Comparative Analysis of Spatio-temporal Gait Parameters Across Dancers’Proficiency Levels Using an AI-based Markerless Motion Capture System

Journal of Sport and Dance Science Vol. 6, No. 1, February, 2026

Journal of Sport and Dance Science Vol. 6, No. 1, February 2026, 355-364

https://doi.org/10.54109/jsds.2026.6.1.25 / pISSN 2799-2918

AI 기반 마커리스 동작 분석 시스템을 활용한 무용 숙련도별 보행의 시공간적 변인 비교 분석

Received: 12 Jan., 2026. Revised: 18 Jan., 2026. Accepted: 19 Jan., 2026

전다윤

Corresponding author: Da-Yun, Jeon E-mail. dy.jeon@yonsei.ac.kr

ⓒ 2026. Journal of Sport and Dance Science. All rights reserved

연세대학교 교육연구소 전문연구원

연세대학교 교육과학대학 강사

Da-Yun, Jeon

The Institute for Educational Research & College of Educational Sciences, Yonsei University, Seoul, Republic of Korea

Objectives The purpose of this study was to investigate differences in spatio-temporal gait parameters among dancers of varying skill levels using an AI-based markerless motion capture system.

Methods Forty-five participants were classified into three groups: the expert-level (EX, n=15), the intermediate level (IM, n=15), and the untrained-control (UC, n=15). Participants performed overground gait trials along a 5-meter walkway at their preferred natural speed while barefoot. Gait data were captured and recorded using the KinaTrax AI markerless system. Statistical analysis was performed using SPSS 28.0 with one-way ANOVA followed by Tukey’s post-hoc tests (p < .05).

Results Significant group differences were found in stride length, swing time, and gait speed. The EX group exhibited significantly greater overall stride length and longer swing time compared to both the IM and UC groups (p < .05). Specifically, the EX group showed a faster gait speed than the UC group (p = .03). In terms of gait asymmetry, all participants displayed excellent gait symmetry, and no statistically significant differences were observed in gait asymmetry among the three groups (p > .05).

Conclusions Expert-level dancers exhibited distinct gait characteristics, specifically longer stride lengths, faster gait speeds, and extended swing times, suggesting enhanced dynamic balance and sophisticated motor control. These proficiency-related differences may reflect gradual physical adaptation to the duration and intensity of dance training. The findings of this study can provide a scientific foundation for developing evidence-based training programs and establishing tailored injury prevention guidelines that account for dancers' skill levels.

Key words Gait analysis, Dance proficiency, AI markerless motion capture system

Introduction

Human gait is a repetitive, symmetric, and rhythmic movement of extremities that involves various interdependent parameters. Since walking requires a complex interaction between neuromuscular coordination and postural control, quantitative gait analysis has become a crucial tool for evaluating overall gait performance and diagnosing gait-related disorders (Lindemann, 2020). Smith et al. (2017) argued that long-term professional dance training induces significant functional adaptations in the musculoskeletal system. Professional dancers experience rigorous physical training that accentuates balance, smooth weight transfer, and accurate body positioning, distinguishing their motor patterns from those of the untrained public (Smith et al., 2017). Gait analysis among dancers indicates changes in physical adaptation due to constant movement in practice or performance routines, which offers insight into the impact of dance training on overall gait dynamics. Previous studies have suggested that the strict and artistic physical demands on dancers lead to a transfer effect, where the specialized motor skills of dance are manifested in fundamental activities such as walking (Rein et al., 2011; Smith et al., 2017).

Specifically, this adaptation is associated with an enhanced ability to regulate the body’s Center of Mass (COM). As demonstrated by Simpkins and Yang (2024), professional dancers can maintain dynamic stability by efficiently controlling the kinematic relationship between the COM and the base of support (BOS), even during gait with higher momentum. These findings suggest that long-term dance practice optimizes the motor control system to prioritize balance during generalized locomotion.

Furthermore, the role of proficiency level is a theoretically meaningful variable because motor adaptation occurs as a function of training dosage and skill acquisition stages. According to the theories of motor refinement, as individuals progress from cognitive to the autonomous stages of learning, their movement patterns undergo a process of “freezing” to “freeing” degrees of freedom (Bernstein, 1967). This means that in the initial “freezing” stage, novices typically simplify the complexity of a task by stiffening multiple joints and limiting their range of motion to maintain basic control. As proficiency increases, performers enter the “freeing” stage, where they release these rigid constraints and learn to exploit passive forces such as gravity, inertia, and reactive forces to achieve more fluid, efficient, and coordinated movement patterns (Bernstein, 1967).

