Lower limb joint biomechanics-based identification of gait transitions in between level walking and stair ambulation

A versatile knee exoskeleton mitigates quadriceps fatigue in lifting, lowering, and carrying tasks

The quadriceps are particularly susceptible to fatigue during repetitive lifting, lowering, and carrying (LLC), affecting worker performance, posture, and ultimately lower-back injury risk. Although robotic exoskeletons have been developed and optimized for specific use cases like lifting-lowering, their controllers lack the versatility or customizability to target critical muscles across many fatiguing tasks. Here, we present a task-adaptive knee exoskeleton controller that automatically modulates virtual springs, dampers, and gravity and inertia compensation to assist squatting, level walking, and ramp and stairs ascent/descent. Unlike end-to-end neural networks, the controller is composed of predictable, bounded components with interpretable parameters that are amenable to data-driven optimization for biomimetic assistance and subsequent application-specific tuning, for example, maximizing quadriceps assistance over multiterrain LLC. When deployed on a backdrivable knee exoskeleton, the assistance torques holistically reduced quadriceps effort across multiterrain LLC tasks (significantly except for level walking) in 10 human users without user-specific calibration. The exoskeleton also significantly improved fatigue-induced deficits in time-based performance and posture during repetitive lifting-lowering. Last, the system facilitated seamless task transitions and garnered a high effectiveness rating postfatigue over a multiterrain circuit. These findings indicate that this versatile control framework can target critical muscles across multiple tasks, specifically mitigating quadriceps fatigue and its deleterious effects.

Deep Learning-Based Identification Algorithm for Transitions Between Walking Environments Using Electromyography Signals Only

Although studies on terrain identification algorithms to control walking assistive devices have been conducted using sensor fusion, studies on transition classification using only electromyography (EMG) signals have yet to be conducted. Therefore, this study was to suggest an identification algorithm for transitions between walking environments based on the entire EMG signals of selected lower extremity muscles using a deep learning approach. The muscle activations of the rectus femoris, vastus medialis and lateralis, semitendinosus, biceps femoris, tibialis anterior, soleus, medial and lateral gastrocnemius, flexor hallucis longus, and extensor digitorum longus of 27 subjects were measured while walking on flat ground, upstairs, downstairs, uphill, and downhill and transitioning between these walking surfaces. An artificial neural network (ANN) was used to construct the model, taking the entire EMG profile during the stance phase as input, to identify transitions between walking environments. The results show that transitioning between walking environments, including continuously walking on a current terrain, was successfully classified with high accuracy of 95.4 % when using all muscle activations. When using a combination of muscle activations of the knee extensor, ankle extensor, and metatarsophalangeal flexor group as classifying parameters, the classification accuracy was 90.9 %. In conclusion, transitioning between gait environments could be identified with high accuracy with the ANN model using only EMG signals measured during the stance phase.

Safety and usability of the MAK exoskeleton in patients with stroke

BACKGROUND AND PURPOSE

Stroke is one of the leading causes of disability in adults worldwide, and one of the main objectives in the rehabilitation of these patients is to recover the gait. New technologies have emerged to cope with this issue, complementing conventional therapy with the use of devices such as exoskeletons. The Marsi Active Knee (MAK) exoskeleton (Marsi Bionics SL, Madrid, Spain) has already been tested, but an updated version was improved to allow the patients to perform functional exercises. The aim of this study was to assess the safety and usability of the MAK in the stroke population as well as its potential clinical effects.

METHODS

A single-group open label intervention trial was conducted. The device was used twice a week for 5 weeks during 1 h per visit. During the visits, sit-to-stand transitions, walking, stair climbing, trunk rotations, and weight-transfer exercises were performed using the device. Adverse events were collected from participants and therapists to assess safety. The Quebec User Evaluation of the Satisfaction with assistive Technology (QUEST 2.0) was used by both therapists and participants to assess usability. To evaluate its clinical effects, active range of motion (ROM) and muscle strength were assessed in the lower limb.

RESULTS

Six participants with stroke were recruited. The device was shown to be safe since no serious adverse events were reported neither by patients nor by therapists. Every proposed exercise was performed. Regarding clinical effects, overall muscle strength showed an increase after the treatment, although ROM measurements did not show any difference.

