Quantitative assessment of thoracolumbosacral orthosis efficacy on the temporal features of gait using inertial measurement unit sensors

Abstract

Prolonged sitting with poor spinal posture alter the biomechanics of spine and the whole body, alleviating gait efficiency and causing fatigue. Evidence suggest that spinal braces can correct postural deviations, enhance proprioception, postural control, and spinal stability. However, the impact of an abnormal posture and spinal brace on the temporal features of gait remains unexplored. This study aimed to assess the quantitative effectiveness of spinal brace on the temporal features of gait using IMU sensors. This randomized crossover experimental study involved 15 lab workers who performed gait trials under two walking conditions: with an abnormal posture, with spinal brace. Temporal gait parameters were analyzed at baseline and post intervention using 10-Meters Walk Tests using foot worn IMU sensors. A paired t-test (p < 0.05) was utilized to compare walking conditions, and Cohen’s d quantified the spinal brace’s effect on gait parameters. Wearing the brace resulted in improved gait symmetry index (p < 0.001, d= -2.54), stride time (p = 0.02, d= -0.29), stance time (p = 0.012, d= -0.57) and cadence (p = 0.048, d = 0.30), with spinal stability and walking efficiency reported by our participants. These findings suggest that the spinal brace is effective in improving gait efficiency in people with postural deviations.

Introduction

Widespread exposure to digital technology and poor work life balance have increased concerns regarding postural deviation among young adults in diverse occupational settings^1,2. Prolonged static postures due to long sitting hours can lead to neck pain, lower back pain, musculoskeletal imbalances, reduced spinal alignment, and ultimately abnormal gait patterns over time^3,4. These postural deviations can affect comfort and productivity and may lead to long-term orthopedic complications due to improper weight distribution on the lower extremities⁵. In this case, the center of gravity (COG) displaces contributing to more energy consumption, unstable and inefficient walking pattern affecting the biomechanics of the whole body. The essential way to alleviate poor posture is to apply corrective forces through casts or spinal braces facilitating immobilization or dynamic postural correction. Spinal orthoses or thoracolumbosacral orthosis (TLSO) type posture correction braces have been designed to correct the postural abnormalities, helps the spine to stay erect, improve proprioception and postural control, to limit the range of motion of spine and trunk, and increase spinal stability⁶.

Gait analysis is a technique in which human movement is assessed by analyzing walking patterns, postures, balance, and walking ability of an individual. It can help with the acquisition of the physiological status, motor skills and potential medical conditions of human body⁷. Various methods have been proposed to conduct gait analysis, including motion capture system, force plates, pressure mats, and wearable sensors (e.g., IMUs, accelerometers)⁸. Recently, wearable inertial measurement units (IMUs) have gained attention as a cost-effective, portable solution for monitoring human motion and analyzing gait in both clinical and research settings^7,9,10. IMUs allow for the real-time tracking of spatiotemporal, kinematic and kinetic features without the need for the motion capture systems which are complex, expensive and confined to the labs^11–13. By utilizing IMU sensors, gait assessments in laboratory could be done with ease, as they could be reliably used as an alternative to optical motion capture systems, which helps researchers to work with fewer participants, while maintaining the credibility of data¹⁴. This advantage of IMU sensors helps to conduct the studies in laboratory environments¹⁵ and can bypass the clinical trials with fewer data sets¹⁶, through increased affordability and decreased computational burden¹⁷.