In the context of dance, expert dancers who have undergone long-term, high-dosage training develop expertise-specific motor memory structures that stabilize complex movements (Bläsing et al., 2012). Intermediate dancers may show a transitional phase of motor adaptation, where gait patterns begin to deviate from untrained individuals but have not yet reached the high level of automatization seen in experts. Therefore, comparing expert, intermediate, and untrained groups allows for a systematic investigation into how the accumulation of dance-specific motor experience progressively reshapes foundational locomotor programs. However, there is a lack of research conducted on how these graded adaptations specifically alter the spatio-temporal structure of gait according to different proficiency levels.

Quantitative assessment of gait has relied on optoelectronic marker-based three-dimensional motion capture analysis (3DMA), which is considered the gold standard for gait analysis in the field of biomechanics. Despite its high precision, this system presents practical challenges, including soft tissue artifacts (STA) caused by the marker placement on soft tissues as well as the potential to change participants’ natural gait due to the physical and/or psychological stress from attached sensors (Mündermann et al., 2006). These drawbacks limit the ability to perform meticulous and effective movement analysis, especially in dance research.

Recently, Artificial Intelligence (AI)-based markerless motion capture systems have actively emerged as a transformative and efficient alternative. These systems utilize deep-learning algorithms with a class of convolutional neural networks (CNNs) that track anatomical landmarks with excellent performance in image classification, computer vision tasks, and pattern recognition (Albawi et al., 2017; LeCun et al., 2015). This cutting-edge alternative system has the advantages of being non-invasive, time-saving, and able to capture uncontrolled movements in a natural setting (Colyer et al., 2018). Ripic (2022) argued that AI-based markerless systems are valid and particularly effective in measuring spatio-temporal variables in walking. These technological changes are highly important in dance studies, where natural movement flows must be captured without interference for reliable data collection.

Spatio-temporal parameters, such as stride length, cadence, and swing time, act as considerable indicators of the stability and efficiency of walking. For instance, the increase in stride length and duration of the optimized swing phase often reflect enhanced lower-body strength and more sophisticated dynamic balance control (Winter, 1991). For professional dancers, the ability to control the center of mass (COM) of the body during the swing phase is crucial for aesthetic execution. In addition, gait symmetry, which is the consistency between left and right extremity movements, is another significant indicator, as severe asymmetry can cause a potential injury risk or functional imbalance (Sadeghi et al., 2000). However, comprehensive data on evaluating these indicators in the dance field remain lacking.

Moreover, even though dancers with varying skill levels are currently actively engaged in the dance field, the specific effects of dance proficiency on the spatio-temporal parameters of gait have not been fully studied. Thus, this study aims to examine the altered gait patterns in different skill levels of dancers by comparing the spatio-temporal parameters among three groups: expert level (EX), intermediate level (IM), and untrained control (UC) using an AI-based markerless motion capture system.

Methods

Comparative Analysis of Spatio-temporal Gait Parameters Across Dancers’Proficiency Levels Using an AI-based Markerless Motion Capture System

1. Participants

Forty-five female participants aged 18 to 25 years participated in the study. The participants were categorized into three groups: fifteen expert-level dancers (EX) who are currently working for professional dance companies in the Miami area, fifteen intermediate-level dancers (IM) who had 3 to 7 years of dance experience from dance classes at the University of Miami, and fifteen untrained-control (UC) group from the university community. Professional status and years of experience were utilized as primary indicators of dance proficiency because these reflect the cumulative training dosage necessary for long-term neuromuscular adaptation.

According to the framework of deliberate practice (Ericsson et al., 1993), the level of expertise is a direct function of the duration and intensity of specialized training. In the context of dance, professional status signifies the attainment of the autonomous stage of motor learning, where foundational locomotor programs are highly refined and automated compared to those of intermediate or untrained individuals (Bläsing et al., 2012).

Each participant visited the biomechanics laboratory at University of Miami for the testing, which lasted approximately 30 minutes. All participants provided signed informed consent forms approved by the Institutional Review Board (IRB No. D4000009E). Subsequently, demographic and anthropometric data were obtained from each participant prior to testing.

