Effect assessment of lifted orthopaedic shoes on gait in people with leg length discrepancy using a deep learning gait detection in the customisation process

doi:10.21203/rs.3.rs-5315073/v1

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Research Article

Effect assessment of lifted orthopaedic shoes on gait in people with leg length discrepancy using a deep learning gait detection in the customisation process

https://doi.org/10.21203/rs.3.rs-5315073/v1

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Background

Gait deviation by leg length discrepancy limits development of motor skills and causes lower limb injuries and pains. Orthopaedic shoes (OSs) have been a widely used treatment for the gait problems. However, their effects on gait performance at their customisation have not been widely studied due to the high complexity and cost in measuring and analysing gait changes. It misses an opportunity for providing optimally lifted OSs to individuals. This study therefore aimed to assess the effects of OSs through simple gait pattern analysis using a vision-based deep learning approach and provide a useful guideline for their customisation.

Methods

Sixteen participants, having the left leg short, underwent walking on straight paths with and without their trial OSs, initially lifted for equalising bilateral leg lengths. The vision-based deep learning model was employed to extract spatiotemporal gait parameters from the participant’s gait videos. Using the parameters, we examined pattern changes between the left and right gaits in terms of harmony, symmetry, regularity, and stability defined in this study. The gait pattern changes were evaluated using paired t-tests.

Results

With the trial OSs, significant improvement (p<0.05) of the gait harmony was shown in the left gait. Conflicting pattern changes between the left and right gaits were observed in the gait symmetry and regularity analyses. The gait symmetry was significantly increased for step length (p<0.05) whereas decreased for step phase (p<0.05) with high variation and considerable gaps in the changes. The left gait became more regular with the increase in step length (p<0.01) and phase on the contrary to the right gait. Regarding step phase, the overall gait regularity was significantly decreased (p<0.05). The gait stability also showed a decreasing tendency. The overall gait performance with the trial OSs was counted as suboptimal, in which further individually-differentiated correction is required in their customisation.

Conclusions

This study raised additional considerations of examining individual gait performance when customising OSs and provided an avenue to develop evidence-based customisation strategies. The gait pattern analysis using a vision-based deep learning approach can be suggested as a feasible method for effective customisation of optimally corrected OSsfor gait rehabilitation.

Leg length discrepancy

Orthopaedic shoes

Evidence-based customisation

Deep learning

Gait pattern analysis

Gait rehabilitation

Leg length discrepancy (LLD) is a condition that the paired limb/leg lengths are unequal. It is a common cause of lower limb injuries and pains, which almost 40–70% of the human population are affected by [1, 2, 3, 4]. LLD is classified into two groups that often present together: a structural LLD is a fixed osseous malformation, and a functional LLD is secondary to asymmetrical pronation of foot and/or soft tissue contractures of the lower limb and spine [1]. Although there is lack of consensus on the magnitude grade of LLD regarding diagnosis and treatment, many studies have demonstrated that LLD is significantly associated with functional and pathological disorders during walking [2, 5, 6, 7, 8]. People with LLD > = 2cm commonly have knee problems on their both legs while those with LLD < = 1cm are more likely to have the development of knee pain and osteoarthritis on their short leg [9]. The discrepancy greater than 1cm is typically accompanied with structural changes of the lumbar spine [10] and associated with the development of lower back pain [11] as well as plantar foot pain [12]. Taken together, LLD promotes the development of abnormal gaits, biomechanical disorders, and pathologies with pains [6, 7, 13, 14, 15, 16].

In preventing such gait problems caused by LLD, orthopaedic shoes (OSs) are the most widely used treatment as they are non-invasive, accessible, and customisable for individuals [11, 13, 14, 17, 18, 19, 20]. Custom-made OSs involve the modification of orthotic shoe soles (insole and outsole) for lifting foot and benefit patients with a wide variety of complications associated with LLD. This compensatory strategy is considered to restore body’s gravity balance, reduce body’s energy expenditure, support biomechanical functions, and soothe pains during walking [19, 21, 22, 23].

Despite the use of OSs is obvious as the initial protocol for LLD treatment, there has not been a useful guideline in customising OSs that can induce optimal gait performance for individuals [20]. The design of OSs has been usually based on the clinical information on foot conditions and focused on equalising the bilateral leg lengths and providing the symmetrical appearance when standing [7, 24]. It has taken no account of walking dynamics or patterns affected by individual motion adaptabilities to different challenges of OSs [20, 22, 25]. Such conventional procedure can produce inappropriate OSs and potentially bring unexpected damages to individuals from the long-term use. This may underpin that the effect assessment of OSs on gait in the literature has been inconsistent with the difference in OS types including orthotic insoles and outsoles [19]. Therefore, it is essential to examine how individual gait patterns are changed by OS for assessing its suitability or optimality for healthier gait performance at its customisation.

Gait patterns can be defined in various ways using gait parameters such as spatiotemporal (e.g. length and phase), kinematic (e.g. range of motion and joint angle), and kinetic (e.g. pressure and ground reaction force) parameters. As shown in Fig. 1, the cyclic gait parameters (e.g. step, stance, swing, etc) are defined by the gait events (heel strike and toe off). In general, these gait parameters are measured using specific tools such as motion capture systems and force plates. Most of studies assessing the effects of foot orthoses including OSs on gait have required a pre-set time and space for using the tools in obtaining biomechanical gait characteristics [19, 20, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36].

However, the high complexity and cost of the tool settings and measurements by professionals made it difficult for individuals not only to routinely examine the change of gait patterns but also to assess the effect of OSs. The assessment evidence in the literature was also not clear with different findings on different gait parameters and measures for the gait pattern analysis [19]. Attempting to address these issues, a vision-based deep learning approach, CNN-transformer hybrid network, was developed to detect the gait events and extract the spatiotemporal parameters upon a walking video [37]. This model was designed to allow individuals to simply examine gait patterns without any specific settings and professionals; thus, it can facilitate effective and efficient analysis of gait patterns and immediate effect assessment of OSs.