Multiple gait analysis technologies have been mentioned in different studies to investigate the biomechanical effects of spinal orthosis on gait. In comparison to walking in a poor posture, use of spinal orthosis can reduce the trunk moments and joint reaction forces, which results in the spinal stability and improved loading during walking⁶. In a comparative study by¹⁸, the spinal orthosis (TLSO) outperformed with significant rigidity in the sagittal plane during walking. Eldemir et al. reported that both the spinomed and biofeedback orthosis improved balance in adults with hyper kyphosis and Spinomed positively influenced cadence, speed, gait cycle duration, elaborated steps, and all symmetry indexes of pelvic angles (p < 0.05)¹⁹. Similarly, Hamlabadi et al. found out that sensor based TLSO reduced the peak medial GRF in individuals with kyphosis (p = 0.017)²⁰. Where, Van Criekinge et al., assessed the impact of TLSO (plastic body jacket) on gait parameters in healthy adults using vicon and reported reduced stride (p = 0.004) and step time (p = 0.013), increased cadence (p = 0.004) and step width (p = 0.002) and some frontal and transverse kinematic changes²¹. In addition, Wang et al. analyzed the influence of spinal orthosis on the kinematic and kinetic parameters of the trunk and lower extremity joints while walking; they reported that wearing spinal orthosis reduced the range of motion of spine, trunk moments and ground reaction forces (p < 0.05)⁶. Moreover, Song et al. concluded that on level walking, step width varied significantly (p < 0.05) during walking with spinal orthosis (plastic body jacket), while on stairs, double stance phase significantly increased during descent (p < 0.05), thus increasing walking stability²².

Despite the efforts^19–23, the evaluation of spinal brace’s efficacy and nature of impact on the temporal gait parameters in people having extended sedentary postures in workplace environments remain unclear. Because, in every population type, the findings are different and cannot be generalized to the whole population. Secondly, most studies have been confined to treadmill-based investigations^24,25 which do not mimic real world situations. Similarly, some studies employed laboratory-based motion capture systems such as Vicon or force plates^6,20,21,24 which are expensive, and lab bound. There is limited evidence related to the investigation of the impact of spinal brace or posture correction brace on gait parameters using wearable sensors like IMUs which are portable solutions to effectively assess the biomechanical impact in real world scenarios. These constraints limit the clinical practicability of the reported findings, as the daily life walking patterns are influenced by dynamic environments and user perceptions. In addition, subject-reported experiences such as comfort or perceived walking improvements have not been evaluated, despite being necessary for compliance and the long-term efficacy of orthotic interventions.

This research focuses on evaluating the effect of prefabricated posture correction brace, a kind of semi-rigid thoracolumbosacral orthosis (TLSO) on the temporal features (stride time, step time, stance time, swing time, and cadence) and gait symmetry index (GSI) using commercially available WT9011DCL IMU sensors from WitMotion Shenzhen Co., Ltd. In this study, we aim to compare the temporal features of two gait conditions: walking with an abnormal posture, and walking while wearing TLSO. The data was gathered in laboratory environment and was evaluated to extract and evaluate the temporal features. Our study revealed that the temporal features and gait symmetry index (GSI) were improved while walking with spinal brace due to better sagittal alignment, with spinal stability and walking efficiency reported by our participants.

Results

Descriptive statistics

Twenty participants were recruited for this investigation, as mentioned in the CONSORT (Fig. 1), out of which five subjects were excluded (n = 1 pes planus, n = 1 had undergone surgery, n = 3 declined to participate), fifteen participants were randomized into two conditions (abnormal posture, with spinal brace) with three trials each for the conditions. The data was collected and further three individuals were excluded due to the problem with signal processing. A total of twelve participants (seven males, five females) were included in the analysis of the study with the age of 25.7 ± 2.34 years, height of 173.3 ± 7.16 cm, weight of 67.4 ± 8.98 kg, body mass index of 22.4 ± 2.48, the descriptive characteristics are mentioned in the Table 1.

Fig. 1.

Consolidated Standards of Reporting Trials Statement (CONSORT) diagram illustrating the process from recruitment to data collection and then analysis.

Table 1.

Descriptive Statistics of the participants, SD: Standard Deviation.

Subjects	Age (Years)	Gender	Height (cm)	Weight (kg)	Body Mass Index
S01	25	F	172	60	20.28
S02	24	F	178	62	19.57
S03	27	F	163	70	26.35
S04	24	M	174	68	22.46
S05	27	M	178	65	20.52
S06	25	M	170	74	25.60
S07	24	F	176	59	19.05
S08	30	M	170	68	23.53
S09	30	M	180	77	23.77
S10	26	M	173	70	23.37
S11	23	F	160	51	19.92
S12	24	M	186	85	24.57

Statistics		Age (Years)	Height (cm)	Weight (kg)	Body Mass Index
Mean		25.7	173.3	67.4	22.4
Median		25	173.5	68	22.9
SD		2.3	7.16	8.98	2.48
Min		23	160	51	19.0
Max		30	186	85	26.3

Temporal gait feature extraction

The gait features were extracted based on the gait phase detection (heel strikes and toe offs) across two walking conditions as shown in the Fig. 2. The values of temporal features (average stride time, average step time, average stance time, average swing time, and cadence) and gait symmetry index (GSI) for respective walking conditions (with an abnormal posture, with spinal brace) are mentioned in the Table 2. The comparison of the temporal features and GSI across two walking conditions (mean ± SD) is shown in Fig. 3.

