Next Article in Journal
Fast-Growing Bio-Based Construction Materials as an Approach to Accelerate United Nations Sustainable Development Goals
Previous Article in Journal
Optimization of Execution Microscopic Extrusion Parameter Characterizations for Color Polycarbonate Grading: General Trend and Box–Behnken Designs
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Applied Sciences
Volume 14
Issue 11
10.3390/app14114849
applsci-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

The Impact of Induced Acceleration Perturbations in Selected Phases of the Gait Cycle on Kinematic and Kinetic Parameters

by
Kajetan Ciunelis
1,
Rafał Borkowski
2 and
Michalina Błażkiewicz
2,*
1
PhD School, The Józef Piłsudski University of Physical Education in Warsaw, 00-968 Warsaw, Poland
2
Faculty of Rehabilitation, The Józef Piłsudski University of Physical Education in Warsaw, 00-968 Warsaw, Poland
*
Author to whom correspondence should be addressed.
Appl. Sci. 2024, 14(11), 4849; https://doi.org/10.3390/app14114849
Submission received: 12 April 2024 / Revised: 1 June 2024 / Accepted: 2 June 2024 / Published: 3 June 2024
Browse Figures
Review Reports Versions Notes

Abstract

:
Background: The prevalence of falls among the older population underscores the imperative of comprehending human adaptations to gait perturbations. Dual-belt treadmills offer a controlled setting for such investigations. The purpose of this study was to examine the effect of the acceleration of one belt of the treadmill during three different phases of the gait cycle on kinematic and kinetic parameters and relate these changes to unperturbed gait. Methods: Twenty-one healthy young females walked on a treadmill in a virtual environment, in which five unexpected perturbations were applied to the left belt at the Initial Contact (IC), Mid Stance (MS), and Pre-Swing (PS) phase of the gait cycle. Data from the undisturbed gait and the first disturbance of each trial were extracted for analysis. Results: All perturbations significantly affected the gait pattern, mainly by decreasing the knee extension angle. The perturbation in the IC phase had the most significant effect, resulting in a 248.48% increase in knee flexion torque. The perturbation in the MS phase mainly affected plantar flexion torque, increasing it by 118.18%, while perturbation in the PS phase primarily increased the hip extension torque by 73.02%. Conclusions: The presence of perturbations in the IC and PS phases caused the most aggressive and significant changes in gait parameters.

1. Introduction

Over the years, researchers have been fascinated by the gait and postural control dynamics and its complicated responses to potential perturbations. In fields such as physics, mathematics, and biology, perturbation refers to a small change or disturbance applied to a system or process, which can lead to alterations in its behaviour, properties, or outcomes [1,2]. These disturbances can arise from external forces [3], such as environmental conditions or physical interactions with objects in the surroundings [4]. Alternatively, they can originate from internal factors, including physiological changes or neural signals within the organism [5]. These disturbances might manifest intentionally, such as during experimental manipulations [6], or they could occur accidentally due to unforeseen events during daily life. It is worth mentioning that external perturbations are not limited to mechanical disturbances. Mechanical disturbances address any physical force or movement that disrupts the balance or proper operation of the system [7]. These may include vibration, impact, pressure, temperature changes, or external forces like wind or water flow. They can also manifest as sensory disturbances, including hearing and vision [8].
Roeles, et al. [8] demonstrated that mechanical perturbations like contralateral sway and treadmill belt deceleration perturbation significantly altered the gait patterns in both young and older adults. In contrast, sensory perturbations (visual, i.e., room darkening, and auditory, i.e., unexpected loud noises) had no significant effect on the gait pattern [8]. Similar conclusions were reached in the studies conducted by Thies, et al. [9] and Rogers, et al. [10]. These findings suggest that an analysis of responses to mechanical perturbations is more justified. To date, many perturbation methods have been proposed in the literature. These include placing obstacles underfoot [11], using slippery or unstable surfaces [12], moving plates [13], and unexpected treadmill accelerations and decelerations [4,11,14]. The field of gait perturbation research, particularly involving both one- and dual-belt treadmill setups, is gaining increasing attention and is presently the most commonly used equipment in such studies [15]. Semaan, et al. [16] demonstrated that treadmill gait is a reliable approximation of overground walking. Notwithstanding the divergence between treadmill gait perturbations and those observed in the ground gait condition [17], Rieger, et al. [18] demonstrated encouraging outcomes regarding motor skills transfer from an artificial context to real life. Furthermore, treadmills offer the advantage of high repeatability in perturbations and walking conditions [19]. Modern dual-belt treadmills, such as GRAIL or CAREN, facilitate the generation of many perturbation types across various gait phases and planes. This comprehensive capability enables a detailed analysis of individual gait characteristics in response to perturbations.
Research focused on perturbations is strictly related to the adaptive functions of the human neuromuscular system that allow the maintenance of balance and a straight position [4,6,20,21,22]. The elderly, particularly vulnerable to falls, are a primary focus due to their diminished ability to adapt to external disturbances, with nearly a quarter of this population at risk [23,24,25]. Understanding the perturbation response is increasingly important as the global population ages. Numerous studies have examined kinematics, kinetics, spatiotemporal parameters, and muscle activity during gait perturbations [15]. Predicting responses in young, healthy individuals provides a baseline for future research and potential training or rehabilitation protocols. In addition, young and healthy individuals can safely handle more severe perturbations under laboratory conditions than older adults [26]. Determining the effects of high-intensity perturbations, such as those induced by the Motek GRAIL system, on kinematic, kinetic, and spatiotemporal parameters is crucial for understanding the body’s response limits and gait pattern changes.
Few studies have investigated perturbations during different gait cycle phases, such as Initial Contact (IC) [27,28], Mid Stance (MS) [29,30], or Pre-Swing (PS) [31]. However, Błażkiewicz and Hadamus [4] investigated how external perturbations induced by treadmill belt acceleration and deceleration during these phases affect gait regularity in young adults. Their findings revealed that, during treadmill deceleration in the Pre-Swing phase, the center of mass (CoM) displacement displayed a consistent pattern in the anterior–posterior (AP) and vertical directions. Conversely, the least regular CoM trajectories were observed during treadmill deceleration in Mid Stance in the AP direction. However, no paper has been found that directly compares the effects of perturbations during all three phases (Initial Contact, Mid Stance, and Pre-Swing) on altered lower limb joint angles and torques compared to undisturbed gait. To address this issue, the purpose of this study was to examine the effect of the acceleration of one belt of a treadmill during three different phases of the gait cycle (Initial Contact, Mid Stance, and Pre-Swing) on kinematic and kinetic parameters and relate these changes to unperturbed gait.

2. Materials and Methods

2.1. Participants

The study group consisted of 21 healthy young females with a mean age of 21.38 ± 1.32 years, body weight of 61.38 ± 6.48 kg, and body height of 165.9 ± 4.53 cm. Eligibility criteria included no muscular or neurological disorders, no lower limb injuries within the last six months, engaging in physical activity at least twice weekly, prior experience with treadmill walking, and right leg dominance [32]. Leg dominance was determined using the kicking test, where participants indicated their preferred lower limb for kicking a ball. In this study, all individuals had the right lower limb dominant. Exclusion criteria encompassed inexperience with treadmill walking, balance issues, or medications affecting the nervous system. All participants provided written informed consent to participate in the study. The study received approval from the University Review Committee (no. SKE01-15/2023) and followed the ethical guidelines and principles of the Declaration of Helsinki.

