A Comparison of Control Strategies in Commercial and Research Knee Prostheses

Toward the Development of User-Centered Neurointegrated Lower Limb Prostheses

The last few years witnessed radical improvements in lower-limb prostheses. Researchers have presented innovative solutions to overcome the limits of the first generation of prostheses, refining specific aspects which could be implemented in future prostheses designs. Each aspect of lower-limb prostheses has been upgraded, but despite these advances, a number of deficiencies remain and the most capable limb prostheses fall far short of the capabilities of the healthy limb. This article describes the current state of prosthesis technology; identifies a number of deficiencies across the spectrum of lower limb prosthetic components with respect to users' needs; and discusses research opportunities in design and control that would substantially improve functionality concerning each deficiency. In doing so, the authors present a roadmap of patients related issues that should be addressed in order to fulfill the vision of a next-generation, neurally-integrated, highly-functional lower limb prosthesis.

A new approach to a powered knee prosthesis: Layering powered assistance onto strictly passive prosthesis behavior

This article describes a novel approach to the control of a powered knee prosthesis where the control system provides passive behavior for most activities and then provides powered assistance only for those activities that require them. The control approach presented here is based on the categorization of knee joint function during activities into four behaviors: resistive stance behavior, active stance behavior, ballistic swing, and non-ballistic swing. The approach is further premised on the assumption that healthy non-perturbed swing-phase is characterized by a ballistic swing motion, and therefore, a replacement of that function should be similarly ballistic. The control system utilizes a six-state finite-state machine, where each state provides different constitutive behaviors (concomitant with the four aforementioned knee behaviors) which are appropriate for a range of activities. Transitions between states and torque control within states is controlled by user motion, such that the control system provides, to the extent possible, knee torque behavior as a reaction to user motion, including for powered behaviors. The control system is demonstrated on a novel device that provides a sufficiently low impedance to enable a strictly passive ballistic swing-phase, while also providing sufficiently high torque to offer powered stance-phase knee-extension during activities such as step-over stair ascent. Experiments employing the knee and control system on an individual with transfemoral amputation are presented that compare the functionality of the power-supplemented nominally passive system with that of a conventional passive microprocessor-controlled knee prosthesis.

Electromyography-Based Control of Lower Limb Prostheses: A Systematic Review

Most amputations occur in lower limbs and despite improvements in prosthetic technology, no commercially available prosthetic leg uses electromyography (EMG) information as an input for control. Efforts to integrate EMG signals as part of the control strategy have increased in the last decade. In this systematic review, we summarize the research in the field of lower limb prosthetic control using EMG. Four different online databases were searched until June 2022: Web of Science, Scopus, PubMed, and Science Direct. We included articles that reported systems for controlling a prosthetic leg (with an ankle and/or knee actuator) by decoding gait intent using EMG signals alone or in combination with other sensors. A total of 1,331 papers were initially assessed and 121 were finally included in this systematic review. The literature showed that despite the burgeoning interest in research, controlling a leg prosthesis using EMG signals remains challenging. Specifically, regarding EMG signal quality and stability, electrode placement, prosthetic hardware, and control algorithms, all of which need to be more robust for everyday use. In the studies that were investigated, large variations were found between the control methodologies, type of research participant, recording protocols, assessments, and prosthetic hardware.

Machine Learning-Based Gait Mode Prediction for Hybrid Knee Prosthesis Control

Recently, hybrid prosthetic knees, which can combine the advantages of passive and active prosthetic knees, have been proposed for individuals with a transfemoral amputation. Users could potentially take advantage of the passive knee mechanics during walking and the active power generation during stair ascent. One challenge in controlling the hybrid knees is accurate gait mode prediction for seamless transitions between passive and active modes. However, data imbalance between passive and active modes may impact the performance of a classifier. In this study, we used a dataset collected from nine individuals with a unilateral transfemoral amputation as they ambulated over level ground, inclines, and stairs. We evaluated several machine learning-based classifiers on the prediction of passive (level-ground walking, incline walking, descending stairs, and donning and doffing the prosthesis) and active mode (ascending stairs). In addition, we developed a generative adversarial network (GAN) to create synthetic data for improving classification performance. The results indicated that linear discriminant analysis and random forest might be the best classifiers regarding sensitivity to the active mode and overall accuracy, respectively. Further, we demonstrated that using the GAN-based synthetic data for training improves the sensitivity of classifiers.