Fig. 2.

Comparison of the gait features across two walking conditions; (a) Condition 1 (Abnormal Posture), (b) Condition 2 (with spinal brace).

Table 2.

Temporal gait features across two walking conditions; C1 (condition 1): walking with an abnormal posture, C2 (condition 2): walking with a spinal brace.

Average Stride Time (s)		Average Step Time (s)		Average Stance Time (s)		Average Swing time (s)		Cadence (steps/min)		Gait Symmetry Index (GSI)
C1	C2	C1	C2	C1	C2	C1	C2	C1	C2	C1	C2
1.13	1.12	0.56	0.56	0.64	0.63	0.48	0.49	105.9	106.4	0.16	0.02
1.10	1.03	0.53	0.52	0.64	0.56	0.46	0.48	111.4	115.6	0.07	0.01
1.12	1.20	0.59	0.60	0.67	0.65	0.50	0.55	101.2	105.6	0.17	0.02
1.10	1.03	0.53	0.52	0.57	0.53	0.49	0.50	112.6	116.1	0.23	0.02
1.10	1.01	0.53	0.51	0.57	0.56	0.49	0.46	112.5	117.9	0.28	0.01
0.97	0.96	0.49	0.48	0.54	0.55	0.43	0.41	122.5	124.2	0.15	0.09
1.22	1.20	0.61	0.60	0.69	0.64	0.53	0.55	98.1	102.2	0.09	0.02
1.11	1.02	0.56	0.51	0.66	0.58	0.46	0.45	107.4	116.6	0.15	0.07
0.96	0.95	0.48	0.48	0.55	0.53	0.42	0.42	123.7	125.7	0.12	0.09
0.97	1.01	0.47	0.50	0.56	0.56	0.41	0.44	127.5	128.0	0.18	0.01
0.96	0.92	0.48	0.47	0.54	0.57	0.43	0.38	124.1	126.5	0.30	0.03
1.13	1.10	0.57	0.55	0.64	0.60	0.45	0.49	105.4	108.4	0.22	0.06

Fig. 3.

Comparison of the temporal gait parameters (mean ± SD) across two walking conditions; Condition 1 (Abnormal Posture), Condition 2 (with spinal brace); (a) Gait Symmetry Index GSI, (b) Stride time (s), (c) Step time (s), (d) Stance time (s), (e) Swing time (s), (f) Cadence.

The statistical analysis involves normality test - Lilliefors (Kolmogorov-Smirnov) to check the normal distribution of the data, where a p > 0.05 suggests the normal distribution pattern, while a p ≤ 0.05 indicates a significant deviation from normality; a paired t-test (p < 0.05) to explore the significance of the influence of spinal brace on the temporal gait parameters; and Cohen’s d to evaluate the brace’s impact on the gait. A normality test shows that the data is normally distributed for all parameters (p > 0.05) as mentioned in the Table 3. Figure 4 shows the comparison of the temporal features across two walking conditions with p-values and d-values mentioned. GSI, average stride time, average stance time and cadence shows statistically significant results (p < 0.05).

Table 3.

Statistical Analysis; Normality test - Lilliefors (Kolmogorov-Smirnov) p > 0.05, paired t-test (p < 0.05) *, Cohen’s d test.

Parameters	Average Stride Time (s)	Average Step Time (s)	Average Stance Time (s)	Average Swing Time (s)	Cadence (steps/min)	Gait Symmetry Index (GSI)
Normality test	0.50	0.24	0.50	0.50	0.39	0.23
Paired t-test	0.02*	0.10	0.01*	0.41	0.04*	0.0001*
Cohen’s d	-0.29	-0.21	-0.57	0.16	0.30	-2.54

Fig. 4.