DISCUSSION

Our results suggest that the MAK device is safe for stroke patients. Nevertheless, further changes to enhance usability are recommended, such as an improvement of the attachment system and an adaptation for the drop foot. Beneficial effects regarding increases in muscle strength were obtained. Further trials with a larger sample size, longer intervention periods, and a control group are needed to verify these results. Also, future research should focus on the usability of the MAK as an assistive technology.

Joint power, joint work and lower limb muscle activity for transitions between level walking and stair ambulation at three inclinations

To enhance human mobility, training interventions and assistive lower limb wearable robotic designs must draw insights from movement tasks from daily life. This study aimed to analyze joint peak power, limb and joint work, and muscle activity of the lower limb during a series of stair ambulation conditions. We recruited 12 subjects (25.4±4.5 yrs, 180.1±4.6 cm, 74.6±7.9 kg) and studied steady gait and gait transitions between level walking, stair ascent and stair descent for three staircase inclinations (low 19°, normal 30.4°, high 39.6°). Our analysis revealed that joint peak power, limb and joint work, and muscle activity increased significantly compared to level walking and with increasing stair inclination for most of the conditions analyzed. Transition strides had no increased requirements compared to the maxima found for steady level walking and steady stair ambulation. Stair ascent required increased lower limb joint positive peak power and work, while stair descent required increased lower limb joint negative peak power and work compared to level walking. The most challenging condition was high stair inclination, which required approximately thirteen times the total lower limb joint positive and negative net work during ascent and descent, respectively. These findings suggest that training interventions and lower limb wearable robotic designs must consider the major increases in lower limb joint and muscle effort during stair ambulation, with specific attention to the demands of ascent and descent, to effectively improve human mobility.

A Locomotion Mode Recognition Algorithm Using Adaptive Dynamic Movement Primitives

Control systems of robotic prostheses should be designed to decode the users' intent to start, stop, or change locomotion; and to select the suitable control strategy, accordingly. This paper describes a locomotion mode recognition algorithm based on adaptive Dynamic Movement Primitive models used as locomotion templates. The models take foot-ground contact information and thigh roll angle, measured by an inertial measurement unit, for generating continuous model variables to extract features for a set of Support Vector Machines. The proposed algorithm was tested offline on data acquired from 10 intact subjects and 1 subject with transtibial amputation, in ground-level walking and stair ascending/descending activities. Following subject-specific training, results on intact subjects showed that the algorithm can classify initiatory and steady-state steps with up to 100.00% median accuracy medially at 28.45% and 27.40% of the swing phase, respectively. While the transitory steps were classified with up to 87.30% median accuracy medially at 90.54% of the swing phase. Results with data of the transtibial amputee showed that the algorithm classified initiatory, steady-state, and transitory steps with up to 92.59%, 100%, and 93.10% median accuracies medially at 19.48%, 51.47%, and 93.33% of the swing phase, respectively. The results support the feasibility of this approach in robotic prosthesis control.

Controlling Powered Prosthesis Kinematics over Continuous Transitions Between Walk and Stair Ascent

One of the primary benefits of emerging powered prosthetic legs is their ability to facilitate step-over-step stair ascent by providing positive mechanical work. Existing control methods typically have distinct steady-state activity modes for walking and stair ascent, where activity transitions involve discretely switching between controllers and often must be initiated with a particular leg. However, these discrete transitions do not necessarily replicate able-bodied joint biomechanics, which have been shown to continuously adjust over a transition stride. This paper presents a phase-based kinematic controller for a powered knee-ankle prosthesis that enables continuous, biomimetic transitions between walking and stair ascent. The controller tracks joint angles from a data-driven kinematic model that continuously interpolates between the steady-state kinematic models, and it allows both the prosthetic and intact leg to lead the transitions. Results from experiments with two transfemoral amputee participants indicate that knee and ankle kinematics smoothly transition between walking and stair ascent, with comparable or lower root mean square errors compared to variations from able-bodied data.