Height and total body mass were measured simultaneously using a calibrated integrated stadiometer and electronic scale. Participants were instructed to stand upright with their heads in the Frankfort plane, wearing light athletic clothing and no shoes. Height was recorded to the nearest 0.1 cm, and body mass was recorded to the nearest 0.1 kg.

One-way Analysis of Variance (ANOVA) was

Table 1. Demographic characteristics of participants

(n=45)

	EX (n=15)	IM (n=15)	UC (n=15)	p-value
Age (yrs)	22.4 ±1.76	21.14 ±1.95	21.33 ±2.16	.17
Height (cm)	165.0 ±5.69	166.4 ±6.20	165.8 ±3.37	.36
Body Mass (kg)	50.8 ±2.89 b,c	56.1 ±4.36 a	58.7 ±4.22 a	<.001*
Dance training (yrs)	13.7 ±2.37 b	6.20 ±1.61 a	N/A	<.001*
All values are Mean±SD. EX=expert level; IM=intermeidiate level; UC=untrained control. a Significantly different from EX, b Significantly different from IM, c Significantly different from UC, *Significant difference among groups (p <.05).

conducted to determine the differences among the three groups. When a significant main effect was found (p < .05), Tukey’s HSD post-hoc test was performed for multiple comparisons. The demographic and anthropometric data are shown in Table 1.

2. Experimental Protocol

Participants performed over-ground gait trials along the 5-meter walkway at their preferred natural speed with their bare feet. The 5-meter walkway protocol was adopted based on recent markerless motion capture research (Ripic, 2022), which confirmed that this distance is adequate for obtaining reliable spatio-temporal gait parameters. In line with this protocol, participants performed five trials, and data from the central portion of the walkway were extracted to ensure the analysis of steady-state gait.

The laboratory was equipped with eight-camera KinaTrax markerless 3DMA system (Cos Inc., Boca Raton, FL), which identifies anatomical landmarks and reconstructs 3D skeletal models without the need for physical markers or specialized clothing.

Participants performed up to 10 walking attempts, and five successful gait trials were recorded for each participant. These five trials were used to ensure the reliability of the spatio-temporal data.

Trials were classified as unsuccessful and excluded from analysis if they met any of the following criteria: (1) the full gait cycle was not captured within the effective measurement volume, (2) the participant exhibited inconsistent gait velocity or unnatural posture (e.g., looking down at the floor).

These trials were selected and repeated immediately to ensure that all five successful trials per participant represented steady-state, natural gait. On average, participants performed 7 to 8 trials to achieve the required 5 successful recordings.

3. Statistical Analysis

All statistical analyses were performed using SPSS 28.0 (IBM Corp., Armonk, NY, USA). A one-way ANOVA was conducted to examine whether there were statistically significant mean differences in each spatio-temporal parameter among the three groups.

Twenty-five spatio-temporal variables, including stride length, cycle time, stance time, swing time, double limb support (DLS), and cadence of each leg during the gait trials were extracted and processed with Visual 3D (C-Motion Inc., Germantown, MD).

Tukey’s HSD post-hoc test was performed for multiple comparisons when significant group differences were found (p < .05).

Results

Results of spatio-temporal parameters and gait asymmetry for all three groups (EX, IM, and UC) are represented in Table 2. The post-hoc analysis of differences in spatio-temporal parameters between groups is displayed in Table 3. A one-way ANOVA revealed statistically significant differences in overall stride length (F (2,42) = 3.31, p = .04, [eqn: eta]2= .13) and swing time among groups (F (2,42) = 14.31, p < .01, [eqn: eta]2 = .41). Post-hoc tests indicated that the EX group had significantly greater stride length than the IM group (MD = .134m, SE = .06m, p = .042, d = .72) and the UC group (MD = .148m, SE = .06m, p = .025, d = .81).

The EX group also exhibited a longer swing time than the IM group (MD = .013s, SE = .005s, p = .03, d = 1.16) as well as the UC group (MD = .023s, SE = .005s, p < .01, d = 1.91). Additionally, the EX group showed a faster gait speed than the UC group (MD = .173m/s, SE = .06m/s, p = .03, d = .45). For each leg, significant group differences were also found for left stride length (F (2,42) = 3.56, p = .03, [eqn: eta]2 = .15), with the EX group having a greater left stride length than the IM (MD = .133m, SE = 06m, p = .04, d = .72) and UC groups (MD = .154m, SE = .06m, p = .02, d = .85). Right step length was significantly longer in the EX group than the IM (MD = .074m, SE = .03m, p = .02, d = .84) and UC groups (MD = .079m, SE = .03m, p = .01, d = .92). Furthermore, the EX group displayed significantly longer left and right swing times than the UC group (left: MD = .025s, SE = .006s, p < .01, d = 1.28; right: MD = .021s, SE = .005s, p < .01, d = 1.8). However, no statistical differences were found in gait asymmetries among the three groups.