Comparison of the temporal features between two conditions (p-values, d-values) using Paired t test and Cohen’s d effect; (a) Gait Symmetry Index GSI, (b) Stride time (s), (c) Step time (s), (d) Stance time (s), (e) Swing time (s), (f) Cadence.

Our findings suggest that the Gait Symmetry Index (GSI) significantly improved with the spinal brace (p = 0.0001, Cohen’s d = -2.54), indicating greater gait symmetry and stability, with GSI values closer to 0. Average Stride Time decreased significantly (p = 0.02, d = -0.29), showing a modest improvement in gait efficiency, while Average Step Time showed a small, non-significant reduction (p = 0.10, d = -0.21). Similarly, Average Stance Time decreased significantly (p = 0.01, d = -0.57), reflecting a moderate improvement, as participants spent less time in the stance phase when walking with the brace, but Swing Time showed no significant change (p = 0.41, d = 0.16). Moreover, Cadence increased significantly (p = 0.04, d = 0.30), indicating a faster, more efficient walking pace with the spinal brace as compared to an abnormal posture, as mentioned in the Fig. 4.

Subjective questionnaire analysis

The questionnaire was completed by the participants, titled ‘Comfort and Perception While Walking with Spinal Brace’. The purpose of this questionnaire was to evaluate the comfort and perception of walking while wearing the spinal brace as compared to walking in an abnormal posture. All the 12 participants (100%) reported that their spinal posture felt relieved when they were walking with spinal brace, 11 participants (91.7%) reported that they felt improvement in walking ability while walking with spinal brace as compared to walking with an abnormal posture. Regarding comfort, 6 participants (50%) found the brace comfortable to wear, with 6 participants (50%) felt uncomfortable while walking with spinal brace. It could be due to poor user adaptability and compliance to the brace in a short period of time. However, fitting was properly done using multiple sizes according to the participant’s physique. The reported improvements in temporal gait features could be due to better sagittal alignment of spine using the brace.

Discussion

This study compared the temporal gait parameters during walking with an abnormal posture and while wearing a spinal brace. Results showed improvements in stride time, step time, stance time, swing time, and cadence (p < 0.05) with the brace, indicating better gait symmetry, reduced postural deviation, and increased walking efficiency. These improvements likely resulted from enhanced proprioception, better trunk alignment, and postural control²⁶. Consequently, they help reduce compensatory trunk motion and plantar pressures²⁷ enabling more coordinated and symmetrical lower-limb movement²⁸.

Additionally, postural deviation (specifically slouching including slumped posture) even in simulated postures may displace the center of mass (COM) forward, resulting in the reduced stability limit and inefficient gait patterns²⁹. Wang et al. (2022) observed increased spinal angles and forward COM shift in poor posture, causing instability⁶. Our intervention (posture correction type semi-rigid thoracolumbosacral orthosis) counteracts this problem by retracting the rounded shoulders passively using shoulder harnesses³⁰ and employing a semi-rigid design in which the rigid bar is positioned on the respective curves of the spine. It creates an extension moment on the spine placing the COM within the base of support¹⁹, resulting in improved gait symmetry.

Moreover, upright trunk positioning enhances force transmission through the lower limbs, conserving energy and improving forward propulsion^31,32. Similarly, the semi-rigid thoracolumbosacral brace integrates both flexible and rigid components, allowing for adaptive contouring to the body while providing structural support to maintain spinal alignment and prevent slouching. It has greater immediate effects on mobility as compared to soft orthoses as evidenced by Karimian et al.³³. Our findings demonstrated increased cadence, reduced stride and stance times, and improved Gait Symmetry Index with the brace in comparison to walking with an abnormal posture. Improved spinal alignment likely reduced strain on the thoracolumbar region, enhanced spino-pelvic rhythm, and restored balance³⁴, resulting in more symmetrical lower-limb movement³⁵.