Continuous Phase Estimation in a Variety of Locomotion Modes Using Adaptive Dynamic Movement Primitives

Accurate gait phase estimation algorithms can be used to synchronize the action of wearable robots to the volitional user movements in real time. Current-day gait phase estimation methods are designed mostly for rhythmic tasks and evaluated in highly controlled walking environments (namely, steady-state walking). Here, we implemented adaptive Dynamic Movement Primitives (aDMP) for continuous real-time phase estimation in the most common locomotion activities of daily living, which are level-ground walking, stair negotiation, and ramp negotiation. The proposed method uses the thigh roll angle and foot-contact information and was tested in real time with five subjects. The estimated phase resulted in an average root-mean-square error of 3.98% ± 1.33% and a final estimation error of 0.60% ± 0.55% with respect to the linear phase. The results of this study constitute a viable groundwork for future phase-based control strategies for lower-limb wearable robots, such as robotic prostheses or exoskeletons.

Effects of community ambulation training with 3D-printed ankle-foot orthosis on gait and functional improvements: a case series of three stroke survivors

Introduction

Many of the patients using ankle-foot orthoses (AFOs) experience poor fit, pain, discomfort, dislike of the aesthetics of the device, and excessive range of motion restrictions, which diminish the use of AFOs. Although 3D-printed ankle-foot orthoses (3D-AFOs) affect patient satisfaction and overall gait functions such as ankle moment, joint range of motion (ROM), and temporal-spatial parameters, the material properties and manufacturing process of 3D-AFOs are still diverse; the clinical effects of community ambulation using 3D-AFOs and satisfaction in patients with stroke are poorly understood.

Case description

Case 1: A 30-year-old man, with a history of right basal ganglia hemorrhage, presented with marked foot drop and genu recurvatum. Case 2: A 58-year-old man, with a history of multifocal scattered infarction, presented with an asymmetrical gait pattern due to abnormal pelvic movement. Case 3: A 47-year-old man, with a history of right putamen hemorrhage, presented with recent poor balance and a prominent asymmetrical gait pattern due to increased ankle spasticity and tremor. All patients could walk independently with AFOs.

Interventions and outcomes

Gait was assessed under three walking (even, uneven, and stair ascent/descent) and four AFO (no shoes, only shoes, shoes with AFOs, and shoes with 3D-AFOs) conditions. After 4 weeks of community ambulation training with 3D-AFO or AFO, the patients were followed up. Spatiotemporal parameters; joint kinematics; muscle efficiency; clinical evaluations including impairments, limitations, and participation; and patient satisfaction with wearing 3D-AFO were evaluated.

Results and conclusion

3D-AFOs were suitable for community ambulation of patients with chronic stroke and effective on step length, stride width, symmetry, ankle range of motion, and muscle efficiency during even surface walking and stair ascent in patients with chronic stroke. The 4-week community ambulation training with 3D-AFOs did not promote patient participation; however, it increased ankle muscle strength, balance, gait symmetry, and gait endurance and reduced depression among patients with a history of stroke. The participants were satisfied with 3D-AFO's thinness, lightweight, comfortable feeling with wearing shoes, and gait adjustability.

Exploring surface electromyography (EMG) as a feedback variable for the human-in-the-loop optimization of lower limb wearable robotics

Human-in-the-loop (HITL) optimization with metabolic cost feedback has been proposed to reduce walking effort with wearable robotics. This study investigates if lower limb surface electromyography (EMG) could be an alternative feedback variable to overcome time-intensive metabolic cost based exploration. For application, it should be possible to distinguish conditions with different walking efforts based on the EMG. To obtain such EMG data, a laboratory experiment was designed to elicit changes in the effort by loading and unloading pairs of weights (in total 2, 4, and 8 kg) in three randomized weight sessions for 13 subjects during treadmill walking. EMG of seven lower limb muscles was recorded for both limbs. Mean absolute values of each stride prior to and following weight loading and unloading were used to determine the detection rate (100% if every loading and unloading is detected accordingly) for changing between loaded and unloaded conditions. We assessed the use of multiple consecutive strides and the combination of muscles to improve the detection rate and estimated the related acquisition times of diminishing returns. To conclude on possible limitations of EMG for HITL optimization, EMG drift was evaluated during the Warmup and the experiment. Detection rates highly increased for the combination of multiple consecutive strides and the combination of multiple muscles. EMG drift was largest during Warmup and at the beginning of each weight session. The results suggest using EMG feedback of multiple involved muscles and from at least 10 consecutive strides (5.5 s) to benefit from the increases in detection rate in HITL optimization. In combination with up to 20 excluded acclimatization strides, after changing the assistance condition, we advise exploring about 16.5 s of walking to obtain reliable EMG-based feedback. To minimize the negative impact of EMG drift on the detection rate, at least 6 min of Warmup should be performed and breaks during the optimization should be avoided. Future studies should investigate additional feedback variables based on EMG, methods to reduce their variability and drift, and should apply the outcomes in HITL optimization with lower limb wearable robots.