Discussion

The EX group exhibited a longer stride length than the IM and UC groups. In addition, the EX group displayed a faster gait speed than the UC group, which is consistent with the arguments of the existing literature (Jorge et al., 2017) that intensive dance training leads to improvements in various gait parameters such as stride length, gait speed, and step regularity. The significantly longer stride length and faster gait speed observed in the expert group (EX) represent a highly adapted motor strategy. Professional dance training involves rigorous conditioning of the lower extremities and core, leading to enhanced muscle power and a greater range of hip joint motion. Expert dancers’ longer stride length can be attributed to both stylistic training and biomechanical adaptations. First, ballet-specific walking

Table 2. Results of spatio-temporal gait parameters and asymmetry among groups

		EX	IM	UC	F (2,42)	[eqn: eta]2	p-value
Combined
	Cycle Time (s)	1.035 ± 0.081	1.049 ± 0.04	1.015 ± 0.044	1.30	.06	.28
	Stride Length (m)	1.486 ± 0.212 b,c	1.351 ± 0.158 a	1.338 ± 0.146 a	3.31	.13	.04*
	Stride Width (m)	0.104 ± 0.033	0.093 ± 0.019	0.098 ± 0.019	.75	.04	.48
	Step Time (s)	0.517 ± 0.02	0.526 ± 0.038	0.507 ± 0.022	1.75	.08	.19
	Stance Time (s)	0.678 ± 0.034	0.686 ± 0.053	0.667 ± 0.037	.77	.04	.47
	Swing Time (s)	0.372 ± 0.012 b,c	0.358 ± 0.012 a	0.348 ± 0.013 a	14.31	.41	<.01*
	Speed (m/s)	1.43 ± 0.227c	1.32 ± 0.177	1.26 ± 0.141 a	3.26	.13	.04*
	DLS (s)	0.323 ± 0.034	0.323 ± 0.035	0.316 ± 0.032	.22	.01	.81
	Cadence (strides∙min-1)	58.02 ± 2.28	57.37 ± 3.92	59.186 ± 2.618	1.39	.06	.26
Left
	Stance Time (s)	0.678 ± 0.031	0.690 ± 0.051	0.670 ± 0.042	.86	.04	.43
	Step Length (m)	0.739 ± 0.117	0.679 ± 0.083	0.671 ± 0.074	2.39	.10	.10
	Step Time (s)	0.515 ± 0.024	0.525 ± 0.037	0.506 ± 0.023	1.64	.07	.20
	Cadence (strides∙min-1)	58.07 ± 2.32	57.29 ± 3.952	59.17 ± 2.753	1.40	.06	.26
	Stride Length (m)	1.477 ± 0.212 b,c	1.344 ± 0.151 a	1.323 ± 0.143 a	3.56	.15	.03*
	Swing Time (s)	0.369 ± 0.021 c	0.356 ± 0.016	0.346 ± 0.014 a	6.70	.24	<.01*
Right
	Stance Time (s)	0.678 ± 0.038	0.683 ± 0.056	0.664 ± 0.035	.75	.04	.48
	Step Length (m)	0.746 ± 0.098 b,c	0.672 ± 0.076 a	0.666 ± 0.074 a	4.28	.17	.02*
	Step Time (s)	0.52 ± 0.018	0.527 ± 0.04	0.509 ± 0.022	1.54	.07	.23
	Cadence (strides∙min-1)	57.964 ± 2.32	57.443 ± 3.944	59.197 ± 2.587	1.32	.06	.28
	Stride Length (m)	1.494 ± 0.214	1.359 ± 0.166	1.342 ± 0.149	3.27	.14	.05
	Swing Time (s)	0.372 ± 0.01 c	0.36 ± 0.01	0.35 ± 0.014 a	13.79	.40	<.01*
Gait Asymmetry (0%=perfect symmetry)
	Stance Time (%)	0.054 ± 2.07	0.98 ± 1.96	0.712 ± 2.964	.60	.03	.55
	Swing Time (%)	1.047 ± 3.39	1.286 ± 3.36	1.561 ± 3.416	.09	.004	.91
	Step Time (%)	0.97 ± 2.73	0.372 ± 2.86	0.050 ± 2.481	.45	.02	.64
	Step Length (%)	1.18 ± 6.22	1.03 ± 3.99	0.692 ± 3.418	.04	.002	.95
	Stride Length (%)	1.13 ± 1.85	0.97 ± 2.16	2.133 ± 1.167	1.89	.08	.16
All values are presented as mean ± standard deviation. EX = expert level dancer; IM = intermediate level dancer; UC = untrained-control, DLS = double limb support, a Significantly different than EX. b Significantly different thanIM. c Significantly different than UC, *Significant difference among groups (p < .05).