As hypothesized, abnormal posture was associated with slower gait, longer stride and stance times, and lower cadence, while the brace promoted efficient gait patterns. Najafi et al. (2019) found that mechanical low back pain and postural issues reduce stride length, step length, gait velocity, and cadence while increasing stride and step times³⁶. Because, poor spinal posture increases mechanical loading and muscle activation on the spine, which contributes to low back pain and altered gait mechanics³⁷. While Eldemier et al. reported that the Spinomed brace increased cadence and speed, reduced gait cycle duration, and improved pelvic symmetry in hyperkyphotic adults by restoring COM alignment¹⁹. Although there is limited evidence related to the quantitative impact of spinal brace in individuals with slumped posture on gait parameters. Moreover, the participants reported relieved spinal posture and enhanced gait efficiency while walking with spinal brace as compared to walking in an abnormal posture, with a few reported that the brace is uncomfortable, possibly due to poor compliance. One of the participants recommended that the shoulder strap should be padded more so that it might not block the blood circulation.

These improvements have significant clinical implications. The spinal brace can serve as a practical rehabilitative intervention for individuals with postural deviations or mechanical low back pain, potentially preventing secondary musculoskeletal problems associated with prolonged sitting or poor posture. Moreover, the use of a brace may enhance gait efficiency in occupational and sedentary populations by promoting awareness of proper posture and reducing mechanical load on the spine. However, long-term brace use has potential drawbacks, e.g. reduced muscle strength, dependency on the brace, and decreased spinal mobility etc. Consequently, combination of brace use and exercise have significant positive effects on spinal alignment and gait.

Despite the significant findings, this pilot study has several limitations. The posture simulation was conducted on healthy laboratory workers, rather than actual patients with postural deviations. The methodology must be validated on actual patients as this study laid the foundation by the provision of the portable IMU-based solution. Data was collected over a short period, with participants wearing the brace for 1.5 h with multiple trials recorded, which according to literature^19,33 sufficient enough for posture and gait adaptation, but longitudinal studies are recommended. Gait patterns may have been influenced by participant awareness (Hawthorne effect)³⁸. The commercial brace used was modified for comfort and proper fit, but custom designs could enhance compliance. Lastly, self-reported questionnaire responses may carry bias, though the inclusion of both subjective and objective analyses helps mitigate this limitation.

Future research should employ a longitudinal study design on patients with postural deviations ensuring long follow up periods to better understand the effect of spinal braces on the gait features. Additionally, multiple IMUs must be used at different locations (on feet, thighs and spinal region) with integration in spinal brace as well and evaluate the real time feedback of posture and gait under given conditions. The integration of EMG with IMU is highly recommended to analyze muscle activity and undergo motion analysis simultaneously, it will allow the assessment of neuromuscular adaptations while wearing brace. Furthermore, machine learning models should be employed to classify gait patterns pre- and post-intervention.

Conclusion

In conclusion, the IMU based assessment of gait showed significant improvements in the temporal features of gait while wearing the spinal brace as compared to an abnormal posture. It demonstrated measurable improvements in gait symmetry, postural alignment and walking efficiency due to proper sagittal alignment of spine. The integration of inertial sensors and posture correction system offers a portable and cost-effective solution of real time monitoring of gait and posture to ensure long-term adherence and self-management, enhancing the functional mobility in individuals with postural deviations or sedentary occupational habits.

Materials and methods

Design and participants

This randomized crossover experimental study involved gait analysis (10 m Walk Test) before and after the intervention. Initially, 20 participants were recruited, of which 5 were excluded as indicated in Fig. 1 (CONSORT diagram). A total of 15 participants completed gait trials, with 3 excluded due to noise and drift in the data. Consequently, the data for 12 participants were included in the subsequent analysis. Ethical approval was obtained from the NUST ethical committee under Ref# NUST/SMME/BMES/ETH/054912/0029 on 1st May, 2025. All methods were performed in accordance with the Declaration of Helsinki.

The inclusion criteria were; (i) Healthy laboratory workers (students, researchers, or clinical staff) with at least 6 months of experience in a laboratory environment involving prolonged sitting with no postural abnormality and back pain; (ii) age 20–40 years; (iii) signing informed consent for participating in gait trials. The exclusion criteria were; (i) no history of significant musculoskeletal or neurological disorders that could affect gait, (ii) hadn’t undergone any kind of orthopedic surgery, and (iii) haven’t used any assistive devices such as orthotics or walking aids.