2.2. Measurement Protocol

The kinematic (joint angles) and kinetic (joint torques and ground reaction forces) parameters of the perturbed and unperturbed gait were measured in the Interactive Gait Real-Time Analysis Laboratory (GRAIL, Motek Medical B.V., Amsterdam, The Netherlands). The GRAIL consisted of a 2200 mm-long and 2 × 500 mm-wide dual-split-belt treadmill (1000 Hz), a motion capture system (Vicon Metrics Ltd., Oxford, UK) with ten Bonita cameras operating at 100 Hz, three video cameras, synchronised virtual reality environments (circular screen with 5 m diameter, 180°, and 3 m height) with three projectors, and a safety harness suspended from the ceiling (Figure 1). The accompanying D-Flow software (Motek Medical B.V., Amsterdam, The Netherlands) was used for model adjustment, initiating perturbations, and data collection.
When the participants arrived, anthropometric measurements were collected, and 25 reflective markers were placed on their bodies according to Human Body Model 2 (HBM2) guidelines (Figure 2). Throughout the study, participants wore their own comfortable sports shoes, which they typically use for walking on a treadmill or exercising at the gym.
Subsequently, participants stepped on the treadmill and aligned in a T-pose to achieve an accurate body model fit in D-Flow 3.26 software. The test protocol included three trials. In each trial, the walking speed was constant at 1.2 m/s. Each trial lasted 80 s and contained five perturbations. The perturbations were introduced at the 30th, 40th, 50th, 60th, and 70th seconds of each trial. The magnitude of the perturbations was five on a scale of 1 to 5. This setting resulted in a change in treadmill belt speed by about 0.5~0.6 m per second. Thus, during the perturbation, the velocity of the left lane of the treadmill was 1.7~1.8 m/s (Figure 3). This setup was in line with that proposed by Sloot, et al. [33]. The duration of the perturbation was ~0.82 s.
Perturbations were introduced only on the left lane of the treadmill, affecting the left limb, which was the non-dominant limb (as described in Section 2.1). Using a high perturbation magnitude facilitated a clearer estimation of peak loads, as the body’s response to significant unexpected forces was more pronounced, making it easier to observe and measure resulting joint loads.
The first trial included perturbations generated only during the Initial Contact (IC) phases. The participants then had a break of about 5 min, during which they did not step off the treadmill. This time was used to save data and set up D-Flow software for the next attempt. Another trial consisted of perturbations during the Mid Stance (MS) phase. Again, this trial was followed by a break of about 5 min. Subsequently, the participants followed the last trial, where perturbations were generated in the Pre-Swing (PS) phase (Figure 4). Therefore, five perturbations were recorded separately for IC, MS, and PS in each of the three trials.
Perturbations were configured in the D-flow software, allowing for the selection of perturbation intensity, event type, treadmill lane, and generation time interval. Since gait phases are defined as intervals rather than specific points in time, generating a perturbation at, for example, the 30th second during the MS phase (which, according to Perry, et al. [34], falls at 10–30% of the gait cycle) could cause a perturbation to occur at 20% of the gait cycle for one person and at 25% for another.
Perturbation presence was most visible for the vertical component of the ground reaction force (vGRF) (Figure 5A). Thus, the definition of the gait cycle, both with and without perturbations, was based on this variable. For gait without and with perturbations, the gait cycle was defined from the first contact of the left foot with the ground to its re-contact with the ground. Additionally, a visual verification was performed using motion data from OpenSim to ensure that the gait cycle was accurately identified, especially during perturbations. For each participant, the undisturbed gait cycle from the second trial, which occurred around the 20th second of gait, was used for analysis, as participants were more accustomed to walking on a treadmill (Figure 5B). For perturbed gait, only the first perturbation gait cycle in each trial was considered, typically occurring around the 30th second of gait (Figure 5C).
Therefore, for analysis, there were 21 gait cycles without perturbations for the left lower limb, 21 gait cycles with perturbations in the IC phase, 21 gait cycles with perturbations in the MS phase, and 21 gait cycles with perturbations in the PS phase.

2.3. Data Processing

Data obtained from D-Flow 3.26 software were saved as *.mox files, and then imported into Matlab 2021a (MathWorks, Natick, MA, USA) using a toolbox developed by Feldhege, et al. [35]. Subsequently, they were transformed into OpenSim 4.2 input files: *.trc, which contained positions of markers placed on a subject at different times during a motion capture trial, and *.mot, which included ground reaction force data [36]. The gait2354_model, a three-dimensional, 23-degree-of-freedom computer model of the human musculoskeletal system with 54 musculotendon actuators representing 27 muscles in the lower extremities and torso, was adjusted to participants’ anthropometry using the Scale Tool. Joint angles were determined using the Inverse Kinematics Tool (IK) based on marker positions during motion. Joint torques were obtained using the Inverse Dynamics Tool (ID) and ground reaction forces (GRFs).
The first perturbed gait cycle for the left leg for each of the three trials, for each person, was determined in the OpenSim software, considering the visualisation of inverse kinematics and GRF. As previously described, it was around the 30th second of gait. The same process was applied to gait without perturbations, but only for the second trial. The gait cycle, which occurred around the 20th second of the trial, was included for each individual. The gait cycle length for each perturbed trial and the gait without perturbations was estimated using the Euclidean distance equation based on the position of the left heel marker at the heel strike, as follows:
s t r i d e   l e n g t h = x 2 − x 1 2 + y 2 − y 1 2 + z 2 − z 1 2 ,
where x1, y1, z1 denote the position of the heel marker at the beginning of the gait cycle, and x2, y2, z2 denote the position of the heel marker at the end of the gait cycle, which was taken into consideration. For subsequent statistical analysis, the stride length, and extreme values (minimal and maximal) of kinematic and kinetic parameters were extracted for gait cycles both with and without perturbations.

2.4. Statistical Analysis

Statistical analysis was performed using PQStat 2021 software v.1.8.2.238 (PQStat Software, Poznań, Poland). Normal distribution of the data was assessed using the Shapiro–Wilk test, indicating normal distributions for extreme values of joint angles and stride length in both unperturbed and perturbed gait conditions (IC, MS, and PS). Extreme values of joint torques and stride length during MS perturbation did not follow a normal distribution.
ANOVA with Tukey’s HSD post hoc test was used to identify statistically significant differences in the extreme values of joint angles achieved in gait cycles with perturbations in IC, MS, and PS and gait cycles without perturbation (N). Friedman’s ANOVA test with Bonferroni’s post hoc test was employed to detect statistically significant differences in the extreme values of joint torques and stride length achieved in the gait cycle with perturbations in IC, MS, and PS, and the gait cycle without perturbation (N).
ANOVA and post hoc tests, with a non-parametric approach using statistical parametric mapping (SPM) [37] was employed on continuous curves, specifically focusing on angles and muscle torques at the lower limb joints. The SPM1D package “(https://www.spm1d.org) (accessed on 14 February 2024)” facilitated these analyses. For each SPM ANOVA, a statistical parametric map SPM{F} was created by calculating the univariate F-Statistic at each point of the gait curve [37]. If SPM{F} crossed the threshold line corresponding to a confidence level equal to 0.95, post hoc SPM{t} maps were calculated for comparing each pair of independent variables. When the SPM{t} map crossed the critical threshold, a significant difference was found between the examined pair of motion tasks. This manuscript only reported comparisons of curves that contained perturbations in the IC, MS, and PS phases, relative to curves recorded during unperturbed gait. All analyses were performed in Matlab R2021a. A similar approach was used in the paper [38].