techniques, characterized by an emphasis on extended knee joints and leading with the toes, become internalized through years of practice, naturally resulting in extended steps compared to typical pedestrian walking (Simpkins & Yang, 2022). Second, professional dancers exhibit increased hip extension and pelvic rotation during the stance phase (Teplá, 2014), which provides an anatomical

Table 3. Pairwise post-hoc comparisons of spatio-temporal parameters

	EX vs. IM		EX vs. UC		IM vs. UC
	MD (SE)	p-value	MD (SE)	p-value	MD (SE)	p-value
Combined Stride Length (m)	.13 (.06)	.04*	.14 (.06)	.02*	.01 (.06)	.82
Combined Swing Time (s)	.013 (<.01)	.03*	.023 (<.01)	< .01*	.01 (<.01)	.17
Combined Speed (s)	.11 (.06)	.20	.17 (.06)	.03*	.05 (.06)	.68
Left Stride Length (m)	.13 (.06)	.04*	.15 (.06)	.01*	.02 (.06)	.73
Left Swing Time (s)	.005 (<.01)	.47	.016 (<.01)	.03*	.01 (<.01)	.15
Right Step Length (m)	.074 (.03)	.02*	.079 (.03)	.01*	.005 (.03)	.86
Right Swing Time (s)	.012 (<.01)	.05*	.021 (<.01)	<.01*	.008 (<.01)	.24
MD: Mean difference; SE: Standard error. EX = expert level dancer; IM = intermediate level dancer; UC = untrained-control, *Significant difference between groups (p < .05).

advantage for lengthening each stride (Kuo, 2007). In line with Clarke et al. (2018) and Simpkins and Yang (2024), who argued that elite dancers demonstrate optimized balance strategies during movement, the significantly faster gait speed in the EX group reflects a high degree of motor control. This indicates that expert dancers can manage higher momentum without compromising postural integrity, distinguishing them from untrained individuals. Also, dancers’ faster gait speed could be closely related to their longer stride length and higher cadence since gait speed is the product of step length and cadence.

From the perspective of motor control theories, these findings suggest that expert dancers have reached the “freeing” stage of degrees of freedom as proposed by Bernstein (1967). While untrained individuals or intermediate dancers may “freeze” their joint movements to maintain stability, the EX group demonstrated the ability to release these constraints, achieving a more fluid and extended gait pattern. This indicates that the distinguishing gait performance of expert dancers is not only a consequence of cumulative training duration, but also an optimized motor strategy that capitalizes on liberated degrees of freedom for enhanced movement efficiency.

Swing time, defined as the time taken from the toe-off to heel strike in gait cycle, was significantly longer in the EX group compared to the IM and UC groups. Tolfrey & Gleeson (1997) stated that dancers had longer preparation time for the next step because they had extended swing time compared to non-dancers while running. This longer preparation time may contribute to their ability to maintain stable control and balance during movement (Tolfrey & Gleeson, 1997). Furthermore, Costa et al. (2013) noted that specialized neuromuscular adaptations from strict dance training lead to superior dynamic balance. This suggests that the EX group’s ability to prolong the swing phase is a direct result of their enhanced capacity to manage COM stability on the supporting leg during the most unstable phase of the gait cycle. Even though gait speed and swing time of the IM and UC groups were not statistically different, the IM group showed a longer swing time and faster gait speed than the UC group, indicating that years of dance training may gradually influence dancers’ gait patterns as they acquired enhanced stability and controlled movements.