Randomization as per CONSORT recommendations

Participants were provided with the detailed information about the nature of study and intervention. They were randomly assigned to the two-walking trial groups; (i) walking in an abnormal posture, (ii) walking with a spinal brace (intervention). The randomization sequence was acquired from the random.org platform. Allocation numbers were printed on blank pages and placed in sealed envelopes, each indicating the assigned groups. A random individual, not involved in the study, selected the envelopes for allocation. Due to the nature of intervention, blinding of participants was not feasible. However, procedures were followed to minimize potential bias during data collection and analysis.

Experimental setup

Two WT9011DCL Inertial Measurement Unit (IMU) sensors from Witmotion, Shenzhen, Co., Ltd. were placed on the dorsum of each foot as shown in the Fig. 5. The placement of IMU sensors on dorsum is supported by studies like³⁹, whose findings suggests that use of one pair of IMUs placed on the feet was as accurate as using a set of seven IMUs for the same application. This data was live recorded via Bluetooth through the WitMotion software. This reduced the overall costs of our work. Furthermore, the data was processed on the MATLAB (MathWorks Inc., Natick, MA). A Prefabricated/off the shelve/commercial Posture correction brace (a kind of Semi-Rigid Thoracolumbosacral Orthosis) was utilized as an intervention with multiple sizes. Posture brace (TLSO) comprised of posterior padded panel with an adjustable bar to align spine along the respective spinal curves. Additionally, it involved multiple harnesses including shoulder harnesses to help with shoulder retraction, and abdominal harnesses to increase intra-abdominal pressure, which in turn unloaded the spine and enhanced spinal stability. It also aided in maintaining proper posture by limiting forward flexion.

Fig. 5.

Experimental Setup – (a) shows 10 m walking track with timed zone from 2 m to 8 m, (b) shows an experimental set up (spinal brace), IMUs, simulated posture instructions and visual cues, post-study questionnaire), (c) shows the placement of IMUs on the dorsum of both feet, (d) shows an abnormal posture (forward flexed posture), (e) shows an erect posture using spinal brace, the line of COG displaces significantly in case of an abnormal posture; while wearing brace, it gets back to its normal alignment (f) shows x, y, z axis of IMU sensor, (g) shows semi-rigid thoracolumbosacral orthosis (spinal brace) having an abdominal belt, rigid bar to align spine and shoulder harnesses for shoulder retraction, (h) shows IMUs set up connected with the software on laptop via Bluetooth adapters.

Potential harms were identified as any adverse event including pain, skin irritation, instability, increased fatigue, or unexpected discomfort associated with brace use or gait trials. Harms were monitored non-systematically via continuous researcher observation and participant self-report following each condition. No harms occurred during the study.

Data collection protocol

The data collection protocol included two conditions with the recording of three gait trials for each condition using 10 m Walk Test: (i) walking on 10 m path with poor spinal posture (forward flexed posture) while wearing IMUs on both feet, where poor posture was simulated by asking the participants to flex their trunk (12–20 degrees of body alignment with respect to trunk). This was performed in the presence of a professional orthotist; (ii) walking on 10 m path with spinal brace while wearing IMUs on both feet. The protocol is shown in the Fig. 5. Each participant completed three trials for each of the two walking conditions (with and without the brace), constituting six trials for each participant. Each trial lasted approximately 1–2 min. Total session duration, including sensor setup, posture guidance and simulation, was approximately 25–30 min with additional one hour of adaptation time for the brace.

Data analysis

Before the collection of data, each IMU sensor was calibrated within the WitMotion software. The raw csv data files were composed of timestamps, acceleration (X, Y, Z), gyroscope data (X, Y, Z), Orientation angles (X, Y, Z) and a port identifier. Data was separated into left and right foot recordings based on COM port numbers where, COM3 data was for right foot and COM4 data was for left foot. Visual data (photos) were stored with the placement of the line of COG in each of the conditions.