3. Results

3.1. Analysis of the Magnitude in Terms of Area Changes

Participants exhibited various stepping strategies in response to the studied perturbations, which were triggered during the IC, MS, and PS phases of the gait cycle by accelerating the left treadmill lane (Figure 4), leading to changes in gait cycle length and both kinematic and kinetic parameter values (Table 1, Figure 6).
When studying the comparison of the curves for ankle angles with perturbation in the IC phase (Figure 6A) and the curve without perturbation, it is noteworthy that statistically significant differences not only occur at the moment of perturbation but also extend to a significantly larger area, as follows: [0–34.6]%, [39.18–64.38]%, and [70.42–88]% of the gait cycle. For the knee angle (Figure 6B), differences manifest in two clusters: [10.62–72.96]% and [90–100]% of the gait cycle. However, for the hip joint (Figure 6C), the differences cover the entire swing phase and part of the support phase, starting from the beginning of the Terminal Stance phase [39–100]% of the gait cycle. Similarly, when examining the behaviour of ankle torque (Figure 6D), it is worth noting that statistically significant differences occur in shifted areas compared to those observed for kinematic parameters. These differences manifest as follows: [5–36]%, [46.74–69.29]%, and [92.11–96.85]% of the gait cycle. For the knee joint (Figure 6E), differences are recorded across almost the entire gait cycle, presenting in six clusters: [0–4]%, [7–35.4]%, [45.6–56.3]%, [60–67]%, [72–74.36]%, and [91.6–95]% of the gait cycle. However, in the case of the hip joint (Figure 6F), differences include the following areas: [0–4]%, [52.6–67]%, and [97–100]% of the gait cycle. In particular, an additional area of [0–4]% appeared for the knee and hip torque, which was not observed for the angles.
When comparing the curves for the ankle joint angle with perturbation in the MS phase (Figure 6A) to the curve for gait without perturbation, three areas exhibit statistically significant differences: [0–9]%, [43–67]%, and [81–92]% of the gait cycle. Similar to previous findings, differences are observed for additional joints but are confined to two clusters: [5–60]% and [93–100]% of the gait cycle for the knee joint (Figure 6B) and [34–46]% and [51–60]% of the gait cycle for the hip joint (Figure 6C). Similar to the previous perturbation, the analysis of muscle torques provides different information. When comparing the curves for ankle torque with perturbation in the Mid Stance phase (Figure 6D) to the curves for gait without perturbation, three areas, similar to those observed for angles, exhibit statistically significant differences: [23.47–51.17]%, [57.81–59.22]%, and [92.36–98]% of the gait cycle. In the knee joint (Figure 6E), differences are evident throughout the entire gait cycle, with as many as six clusters: [2–4.22]%, [6.63–20]%, [31.31–37.02]%, [52–59]%, [62–69]%, and [85.5–93]% of the gait cycle. However, for the hip joint (Figure 6F), differences manifest in two clusters: [6–22.1]% and [62.77–85.80]% of the gait cycle.
Most differences were observed for curves containing perturbations during the Pre-Swing phase. Regarding the ankle joint, four clusters with differences were identified, spanning the entire gait cycle with small 10% intervals: [9–17]%, [43–48]%, and [66–77]%. For the knee joint, similar to previous perturbations, two clusters with noticeable differences are apparent: [17–55]% and [88–100]% of the gait cycle. In the case of the hip joint, differences were identified in the following regions: [16–45]%, and [81–100]% of the gait cycle. When comparing the curves for ankle torque with perturbation in the Pre-Swing phase (Figure 6D) to the curve for gait without perturbation, it is noteworthy that statistically significant differences extend beyond the moment of perturbation, covering a significantly larger area: [5–36]%, [46.74–69.29]%, and [92.11–96.85]% of the gait cycle. In the knee joint (Figure 6E), differences are observed in three segments: [13–21.18]%, [36.31–52.14]%, and [72.69–98.44]% of the gait cycle. However, for the hip joint (Figure 6F), differences manifest in two areas: [55–60]% and [72.41–95]% of the gait cycle. The above description illustrates how individual perturbations radiate and impact the affected areas, which do not overlap for kinematic and kinetic parameters.

3.2. Analysis of the Volume of Changes

To understand the extent of changes induced by perturbations, it is valuable to not only compare the affected areas, but also assess their magnitude by comparing the peak values (Table 2) observed in each curve with those documented during unperturbed gait.
In analysing extreme values for gait with perturbations compared to gait without them, it becomes evident that most substitutions occurred sequentially during the perturbation in the Initial Contact phase (six significant for kinematic parameters and four for kinetic parameters), Pre-Swing (five significant for kinematic parameters and two for kinetic parameters), and Mid Stance phase (two for kinematic parameters and three for kinetic parameters). Furthermore, in most cases, there were significant increases in extreme values in the presence of perturbations.
It is worth highlighting that the most substantial increases in kinematic parameters are for ankle plantar flexors, which exceed those achieved during treadmill gait by 382.06%, 321.98%, and 241.13% for perturbations in the IC, PS, and MS phases, respectively. A less marked but consistent trend is evident for ankle dorsiflexors, with values for IC, PS, and MS perturbations exceeding those recorded for treadmill gait by 36.61%, 23.32%, and 12.84%, respectively. The hip joint tends to reduce its maximal extension compared to treadmill gait by 36.62%, 1.97%, and 22.96% for perturbations in the IC, PS, and MS phases, respectively. Simultaneously, flexion values increased by 8.8% for gait with perturbation in the MS phase and by 25.43% and 29.71% during perturbation in the PS and IC phases, respectively. The knee joint remains in flexion, with maximum values increasing by 12.26% during perturbations in the IC phase and by 10.13% during PS phase perturbations. Notably, knee extension was observed during treadmill gait, but this phenomenon was not repeated during any perturbation (Table 2).
The response to changes in kinematic parameters is also evident for kinetic parameters. Notably, the alterations in ankle joint torques are relatively minor compared to the previously discussed kinematic values. This phenomenon can probably be attributed to the involvement of the bi-articular muscles, in this case directing their influence mainly to the knee joint. Within the knee joint, a significant increase of 248.48% is observed in the flexion torque when perturbation is introduced during the IC phase, in contrast to gait without perturbation. Meanwhile, the hip joint experiences a substantial increase in the activation of hip flexor muscles, particularly during perturbations in the IC and PS phases, with increases of 78.69% and 14.75%, respectively (Table 2).