Development of an Integrated Powered Hip and Microprocessor-Controlled Knee for a Hip-Knee-Ankle-Foot Prosthesis

Hip-knee-ankle-foot prostheses (HKAF) are full lower-limb devices for people with hip amputations that enable individuals to regain their mobility and move freely within their chosen environment. HKAFs typically have high rejection rates among users, as well as gait asymmetry, increased trunk anterior-posterior lean, and increased pelvic tilt. A novel integrated hip-knee (IHK) unit was designed and evaluated to address the limitations of existing solutions. This IHK combines powered hip and microprocessor-controlled knee joints into one structure, with shared electronics, sensors, and batteries. The unit is also adjustable to user leg length and alignment. ISO-10328:2016 standard mechanical proof load testing demonstrated acceptable structural safety and rigidity. Successful functional testing involved three able-bodied participants walking with the IHK in a hip prosthesis simulator. Hip, knee, and pelvic tilt angles were recorded and stride parameters were analyzed from video recordings. Participants were able to walk independently using the IHK and data showed that participants used different walking strategies. Future development of the thigh unit should include completion of a synergistic gait control system, improved battery-holding mechanism, and amputee user testing.

The effects of swing assistance in a microprocessor-controlled transfemoral prosthesis on walking at varying speeds and grades

This article proposes, describes, and tests a swing-assist walking controller for a stance-controlled, swing-assisted knee prosthesis that aims to combine benefits of passive swing mechanics (e.g., quiet operation, biomimetic function, and low power requirements) with benefits of powered swing assistance (e.g., increased robustness of swing-phase motion and specifically increased toe clearance). A three-participant, multislope, multispeed treadmill walking study was performed using the swing-assist prosthesis and controller, as well as using the participants' prescribed microprocessor knee devices. The swing-assist device and approach were found to improve user minimum foot clearance during walking at slopes and speeds, and also to improve symmetry of knee motion. Hip power inputs from stance knee release to heel strike indicated that, on average, less hip power was required when using the swing-assist prosthesis, indicating that the observed benefits were likely the result of the knee device and its control methodology, rather than a result of increased hip joint effort.

Control strategies used in lower limb exoskeletons for gait rehabilitation after brain injury: a systematic review and analysis of clinical effectiveness

BACKGROUND

In the past decade, there has been substantial progress in the development of robotic controllers that specify how lower-limb exoskeletons should interact with brain-injured patients. However, it is still an open question which exoskeleton control strategies can more effectively stimulate motor function recovery. In this review, we aim to complement previous literature surveys on the topic of exoskeleton control for gait rehabilitation by: (1) providing an updated structured framework of current control strategies, (2) analyzing the methodology of clinical validations used in the robotic interventions, and (3) reporting the potential relation between control strategies and clinical outcomes.

METHODS

Four databases were searched using database-specific search terms from January 2000 to September 2020. We identified 1648 articles, of which 159 were included and evaluated in full-text. We included studies that clinically evaluated the effectiveness of the exoskeleton on impaired participants, and which clearly explained or referenced the implemented control strategy.

RESULTS

(1) We found that assistive control (100% of exoskeletons) that followed rule-based algorithms (72%) based on ground reaction force thresholds (63%) in conjunction with trajectory-tracking control (97%) were the most implemented control strategies. Only 14% of the exoskeletons implemented adaptive control strategies. (2) Regarding the clinical validations used in the robotic interventions, we found high variability on the experimental protocols and outcome metrics selected. (3) With high grade of evidence and a moderate number of participants (N = 19), assistive control strategies that implemented a combination of trajectory-tracking and compliant control showed the highest clinical effectiveness for acute stroke. However, they also required the longest training time. With high grade of evidence and low number of participants (N = 8), assistive control strategies that followed a threshold-based algorithm with EMG as gait detection metric and control signal provided the highest improvements with the lowest training intensities for subacute stroke. Finally, with high grade of evidence and a moderate number of participants (N = 19), assistive control strategies that implemented adaptive oscillator algorithms together with trajectory-tracking control resulted in the highest improvements with reduced training intensities for individuals with chronic stroke.