By investigating spatio-temporal parameters during gait among the three groups, the findings suggest that future studies should consider the impact of years of dance training on dancers’ movement efficiency and injury resilience (Whittle, 2014; Baker, 2006). Moreover, how those altered gait patterns from gradual physical adaptation of the different periods of dance training impact the function of dancers’ daily life should be studied.

The range of normal gait asymmetry for healthy young adults is between 5% and 15%. All three groups exhibited less than 5% of gait asymmetry, indicating that all participants had excellent gait symmetry. To some degree, gait asymmetry is a common trait in healthy young adults during gait, although the extent and nature of the asymmetry may vary depending on several factors, such as age, level of physical activity, and the presence of any underlying disease or injuries (Stief et al., 2021).

Conclusion

This study explored the differences in spatio-temporal gait parameters among EX, IM, and UC groups. The results indicated that higher dance proficiency is significantly associated with faster gait speed and longer stride length, which may be attributed to ballet-specific walking techniques and enhanced hip range of motion. Furthermore, the extended swing time observed in the EX group suggests a strategic preparation interval for superior postural control.

However, as a cross-sectional study, these findings should be interpreted as associations rather than direct causalities, and the possibility of self-selection bias among voluntary participants should also be acknowledged.

Nevertheless, these results can offer a scientific foundation for designing evidence-based dance training and injury prevention protocols for dancers with varying skill levels.

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국문초록

Journal of Sport and Dance Science Vol. 1, No. 2 , December 2021, 1-12

Journal of Sport and Dance Science Vol. 1, No. 1, March 2021, 1-5

https://doi.org/10.00000/xxxx.2021.1.1.1 / pISSN 0000-0000

AI 기반 마커리스 동작 분석 시스템을 활용한 무용 숙련도별 보행의 시공간적 변인 비교 분석

전다윤

연세대학교 교육연구소 전문연구원 & 연세대학교 교육과학대학 강사

본 연구는 AI 기반 마커리스 동작 분석 시스템을 활용하여 무용 숙련도에 따른 보행의 시공간적 변인 차이를 규명하고자 하였다. 이를 위해 총 45명의 연구대상자를 숙련도에 따라 전문 무용수 (EX, n=15), 중급 무용수 (IM, n=15), 비무용수 (UC, n=15) 세 그룹으로 분류하였으며, 참여자들은 5m 보행로에서 각자 선호하는 자연스러운 속도로 보행을 수행하였다. 보행 데이터는 KinaTrax AI 마커리스 시스템을 통해 수집 및 기록되었다. 수집된 데이터의 통계 처리는 SPSS Statistics 28.0 프로그램을 이용하여 일원분산분석(One-way ANOVA)을 실시하였다. 연구 결과, 보장(stride length), 스윙 시간(swing time), 보행 속도 (gait speed) 에서 그룹 간 유의미한 차이가 나타났다. 전문 무용수 그룹 (EX)은 중급 (IM) 및 비무용수 (UC) 그룹에 비해 전체적인 보장이 유의하게 길었으며, 스윙 시간 또한 더 긴 것으로 확인되었다 (p < .05). 또한, 전문 무용수 그룹은 비무용수 그룹보다 유의하게 빠른 보행 속도를 보였다 (p = .04). 보행 비대칭성의 경우 모든 참여자가 우수한 대칭성을 나타냈으며, 그룹 간 통계적으로 유의미한 차이는 발견되지 않았다 (p > .05). 결론적으로, 전문적인 무용 훈련을 받은 무용수들은 긴 보장과 스윙 시간을 특징으로 하는 차별화된 보행 특성을 보였으며, 이는 보행의 안정적인 균형 유지와 정교한 운동 제어 능력의 향상을 시사한다. 이러한 숙련도에 따른 보행 특성의 차이는 무용 훈련의 기간과 강도에 따른 점진적인 신체적 적응의 결과로 판단되며, 본 연구의 결과는 데이터 기반의 무용 훈련 프로그램 개발 및 숙련도를 고려한 맞춤형 부상 방지 가이드라인 수립을 위한 기초 자료로 활용될 수 있을 것이다.

주제어: 보행 분석, 무용 숙련도, AI 기반 마커리스 동작 분석 시스템

Journal of Sport and Dance Science Vol. 6, No. 1, February 2026, 355-364

https://doi.org/10.54109/jsds.2026.6.1.25 / pISSN 2799-2918