4. Discussion

An understanding of the influence of external perturbations on gait dynamics can assist clinicians and researchers in the design of personalised rehabilitation programmes aimed at improving balance, stability, and overall gait function. These programmes can reduce the risk of falls and enhance mobility and quality of life for individuals with neuromuscular impairments or age-related declines in gait performance. This study aimed to provide support for this field by identifying the most vulnerable phase of the gait cycle to perturbation and the highest overloads that occur during the perturbation. This study employed a perturbation protocol using the Grail system (Motek Medical BV, Amsterdam, The Netherlands), which allowed for the precise timing and intensity control of perturbations. The findings indicate that all perturbations significantly influenced the examined parameters, probably due to the application of high perturbation force [4]. A consistent significant alteration observed across all perturbations was the reduction in full knee extension, as evidenced by the knee joint angle curves (Figure 6B) and extreme values for the knee extension angle (Table 2). All this suggests a critical role of the knee joint in adapting to perturbations, a trend similarly noted by Shokouhi, et al. [39], who examined adjustments in lower limb joint power and work in response to trip-induced perturbations. In contrast to Shokouhi et al.’s [39] trip-like perturbation, which involved rapidly decelerating the belt under the dominant foot, this study utilised accelerations. Additionally, Błażkiewicz and Hadamus [4] assessed gait regularity during external perturbations caused by treadmill belt acceleration and deceleration across different phases of the gait cycle. They found that perturbations caused by treadmill belt deceleration contribute to higher irregularities, with the most irregular behaviour observed during the Pre-Swing phase (vertical direction) among those caused by acceleration. In this paper, an analysis of the kinetic and kinematic values of the perturbed gait indicated that perturbations induced during the Initial Contact (IC) phase were the most intense. Perturbations in the Pre-Swing (PS) phase also had a significant impact on joint angular values, but they did not result in substantial changes in torque values, thus making them less likely to cause overloads. Perturbations in the Mid Stance (MS) phase were determined to have the least significance. These findings will be discussed sequentially, starting with the most impactful perturbations.

4.1. Perturbations in the Initial Contact Phase

The Initial Contact (IC) phase, marking the beginning of the gait cycle, plays a fundamental role in locomotion [34]. It requires good co-ordination, balance, and effective shock absorption capabilities. The perturbation induced in the IC phase affects the entire gait cycle, which is probably the reason why it accounts for the most significant changes observed in the examined parameters.
The most noteworthy discovery revolved around the alteration in knee joint torques, as the areas showing significant changes covered 55.86% of the gait cycle (Figure 6E). Specifically, the peak knee flexion torque reached 1.15 ± 0.29 Nm/kg, marking a staggering 248.48% increase compared to unperturbed gait. It was the most substantial kinetic shift across all perturbations and joints. Additionally, there was a noteworthy rise in peak knee extension torque −0.66 ± 0.21 Nm/kg in the presence of perturbation, compared to the −0.31 ± 0.03 Nm/kg recorded during unperturbed gait. Moreover, the perturbation during the Initial Contact (IC) phase induced substantial alterations in knee kinematics, affecting up to 72.34% of the gait cycle. However, there was only a minor increase of 12.26% in extreme flexion values.
Perturbations significantly altered the ankle joint angle curve values in 77.38% of the gait cycle area compared to a typical gait pattern. These alterations were most notable in the extremes of plantar flexion, exhibiting a substantial increase from −23.91 ± 9.42 deg to −4.96 ± 2.25 deg. Despite this pronounced shift, the torque exerted by the plantar flexors experienced a notable decrease of 27.72% compared to the torque observed during unperturbed gait.
Perturbations also significantly affected the kinematic parameters associated with the hip joint. These alterations impacted 61% of the gait cycle, particularly visible during the late stance and all swing phases. Notably, perturbation during the Initial Contact phase induced the most substantial changes in the extreme angular values of the hip joint, with hip flexion increasing by 29.71% and hip extension by 36.62%. In terms of torque, there was a noteworthy 78.69% increase in hip flexion torque.
It is worth noting that reviews by Taylor, et al. [15], as well as Ferreira, et al. [11], have underscored that heel strike perturbations are prevalent. This study corroborates this finding, demonstrating that perturbations during the IC phase significantly impacted all examined parameters except torques related to dorsiflexion and hip extension.
Notably, knee flexion torque exhibited a nearly threefold increase, emphasising the pivotal role of the knee joint and quadriceps muscle in responding to such perturbations. This observation is particularly relevant given that even short periods of immobilisation can lead to rapid muscle weakness [40], potentially heightening the risk of falls triggered by perturbations.

4.2. Perturbations in the Pre-Swing Phase

The Pre-Swing phase occurs at 50–60% of the gait cycle, immediately preceding the swing phase. Perturbation induced in this phase had the most significant impact on increasing step length, artificially accelerating the swing phase, and forcing participants to actively decelerate the limb. However, concerns arise as the gait cycle for perturbations in the PS phase was disrupted even before the perturbation occurred.
This disruption does not seem attributable to mistimed perturbation induction, as Golyski, et al. [29] reported an average delay of 56 ms in perturbation induction by the GRAIL and D-Flow systems, nor does it appear to stem from changes in percentage intervals due to gait cycle shortening or lengthening. A more plausible explanation seems to be that the perturbation in the PS phase was induced last, prompting participants to adopt a different gait pattern in anticipation, as observed in studies by Swart, et al. [41]. The hypothesis that participants adopted a different gait strategy is also supported by the kinematic curves of the knee joint, showing a lack of full extension compared to unperturbed gait. This relationship is particularly visible in the area of [7–35.4]% of the gait cycle (Figure 6E). Significant changes also occur in the hip joint [16–45]% and in the ankle joint [9–17]%, and [43–48]% in areas before the disturbance. The perturbation in the PS phase significantly affected all extreme kinematic values, except for the hip extension angle. The greatest changes compared to unperturbed gait were observed for plantar flexion −20.93 ± 8.22 vs. −4.96 ± 2.25 deg and knee extension 3.4 ± 3.81 vs. −3.28 ± 1.95 deg.
As for the analysis of kinetic parameters, changes in the gait pattern appeared in the [5–36]% of the gait cycle for the ankle torque and within [13–21.18]%, and [36.31–52.14]% of the gait cycle for the knee joint torque curves. However, changes in the kinetic parameters of the hip joint are directly related to the moment of perturbation, appearing, respectively, in the areas of [55–60]% and [72.41–95]% of the gait cycle. The most significant alteration in extreme values occurred at the hip joint, with the hip extension torque increasing by 73.02%. This represented the most substantial change in this parameter among all examined perturbations. These results are consistent with Vlutters, et al. [42], who also identified the hip joint as a primary control for this type of disturbance.
Regarding the perturbation during the Pre-Swing (PS) phase, the most notable changes were noted for the hip extension torque. However, the existence of significant alterations preceding the induced perturbation is intriguing and challenging to explain. These pre-perturbation changes are possibly attributable to anticipatory muscle activation in preparation for the impending perturbation. But this issue requires further research.