CONCLUSIONS

Despite the efforts to develop novel and more effective controllers for exoskeleton-based gait neurorehabilitation, the current level of evidence on the effectiveness of the different control strategies on clinical outcomes is still low. There is a clear lack of standardization in the experimental protocols leading to high levels of heterogeneity. Standardized comparisons among control strategies analyzing the relation between control parameters and biomechanical metrics will fill this gap to better guide future technical developments. It is still an open question whether controllers that provide an on-line adaptation of the control parameters based on key biomechanical descriptors associated to the patients' specific pathology outperform current control strategies.

A gait phase prediction model trained on benchmark datasets for evaluating a controller for prosthetic legs

Powered lower-limb assistive devices, such as prostheses and exoskeletons, are a promising option for helping mobility-impaired individuals regain functional gait. Gait phase prediction plays an important role in controlling these devices and evaluating whether the device generates a gait similar to that of individuals with intact limbs. This study proposes a gait phase prediction method based on a deep neural network (DNN). The long short-term memory (LSTM)-based model predicts a continuous gait phase from the 250 ms history of the vertical load, thigh angle, knee angle, and ankle angle, commonly available on powered lower-limb assistive devices. One unified model was trained using publicly available benchmark datasets containing intact limb gaits for level-ground walking (LGW) and ascending stairs (SA). A phase prediction error of 1.28% for all benchmark datasets was obtained. The model was subsequently applied to a state machine-controlled powered prosthetic leg dataset collected from four individuals with unilateral transfemoral amputation. The gait phase prediction results (a phase prediction error of 5.70%) indicate that the model trained on benchmark data can be used for a system not included in the training dataset with no post-processing, such as model adaptation. Furthermore, it provided information regarding evaluation of the controller: whether the prosthetic leg generated normal gait. In conclusion, the proposed gait phase prediction model will facilitate efficient gait prediction and evaluation of controllers for powered lower-limb assistive devices.

Design, fabrication and experiments of a hydraulic active-passive hybrid prosthesis knee

BACKGROUND

Due to low friction, passive mechanical prostheses move compliantly followed by the stump and are used widely. Advanced semi-active prostheses can both move passively like passive prostheses and provide active torque under specific conditions. However, the current mechanical-hydraulic coupling driven semi-active prostheses, in order to meet the low passive friction requirements with a low active transmission ratio, lead to a significant problem of insufficient active torque.

OBJECTIVE

A hybrid active and passive prosthesis was developed to solve the incompatibility problem of low passive friction and high active driving torque of semi-active prostheses.

METHODS

The mechanical structure and control strategy of the prosthesis were demonstrated. The performance of the prosthesis was tested by bench and human tests.

RESULTS

Passive subsystem damping adjustment ranges from 0.4 N⋅(mm/s)-1 to 300 N⋅(mm/s)-1. The switching time between the damping and the active subsystem is 32 ± 2 ms. The continuous active torque output is more than 24 Nm. In level walking, the peak torque is about 28 Nm.

CONCLUSION

The proposed active-passive hybrid hydraulic prosthesis could satisfy both low passive friction and high active actuation.

Mechanisms and component design of prosthetic knees: A review from a biomechanical function perspective

Prosthetic knees are state-of-the-art medical devices that use mechanical mechanisms and components to simulate the normal biological knee function for individuals with transfemoral amputation. A large variety of complicated mechanical mechanisms and components have been employed; however, they lack clear relevance to the walking biomechanics of users in the design process. This article aims to bridge this knowledge gap by providing a review of prosthetic knees from a biomechanical perspective and includes stance stability, early-stance flexion and swing resistance, which directly relate the mechanical mechanisms to the perceived walking performance, i.e., fall avoidance, shock absorption, and gait symmetry. The prescription criteria and selection of prosthetic knees depend on the interaction between the user and prosthesis, which includes five functional levels from K0 to K4. Misunderstood functions and the improper adjustment of knee prostheses may lead to reduced stability, restricted stance flexion, and unnatural gait for users. Our review identifies current commercial and recent studied prosthetic knees to provide a new paradigm for prosthetic knee analysis and facilitates the standardization and optimization of prosthetic knee design. This may also enable the design of functional mechanisms and components tailored to regaining lost functions of a specific person, hence providing individualized product design.