Overall, the objective of this study was to examine the change of gait patterns and assess the immediate effect of trial OSs, initially lifted for the symmetrical appearance of both leg lengths, in the early process of its customisation. For the gait pattern analysis using the CNN-transformer hybrid network, we investigated and defined gait pattern metrics with mainly available analytical pattern indices (harmony, symmetry, regularity, and stability) and the most suitable measures for each index. The metrics were consolidated to characterise gait performance, delineating pattern differences of walking without and with the trial OSs. For assessing the trial OS effects on gait performance, we compared the changes in gait patterns between the short and long legs, exploring how they affect the overall gait performance in the light of optimisation.

Subjects

Data of patients with LLD were collected from Department of Rehabilitation Medicine, Chungnam National University in South Korea. The patients were clinically diagnosed with LLD, being assessed and given the magnitude difference between the left and right lower limbs. The differentiation of their structural LLD was clarified using a scenography. The exact length of the femur and tibia was examined using their lower limb X-rays and angulometer measuring the limb length from the ground to the highest point of the iliac crest of pelvic bone.

A participating group were recruited from the university hospital for this study. Among them, a majority of participants (16) having left leg short were included in this study to examine the change direction of gait patterns by their trial OS given for their left leg length adjustment. They consisted of 8 males and 8 females in a range of age 7–32 (14.6 ± 6) years old (2 children, 11 adolescents, and 3 young adults) with 153.44 ± 11.94 cm height and 2.4 ± 0.65 cm shorter on the left leg. Regarding age, the participants could be grouped as young individuals (< 35 years old) as having similar gait adaptabilities in daily and social activities that considerably differ from old individuals [38, 39]. The OS effects on gait performance might also be more specific for the young participants as they could be more responsive to the change of body motions and functions [23]. They were all satisfied the inclusion criteria of this study: (1) 1cm < = LLD < = 4cm (mild LLD not requiring surgery [2]); (2) no prior correction or surgical operation of LLD; (3) capable of walking on their own without any other diseases that affected walking ability. The written informed consents were obtained from all participants and legal guardians for children and adolescents aged 7–18, under Institutional Review Board approval.

Procedures

A custom-made trial OS such as shown in Fig. 2 was provided to each participant for the purpose of gait rehabilitation. Every OS was designed by an orthotist based on the information of negative foot casts for foot shape and contours and biomechanical assessment of static foot and lower limb from the Biomechanics Lab. Using an extrinsically lifted shoe with outsole (including their ordinary footwear), insole was fabricated and fitted intrinsically to give the symmetrical appearance of the bilateral leg lengths.

The 16 participants were invited to the Biomechanics Lab and made their usual walk around 6m straight paths. A digital camera with 60 Hz frame rate was set to film their walking. Two gait videos for each participant were collected walking without and with their custom-made OS on trial.

Deep learning model

The collected videos for the 16 participants were given to the deep learning model, CNN-transformer hybrid network [37]. This vision-based gait analysis detected the gait events (heel strike and toe off) from a gait video with the F1 score of 0.992 (refer to Fig. 3). The model extracted the gait parameters described in Fig. 1 with their spatial information in length and width (cm) and temporal information in time (second) and phase (%) for the left and right gaits. Additionally, the information of gait velocity and cadence was also obtained for the change evaluation of gait patterns.

Gait pattern metrics

The spatiotemporal parameters were used to measure gait patterns in terms of harmony, symmetry, regularity, and stability. In this study, we investigated and defined these four indices of gait patterns with their measuring methods. The interest for these metrics was justified by their potential role with their unique contribution in analysing gait patterns. The overall gait performance was identified by consolidating the metrics and presenting the pattern changes between the left and right gaits, taking their optimal balance into consideration.

Harmony

The gait harmony (H) represents bilateral rhythmicity or smoothness of gait movement along with the whole-body balance. It is defined by two harmonic ratios in the following equations using the temporal gait parameters of stride, stance, and swing in phase (%) for the left and right gaits [30, 35, 36, 40, 41, 42]:

$\:H1=\frac{\text{S}\text{t}\text{a}\text{n}\text{c}\text{e}\:\text{P}\text{h}\text{a}\text{s}\text{e}}{\text{S}\text{w}\text{i}\text{n}\text{g}\:\text{P}\text{h}\text{a}\text{s}\text{e}}\:\:for\:the\:left\:and\:right\:gaits$ , and (1)

$$\:H2=\frac{\text{S}\text{t}\text{r}\text{i}\text{d}\text{e}\:\text{P}\text{h}\text{a}\text{s}\text{e}\:}{\text{S}\text{t}\text{a}\text{n}\text{c}\text{e}\:\text{P}\text{h}\text{a}\text{s}\text{e}\:}\:for\:the\:left\:and\:right\:gaits$$

The harmonic properties were derived from the golden ratio (= 1.618), which the proportion between the shorter part to the longer one is the same as the longer part to the whole, defined by Euclid in the third century BC [43]. Considering a gait cycle on a straight line, the short part is swing whereas the longer one is stance for the whole, stride. With the relevance between these three parameters for a gait cycle, the normal phase of stride, stance, and swing are respectively defined as 100%, 62% and 38% in the literature [35, 41, 43]. The harmonic ratios in Equations (1) and (2) are expected to be closer to the golden ratio if a gait is normal and healthy. Thus, the gait harmony can act as an indicator for the efficiency of motor control in performing gaits [35, 36, 41].

Symmetry

The gait symmetry (S) describes similarity in the bilateral behaviour of both legs during walking. It is defined by two symmetric ratios in the following equations using the spatiotemporal information of contralateral steps in length (cm) and phase (%) [18, 27, 29, 44]:

$\:S1=\frac{\text{B}\text{i}\text{g}\text{g}\text{e}\text{r}\:\text{S}\text{t}\text{e}\text{p}\:\text{L}\text{e}\text{n}\text{g}\text{t}\text{h}\:}{\text{S}\text{m}\text{a}\text{l}\text{l}\text{e}\text{r}\:\text{S}\text{t}\text{e}\text{p}\:\text{L}\text{e}\text{n}\text{g}\text{t}\text{h}}\:between\:the\:left\:and\:right\:gaits$ , and (3)

$$\:S2=\frac{\text{B}\text{i}\text{g}\text{g}\text{e}\text{r}\:\text{S}\text{t}\text{e}\text{p}\:\text{P}\text{h}\text{a}\text{s}\text{e}\:}{\text{S}\text{m}\text{a}\text{l}\text{l}\text{e}\text{r}\:\text{S}\text{t}\text{e}\text{p}\:\text{P}\text{h}\text{a}\text{s}\text{e}}\:between\:the\:left\:and\:right\:gaits.$$