4.3. Perturbations in the Mid Stance Phase

The perturbations induced during the Mid Stance phase had minimal impact on the parameters studied compared to the previously mentioned perturbations. Notably, only this perturbation did not alter stride length compared to unperturbed gait. However, this finding contrasts with the Madehkhaksar, et al. [43] report, which observed a significantly shorter step length under similar conditions.
The most common strategy adopted in response to this perturbation involved rising onto the toes (Figure 3). This is evident in the notable deviations observed in ankle plantar flexion occurring within the intervals [23.47–51.17; 57.81–59.22]% of the gait cycle. Moreover, extreme torque values for plantar flexion increased by 118.18% compared to those recorded during undisturbed gait, marking the most significant change in this parameter of all the perturbations. Furthermore, there was a significant increase in extreme torque values for knee flexion—93.94%—compared to unaffected gait. Aligned with the findings presented in this study are the results of Taborri, et al. [38]. Taborri, et al. [38] demonstrated a noticeable decrease in ankle dorsiflexion and plantar flexion when locomotion was perturbed. However, it is worth noting that the authors analysed ranges of motion rather than angles.
Before perturbation, as for perturbations in the Pre-Swing phase, some areas showed significant differences from the unperturbed gait cycle. These included changes in [0–9]% and [5–60]% of the gait cycle for ankle and knee kinematics, respectively. For kinetic parameters, the change regions were [2–4.22; 6.63–20]% and [6–22.1]% of the gait cycle for the knee and hip joint, respectively (Figure 6). It seems that the explanation for this phenomenon may be similar to that for perturbation in the Pre-Swing phase, and it relates to the premature activation of muscles in subconscious anticipation of a perturbation, as this type of perturbation was studied as the second one in the experiment. Santuz, et al. [44], by applying the muscle synergy theory, demonstrated that humans are able to modify their motor control strategies, especially in terms of timing, when walking in unsteady conditions.

4.4. Study Limitations

This study has some limitations. First, due to the technical limitations of the treadmill, the perturbations did not occur at the same time of the gait cycle, only in the same interval, which could lead to different muscle activations and, thus, different behaviours. However, as presented, the results were pretty consistent within the perturbations. Another limitation concerns the analysis of the perturbed limb only. In subsequent studies, it would also be worthwhile to consider the recovery limb. Given that there was a delay in the triggering of the perturbation due to limitations in the configuration of the treadmill device, the frame in which the perturbation was presented could not be detected, making point analysis impossible and forcing an interval analysis (SPM). It is not known whether the extreme values were after the perturbation or at the time of its occurrence. Thus, some cautions in interpreting the results should be taken into account, considering that, depending on the moment of the perturbation within the gait cycle, the response is different.

4.5. Future Research

In future research, it would be beneficial to investigate the response to perturbations of both young and elderly individuals through comprehensive assessments of kinematic and kinetic parameters. However, challenges may arise due to differences in treadmill walking experience and the need to adjust the walking speed accordingly. Another avenue for future research could be to compare the impact of perturbations on the Motek GRAIL system to that of more affordable tools, such as a moving platform or other similar devices that are less expensive to maintain. Furthermore, the literature on induced perturbations uses the terms slip, trip, or stumble misleadingly, as authors use the same terms to describe different types of perturbations. Therefore, it would be beneficial for future research to provide a clear and detailed explanation for all induced perturbations and propose a common terminology.

5. Conclusions

The research findings suggest that each type of examined one-belt-acceleration perturbation presents a challenge to balance capabilities. However, the perturbation in the IC phase was associated with the highest loads, particularly in terms of the extreme values of the knee flexion torque, which were three times higher compared to normal gait.
Perturbations in the PS phase primarily influenced the hip joint, with the most significant change observed in the extension torque. The perturbations that occurred in the MS phase mainly affected the ankle joint, with a notable influence on the plantar flexion torque.

6. Practical Applications

Research conducted on healthy young people employing high treadmill speeds and perturbing forces may provide a valuable reference point for future studies and the development of rehabilitation protocols. In particular, it highlights the importance of implementing tailored interventions to improve stability during the IC phase. By directing resources towards interventions that will enhance knee extensor muscle function, clinicians can strategically improve gait dynamics and potentially reduce the incidence of falls. However, clinicians and researchers must exercise caution when tailoring interventions to patients and participants based on their health status and clinical experience. It is important to note that findings from studies conducted in young, healthy people may not directly transfer to other patient groups.

Author Contributions

Conceptualisation, M.B.; methodology, K.C. and M.B.; software, K.C. and M.B.; validation, R.B.; formal analysis, K.C. and M.B.; investigation, M.B. and K.C.; resources, M.B.; data curation, K.C.; writing—original draft preparation, K.C., R.B. and M.B.; writing—review and editing, K.C., R.B. and M.B.; visualisation, M.B.; supervision, M.B.; project administration, M.B.; funding acquisition, M.B. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Józef Piłsudski University of Physical Education in Warsaw, grant number UPB No. 2 (114/12/PRO/2023).

Institutional Review Board Statement

This study was conducted in accordance with the Declaration of Helsinki, and approved by the Institutional Review Board of Józef Piłsudski University of Physical Education in Warsaw, Poland (protocol code SKE01-15/2023 and date of approval 24 March 2023).

Informed Consent Statement

Written informed consent has been obtained from the patient(s) to publish this paper.

Data Availability Statement

The measurement data used to support the findings of this study are available from the corresponding author upon request.