A Primarily-Passive Knee Prosthesis with Powered Stance and Swing Assistance

This paper describes a knee prosthesis that provides primarily-passive behavior akin to a state-of-the-art modulated passive microprocessor-controlled knee prosthesis (MPK), but also offers powered stance and swing assistance, which are layered on top of the passive functionality. The combination of both passive and powered behaviors is enabled by a unique electronically-selectable two-speed transmission, which enables the combination of very low output impedance and high resistive torques necessary for passive functionality, while also enabling the generative torque-speed characteristics suitable for stance and swing phase assistance. A control system is described that consolidates behaviors appropriate for a wide range of activities of daily living (ADLs) into six states, with each state providing adaptive torque behaviors appropriate for different phases of gait, whether powered or passive. Experimental results on an individual with transfemoral amputation suggest that the prosthesis and controller provide similar or improved passive behaviors relative to a commercially-available MPK, in addition to providing powered functionality commensurate with a powered prosthesis.

Ambulation Control System Design for a Hybrid Knee Prosthesis

Prosthetic knees available to individuals with transfemoral amputation seek to restore functional ability to the user. Passive prosthetic knees are lightweight but can restore only limited, dissipative ambulation activities whereas active knees can provide energy to restore additional ambulation activities such as stair climbing and standing up from a chair. Semi-active prosthetic devices aim to only power a subset of activities and use passive components and control when that power is not necessary. Here, we outline an ambulation control system for a lightweight Hybrid Knee prosthesis that is powered for climbing stairs and passive for other ambulation activities (level-ground walking, walking on an incline, and stair descent). We include preliminary offline and online intent recognition system results for one able-bodied user and one individual with a transfemoral amputation demonstrating low error rates in predicting between active and passive control.

Data-Mined Continuous Hip-knee Coordination Mapping with Motion Lag for Lower-limb Prosthesis Control

Trajectory planning of the knee joint plays an essential role in controlling the lower limb prosthesis. Nowadays, the idea of mapping the trajectory of the healthy limb to the motion trajectory of the prosthetic joint has begun to emerge. However, establishing a simple and intuitive coordination mapping is still challenging. This paper employs the method of experimental data mining to explore such a coordination mapping. The coordination indexes, i.e., the mean absolute relative phase (MARP) and the deviation phase (DP), are obtained from experimental data. Statistical results covering different subjects indicate that the hip motion possesses a stable phase difference with the knee, inspiring us to construct a hip-knee Motion-Lagged Coordination Mapping (MLCM). The MLCM first introduces a time lag to the hip motion to avoid conventional integral or differential calculations. The model in polynomials, which is proved more efficient than Gaussian process regression and neural network learning, is then constructed to represent the mapping from the lagged hip motion to the knee motion. In addition, a strong linear correlation between hip-knee MARP and hip-knee motion lag is discovered for the first time. By using the MLCM, one can generate the knee trajectory for the prosthesis control only via the hip motion of the healthy limb, indicating less sensing and better robustness. Numerical simulations show that the prosthesis can achieve normal gaits at different walking speeds.

Powered Knee and Ankle Prosthesis with Adaptive Control Enables Climbing Stairs with Different Stair Heights, Cadences, and Gait Patterns

Powered prostheses can enable individuals with above-knee amputations to ascend stairs step-over-step. To accomplish this task, available stair ascent controllers impose a pre-defined joint impedance behavior or follow a pre-programmed position trajectory. These control approaches have proved successful in the laboratory. However, they are not robust to changes in stair height or cadence, which is essential for real-world ambulation. Here we present an adaptive stair ascent controller that enables individuals with above-knee amputations to climb stairs of varying stair heights at their preferred cadence and with their preferred gait pattern. We found that modulating the prosthesis knee and ankle position as a function of the user's thigh in swing provides toe clearance for varying stair heights. In stance, modulating the torque-angle relationship as a function of the prosthesis knee position at foot contact provides sufficient torque assistance for climbing stairs of different heights. Furthermore, the proposed controller enables individuals to climb stairs at their preferred cadence and gait pattern, such as step-by-step, step-over-step, and two-steps. The proposed adaptive stair controller may improve the robustness of powered prostheses to environmental and human variance, enabling powered prostheses to more easily move from the lab to the real-world.

