present the updated understanding in the field of lower limb rehabilitation robotics and identify future research avenues.

METHODS

Groups of keywords relating to assistive technology, rehabilitation robotics, and lower limb were combined and searched in EMBASE, IEEE Xplore Digital Library, Scopus, Web of Science and Google Scholar database.

RESULTS

Based on the literature collected from the databases we provide an overview of the understanding of robotics in rehabilitation and state of the art devices for lower limb rehabilitation. Technological advancements in rehabilitation robotic architecture (sensing, actuation and control) and biomechanical considerations in design have been discussed. Finally, a discussion on the major advances, research directions, and challenges is presented.

CONCLUSIONS

Although the use of robotics has shown a promising approach to rehabilitation and reducing the burden on caregivers, extensive and innovative research is still required in both cognitive and physical human-robot interaction to achieve treatment efficacy and efficiency.

Design of Hybrid Phase Sliding Mode Control Scheme for Lower Extremity Exoskeleton

Aiming at a pneumatic artificial muscle (PAM) lower extremity exoskeleton, a control mechanism based on hybrid phase sliding mode control (SMC) is proposed. First of all, the human gait cycle is mainly divided into the swing phase and stance phase, and the lower extremity exoskeleton phase models are established by the Euler–Lagrange method, respectively. Secondly, the lower limb exoskeleton is inevitably affected in the diverse working environment, and the exoskeleton model has nonlinear and strong coupling characteristics, which both increase the control difficulty. In this situations, a robust sliding mode control method is designed based on an Extended State Observer (ESO). Thirdly, the pneumatic muscle takes time to contract and relax, and then the joint input torque cannot jump when the gait phase changes, hence, the smoothing switching of the assistive control scheme is introduced to solve it. The smoothing switching time is detected by a phase detector, and the phase detector is designed by the plantar pressure information. Finally the comparative simulation shows that this control strategy has the advantages of fast time, high control precision and no jump during control torque switching. Pneumatic artificial muscle contraction rate curve shows that the pneumatic muscles’ motion range meets the control requirement of the exoskeleton.

Simultaneous Floating-Base Estimation of Human Kinematics and Joint Torques

The paper presents a stochastic methodology for the simultaneous floating-base estimation of the human whole-body kinematics and dynamics (i.e., joint torques, internal and external forces). The paper builds upon our former work where a fixed-base formulation had been developed for the human estimation problem. The presented approach is validated by presenting experimental results of a health subject equipped with a wearable motion tracking system and a pair of shoes sensorized with force/torque sensors while performing different motion tasks, e.g., walking on a treadmill. The results show that joint torque estimates obtained by using floating-base and fixed-base approaches match satisfactorily, thus validating the present approach.

Dynamic Parameter Identification of a Lower Extremity Exoskeleton Using RLS-PSO

The lower extremity exoskeleton is a device for auxiliary assistance of human movement. The interaction performance between the exoskeleton and the human is determined by the lower extremity exoskeleton’s controller. The performance of the controller is affected by the accuracy of the dynamic equation. Therefore, it is necessary to study the dynamic parameter identification of lower extremity exoskeleton. The existing dynamic parameter identification algorithms for lower extremity exoskeletons are generally based on Least Square (LS). There are some internal drawbacks, such as complicated experimental processes and low identification accuracy. A dynamic parameter identification algorithm based on Particle Swarm Optimization (PSO) with search space defined by Recursive Least Square (RLS) is developed in this investigation. The developed algorithm is named RLS-PSO. By defining the search space of PSO, RLS-PSO not only avoids the convergence of identified parameters to the local minima, but also improves the identification accuracy of exoskeleton dynamic parameters. Under the same experimental conditions, the identification accuracy of RLS-PSO, PSO and LS was quantitatively compared and analyzed. The results demonstrated that the identification accuracy of RLS-PSO is higher than that of LS and PSO.

Development, Dynamic Modeling, and Multi-Modal Control of a Therapeutic Exoskeleton for Upper Limb Rehabilitation Training

Robot-assisted training is a promising technology in clinical rehabilitation providing effective treatment to the patients with motor disability. In this paper, a multi-modal control strategy for a therapeutic upper limb exoskeleton is proposed to assist the disabled persons perform patient-passive training and patient-cooperative training. A comprehensive overview of the exoskeleton with seven actuated degrees of freedom is introduced. The dynamic modeling and parameters identification strategies of the human-robot interaction system are analyzed. Moreover, an adaptive sliding mode controller with disturbance observer (ASMCDO) is developed to ensure the position control accuracy in patient-passive training. A cascade-proportional-integral-derivative (CPID)-based impedance controller with graphical game-like interface is designed to improve interaction compliance and motivate the active participation of patients in patient-cooperative training. Three typical experiments are conducted to verify the feasibility of the proposed control strategy, including the trajectory tracking experiments, the trajectory tracking experiments with impedance adjustment, and the intention-based training experiments. The experimental results suggest that the tracking error of ASMCDO controller is smaller than that of terminal sliding mode controller. By optimally changing the impedance parameters of CPID-based impedance controller, the training intensity can be adjusted to meet the requirement of different patients.