Acknowledgments

We would like to thank Karol Kowieski, Aneta Łuć, Martyna Jarocka, Katarzyna Kaczmarczyk, and Agnieszka Zdrodowska for their help in data collection.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Bender, C.; Orszag, S. Advanced Mathematical Methods for Scientists and Engineers: Asymptotic Methods and Perturbation Theory; Springer: Berlin/Heidelberg, Germany, 1999; Volume 1. [Google Scholar]
  2. Holmes, M.H. Introduction to Perturbation Methods; Springer Science and Business Media LLC: Dordrecht, The Netherlands, 2013. [Google Scholar] [CrossRef]
  3. Rogers, M.W.; Mille, M.-L. Chapter 5—Balance perturbations. In Handbook of Clinical Neurology; Day, B.L., Lord, S.R., Eds.; Elsevier: Amsterdam, The Netherlands, 2018; Volume 159, pp. 85–105. [Google Scholar]
  4. Błażkiewicz, M.; Hadamus, A. Influence of Perturbation’s Type and Location on Treadmill Gait Regularity. Appl. Sci. 2024, 14, 493. [Google Scholar] [CrossRef]
  5. Blazkiewicz, M.; Wiszomirska, I.; Kaczmarczyk, K.; Brzuszkiewicz-Kuzmicka, G.; Wit, A. Mechanisms of compensation in the gait of patients with drop foot. Clin. Biomech. 2017, 42, 14–19. [Google Scholar] [CrossRef]
  6. Vonesch, A.; Duhot, C.; Lelard, T.; Léonard, G.; Błażkiewicz, M.; Mouras, H. Non-Linear Measures of Postural Control in Response to Painful and Non-Painful Visual Stimuli. Entropy 2023, 25, 1561. [Google Scholar] [CrossRef]
  7. McCrum, C.; Epro, G.; Meijer, K.; Zijlstra, W.; Brüggemann, G.P.; Karamanidis, K. Locomotor stability and adaptation during perturbed walking across the adult female lifespan. J. Biomech. 2016, 49, 1244–1247. [Google Scholar] [CrossRef]
  8. Roeles, S.; Rowe, P.J.; Bruijn, S.M.; Childs, C.R.; Tarfali, G.D.; Steenbrink, F.; Pijnappels, M. Gait stability in response to platform, belt, and sensory perturbations in young and older adults. Med. Biol. Eng. Comput. 2018, 56, 2325–2335. [Google Scholar] [CrossRef]
  9. Thies, S.B.; Richardson, J.K.; Ashton-Miller, J.A. Effects of surface irregularity and lighting on step variability during gait: A study in healthy young and older women. Gait Posture 2005, 22, 26–31. [Google Scholar] [CrossRef]
  10. Rogers, H.L.; Cromwell, R.L.; Grady, J.L. Adaptive changes in gait of older and younger adults as responses to challenges to dynamic balance. J. Aging Phys. Act. 2008, 16, 85–96. [Google Scholar] [CrossRef]
  11. Ferreira, R.N.; Ribeiro, N.F.; Figueiredo, J.; Santos, C.P. Provoking Artificial Slips and Trips towards Perturbation-Based Balance Training: A Narrative Review. Sensors 2022, 22, 9254. [Google Scholar] [CrossRef]
  12. Kim, S.; Joo, K.S.; Liu, J.; Sohn, J.H. Lower extremity kinematics during forward heel-slip. Technol. Health Care Off. J. Eur. Soc. Eng. Med. 2019, 27, 345–356. [Google Scholar] [CrossRef]
  13. Sawers, A.; Pai, Y.C.; Bhatt, T.; Ting, L.H. Neuromuscular responses differ between slip-induced falls and recoveries in older adults. J. Neurophysiol. 2017, 117, 509–522. [Google Scholar] [CrossRef]
  14. Liu, X.; Bhatt, T.; Wang, S.; Yang, F.; Pai, Y.C. Retention of the “first-trial effect” in gait-slip among community-living older adults. GeroScience 2017, 39, 93–102. [Google Scholar] [CrossRef] [PubMed]
  15. Taylor, Z.; Walsh, G.S.; Hawkins, H.; Inacio, M.; Esser, P. Perturbations during Gait: A Systematic Review of Methodologies and Outcomes. Sensors 2022, 22, 5927. [Google Scholar] [CrossRef]
  16. Semaan, M.B.; Wallard, L.; Ruiz, V.; Gillet, C.; Leteneur, S.; Simoneau-Buessinger, E. Is treadmill walking biomechanically comparable to overground walking? A systematic review. Gait Posture 2022, 92, 249–257. [Google Scholar] [CrossRef] [PubMed]
  17. Siragy, T.; Russo, Y.; Young, W.; Lamb, S.E. Comparison of over-ground and treadmill perturbations for simulation of real-world slips and trips: A systematic review. Gait Posture 2023, 100, 201–209. [Google Scholar] [CrossRef] [PubMed]
  18. Rieger, M.M.; Papegaaij, S.; Pijnappels, M.; Steenbrink, F.; van Dieën, J.H. Transfer and retention effects of gait training with anterior-posterior perturbations to postural responses after medio-lateral gait perturbations in older adults. Clin. Biomech. 2020, 75, 104988. [Google Scholar] [CrossRef]
  19. Tesio, L.; Rota, V. Gait Analysis on Split-Belt Force Treadmills: Validation of an Instrument. Am. J. Phys. Med. Rehabil. 2008, 87, 515–526. [Google Scholar] [CrossRef] [PubMed]
  20. Visser, J.E.; Carpenter, M.G.; van der Kooij, H.; Bloem, B.R. The clinical utility of posturography. Clin. Neurophysiol. Off. J. Int. Fed. Clin. Neurophysiol. 2008, 119, 2424–2436. [Google Scholar] [CrossRef] [PubMed]
  21. Borkowski, R.; Błażkiewicz, M. Postural Reactions to External Mediolateral Perturbations: A Review. Appl. Sci. 2023, 13, 1696. [Google Scholar] [CrossRef]
  22. Bartsch-Jimenez, A.; Błażkiewicz, M.; Azadjou, H.; Novotny, R.; Valero-Cuevas, F.J. “Fine synergies” describe motor adaptation in people with drop foot in a way that supplements traditional “coarse synergies”. Front. Sports Act. Living 2023, 5, 1080170. [Google Scholar] [CrossRef] [PubMed]
  23. Thibaud, M.; Bloch, F.; Tournoux-Facon, C.; Brèque, C.; Rigaud, A.S.; Dugué, B.; Kemoun, G. Impact of physical activity and sedentary behaviour on fall risks in older people: A systematic review and meta-analysis of observational studies. Eur. Rev. Aging Phys. Act. 2012, 9, 5–15. [Google Scholar] [CrossRef]
  24. Jiang, Y.; Wang, M.; Liu, S.; Ya, X.; Duan, G.; Wang, Z. The association between sedentary behavior and falls in older adults: A systematic review and meta-analysis. Front. Public Health 2022, 10, 1019551. [Google Scholar] [CrossRef] [PubMed]
  25. Salari, N.; Darvishi, N.; Ahmadipanah, M.; Shohaimi, S.; Mohammadi, M. Global prevalence of falls in the older adults: A comprehensive systematic review and meta-analysis. J. Orthop. Surg. Res. 2022, 17, 334. [Google Scholar] [CrossRef] [PubMed]
  26. Batcir, S.; Shani, G.; Shapiro, A.; Melzer, I. Characteristics of step responses following varying magnitudes of unexpected lateral perturbations during standing among older people—A cross-sectional laboratory-based study. BMC Geriatr. 2022, 22, 400. [Google Scholar] [CrossRef] [PubMed]
  27. Aprigliano, F.; Martelli, D.; Kang, J.; Kuo, S.-H.; Kang, U.J.; Monaco, V.; Micera, S.; Agrawal, S.K. Effects of repeated waist-pull perturbations on gait stability in subjects with cerebellar ataxia. J. NeuroEng. Rehabil. 2019, 16, 50. [Google Scholar] [CrossRef] [PubMed]
  28. Debelle, H.; Harkness-Armstrong, C.; Hadwin, K.; Maganaris, C.N.; O’Brien, T.D. Recovery from a Forward Falling Slip: Measurement of Dynamic Stability and Strength Requirements Using a Split-Belt Instrumented Treadmill. Front. Sports Act. Living 2020, 2, 82. [Google Scholar] [CrossRef]