Signal pre-processing

Gait data has been visualized in MATLAB R2017a (MathWorks Inc., Natick, MA). One trial was selected for data analysis among the three trials for each condition, based on the quality of data and presence or absence of noise and drift. The data from first 2 m and last 2 m was removed for further feature extraction to avoid inclusion of incomplete steps in the starting and ending point, 6 meters’ data was selected for further analysis in all subsequent trials. Low pass butter-worth filter with cut off frequency of 6 Hz has been applied on the accelerometer and gyroscope data. The filtered AccZ has been plotted separately for each foot.

Gait feature extraction

After the signal pre-processing, Peak and Valley Detection method^40–42 was employed to mark heel strikes and toe offs. Based on our data, the gait phases and sub phases were estimated as shown in the Fig. 6. The acceleration signal (axis z) is shown against time (s). The right foot gait cycle is demonstrated, starting with the initial contact of right foot and concluding with the next initial contact of right foot (one stride). It starts with the initial contact of right foot marked with the peak followed by the loading response where the decelerations start as the foot is landed on the ground, the signal is steady at the moment showing mid stance. In the meantime, the contralateral foot (left foot) is undergoing swing phase.

Fig. 6.

Gait cycle events identified according to peak and valley detection method. A stride begins at the initial contact (heel strike), marked by a distinct peak in acceleration. This is followed by the loading response and midstance, during which the signal typically becomes flat, and body weight is transferred. The transition to terminal stance produces a valley indicating pre-swing (toe-off). The initial swing phase is characterized by a rapid change in acceleration forming a peak, followed by mid-swing and terminal swing. The cycle completes with the heel strike of the same foot (right foot in this case). Overall, the stance phase accounts for approximately 60%, while the swing phase composes of 40%, making up a complete gait cycle.

According to the gait phase detection, the temporal gait features were extracted using custom code on MATLAB in the given two conditions (abnormal posture, with spinal brace). They included Stride time (s), Step time (s), Stance time (s), Swing time (s), Cadence (step/min) and Gait Symmetry Index (GSI). The stride time is the time from the initial contact of one foot to the initial contact of the same foot. The step time is the time from the initial contact of one foot to the initial contact of contralateral foot. The stance time is the time when foot is on the ground (initial contact to toe off) while swing phase is the time when the foot is not on the ground (toe off to initial contact). Cadence is the number of steps per minute. Gait Symmetry Index (GSI) measures symmetry between left and right limb measurements. Based on average step times of the both feet, gait symmetry index (GSI)^43–45 was calculated by the following formula:

Statistical analysis

The statistical methods involved descriptive statistics (mean, SD), inferential statistics (paired t-test for significance and Cohen’s d for effect size). The purpose of paired t-test was used to compare temporal features before and after the intervention on the same subjects. It checked whether the mean difference is statistically significant. The alpha (α) value was 0.05. The null hypothesis (H₀) means no difference in the variables (brace has no effect) and if p-value < 0.05, it means there is a significant difference (brace is effective). The purpose of Cohen’s d test was to measure the effect size (magnitude of difference) between pre and post-intervention gait temporal values. The d values around 0.2 indicated small effect, 0.5 as moderate effect, > 0.8 as large effect. A higher d was considered to have stronger impact of the spinal brace on improving symmetry and gait parameters.

Author contributions

All the authors – Zahra Gull, Khalid Ansari, Asim Waris, Xing Chen, Muhammad Anees Ijaz, Muhammad Jawad Khan, Muhammad Adeel Ijaz, Falah Alanazi, Syed Omer Gilani – were actively involved in manuscript drafting, literature review, data collection, and data analysis, and contributed equally to the completion of this study.

Funding

This work was supported by an Institutional Research Grant (IRG: MHS_001-2025), Liwa University, Abu Dhabi, and the National Key Research and Development Program of China (2024YFC2418800).

Data availability

The data supporting the findings of this study are fully included in the manuscript.

Declarations

Competing interests

The authors declare no competing interests.

Ethical approval

The data collection and experimental protocol received approval from the NUST ethical committee under Ref# Ref# NUST/SMME/BMES/ETH/054912/0029.

Informed consent

Written informed consent was obtained from all participants for participation in the study and for the publication of any potentially identifiable data and images in this open-access journal.