Design of a Bio-Inspired Gait Phase Decoder Based on Temporal Convolution Network Architecture With Contralateral Surface Electromyography Toward Hip Prosthesis Control

Inter-leg coordination is of great importance to guarantee the safety of the prostheses wearers, especially for the subjects at high amputation levels. The mainstream of current controllers for lower-limb prostheses is based on the next motion state estimation by the past motion signals at the prosthetic side, which lacks immediate responses and increases falling risks. A bio-inspired gait pattern generation architecture was proposed to provide a possible solution to the bilateral coordination issue. The artificial movement pattern generator (MPG) based on the temporal convolution network, fusing with the motion intention decoded from the surface electromyography (sEMG) measured at the impaired leg and the motion status from the kinematic modality of the prosthetic leg, can predict four sub gait phases. Experiment results suggested that the gait phase decoder exhibited a relatively high intra-subject consistency in the gait phase inference, adapted to various walking speeds with mean decoding accuracy ranging from 89.27 to 91.16% across subjects, and achieved an accuracy of 90.30% in estimating the gait phase of the prosthetic leg in the hip disarticulation amputee at the self-selected pace. With the proof of concept and the offline experiment results, the proposed architecture improves the walking coordination with prostheses for the amputees at hip level amputation.

EMG-driven control in lower limb prostheses: a topic-based systematic review

BACKGROUND

The inability of users to directly and intuitively control their state-of-the-art commercial prosthesis contributes to a low device acceptance rate. Since Electromyography (EMG)-based control has the potential to address those inabilities, research has flourished on investigating its incorporation in microprocessor-controlled lower limb prostheses (MLLPs). However, despite the proposed benefits of doing so, there is no clear explanation regarding the absence of a commercial product, in contrast to their upper limb counterparts.

OBJECTIVE AND METHODOLOGIES

This manuscript aims to provide a comparative overview of EMG-driven control methods for MLLPs, to identify their prospects and limitations, and to formulate suggestions on future research and development. This is done by systematically reviewing academical studies on EMG MLLPs. In particular, this review is structured by considering four major topics: (1) type of neuro-control, which discusses methods that allow the nervous system to control prosthetic devices through the muscles; (2) type of EMG-driven controllers, which defines the different classes of EMG controllers proposed in the literature; (3) type of neural input and processing, which describes how EMG-driven controllers are implemented; (4) type of performance assessment, which reports the performance of the current state of the art controllers.

RESULTS AND CONCLUSIONS

The obtained results show that the lack of quantitative and standardized measures hinders the possibility to analytically compare the performances of different EMG-driven controllers. In relation to this issue, the real efficacy of EMG-driven controllers for MLLPs have yet to be validated. Nevertheless, in anticipation of the development of a standardized approach for validating EMG MLLPs, the literature suggests that combining multiple neuro-controller types has the potential to develop a more seamless and reliable EMG-driven control. This solution has the promise to retain the high performance of the currently employed non-EMG-driven controllers for rhythmic activities such as walking, whilst improving the performance of volitional activities such as task switching or non-repetitive movements. Although EMG-driven controllers suffer from many drawbacks, such as high sensitivity to noise, recent progress in invasive neural interfaces for prosthetic control (bionics) will allow to build a more reliable connection between the user and the MLLPs. Therefore, advancements in powered MLLPs with integrated EMG-driven control have the potential to strongly reduce the effects of psychosomatic conditions and musculoskeletal degenerative pathologies that are currently affecting lower limb amputees.

Optimization of immune receptor-related hypersensitive cell death response assay using agrobacterium-mediated transient expression in tobacco plants

BACKGROUND

The study of the regulatory mechanisms of evolutionarily conserved Nucleotide-binding leucine-rich repeat (NLR) resistance (R) proteins in animals and plants is of increasing importance due to understanding basic immunity and the value of various crop engineering applications of NLR immune receptors. The importance of temperature is also emerging when applying NLR to crops responding to global climate change. In particular, studies of pathogen effector recognition and autoimmune activity of NLRs in plants can quickly and easily determine their function in tobacco using agro-mediated transient assay. However, there are conditions that should not be overlooked in these cell death-related assays in tobacco.

RESULTS

Environmental conditions play an important role in the immune response of plants. The system used in this study was to establish conditions for optimal hypertensive response (HR) cell death analysis by using the paired NLR RPS4/RRS1 autoimmune and AvrRps4 effector recognition system. The most suitable greenhouse temperature for growing plants was fixed at 22 °C. In this study, RPS4/RRS1-mediated autoimmune activity, RPS4 TIR domain-dependent cell death, and RPS4/RRS1-mediated HR cell death upon AvrRps4 perception significantly inhibited under conditions of 65% humidity. The HR is strongly activated when the humidity is below 10%. Besides, the leaf position of tobacco is important for HR cell death. Position #4 of the leaf from the top in 4-5 weeks old tobacco plants showed the most effective HR cell death.