  29. Golyski, P.R.; Vazquez, E.; Leestma, J.K.; Sawicki, G.S. Onset timing of treadmill belt perturbations influences stability during walking. J. Biomech. 2022, 130, 110800. [Google Scholar] [CrossRef] [PubMed]
  30. Molina, L.K.; Small, G.H.; Neptune, R.R. The influence of step width on balance control and response strategies during perturbed walking in healthy young adults. J. Biomech. 2023, 157, 111731. [Google Scholar] [CrossRef] [PubMed]
  31. Lee, B.C.; Kim, C.S.; Seo, K.H. The Body’s Compensatory Responses to Unpredictable Trip and Slip Perturbations Induced by a Programmable Split-Belt Treadmill. IEEE Trans. Neural Syst. Rehabil. Eng. A Publ. IEEE Eng. Med. Biol. Soc. 2019, 27, 1389–1396. [Google Scholar] [CrossRef]
  32. Promsri, A.; Haid, T.; Federolf, P. How does lower limb dominance influence postural control movements during single leg stance? Hum. Mov. Sci. 2018, 58, 165–174. [Google Scholar] [CrossRef]
  33. Sloot, L.H.; van den Noort, J.C.; van der Krogt, M.M.; Bruijn, S.M.; Harlaar, J. Can Treadmill Perturbations Evoke Stretch Reflexes in the Calf Muscles? PLoS ONE 2015, 10, e0144815. [Google Scholar] [CrossRef]
  34. Perry, J.; Burnfield, J.M. Gait Analysis. Normal and Pathological Function, 2nd ed.; Slack: San Francisco, CA, USA, 2010. [Google Scholar]
  35. Feldhege, F.; Richter, K.; Bruhn, S.; Fischer, D.C.; Mittlmeier, T. MATLAB-based tools for automated processing of motion tracking data provided by the GRAIL. Gait Posture 2021, 90, 422–426. [Google Scholar] [CrossRef] [PubMed]
  36. Delp, S.L.; Anderson, F.C.; Arnold, A.S.; Loan, P.; Habib, A.; John, C.T.; Guendelman, E.; Thelen, D.G. OpenSim: Open-source software to create and analyze dynamic simulations of movement. IEEE Trans. Biomed. Eng. 2007, 54, 1940–1950. [Google Scholar] [CrossRef] [PubMed]
  37. Pataky, T.C. Generalized n-dimensional biomechanical field analysis using statistical parametric mapping. J. Biomech. 2010, 43, 1976–1982. [Google Scholar] [CrossRef] [PubMed]
  38. Taborri, J.; Santuz, A.; Brüll, L.; Arampatzis, A.; Rossi, S. Measuring Kinematic Response to Perturbed Locomotion in Young Adults. Sensors 2022, 22, 672. [Google Scholar] [CrossRef]
  39. Shokouhi, S.; Mokhtarzadeh, H.; Lee, P.V.-S. Lower extremity joint power and work during recovery following trip-induced perturbations. Gait Posture 2024, 107, 1–7. [Google Scholar] [CrossRef] [PubMed]
  40. Marusic, U.; Narici, M.; Simunic, B.; Pisot, R.; Ritzmann, R. Nonuniform loss of muscle strength and atrophy during bed rest: A systematic review. J. Appl. Physiol. Bethesda Md. 1985 2021, 131, 194–206. [Google Scholar] [CrossRef]
  41. Swart, S.B.; den Otter, R.; Lamoth, C.J.C. Anticipatory control of human gait following simulated slip exposure. Sci. Rep. 2020, 10, 9599. [Google Scholar] [CrossRef] [PubMed]
  42. Vlutters, M.; van Asseldonk, E.H.F.; van der Kooij, H. Lower extremity joint-level responses to pelvis perturbation during human walking. Sci. Rep. 2018, 8, 14621. [Google Scholar] [CrossRef]
  43. Madehkhaksar, F.; Klenk, J.; Sczuka, K.; Gordt, K.; Melzer, I.; Schwenk, M. The effects of unexpected mechanical perturbations during treadmill walking on spatiotemporal gait parameters, and the dynamic stability measures by which to quantify postural response. PLoS ONE 2018, 13, e0195902. [Google Scholar] [CrossRef]
  44. Santuz, A.; Ekizos, A.; Eckardt, N.; Kibele, A.; Arampatzis, A. Challenging human locomotion. Sci. Rep. 2018, 8, 2740. [Google Scholar] [CrossRef]
Applsci 14 04849 g001
Figure 1. A view of the Interactive Gait Real-Time Analysis Laboratory (GRAIL, Motek Medical B.V., Amsterdam, The Netherlands).
Figure 1. A view of the Interactive Gait Real-Time Analysis Laboratory (GRAIL, Motek Medical B.V., Amsterdam, The Netherlands).
Applsci 14 04849 g001
Applsci 14 04849 g002
Figure 2. Front, side, and rear view of the marker set used in the Human Body Model. Green markers are anatomical markers used to define the skeleton during initialisation, where STRN—jugular notch of the sternum, XYPH—xiphoid process of the sternum, NAVE—navel, T10—10th thoracic vertebra, LASIS and RASIS—left and right anterior superior iliac spine, LPSIS and RPSIS—left and right posterior superior iliac spine, SACR—in the mid-way between the LPSIS and RPSIS points, LGTRO and RGTRO—left and right greater trochanter, I FLTHI and FRTH—left and right thigh, lateral (1/2 on the line between the left greater trochanter and LLEK/RLEK), LLEK and RLEK—left and right lateral epicondyle of the knee, LATI and RATI—left and right shank, lateral (1/2 on the line between LLEK/RLEK and LLM/RLM), LLM and RLM—left and right lateral malleolus of the ankle, LMT5 and RMT5—left and right 5th metatarsal, LTOE and RTOE—tip of the left and left toe, and LHEE and RHEE—left and right heel.
Figure 2. Front, side, and rear view of the marker set used in the Human Body Model. Green markers are anatomical markers used to define the skeleton during initialisation, where STRN—jugular notch of the sternum, XYPH—xiphoid process of the sternum, NAVE—navel, T10—10th thoracic vertebra, LASIS and RASIS—left and right anterior superior iliac spine, LPSIS and RPSIS—left and right posterior superior iliac spine, SACR—in the mid-way between the LPSIS and RPSIS points, LGTRO and RGTRO—left and right greater trochanter, I FLTHI and FRTH—left and right thigh, lateral (1/2 on the line between the left greater trochanter and LLEK/RLEK), LLEK and RLEK—left and right lateral epicondyle of the knee, LATI and RATI—left and right shank, lateral (1/2 on the line between LLEK/RLEK and LLM/RLM), LLM and RLM—left and right lateral malleolus of the ankle, LMT5 and RMT5—left and right 5th metatarsal, LTOE and RTOE—tip of the left and left toe, and LHEE and RHEE—left and right heel.
Applsci 14 04849 g002
Applsci 14 04849 g003
Figure 3. Graphical representation of the treadmill left belt speed (A): during the length of one trial, (B); and during the first perturbation.
Figure 3. Graphical representation of the treadmill left belt speed (A): during the length of one trial, (B); and during the first perturbation.
Applsci 14 04849 g003
Applsci 14 04849 g004
Figure 4. The direction of the treadmill belt’s movement and acceleration relative to the gait direction. Example of a participant’s typical response. The left (red) lower extremity was perturbed, while the right (green) lower limb was recovered. The shadow at the behind shows a gait without perturbation. The moments when the perturbation occurred are marked in red at the top of the figures, representing the phase of the gait cycle.