There are various ways in measuring the gait symmetry: symmetry ratio, symmetry index, gait asymmetry, ratio index, and symmetry angle. Among them, the symmetry ratio was recommended for the standard use with its simplicity, lowest variability, and easiest interpretation [29]. This study used the symmetry ratio based on contralateral steps as they are only the independently successive parameters in measuring the symmetry between the left and right gaits. The perfect symmetry value is 1, and the symmetry in Equations (3) and (4) can be greater than 1 if asymmetric. Thus, the value gap from 1 can indicate how much a gait is asymmetric and leans on the side (left or right) with bigger value. A normal gait for young individuals was found to be fairly symmetric if the ratios are within a range of up to 4–6% asymmetry [45, 46]. The symmetry properties can reflect the central distribution of mechanical loads in performing gaits [22].

Regularity

The gait regularity (R) implies consistency of successive and/or contralateral steps of the left and right gaits. In this study, it is formulated by coefficient of variation or variability ratio and defined by three regularity ratios in the following equations using the spatiotemporal information of steps in length (cm) and phase (%) [27, 44, 47]:

$$\:R1=1-\:\frac{\text{S}\text{D}\:\left(\text{S}\text{t}\text{e}\text{p}\:\text{L}\text{e}\text{n}\text{g}\text{t}\text{h}\right)}{\text{M}\text{E}\text{A}\text{N}\:\left(\text{S}\text{t}\text{e}\text{p}\:\text{L}\text{e}\text{n}\text{g}\text{t}\text{h}\right)}\:for\:the\:left\:and\:right\:gaits$$

$\:R2=1-\:\frac{\text{S}\text{D}\:\left(\text{S}\text{t}\text{e}\text{p}\:\text{P}\text{h}\text{a}\text{s}\text{e}\right)}{\text{M}\text{E}\text{A}\text{N}\:\left(\text{S}\text{t}\text{e}\text{p}\:\text{P}\text{h}\text{a}\text{s}\text{e}\right)}\:for\:the\:left\:and\:right\:gaits$ , and (6)

$$\:R3=1-\:\frac{\:\left|\text{L}\text{e}\text{f}\text{t}\:\text{S}\text{t}\text{e}\text{p}\:\text{P}\text{h}\text{a}\text{s}\text{e}-\text{R}\text{i}\text{g}\text{h}\text{t}\:\text{S}\text{t}\text{e}\text{p}\:\text{P}\text{h}\text{a}\text{s}\text{e}\right|}{\text{M}\text{A}\text{X}\:\left(\text{L}\text{e}\text{f}\text{t}\:\text{S}\text{t}\text{e}\text{p}\:\text{P}\text{h}\text{a}\text{s}\text{e},\:\:\:\text{R}\text{i}\text{g}\text{h}\text{t}\:\text{S}\text{t}\text{e}\text{p}\:\text{P}\text{h}\text{a}\text{s}\text{e}\right)}\:between\:the\:left\:and\:right\:gaits.\:\:\:\:\:\:\:\:\:\:\:\:$$

The perfect regularity value is 1, and the regularities in Equations (5)–(7) can be less than 1 if irregular. Thus, the value gap from 1 can indicate how much a gait is irregular or diverse. A gait is more regular if the ratios are closer to 1 with the lower variability for the left and right steps (Equations (5) and (6)) and between both steps (Eq. (7)). The terms, symmetry and regularity are often interchangeably used in the interpretation of gait patterns. However, there is a distinction between the two terms with their different measurements that can provide more unique and deeper information about gait patterns. The regularity or variability properties can play a role for estimating power balance or force capacity of the lower limb muscles generated while performing gaits [44].

Stability

The gait stability expresses gait repeatability and energy expenditure of the body. It can be defined by the walk ratio (W), which is a ratio of step length (cm) to cadence (step frequency or rate, steps/min), in the following equation [48, 49, 50, 51, 52]:

$$\:W=\frac{\text{T}\text{o}\text{t}\text{a}\text{l}\:\text{S}\text{t}\text{e}\text{p}\:\text{L}\text{e}\text{n}\text{g}\text{t}\text{h}\:}{\text{C}\text{a}\text{d}\text{e}\text{n}\text{c}\text{e}\:}\:for\:the\:left\:and\:right\:gaits\:(cm/(steps/min\left)\right)\:$$

The normal walk ratio for young individuals was found constant at the value of 0.65 ± 0.08 cm/(step/min), which is reliable as invariant with gait speed [28, 50, 53]. Thus, unstable gaits can be identified by the value of W in Eq. (8) whether it is lower (shorter step length with higher step rate) or higher (longer step length with lower step rate) than the normal value, specified as 0.65 cm/(step/min) [48, 53, 54]. As the walk ratio is related to the amplitude and frequency of leg movement in the forward progression of body, it can represent the temporal and spatial gait coordination [28, 42, 53]. This makes the gait stability is used to indicate the upper body balance as well in performing gaits [53]. Therefore, gait patterns with an invariant walk ratio can further characterise the optimal gait performance.

Statistical analysis

Although such gait pattern analysis should be performed for each individual in the actual customisation process of their own OS, this study was set to analyse the extent of gait pattern changes by the trial OSs among the participants. Thus, the spatiotemporal parameters used in this study were averaged across the gait cycles of each participant from walking without and with their trial OS. The pattern indices were also averaged for each participant in the gait pattern analysis (four indices with twelve measures in total). The pattern differences of the left and right gaits - four harmony measures, two symmetry measures, five regularity measures, and a stability measure – in the 16 participants were compared to assess their gait performance and the effects of their trial OS. Paired t-tests (one-tailed for the positive change direction) were used to evaluate the changes in gait patterns with the significance level at less than 0.1.

Gait parameter change

Using the vision-based deep learning model, the spatiotemporal gait parameters for the 16 participants were extracted from walking without and with their trial OSs. Table 1 presents mean and standard deviation (SD) of each parameter, including gait velocity and cadence, for the left and right gaits. The changes of each parameter by the trial OSs were also expressed with significance, p value.