CONCLUSIONS

As whole genome sequencing (WGS) or resistance gene enrichment sequencing (RenSeq) of various crops continues, different types of NLRs and their functions will be studied. At this time, if we optimize the conditions for evaluating NLR-mediated HR cell death, it will help to more accurately identify the function of NLRs. In addition, it will be possible to contribute to crop development in response to global climate change through NLR engineering.

Evaluating Electromyography and Sonomyography Sensor Fusion to Estimate Lower-Limb Kinematics Using Gaussian Process Regression

Research on robotic lower-limb assistive devices over the past decade has generated autonomous, multiple degree-of-freedom devices to augment human performance during a variety of scenarios. However, the increase in capabilities of these devices is met with an increase in the complexity of the overall control problem and requirement for an accurate and robust sensing modality for intent recognition. Due to its ability to precede changes in motion, surface electromyography (EMG) is widely studied as a peripheral sensing modality for capturing features of muscle activity as an input for control of powered assistive devices. In order to capture features that contribute to muscle contraction and joint motion beyond muscle activity of superficial muscles, researchers have introduced sonomyography, or real-time dynamic ultrasound imaging of skeletal muscle. However, the ability of these sonomyography features to continuously predict multiple lower-limb joint kinematics during widely varying ambulation tasks, and their potential as an input for powered multiple degree-of-freedom lower-limb assistive devices is unknown. The objective of this research is to evaluate surface EMG and sonomyography, as well as the fusion of features from both sensing modalities, as inputs to Gaussian process regression models for the continuous estimation of hip, knee and ankle angle and velocity during level walking, stair ascent/descent and ramp ascent/descent ambulation. Gaussian process regression is a Bayesian nonlinear regression model that has been introduced as an alternative to musculoskeletal model-based techniques. In this study, time-intensity features of sonomyography on both the anterior and posterior thigh along with time-domain features of surface EMG from eight muscles on the lower-limb were used to train and test subject-dependent and task-invariant Gaussian process regression models for the continuous estimation of hip, knee and ankle motion. Overall, anterior sonomyography sensor fusion with surface EMG significantly improved estimation of hip, knee and ankle motion for all ambulation tasks (level ground, stair and ramp ambulation) in comparison to surface EMG alone. Additionally, anterior sonomyography alone significantly improved errors at the hip and knee for most tasks compared to surface EMG. These findings help inform the implementation and integration of volitional control strategies for robotic assistive technologies.

Understanding LSTM Network Behaviour of IMU-Based Locomotion Mode Recognition for Applications in Prostheses and Wearables

Human Locomotion Mode Recognition (LMR) has the potential to be used as a control mechanism for lower-limb active prostheses. Active prostheses can assist and restore a more natural gait for amputees, but as a medical device it must minimize user risks, such as falls and trips. As such, any control system must have high accuracy and robustness, with a detailed understanding of its internal operation. Long Short-Term Memory (LSTM) machine-learning networks can perform LMR with high accuracy levels. However, the internal behavior during classification is unknown, and they struggle to generalize when presented with novel users. The target problem addressed in this paper is understanding the LSTM classification behavior for LMR. A dataset of six locomotive activities (walking, stopped, stairs and ramps) from 22 non-amputee subjects is collected, capturing both steady-state and transitions between activities in natural environments. Non-amputees are used as a substitute for amputees to provide a larger dataset. The dataset is used to analyze the internal behavior of a reduced complexity LSTM network. This analysis identifies that the model primarily classifies activity type based on data around early stance. Evaluation of generalization for unseen subjects reveals low sensitivity to hyper-parameters and over-fitting to individuals’ gait traits. Investigating the differences between individual subjects showed that gait variations between users primarily occur in early stance, potentially explaining the poor generalization. Adjustment of hyper-parameters alone could not solve this, demonstrating the need for individual personalization of models. The main achievements of the paper are (i) the better understanding of LSTM for LMR, (ii) demonstration of its low sensitivity to learning hyper-parameters when evaluating novel user generalization, and (iii) demonstration of the need for personalization of ML models to achieve acceptable accuracy.