Wearable-Based Stair Climb Power Estimation and Activity Classification

Stair climb power (SCP) is a clinical measure of leg muscular function assessed in-clinic via the Stair Climb Power Test (SCPT). This method is subject to human error and cannot provide continuous remote monitoring. Continuous monitoring using wearable sensors may provide a more comprehensive assessment of lower-limb muscular function. In this work, we propose an algorithm to classify stair climbing periods and estimate SCP from a lower-back worn accelerometer, which strongly agrees with the clinical standard (r = 0.92, p < 0.001; ICC = 0.90, [0.82, 0.94]). Data were collected in-lab from healthy adults (n = 65) performing the four-step SCPT and a walking assessment while instrumented (accelerometer + gyroscope), which allowed us to investigate tradeoffs between sensor modalities. Using two classifiers, we were able to identify periods of stair ascent with >89% accuracy [sensitivity = >0.89, specificity = >0.90] using two ensemble machine learning algorithms, trained on accelerometer signal features. Minimal changes in model performances were observed using the gyroscope alone (±0−6% accuracy) versus the accelerometer model. While we observed a slight increase in accuracy when combining gyroscope and accelerometer (about +3−6% accuracy), this is tolerable to preserve battery life in the at-home environment. This work is impactful as it shows potential for an accelerometer-based at-home assessment of SCP.

Real-Time Adaptation of an Artificial Neural Network for Transfemoral Amputees Using a Powered Prosthesis

OBJECTIVE

We evaluated a two-step method to improve control accuracy for a powered prosthetic leg using machine learning and adaptation, while reducing subject training time.

METHODS

First, information from three transfemoral amputees was grouped together, to create a baseline control system that was subsequently tested using data from a fourth subject (user-independent classification). Second, online adaptation was investigated, whereby the fourth subject's data were used to improve the baseline control system in real-time. Results were compared for user-independent classification and for user-dependent classification (data collected from and tested in the same subject), with and without adaptation.

RESULTS

The combination of a user-independent classifier with real-time adaptation based on a unique individual's data produced a classification error rate as low as 1.61% [0.15 standard error of the mean (SEM)] without requiring collection of initial training data from that individual. Training/testing using a subject's own data (user-dependent classification), combined with adaptation, resulted in a classification error rate of 0.9% [0.12 SEM], which was not significantly different (P 0.05) but required, on average, an additional 7.52 hours [0.92 SEM] of training sessions.

CONCLUSION AND SIGNIFICANCE

We found that the combination of a user-independent dataset with adaptation resulted in error rates that were not significantly different from using a user-dependent dataset. Furthermore, this method eliminated the need for individual training sessions, saving many hours of data collection time.

Performance of Sonomyographic and Electromyographic Sensing for Continuous Estimation of Joint Torque During Ambulation on Multiple Terrains

Advances in powered assistive device technology, including the ability to provide net mechanical power to multiple joints within a single device, have the potential to dramatically improve the mobility and restore independence to their users. However, these devices rely on the ability of their users to continuously control multiple powered lower-limb joints simultaneously. Success of such approaches rely on robust sensing of user intent and accurate mapping to device control parameters. Here, we compare two non-invasive sensing modalities: surface electromyography and sonomyography, (i.e., ultrasound imaging of skeletal muscle), as inputs to Gaussian process regression models trained to estimate hip, knee and ankle joint moments during varying forms of ambulation. Experiments were performed with ten non-disabled individuals instrumented with surface electromyography and sonomyography sensors while completing trials of level, incline (10°) and decline (10°) walking. Results suggest sonomyography of muscles on the anterior and posterior thigh can be used to estimate hip, knee and ankle joint moments more accurately than surface electromyography. Furthermore, these results can be achieved by training Gaussian process regression models in a task-independent manner; i.e., incorporating features of level and ramp walking within the same predictive framework. These findings support the integration of sonomyographic and electromyographic sensing within powered assistive devices to continuously control joint torque.