Table 1

Mean and SD of the left and right gait parameters for walking without and with the trial OSs and significance (p value) of the gait pattern changes (P* < 0.1, p** < 0.05, p*** < 0.01, p**** < 0.001; (-) decreased value).
Parameter	Gait	Without OS (mean ± sd)	With OS (mean ± sd)	P value
Step Length (cm)	Left	32.91 ± 4.18	34.24 ± 4.02	< 0.001****
Step Length (cm)	Right	35.63 ± 4.07	36.21 ± 3.86	0.089*
Step Phase (%)	Left	50.32 ± 1.02	50.01 ± 1.76	(-) 0.224
Step Phase (%)	Right	49.82 ± 0.68	50.06 ± 1.08	0.160
Stance Phase (%)	Left	65.26 ± 1.15	64.49 ± 1.53	(-) 0.020**
Stance Phase (%)	Right	65.34 ± 0.72	65.04 ± 0.91	(-) 0.136
Swing Phase (%)	Left	34.74 ± 1.15	35.51 ± 1.53	0.020**
Swing Phase (%)	Right	34.66 ± 0.72	34.96 ± 0.91	0.136
Velocity (cm/s)	Left	59.15 ± 7.72	63.78 ± 8.50	< 0.001****
Velocity (cm/s)	Right	64.77 ± 7.72	67.55 ± 7.06	0.020**
Cadence (steps/min)	Left	108.00 ± 7.71	111.94 ± 9.14	0.027**
Cadence (steps/min)	Right	109.13 ± 7.31	112.19 ± 7.58	0.020**

From the gait analysis shown in Table 1, the participant gaits were found as relatively abnormal by longer stance phases (64–66.5% without and 63–66% with trial OS) and shorter swing phases (33.5–36% without and 34–37% with trial OS) than a normal gait, which stance and swing phases are accordingly 62% and 38% within a stride (100%) [35, 41, 43]. When walking with the trial OSs, stance phases were decreased whereas swing phases were increased for both left and right gaits towards normal. The left gaits showed the significantly positive changes in both stance and swing phases with p < 0.05.

The left and right step lengths were also significantly increased by the trial OSs with p < 0.001 and p < 0.1, respectively. On the other hand, step phases were decreased for the left gaits whereas increased for the right ones, showing both insignificant changes. The individual changes of step length and phase are respectively presented in Fig. 4 (a) and (b). They show low variation in the step length changes (mostly paired with increases especially for the left gaits) but high variation with considerable gaps (0.7-5.0%) in the step phase changes (almost all paired with mixed increase and decrease between the left and right gaits). The step length and phase changes resulted in the significant increases of both gait velocity and cadence.

Gait pattern change

The gait patterns with four indices – harmony, symmetry, regularity, and stability - defined in this study were analysed using the gait parameters in Table 1. The pattern changes for every index (by equations) is displayed with significance (p value) in Table 2. The change details for each index are further described in the following.

Table 2

Significance (p value) of the gait pattern changes by the trial OSs for each index (P* < 0.1, p** < 0.05, p*** < 0.01, p**** < 0.001; (-) decreased value).
Index	Left/Right	Equation	P value
Harmony	Left	(1) $\:H1=\frac{\text{L}\text{e}\text{f}\text{t}\:\text{S}\text{t}\text{a}\text{n}\text{c}\text{e}\:\text{P}\text{h}\text{a}\text{s}\text{e}}{\text{L}\text{e}\text{f}\text{t}\:\text{S}\text{w}\text{i}\text{n}\text{g}\:\text{P}\text{h}\text{a}\text{s}\text{e}}$	0.022**
	Left	(2)$\:\:H2=\frac{\text{L}\text{e}\text{f}\text{t}\:\text{S}\text{t}\text{r}\text{i}\text{d}\text{e}\:\text{P}\text{h}\text{a}\text{s}\text{e}\:}{\text{L}\text{e}\text{f}\text{t}\:\text{S}\text{t}\text{a}\text{n}\text{c}\text{e}\:\text{P}\text{h}\text{a}\text{s}\text{e}\:}$	0.018**
	Right	(1) $\:H1=\frac{\text{R}\text{i}\text{g}\text{h}\text{t}\:\text{S}\text{t}\text{a}\text{n}\text{c}\text{e}\:\text{P}\text{h}\text{a}\text{s}\text{e}}{\text{R}\text{i}\text{g}\text{h}\text{t}\:\text{S}\text{w}\text{i}\text{n}\text{g}\:\text{P}\text{h}\text{a}\text{s}\text{e}}$	0.156
	Right	(2)$\:\:H2=\frac{\text{R}\text{i}\text{g}\text{h}\text{t}\:\text{S}\text{t}\text{r}\text{i}\text{d}\text{e}\:\text{P}\text{h}\text{a}\text{s}\text{e}\:}{\text{R}\text{i}\text{g}\text{h}\text{t}\:\text{S}\text{t}\text{a}\text{n}\text{c}\text{e}\:\text{P}\text{h}\text{a}\text{s}\text{e}\:}$	0.146
Symmetry	Left & Right	(3) $\:S1=\frac{\text{B}\text{i}\text{g}\text{g}\text{e}\text{r}\:\text{S}\text{t}\text{e}\text{p}\:\text{L}\text{e}\text{n}\text{g}\text{t}\text{h}\:\text{b}\text{e}\text{t}\text{w}\text{e}\text{e}\text{n}\:\text{L}\text{e}\text{f}\text{t}\:\text{a}\text{n}\text{d}\:\text{R}\text{i}\text{g}\text{h}\text{t}\:}{\text{S}\text{m}\text{a}\text{l}\text{l}\text{e}\text{r}\:\text{S}\text{t}\text{e}\text{p}\:\text{L}\text{e}\text{n}\text{g}\text{t}\text{h}\:\text{b}\text{e}\text{t}\text{w}\text{e}\text{e}\text{n}\:\text{L}\text{e}\text{f}\text{t}\:\text{a}\text{n}\text{d}\:\text{R}\text{i}\text{g}\text{h}\text{t}}$	0.039**
Symmetry	Left & Right	(4) $\:S2=\frac{\text{B}\text{i}\text{g}\text{g}\text{e}\text{r}\:\text{S}\text{t}\text{e}\text{p}\:\text{L}\text{e}\text{n}\text{g}\text{t}\text{h}\:\text{b}\text{e}\text{t}\text{w}\text{e}\text{e}\text{n}\:\text{L}\text{e}\text{f}\text{t}\:\text{a}\text{n}\text{d}\:\text{R}\text{i}\text{g}\text{h}\text{t}\:}{\text{S}\text{m}\text{a}\text{l}\text{l}\text{e}\text{r}\:\text{S}\text{t}\text{e}\text{p}\:\text{L}\text{e}\text{n}\text{g}\text{t}\text{h}\:\text{b}\text{e}\text{t}\text{w}\text{e}\text{e}\text{n}\:\text{L}\text{e}\text{f}\text{t}\:\text{a}\text{n}\text{d}\:\text{R}\text{i}\text{g}\text{h}\text{t}}$	(-) 0.018**
Regularity	Left	(5) $\:R1=\frac{\text{S}\text{D}\:\left(\text{L}\text{e}\text{f}\text{t}\:\text{S}\text{t}\text{e}\text{p}\:\text{L}\text{e}\text{n}\text{g}\text{t}\text{h}\right)\:}{\text{M}\text{E}\text{A}\text{N}\:\left(\text{L}\text{e}\text{f}\text{t}\:\text{S}\text{t}\text{e}\text{p}\:\text{L}\text{e}\text{n}\text{g}\text{t}\text{h}\right)}$	0.009***
	Left	(6) $\:R2=\frac{\text{S}\text{D}\:\left(\text{L}\text{e}\text{f}\text{t}\:\text{S}\text{t}\text{e}\text{p}\:\text{P}\text{h}\text{a}\text{s}\text{e}\right)}{\text{M}\text{E}\text{A}\text{N}\:\left(\text{L}\text{e}\text{f}\text{t}\:\text{S}\text{t}\text{e}\text{p}\:\text{P}\text{h}\text{a}\text{s}\text{e}\right)}$	0.123
	right	(5) $\:R1=\frac{\text{S}\text{D}\:\left(\text{R}\text{i}\text{g}\text{h}\text{t}\:\text{S}\text{t}\text{e}\text{p}\:\text{L}\text{e}\text{n}\text{g}\text{t}\text{h}\right)\:}{\text{M}\text{E}\text{A}\text{N}\:\left(\text{R}\text{i}\text{g}\text{h}\text{t}\:\text{S}\text{t}\text{e}\text{p}\:\text{L}\text{e}\text{n}\text{g}\text{t}\text{h}\right)}$	(-) 0.306
	right	(6) $\:R2=\frac{\text{S}\text{D}\:\left(\text{R}\text{i}\text{g}\text{h}\text{t}\:\text{S}\text{t}\text{e}\text{p}\:\text{P}\text{h}\text{a}\text{s}\text{e}\right)}{\text{M}\text{E}\text{A}\text{N}\:\left(\text{R}\text{i}\text{g}\text{h}\text{t}\:\text{S}\text{t}\text{e}\text{p}\:\text{P}\text{h}\text{a}\text{s}\text{e}\right)}$	(-) 0.488
	Left & Right	(7) $\:R3=\frac{\:\|\text{L}\text{e}\text{f}\text{t}\:\text{S}\text{t}\text{e}\text{p}\:\text{P}\text{h}\text{a}\text{s}\text{e}-\text{R}\text{i}\text{g}\text{h}\text{t}\:\text{S}\text{t}\text{e}\text{p}\:\text{P}\text{h}\text{a}\text{s}\text{e}\|}{\text{M}\text{A}\text{X}\:(\text{L}\text{e}\text{f}\text{t}\:\text{S}\text{t}\text{e}\text{p}\:\text{P}\text{h}\text{a}\text{s}\text{e},\:\:\:\text{R}\text{i}\text{g}\text{h}\text{t}\:\text{S}\text{t}\text{e}\text{p}\:\text{P}\text{h}\text{a}\text{s}\text{e})}$	(-) 0.018**
Stability	Left & Right	(8) $\:W=\frac{\text{T}\text{o}\text{t}\text{a}\text{l}\:\text{S}\text{t}\text{e}\text{p}\:\text{L}\text{e}\text{n}\text{g}\text{t}\text{h}\:\text{o}\text{f}\:\text{L}\text{e}\text{f}\text{t}\:\text{a}\text{n}\text{d}\:\text{R}\text{i}\text{g}\text{h}\text{t}\:}{\text{C}\text{a}\text{d}\text{e}\text{n}\text{c}\text{e}\:}$	(-) 0.413

Harmony

The harmonic ratios between stride, stance, and swing for the left and right gaits showed the positive changes closer to the golden ratio (= 1.618) [43] with the trial OSs (refer to Fig. 5). As seen in Table 2, the left gait harmony was significantly increased with p < 0.05 while the right gait one was increased without significance. This is directly related to the shorter stance and longer swing changes of the left gaits than the right ones as seen in Table 1. With the trial OSs, the participants were able to perform more rhythmic gaits for both legs.

Symmetry

The symmetry ratios of both step length and phase showed the significant but conflicting changes (p < 0.05) by the trial OSs. Figure 6 shows that the gait symmetry in step lengths (Eq. (3)) was significantly increased (closer to the perfect symmetry = 1), getting in the fairly symmetric range (within 4–6% from 1) [45, 46]. On the contrary, the gait symmetry in step phases (Eq. (4)) was significantly decreased (further away from the perfect symmetry = 1). As seen the opposite changes in step phase for each of the left and right gaits (refer to Fig. 4 (b)), the bilateral behaviour of both legs became less similar during walking with the trial OSs.

Regularity

The regularity ratios also showed the contrary changes between the left and right gaits. As shown in Fig. 7, the regularity ratios for both step length and phase were increased for the left gaits (p < 0.01 for step length) whereas decreased for the right gaits. This might also be related to the negative changes in step length with relatively bigger gaps for the right gaits. The inconsistent pattern changes especially in step phase between the left and right gaits in Fig. 4 (b) resulted in the significant decrease (p < 0.05) of the overall regularity (R3 in Eq. (7)). This showed that the right gaits in particular became much more irregular while making the left gaits more regular by the trial OSs.

Stability

Based on the spatiotemporal information of step, both gait velocity and cadence were significantly increased for almost all participants with p < 0.01 and p < 0.05, respectively (see Table 1). On the other hand, the walk ratio for the young participants was further away from the normal value, 0.65 cm/(step/min) [28, 50, 53]. Figure 8 shows a moderately positive correlation ((a) r = 0.52 without and (b) r = 0.46 with the trial OSs) between walk ratio and gait velocity. However, the increase of gait velocity had no apparent correlation (r = 0.07) with walk ratio as shown in Fig. 9. Although the stability showed a decreasing tendency in average (from 0.6352 to 0.6338), the individual Ws still stayed within the fairly stable range of 0.65 ± 0.08 cm/(step/min), not requiring much more energy for performing gaits with the trial OSs.

Effects of the trial OSs

The effects of the trial OSs were assessed by evaluating the gait performance based on the gait pattern analysis with the indices – harmony, symmetry, regularity, and stability. The trial OSs could support the participants to perform their gaits more rhythmically and keep their walking fairly stable. However, most of them could not allow the participants to optimally control their steps to be symmetric and regular as their full-length correction for the left leg brought statistically significant negative effects on the right gaits while empowering the left gaits with the positive effects.

There was limited understanding of the effects of lifted OSs on gait performance and how it should be considered in relation to gait pattern changes for better treatment support. LLD should be treated operatively by considering the changes of dynamic motor functions of the lower limbs (walking patterns) [55]. The conservative treatment such as lifted OSs is crucial to deliver good walking capabilities, which individually differ according to what an OS is provided. With the concerns, this study analysed the gait pattern changes in people with LLD, commonly having the left leg short, by their trial OSs in the customisation process. The gait pattern analysis allowed to evaluate the gait performance using the consolidated metrics, which in turn to assess the effects of the trial OSs. The metrics provided their unique information differentiating the pattern changes in the left and right gaits and various aspects in customising appropriate Oss.

The gait abnormality based on stance and swing phases was also identified with the harmonic ratios obtained between 1.8 and 1.9 in average for both left and right gaits without and with the trial OSs. The harmonic ratio between stance and swing phases for healthy young individuals was found as 1.629 ± 0.173, approximating its normal range, 1.5–1.8 [41]. Referring to the normal range, the participant gaits would not be seriously abnormal as gaits in people with other specific pathologies. For instance, the harmonic ratio of Parkinson’s disease was studied with a greater extent to 2.12 (stance: swing = 68%: 32%) [56]. In regard to speed, stance phase was found directly correlated with walking speed as decreasing with fast walking (e.g. 63% at 1m/s and 59% at 1.9m/s) [57, 58]. However, gait performance can be disharmonised by fast walking if stance phase is much decreased to beyond the normal range (60–62%). This highlights the importance of providing OS that can support people with LLD to keep their harmonic ratios closer to the golden ratio (1.618) during walking. In this respect, the trial OSs with the positive changes in the gait harmony were acceptable for the participants to efficiently transfer their body balance into more rhythmic movement especially when performing the left gaits.

The participant’s daily gaits were asymmetric as their walking balance was moved to their (short) left leg, making its step length shorter but phase longer (refer to Table 1). With the trial OSs, their gaits were still being asymmetric but became fairly symmetric in spite of the conflicting changes between step length and phase with high variation and considerable gaps (refer to Table 2). This may reflect that gaits could concurrently involve different aspects such being symmetric and asymmetric [59, 60]. Unsuitable OSs could force individuals more than needs to change their walking posture and maintain their walking balance, in which cause the adverse effects on the gait performance [61, 62]. This could bring additional considerations into the customisation of OSs that can support performing gaits with increased step lengths and consistent step phases for both left and right legs. In this regard, the trial OSs were not yet optimally designed for the participants in controlling their gait balance and mechanical loads.

The conflicting changes in the gait symmetry were further detailed in the gait regularity analysis. Some studies have defined the gait symmetry and regularity as the same indicators in analysing gait patterns in terms of the gait variability [27, 62, 63]. Following the gait symmetry results, the regularity analysis demonstrated further inconsistent changes in step length and phase for the left and right gaits. Regarding step phase, the overall regularity between the left and right gaits was significantly decreased with the increased variability. This could mean that the trial OSs with the full leg length correction would distract the force distribution of the right limb muscles when successively performing the right gait to the left gait [44]. It indicates the importance of customising OSs that can allow people with LLD to cope with their force distribution balanced between the left and right gaits.

With the trial OSs, no significant difference was detected in the gait stability. However, the negative changes in walk ratio represented that the participants experienced difficulties in balancing their walking. The walk ratio is correlated to gait velocity in terms of step length and rate [28, 50, 53, 54]. Although moderately positive correlations were observed between walk ratio and gait velocity from both walking without and with the trial OSs in this study, the participants did not perform their gaits more stable with the increase in their gait velocity. This convinces that the gait stability is speed independent, which was consistent with previous findings [48, 50, 54]. Gait characteristics with LLD included a contradictive relation between gait velocity (increase) and cadence (decrease) on the short leg corrected by OS [2, 64]. In contrast, this study found the significantly increased gait velocity and cadence, indicating inappropriate correction of the trial OSs for the participants. The gait stability together with step length and phase could be more evident in understanding the gait performance and the OS effects. As evidence in this study, the lower stability could be associated with losing ordinary force control capacity and consuming more body movement energy (including lower and upper bodies) at the early stage of rectifying impaired central balance for performing gaits. This comprehends that the customisation of OSs needs to consider the development of walking ability based on the individual adaptability for walking with.

This study identified the conflicting gait pattern changes between the short and long legs and alerted to customise OSs for optimal gait performance. The left (short) gait patterns were improved whereas the right (long) gait patterns were disrupted by the trial OSs across the individuals. The participant gaits could be optimally performed when OS brings about the maximised positive changes in the gait patterns/metrics and the minimised change gaps between the left and right gaits. The treatment strategy with OSs should involve the gait pattern analysis for individuals with a special attention to controlling their walking balance and restoring their walking ability for inducing optimal performance in both gaits. These concerns can be reflected by some strategies applied to the design of OSs with 50% correction of the LLD magnitude as full leg length equalisation is not effective especially for the young ones with different types of LLD [20, 23]. Therefore, it is worthwhile to assess the effects of OS corrections on height offset through the gait pattern analysis in order to provide optimal treatment for effective individual rehabilitation.

This study could reveal insights on the effect assessment of OSs through the gait pattern analysis based on the consolidated metrics using the simple vision-based deep learning model. The approach can potentially guide individuals in customising appropriate OSs, allowing for early detection of defective gait performance. It can also be applied to study further progression of gait monitoring for a long-term effect assessment of OSs and management of LLD. While it cannot be assumed that the trial OSs may not be suitable for the treatment, the findings in this study demonstrated the need for the gait pattern analysis in their customisation process to support optimal gait performance. The propositions on the study results require future investigation to confirm the validity with clinical investigation and useability survey. Upon the gait pattern analysis, it is still difficult to assess the total effect of OSs in relation to clinical problems with injuries and pains. However, the understanding of gait pattern changes may be beneficial in the correction of OSs to prevent any associated complications and ensure optimal treatment for everyday-based rehabilitation [27].

Limitations

There are a few limitations in this study.

This is a descriptive study of observed gait patterns in a group of participants having left leg short to assess the effects of OSs on gait performance but not to analyse the underlying reasons. It is possible that some participants with the trial OSs made defective gait performance for undeclared reasons.
The number of participants is relatively small to generalise the findings in the current study setting. With the limitation of available data, the results should be interpreted with care for generalisation.
The study could not facilitate a comparative analysis with gait pattern changes for different LLD (e.g. right leg short) or by differently corrected OSs, which remained as future works when enough data are available for better targeting.

Nonetheless, the gait pattern analysis using the advanced deep learning model seems to be apposite to and informative for facilitating the evidence-based customisation and provision of OSs in optimally offsetting individual LLD.

Future Directions

This study employed a vision-based deep learning model to analyse gait parameters and patterns for understanding gait performance in the effect assessment of OSs. This is of vital importance in the process of customisation of OSs and encouraged for functionally and timely optimal treatment for people with LLD. In the future perspective, the OS effects would be further assessed by utilising more advanced deep learning models developed for other gait evaluation with kinematic and kinetic parameters in addition to the spatiotemporal gait analysis. In the light of our results, further analyses for OSs with different correction magnitudes are also recommended to complement this research and shed new light on this research. The other effect factors related to shoe comfort and pain relief can further be assessed by integrating usability and clinical tests in the customisation process.

This study confirmed the need of assessing the effects of OSs at their customisation by examining gait performance in people with LLD in terms of harmony, symmetry, regularity, and stability. Findings on conflicting pattern changes between the short left and long right gaits by the trial OSs, lifted with full length equalisation, provided an avenue to develop evidence-based customisation to strategically enhance gait performance for both legs. Contributing to a better understanding of the immediate effects of OSs on biomechanical pattern changes in gait, the gait pattern analysis using the vision-based deep learning model can be suggested as a useful guideline for customising optimal OSs for individual gait rehabilitation.

LLD: Leg length discrepancy

OS: Orthopaedic shoes (a pair of)

CNN: Convolutional Neural Network

H: Gait harmony

S: Gait symmetry

R: Gait regularity

W: Walk ratio

SD: Standard deviation

Ethical approval and consent to participate

Ethics approval for using the video data filmed participant walking without and with their custom-made trial OS was granted by the Institutional Review Board of Chungnam National University Hospital (CNUH 2024-03-004-006).

Availability of data and materials

The data are not publicly available due to their containing information that could compromise the privacy of research participants.

Competing interests

The authors declare that they have no competing interests.

Funding

This study was supported by the Korea Medical Device Development Fund grant funded by the Korea government (the Ministry of Science and ICT, the Ministry of Trade, Industry and Energy, the Ministry of Health Welfare, the Ministry of Food and Drug Safety) (Project Number: 1711192863, RS-2022-00164554)

Authors’ contribution

YC and JK contributed equally to the study, making substantial contributions to study conception, design, and analysis. YC was a major contributor in the study conception, study design and review, data analysis, result interpretation, and the first draft and final revision of the manuscript. IP and SB made a substantial contribution to background understanding and result interpretation with data acquisition. AJ and KM mainly contributed to the data analysis using the deep learning model. All authors read and approved the final manuscript.

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Effect assessment of lifted orthopaedic shoes on gait in people with leg length discrepancy using a deep learning gait detection in the customisation process

Status:

Version 1

Abstract

Figures

Background

Methods

Subjects

Procedures

Deep learning model

Gait pattern metrics

Harmony

Symmetry

Regularity

Stability

Statistical analysis

Results

Gait parameter change

Gait pattern change

Harmony

Symmetry

Regularity

Stability

Effects of the trial OSs

Discussion

Limitations

Future Directions

Conclusions

Abbreviations

Declarations

References

Additional Declarations

Status:

Version 1

Index	Left/Right	Equation	P value
Harmony	Left	(1) \(\:H1=\frac{\text{L}\text{e}\text{f}\text{t}\:\text{S}\text{t}\text{a}\text{n}\text{c}\text{e}\:\text{P}\text{h}\text{a}\text{s}\text{e}}{\text{L}\text{e}\text{f}\text{t}\:\text{S}\text{w}\text{i}\text{n}\text{g}\:\text{P}\text{h}\text{a}\text{s}\text{e}}\)	0.022**
	Left	(2)\(\:\:H2=\frac{\text{L}\text{e}\text{f}\text{t}\:\text{S}\text{t}\text{r}\text{i}\text{d}\text{e}\:\text{P}\text{h}\text{a}\text{s}\text{e}\:}{\text{L}\text{e}\text{f}\text{t}\:\text{S}\text{t}\text{a}\text{n}\text{c}\text{e}\:\text{P}\text{h}\text{a}\text{s}\text{e}\:}\)	0.018**
	Right	(1) \(\:H1=\frac{\text{R}\text{i}\text{g}\text{h}\text{t}\:\text{S}\text{t}\text{a}\text{n}\text{c}\text{e}\:\text{P}\text{h}\text{a}\text{s}\text{e}}{\text{R}\text{i}\text{g}\text{h}\text{t}\:\text{S}\text{w}\text{i}\text{n}\text{g}\:\text{P}\text{h}\text{a}\text{s}\text{e}}\)	0.156
	Right	(2)\(\:\:H2=\frac{\text{R}\text{i}\text{g}\text{h}\text{t}\:\text{S}\text{t}\text{r}\text{i}\text{d}\text{e}\:\text{P}\text{h}\text{a}\text{s}\text{e}\:}{\text{R}\text{i}\text{g}\text{h}\text{t}\:\text{S}\text{t}\text{a}\text{n}\text{c}\text{e}\:\text{P}\text{h}\text{a}\text{s}\text{e}\:}\)	0.146
Symmetry	Left & Right	(3) \(\:S1=\frac{\text{B}\text{i}\text{g}\text{g}\text{e}\text{r}\:\text{S}\text{t}\text{e}\text{p}\:\text{L}\text{e}\text{n}\text{g}\text{t}\text{h}\:\text{b}\text{e}\text{t}\text{w}\text{e}\text{e}\text{n}\:\text{L}\text{e}\text{f}\text{t}\:\text{a}\text{n}\text{d}\:\text{R}\text{i}\text{g}\text{h}\text{t}\:}{\text{S}\text{m}\text{a}\text{l}\text{l}\text{e}\text{r}\:\text{S}\text{t}\text{e}\text{p}\:\text{L}\text{e}\text{n}\text{g}\text{t}\text{h}\:\text{b}\text{e}\text{t}\text{w}\text{e}\text{e}\text{n}\:\text{L}\text{e}\text{f}\text{t}\:\text{a}\text{n}\text{d}\:\text{R}\text{i}\text{g}\text{h}\text{t}}\)	0.039**
Symmetry	Left & Right	(4) \(\:S2=\frac{\text{B}\text{i}\text{g}\text{g}\text{e}\text{r}\:\text{S}\text{t}\text{e}\text{p}\:\text{L}\text{e}\text{n}\text{g}\text{t}\text{h}\:\text{b}\text{e}\text{t}\text{w}\text{e}\text{e}\text{n}\:\text{L}\text{e}\text{f}\text{t}\:\text{a}\text{n}\text{d}\:\text{R}\text{i}\text{g}\text{h}\text{t}\:}{\text{S}\text{m}\text{a}\text{l}\text{l}\text{e}\text{r}\:\text{S}\text{t}\text{e}\text{p}\:\text{L}\text{e}\text{n}\text{g}\text{t}\text{h}\:\text{b}\text{e}\text{t}\text{w}\text{e}\text{e}\text{n}\:\text{L}\text{e}\text{f}\text{t}\:\text{a}\text{n}\text{d}\:\text{R}\text{i}\text{g}\text{h}\text{t}}\)	(-) 0.018**
Regularity	Left	(5) \(\:R1=\frac{\text{S}\text{D}\:\left(\text{L}\text{e}\text{f}\text{t}\:\text{S}\text{t}\text{e}\text{p}\:\text{L}\text{e}\text{n}\text{g}\text{t}\text{h}\right)\:}{\text{M}\text{E}\text{A}\text{N}\:\left(\text{L}\text{e}\text{f}\text{t}\:\text{S}\text{t}\text{e}\text{p}\:\text{L}\text{e}\text{n}\text{g}\text{t}\text{h}\right)}\)	0.009***
	Left	(6) \(\:R2=\frac{\text{S}\text{D}\:\left(\text{L}\text{e}\text{f}\text{t}\:\text{S}\text{t}\text{e}\text{p}\:\text{P}\text{h}\text{a}\text{s}\text{e}\right)}{\text{M}\text{E}\text{A}\text{N}\:\left(\text{L}\text{e}\text{f}\text{t}\:\text{S}\text{t}\text{e}\text{p}\:\text{P}\text{h}\text{a}\text{s}\text{e}\right)}\)	0.123
	right	(5) \(\:R1=\frac{\text{S}\text{D}\:\left(\text{R}\text{i}\text{g}\text{h}\text{t}\:\text{S}\text{t}\text{e}\text{p}\:\text{L}\text{e}\text{n}\text{g}\text{t}\text{h}\right)\:}{\text{M}\text{E}\text{A}\text{N}\:\left(\text{R}\text{i}\text{g}\text{h}\text{t}\:\text{S}\text{t}\text{e}\text{p}\:\text{L}\text{e}\text{n}\text{g}\text{t}\text{h}\right)}\)	(-) 0.306
	right	(6) \(\:R2=\frac{\text{S}\text{D}\:\left(\text{R}\text{i}\text{g}\text{h}\text{t}\:\text{S}\text{t}\text{e}\text{p}\:\text{P}\text{h}\text{a}\text{s}\text{e}\right)}{\text{M}\text{E}\text{A}\text{N}\:\left(\text{R}\text{i}\text{g}\text{h}\text{t}\:\text{S}\text{t}\text{e}\text{p}\:\text{P}\text{h}\text{a}\text{s}\text{e}\right)}\)	(-) 0.488
	Left & Right	(7) \(\:R3=\frac{\:\|\text{L}\text{e}\text{f}\text{t}\:\text{S}\text{t}\text{e}\text{p}\:\text{P}\text{h}\text{a}\text{s}\text{e}-\text{R}\text{i}\text{g}\text{h}\text{t}\:\text{S}\text{t}\text{e}\text{p}\:\text{P}\text{h}\text{a}\text{s}\text{e}\|}{\text{M}\text{A}\text{X}\:(\text{L}\text{e}\text{f}\text{t}\:\text{S}\text{t}\text{e}\text{p}\:\text{P}\text{h}\text{a}\text{s}\text{e},\:\:\:\text{R}\text{i}\text{g}\text{h}\text{t}\:\text{S}\text{t}\text{e}\text{p}\:\text{P}\text{h}\text{a}\text{s}\text{e})}\)	(-) 0.018**
Stability	Left & Right	(8) \(\:W=\frac{\text{T}\text{o}\text{t}\text{a}\text{l}\:\text{S}\text{t}\text{e}\text{p}\:\text{L}\text{e}\text{n}\text{g}\text{t}\text{h}\:\text{o}\text{f}\:\text{L}\text{e}\text{f}\text{t}\:\text{a}\text{n}\text{d}\:\text{R}\text{i}\text{g}\text{h}\text{t}\:}{\text{C}\text{a}\text{d}\text{e}\text{n}\text{c}\text{e}\:}\)	(-) 0.413