Ultrasound Features of Skeletal Muscle Can Predict Kinematics of Upcoming Lower-Limb Motion

Seamless integration of lower-limb assistive devices with the human body requires an intuitive human-machine interface, which would benefit from predicting the intent of individuals in advance of the upcoming motion. Ultrasound imaging was recently introduced as an intuitive sensing interface. The objective of the present study was to investigate the predictability of joint kinematics using ultrasound features of the rectus femoris muscle during a non-weight-bearing knee extension/flexion. Motion prediction accuracy was evaluated in 67 ms increments, up to 600 ms in time. Statistical analysis was used to evaluate the feasibility of motion prediction, and the linear mixed-effects model was used to determine a prediction time window where the joint angle prediction error is barely perceivable by the sample population, hence clinically reliable. Surprisingly, statistical tests revealed that the prediction accuracy of the joint angle was more sensitive to temporal shifts than the accuracy of the joint angular velocity prediction. Overall, predictability of the upcoming joint kinematics using ultrasound features of skeletal muscle was confirmed, and a time window for a statistically and clinically reliable prediction was found between 133 and 142 ms. A reliable prediction of user intent may provide the time needed for processing, control planning, and actuation of the assistive devices at critical points during ambulation, contributing to the intuitive behavior of lower-limb assistive devices.

Relying on more sense for enhancing lower limb prostheses control: a review

Modern lower limb prostheses have the capability to replace missing body parts and improve the patients' quality of life. However, missing environmental information often makes a seamless adaptation to transitions between different forms of locomotion challenging. The aim of this review is to identify the progress made in this area over the last decade, addressing two main questions: which types of novel sensors for environmental awareness are used in lower limb prostheses, and how do they enhance device control towards more comfort and safety. A literature search was conducted on two Internet databases, PubMed and IEEE Xplore. Based on the criteria for inclusion and exclusion, 32 papers were selected for the review analysis, 18 of those are related to explicit environmental sensing and 14 to implicit environmental sensing. Characteristics were discussed with a focus on update rate and resolution as well as on computing power and energy consumption. Our analysis identified numerous state-of-the-art sensors, some of which are able to "look through" clothing or cosmetic covers. Five control categories were identified, how "next generation prostheses" could be extended. There is a clear tendency towards more upcoming object or terrain prediction concepts using all types of distance and depth-based sensors. Other advanced strategies, such as bilateral gait segmentation from unilateral sensors, could also play an important role in movement-dependent control applications. The studies demonstrated promising accuracy in well-controlled laboratory settings, but it is unclear how the systems will perform in real-world environments, both indoors and outdoors. At the moment the main limitation proves to be the necessity of having an unobstructed field of view.

Prediction of Distal Lower-Limb Motion Using Ultrasound-Derived Features of Proximal Skeletal Muscle

Control of lower-limb assistive devices would benefit from predicting the intent of individuals in advance of upcoming motion, rather than estimating the current states of their motion. Human lower-limb motion estimation using ultrasound (US) image derived features of skeletal muscle has been demonstrated. However, predictability of motion in time remains an open question. The objective of this study was to assess the predictability of distal lower-limb motion using US image features of rectus femoris (RF) muscle during non-weight-bearing knee flexion/extension. A series of time shifts was introduced between the US features and the joint position in 67 ms steps from 0 ms (i.e., estimation, no prediction) up to predicting 467 ms in advance. A US-based algorithm to estimate lower-limb motion was then used to predict the knee joint position in time using the US features after introducing the time shifts. The accuracy of joint motion prediction after each time shift was compared to the accuracy of joint motion estimation. The reliability of the prediction was then assessed using an analysis of variance (ANOVA) test. The motion prediction accuracy was found to be reliable up to 200 ms, where the average root mean square error (RMSE) of prediction across 9 healthy subjects was 0.89 degrees greater than the average RMSE (7.39 degrees) of motion estimation for the same group of subjects. These findings suggest a reliable prediction of upcoming lower-limb motion is feasible using the US features of skeletal muscle up to a certain point. A reliable prediction may provide lower-limb assistive device control systems with a time-window for processing and control planning, and actuation hence improving the volitional control behaviors of lower-limb assistive devices.