Figure 4. The direction of the treadmill belt’s movement and acceleration relative to the gait direction. Example of a participant’s typical response. The left (red) lower extremity was perturbed, while the right (green) lower limb was recovered. The shadow at the behind shows a gait without perturbation. The moments when the perturbation occurred are marked in red at the top of the figures, representing the phase of the gait cycle.
Applsci 14 04849 g004
Applsci 14 04849 g005
Figure 5. Procedure for determining the gait cycle containing perturbations: (A) example of the vertical component of ground reaction force (vGRF) for a single participant, with perturbations occurring every ten seconds during the Pre-Swing phase; (B) example of vGRF without perturbations for comparison; and (C) vGRF containing perturbations at 30, 40, 50, 60, and 70 s of gait. The gait cycle with the first perturbation, highlighted by the bold line, was selected for further analysis.
Figure 5. Procedure for determining the gait cycle containing perturbations: (A) example of the vertical component of ground reaction force (vGRF) for a single participant, with perturbations occurring every ten seconds during the Pre-Swing phase; (B) example of vGRF without perturbations for comparison; and (C) vGRF containing perturbations at 30, 40, 50, 60, and 70 s of gait. The gait cycle with the first perturbation, highlighted by the bold line, was selected for further analysis.
Applsci 14 04849 g005
Applsci 14 04849 g006
Figure 6. The means and standard deviations of curves across all individuals for the (A) ankle angle joint, (B) knee angle joint, (C) hip angle joint, (D) ankle torque, (E) knee torque, and (F) hip torque throughout the gait cycle. The first column shows the curves for the affected limb without perturbation (black), with perturbation in the Initial Contact (red), Mid Stance (green) and Pre-Swing (blue) phase of gait cycle. The second, third, and fourth columns display the post hoc analysis, with critical threshold (t*) values located on the right axis. Highlighted areas indicate ranges where statistically significant differences exist compared to gait without perturbation, where df—dorsiflexion, pf—plantarflexion, flex—flexion, and ext—extension.
Figure 6. The means and standard deviations of curves across all individuals for the (A) ankle angle joint, (B) knee angle joint, (C) hip angle joint, (D) ankle torque, (E) knee torque, and (F) hip torque throughout the gait cycle. The first column shows the curves for the affected limb without perturbation (black), with perturbation in the Initial Contact (red), Mid Stance (green) and Pre-Swing (blue) phase of gait cycle. The second, third, and fourth columns display the post hoc analysis, with critical threshold (t*) values located on the right axis. Highlighted areas indicate ranges where statistically significant differences exist compared to gait without perturbation, where df—dorsiflexion, pf—plantarflexion, flex—flexion, and ext—extension.
Applsci 14 04849 g006
Table 1. The impact of induced acceleration perturbation at the selected gait cycle phases on the step length, to gait without perturbations.
Table 1. The impact of induced acceleration perturbation at the selected gait cycle phases on the step length, to gait without perturbations.
ParameterInitial Contact (IC)Mid Stance (MS)Pre-Swing (PS)Treadmill Gait (N)IC vs. NMS vs. NPS vs. N
Stride length [m]0.13 ± 0.0730.051 ± 0.0290.142 ± 0.0870.024 ± 0.019p = 0.0001-p = 0.0001
IC—Initial Contact, MS—Mid Stance, PS—Pre-Swing gait phases, N—standard treadmill gait, and p-value—significance level set at p < 0.05.
Table 2. Mean, standard deviation, and percentages of extreme values for lower limb joint kinematics and kinetics curves calculated across all participants containing the impact of induced acceleration perturbation at the selected gait phases, compared to gait without perturbations.
Table 2. Mean, standard deviation, and percentages of extreme values for lower limb joint kinematics and kinetics curves calculated across all participants containing the impact of induced acceleration perturbation at the selected gait phases, compared to gait without perturbations.
ParameterInitial Contact (IC)Mid Stance (MS)Pre-Swing (PS)Treadmill Gait (N)IC vs. NMS vs. NPS vs. N
Kinematic parameters [deg]
Ankle max (df)21.38 ± 3.9617.66 ± 4.7519.3 ± 3.5415.65 ± 2.1836.61% *12.84%23.32% *
Ankle min (pf)−23.91 ± 9.42−16.92 ± 9.26−20.93 ± 8.22−4.96 ± 2.25382.06% *241.13% *321.98% *
Knee max (flex)79.21 ± 8.3468.08 ± 6.6277.71 ± 6.6970.56 ± 3.1912.26% *−3.51%10.13% *
Knee min (ext)3.66 ± 2.912.57 ± 3.853.4 ± 3.81−3.28 ± 1.95−211.59% *−178.35% *−203.66% *
Hip max (flex)40.25 ± 5.0633.76 ± 4.4138.92 ± 5.2131.03 ± 3.1929.71% *8.8%25.43% *
Hip min (ext)−9.33 ± 4.15−11.34 ± 4.22−14.43 ± 3.57−14.72 ± 2.64−36.62% *−22.96%−1.97%
Kinetic parameters [Nm/kg]
Ankle max (df)1.57 ± 0.371.75 ± 0.231.88 ± 0.151.75 ± 0.09−10.29%0%7.43%
Ankle min (pf)−0.08 ± 0.09−0.24 ± 0.04−0.11 ± 0.05−0.11 ± 0.04−27.72% *118.18% *0%
Knee max (flex)1.15 ± 0.290.64 ± 0.260.36 ± 0.050.33 ± 0.04248.48% *93.94% *9.09%
Knee min (ext)−0.66 ± 0.21−0.38 ± 0.08−0.51 ± 0.08−0.31 ± 0.03112.9% *22.58%64.52% *
Hip max (flex)1.09 ± 0.230.55 ± 0.10.7 ± 0.160.61 ± 0.1178.69% *−9.84%14.75%
Hip min (ext)−0.54 ± 0.15−0.78 ± 0.16−1.09 ± 0.23−0.63 ± 0.05−14.29%23.81% *73.02% *
IC—Initial Contact, MS—Mid Stance, PS—Pre-Swing gait phases, N—standard treadmill gait, df—dorsiflexion, pf—plantarflexion, flex—flexion, ext—extension, and *—significance level p < 0.05. Negative values alongside percentages indicate the extent of decrease compared to treadmill gait or whether the sign has been reversed.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Ciunelis, K.; Borkowski, R.; Błażkiewicz, M. The Impact of Induced Acceleration Perturbations in Selected Phases of the Gait Cycle on Kinematic and Kinetic Parameters. Appl. Sci. 2024, 14, 4849. https://doi.org/10.3390/app14114849

AMA Style

Ciunelis K, Borkowski R, Błażkiewicz M. The Impact of Induced Acceleration Perturbations in Selected Phases of the Gait Cycle on Kinematic and Kinetic Parameters. Applied Sciences. 2024; 14(11):4849. https://doi.org/10.3390/app14114849

Chicago/Turabian Style

Ciunelis, Kajetan, Rafał Borkowski, and Michalina Błażkiewicz. 2024. "The Impact of Induced Acceleration Perturbations in Selected Phases of the Gait Cycle on Kinematic and Kinetic Parameters" Applied Sciences 14, no. 11: 4849. https://doi.org/10.3390/app14114849

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop