Edited by: C. David Remy, University of Michigan, United States
Reviewed by: Priyanshu Agarwal, Rice University, United States; Erwei Yin, China Astronaut Research and Training Center, China
Specialty section: This article was submitted to Biomedical Robotics, a section of the journal Frontiers in Robotics and AI
This is an open-access article distributed under the terms of the
Exoskeletons and other wearable robotic devices have a wide range of potential applications, including assisting patients with walking pathologies, acting as tools for rehabilitation, and enhancing the capabilities of healthy humans. However, applying these devices effectively in a real-world setting can be challenging, as the optimal design features and control commands for an exoskeleton are highly dependent on the current user, task and environment. Consequently, robust metrics and methods for quantifying exoskeleton performance are required. This work presents an analysis of walking data collected for healthy subjects walking with an active pelvis exoskeleton over three assistance scenarios and five walking contexts. Spatial and temporal, kinematic, kinetic and other novel dynamic gait metrics were compared to identify which metrics exhibit desirable invariance properties, and so are good candidates for use as a stability metric over varying walking conditions. Additionally, using a model-based approach, the average metabolic power consumption was calculated for a subset of muscles crossing the hip, knee and ankle joints, and used to analyse how the energy-reducing properties of an exoskeleton are affected by changes in walking context. The results demonstrated that medio-lateral centre of pressure displacement and medio-lateral margin of stability exhibit strong invariance to changes in walking conditions. This suggests that these dynamic gait metrics are optimised in human gait and are potentially suitable metrics for optimising in an exoskeleton control paradigm. The effectiveness of the exoskeleton at reducing human energy expenditure was observed to increase when walking on an incline, where muscles aiding in hip flexion were assisted, but decrease when walking at a slow speed. These results underline the need for adaptive control algorithms for exoskeletons if they are to be used in varied environments.
Increasingly, exoskeletons are being used to great effect for the rehabilitation of people with lower-limb pathologies (
Current control paradigms frequently use normalised kinematic trajectories (
It is known that the human neuromuscular system optimises stability (
The human neuromuscular system also optimises energy efficiency (
In this study, a neuromuscular human and exoskeleton model is presented. Experimental data was collected using a unique setup, combining kinematic, kinetic, and exoskeleton angular and torque data. Using this data, stability metrics and metabolic energy consumption were compared between three walking scenarios: walking without an exoskeleton, walking with an exoskeleton in transparent mode, and walking with an exoskeleton in assistive mode. For each of these scenarios five different walking contexts were investigated: walking at baseline speed, walking up an incline, walking down an incline, fast walking, and slow walking. To carry out the analysis a range of spatial and temporal, kinematic, kinetic, and dynamic gait metrics (such as centre of mass displacement) were selected.. The selected metrics were compared to identify those which demonstrated the most invariance and therefore would be suitable for optimising in an exoskeleton control paradigm. In addition, metabolic energy consumption was calculated and is reported for a subset of muscles crossing the hip, knee and ankle joints, and the effect of variations in walking context and exoskeleton assistance level on these representative muscles is discussed.
The exoskeleton which we use to provide assistance is the Active Pelvis Orthosis (APO), a revised version of the device presented by Giovacchini et al. (
The APO developers adapted work by Ronsse et al. (
We developed a unique model of a human subject wearing the APO (see
Data was collected for each subject while they walked on a treadmill in a variety of walking contexts and exoskeleton assistance scenarios. Reflective markers were attached to each subject to accurately track their movements using a six camera motion capture system (Vicon, Oxford, UK). The marker set used was adapted from the Cleveland marker set and consisted of 33 markers, 8 of which were solely used for the purpose of scaling the dynamic model. Ground reaction forces and moments were collected using a six axis, split belt instrumented treadmill (Motekforce Link, Amsterdam, Netherlands). The torques applied by the APO were measured directly from the device.
To capture data in different walking contexts, a script was implemented in the Motek D-Flow software to programmatically change the speed or incline of the treadmill appropriately. Each subject was made to walk in five different walking contexts, as follows: at baseline walking speed with no incline (BW), at baseline walking speed with an incline of 5 degrees (UW), at baseline walking speed with an incline of −5 degrees (DW), at a fast walking speed with no incline (FW), and at a slow walking speed with no incline (SW). The baseline walking speed used for the BW context was calculated using the principle of dynamic similarity as described by the Froude number (
where
The contexts were repeated for 3 different assistance scenarios: one without wearing the APO (NE), one wearing the APO set in transparent mode (ET) and one wearing the APO set in assistive mode (EA) with the virtual stiffness set to 15 Nm/rad. For each subject, a static pose was collected in both the NE and ET assistance scenarios.
Before the data could be analysed, several post-processing steps had to be undertaken. Any gaps in the raw recorded motion capture data were filled using Vicon’s software Nexus. A combination of the built in algorithms were used including the spline fill, the pattern fill, and the cyclic fill. The MoNMS toolbox (
For the ground reaction forces and moments, custom scripts were written in MATLAB. The first step was to compensate for data collected when the treadmill was tilted, and therefore causing gravity to work in a different direction to the force plate sensors. The ground reaction forces were then filtered using a zero-lag 4th order Butterworth filter with a 6 Hz cut-off. For the next step a threshold filter was applied to the ground reaction forces and moments that set all values equal to zero when the vertical force was less than 40 N. This was implemented because the CoP values were noisy when the vertical ground reaction forces were low. Additionally, it filtered out any noise in the force measurements during the swing phase of the gait cycle when there should be no forces applied to the foot. After applying the threshold, the CoPs were calculated and the global force plate moments were converted into free moments around the foot. Finally, the D-flow axis system was transformed to the OpenSim axis system.
The problem of joint misalignment is well known when dealing with physically coupled systems, e.g. humans wearing exoskeletons (
Measure the distance from a fixed reflective marker on the back of the APO to the left and right exoskeleton joint centres.
Using the static pose data from this fixed marker, calculate the position in the ground frame of the left and right exoskeleton joint centres for each subject.
Using the reflective markers situated on the pelvis, coupled with a variation of the Harrington method (
Calculate the offset between the exoskeleton and human joint centres.
The above steps were undertaken for all subjects using the corresponding static pose data. The offsets for each subject are summarised in
The static joint offsets between the hip joints of each subject and the APO joint centres.
Subject | Right hip offset (m) | Left hip offset (m) | ||
S1 | 0.0818 | 0.0158 | 0.0817 | 0.0097 |
S2 | 0.0546 | −0.0222 | 0.0621 | −0.0288 |
S3 | 0.0847 | 0.0127 | 0.0843 | 0.0141 |
S4 | 0.0770 | 0.0057 | 0.0636 | 0.0105 |
S5 | 0.0929 | 0.0067 | 0.0648 | 0.0017 |
S6 | 0.0615 | −0.0017 | 0.0584 | 0.0006 |
S7 | 0.0881 | 0.0209 | 0.1081 | 0.0213 |
S8 | 0.0753 | 0.0024 | 0.0580 | 0.0022 |
Note that the
A graphical representation of the APO force models for a single gait cycle. The ideal force model (With Offsets) and compliant force model (Compliant) are shown relative to the measured APO torques (No Offsets). Left: the torque applied to the human femur body. Note the delayed onset of the peaks in the compliant force model. Right: the introduction of undesired interaction forces directed parallel to the thigh. These forces are introduced due to the presence of joint offsets.
Due to the presence of compliance in human-exoskeleton systems, largely due to flexible straps and soft biological tissues, power loss occurs between the torques generated by the exoskeleton and the torques experienced by the human subject. A relatively limited number of previous studies have investigated these interface dynamics in more detail, using a mix of kinematic and load sensing measurement devices to estimate the visco-elastic properties of the human-exoskeleton system (
In subsequent metabolic analyses of the APO in assistive mode, two models were used for the transmission of exoskeleton torques. Both models account for human-exoskeleton joint misalignment. The first model, hereafter referred to as the
The compliant torque transmission model partitions the APO torque signal in to phases categorised as
The processed data for each subject was divided in to 10 gait cycles per combination of walking context and assistance scenario. A range of analyses were then carried out using OpenSim tools in combination with the gait2392 and human/APO musculoskeletal models.
The first step was to scale the generic versions of these models to fit each subject using the static pose data and the
The
The
The
The
Each of the dynamic analyses was performed twice for the EA assistance scenario; once using the ideal APO torque transmission model and again using the compliant model. The outputs of these analyses were used in the calculation of the stability metrics and metabolic energy consumption. A schematic of the overall data processing and analysis pipeline is provided in
A schematic outlining the data collection and analysis pipeline.
A set of candidate stability metrics were chosen so as to cover a range of spatial, temporal and derived metrics. It is posited that those gait metrics which exhibit a strong invariance to changes in walking context or exoskeleton assistance scenario are good candidates for use as a measure of gait stability in variable walking conditions. The metrics and corresponding definitions were as follows:
Step width was determined as the medial-lateral distance between the lateral malleolus markers at the heel strikes of consecutive steps.
Step frequency was calculated as the inverse of the time between the heel strikes of consecutive steps.
The hip range of motion (
Hip peak to peak torques (
The CoM displacement was calculated by subtracting the maximum CoM position from the minimum over the gait cycle. This was calculated in both the vertical and medio-lateral directions, resulting in two distinct metrics:
The CoP displacement was calculated by subtracting the maximum CoP position from the minimum over the stance phase period. This was calculated in both the anterior-posterior and medio-lateral directions, resulting in two distinct metrics:
The margin of stability was calculated as specified by Hof (
where
with
These metrics are summarised for reference in
The direction, notation, and units of each metric.
Metric | Direction | Notation | Unit |
Step width | N/A | N/A | cm |
Step frequency | N/A | N/A | steps/min |
Sagittal hip angle range of motion | N/A | degrees | |
Sagittal peak to peak hip torque | N/A | Nm/kg | |
CoM displacement | Vertical | mm | |
CoM displacement | Medio-lateral | mm | |
CoP displacement | Medio-lateral | mm | |
CoP displacement | Anterior-posterior | mm | |
Margin of stability | Medio-lateral | mm | |
Margin of stability | Anterior-posterior | mm |
Note that stability was considered for the system as a whole, and therefore the net kinematic and ground reaction force data was used for metric calculations within the EA assistance scenario. The APO force models were not used to distinguish between the human and exoskeleton torque contributions in this case.
The calculation of metabolic power consumption followed a strategy used in other works (
The gait2392 model and our adapted human/APO musculoskeletal model both use Hill-type muscle models implemented by the OpenSim developers, which represent muscles as musculo-tendon units (MTUs) consisting of a tendon in series with a contractile muscle (
Once calculated, the instantaneous metabolic power consumption of each muscle was integrated over the gait cycle and divided by total subject mass (
Normalised averaged metabolic power consumption was calculated both using the ideal APO torque transmission model and, separately, the compliant model.
The mean and SD of each stability metric was averaged over all recorded gait cycles and all subjects, for each combination of walking context and assistance scenario. Therefore, the number of samples for each combination of stability metric, walking context and assistance scenario was
The mean and SD of the normalised average metabolic power consumption was calculated in the same way as outlined above for each muscle in the musculoskeletal model, however only half of the recorded gait cycles were used for each subject to reduce the time taken to perform the simulations. Consequently, the number of samples for each combination of muscle, walking context and assistance scenario was
To investigate the effects of the exoskeleton assistance and the walking context on the stability metrics, a two-way ANOVA was used. For the post-hoc analysis, the MATLAB multiple comparison procedure “multcompare” was used with the comparison type based on Tukey’s honestly significant difference criterion. The statistical significance level was set at
For combinations of walking context and assistance scenario which demonstrated a significant difference in the mean of a metric, the effect size was measured by computing the absolute value
A mapping from qualitative descriptions of effect size to the corresponding range of Cohen’s
Effect size | Cohen’s |
Very small | 0.01 ≤ |
Small | 0.20 ≤ |
Medium | 0.50 ≤ |
Large | 0.80 ≤ |
Very large | 1.20 ≤ |
Huge | |
The two-way ANOVA and multiple comparison procedure was repeated with the same statistical significance level to investigate changes in normalised average metabolic power consumption for a subset of muscles crossing the hip, knee and ankle joints. The included muscles were as follows: the adductor brevis, adductor longus, adductor magnus, psoas, gluteus maximus, biceps femoris, rectus femoris, vastus medialis, medial gastrocnemius and soleus. For reference these muscles and their main actions are listed in
The muscles for which a two-way ANOVA analysis was carried out, along with their main actions.
Muscle | Actions |
Adductor brevis | Hip adduction |
Adductor longus | Hip adduction, hip flexion |
Adductor magnus | Hip adduction, hip flexion, hip extension |
Psoas | Hip flexion |
Gluteus maximus | Hip extension, hip rotation |
Biceps femoris long head | Knee flexion, hip extension |
Rectus femoris | Knee extension, hip flexion |
Vastus medialis | Knee extension |
Medial gastrocnemius | Ankle plantarflexion, knee flexion |
Soleus | Ankle plantarflexion |
In order to directly compare the effect of active exoskeleton assistance between different contexts, a one-way ANOVA was performed for each context over all assistance levels and for each muscle. Within each context, the muscles which had significantly different average metabolic power consumption when in active-ideal assistance mode (EA-I) or active-compliant assistive mode (EA-C) compared to transparent mode (ET) were identified. The relative change in metabolic power consumption going from transparent mode to active mode was then calculated as a percentage in order to quantify the effectiveness of the exoskeleton assistance.
The anthropometric measurements and calculated walking velocities for each subject are presented in
The subjects’ anthropometric features and walking velocities. ©IEEE 2017
Subject | Height (m) | Weight (kg) | Walking velocity (m/s) | ||
BW | FW | SW | |||
S1 | |||||
S2 | |||||
S3 | |||||
S4 | |||||
S5 | |||||
S6 | |||||
S7 | |||||
S8 |
Running the RRA tool for all the data sets generated residual forces and moments, which are applied to the pelvis in simulation to account for the dynamic inconsistency between the dynamic model and the recorded data. These residuals should be low to ensure accurate simulations. All of the average residual forces measured during our simulations were less than the thresholds specified by the OpenSim developers (see
RRA residuals in OpenSim. ©IEEE 2017
Quantity | Value | OpenSim Benchmark |
RMS Residual force (N) | ||
Peak Residual force (N) | ||
RMS Residual moment (Nm) | ||
Peak Residual moment (Nm) |
For each metric and for every context and assistance scenario the percentage difference from the baseline condition (no exoskeleton assistance and walking at baseline speed) is shown in
Percentage difference from baseline, categorised by walking context and assistance scenario, for
Percentage difference from baseline, categorised by walking context and assistance scenario, for
The effect sizes for each metric, averaged separately by walking context and by assistance scenario, are displayed in
The effect sizes for each gait metric, averaged over
Comparing the effect sizes averaged over assistance scenario, the metrics which exhibit the lowest average effect size are
In
The effect sizes for each gait metric, averaged over all combinations of walking context and assistance scenarios. ©IEEE 2017
For each muscle in
The % difference in average metabolic power consumption of the
The % difference in average metabolic power consumption of the
Given the cases where significant differences in the average metabolic power consumption were observed, the effect size was averaged along assistance and context. The results are presented in
The values of Cohen‘s
In general, the observed effect of assistance level on the average metabolic power consumption of the muscles was small, with all effect sizes lying within the range
In contrast, the muscles we considered experienced a wider range of effect sizes due to changing walking context. The psoas, gluteus maximus, biceps femoris long head, medial gastrocnemius and soleus muscles all exhibited large effect sizes, with a Cohen’s
From
The muscles from the full set which were identified via the context-specific one-way ANOVAS to experience a significant change in metabolic energy consumption between the ET and EA assistance levels are listed by context in
The muscles which are significantly affected by exoskeleton assistance for each context.
Baseline walking | Uphill walking | Downhill walking | Slow walking |
Adductor brevis | Iliacus | Adductor longus | Adductor brevis |
Rectus femoris | Psoas | Tibialis posterior | Adductor brevis |
Quadratus femoris | Biceps femoris long head | ||
Medial gastrocnemius | Lateral gastrocnemius | ||
Flexor hallucis longus | |||
Peroneus brevis | |||
Peroneus longus |
The percentage change in average normalised metabolic energy consumption of muscles which show a significant change between the ET and EA assistance modes during
The overall metabolic effect of exoskeleton assistance in each walking condition, for both the ideal APO force model (blue) and the compliant APO force model (red). Absence of data denotes that no significant differences were observed in this case.
It is well known that walking speed is a cause of gait variability for kinematic, kinetic, and
The effect of walking up and down an incline has previously been demonstrated to have significant kinematic and kinetic changes (
This study demonstrates there are significant differences between the
All gait metrics exhibited some significant differences due to the changes in walking context and assistance scenario. After factoring in the effect sizes the most invariant metrics were shown to be step width,
In general, we observed that applying exoskeleton assistance had significantly less effect on metabolic energy consumption than changes in walking context. However, this analysis was limited by the fact that while there were three assistance scenarios, only one of these scenarios explored active assistance, and therefore comparison between different magnitudes of active assistance was not explored. A source of further work could be to collect data using a wider range of virtual stiffness levels, which would allow for an analysis of how the metabolic effect of active assistance varies with assistance magnitude. It should be noted that the motor torques commanded to the APO during active assistance trials were already close to the torque limitations in place on the device.
The relative effect of applying exoskeleton assistance was most pronounced in the flat walking, uphill walking, downhill walking and slow walking scenarios. In the latter two of these scenarios, both the ideal and compliant APO force models predicted increased metabolic cost. The compliant model predicted that flat walking benefited from assistance, while the ideal model predicted that uphill walking benefited from assistance. Anecdotally, this result agrees with feedback from subjects following data collection (e.g., the exoskeleton assistance was most beneficial when walking uphill). The negative effect of the exoskeleton when walking at slow speed may be a result of the choice of control algorithm. As discussed in Section 2.1, the control algorithm used is based on adaptive oscillators, which requires synchronisation to input joint angles. Therefore, the decrease in performance in the slow walking context may have been due to a suboptimal synchronisation. However, during walking trials, time was allowed both for subject familiarisation with the new context and for APO controller synchronisation.
A limiting factor of our study is that the adaptive oscillator control sceheme was the only exoskeleton controller tested. A source of further work could be to apply or develop additional control paradigms, so that their relative performance over different contexts can be analysed. Additionally, external measurement devices such as calorimetry systems, which have recently been used for investigations in to metabolic cost reductions of soft exosuits (
It should be noted that the implementation of a compliant APO force model was intended largely as a point of qualitative comparison between the results from the ideal APO force model. Indeed, several differences between the experimental setup used for this work and the work on interface dynamics (
Overall, our results quantify the effect that varying walking context has on the effectiveness of active exoskeleton assistance. If exoskeletons are to be applied in real-world settings where subjects may frequently adapt their walking speed or incline, they must be able to rely on adaptive control algorithms which can account for these changes in walking context. Failure to do so can result in increased, rather than decreased, metabolic energy costs, as shown by our analysis. Techniques based on musculoskeletal modelling over various walking conditions can be useful and non-invasive tools for testing how well exoskeleton control algorithms perform in this regard.
To carry out the experiments approval from the School of Informatics' Ethics Panel was received. Eight participants were recruited to undertake data collection after providing written informed consent.
DG and GH jointly created the human/APO model, devised the data collection protocol and carried out the experiments. DG implemented the batch OpenSim analysis pipeline and the metabolics calculations. GH implemented the post-processing pipeline and the gait metric calculations. SV provided continuous supervision and critical feedback on methods and results. All authors contributed to the drafting of the article.
The authors declare that this research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
This article is an extension of a conference paper titled
The Supplementary Material for this article can be found online at:
The data from one subject had to be discarded due to an issue with the force plate calibration.
The decision to take the absolute value was motivated by an interest in the magnitude of an effect rather than its direction.