Multi-Modal Ergonomic Evaluation of High-Speed Rail Seating by Integrating Electromyography, Skin Conductance Response, and Scenario-Based Analysis

Global mobility has been transformed by the quick development of high-speed rail (HSR) networks, which shorten travel times and improve accessibility. Even as HSR infrastructure has undergone constant technical improvements, seating comfort, especially during extended travel, remains a crucial yet understudied component of the passenger experience. According to earlier studies, badly made seats can lead to chronic musculoskeletal strain, discomfort, and muscular fatigue^1,2,3. These problems are made worse in lower-tier seating classes, where cost-driven design trade-offs frequently result in inflexible, non-adjustable constructions that are unable to take into account a variety of body shapes and postural requirements. International train systems, like France's TGV and Japan's Shinkansen, have incorporated ergonomic principles to improve seating designs, but there is still a dearth of systematic, empirical research on HSR seating comfort, especially about physiological muscle stress⁴. A deeper understanding of passenger comfort and tiredness accumulation can be obtained by implementing human factors approaches⁵. Creating a reliable evaluation system that measures biomechanical strain while taking contextual seat usage and real-world passenger behaviors into account is the main problem.

Recent research in ergonomics and human factors has demonstrated that EMG and SCR could provide objective real-time information on physiological responses associated with muscle fatigue and seated pain⁶. EMG is a widespread technique of evaluating localized strain of muscles during sitting postures and is an electrical measure of muscle activity. Moreover, SCR produces autonomic nervous system responses, which provide an indirect measure of physical discomfort and stress, particularly in prolonged static poses that are static⁷. These methods give a more detailed understanding of ergonomic weaknesses in the design of the seats by measuring voluntary and involuntary physiological reactions. It is worth noting that previous studies have successfully applied EMG and SCR to research task performance in high-cognitively challenging settings and digital interaction systems to demonstrate their versatility in a human-centered design evaluation. However, they are yet to be directly applied in research on seating of rail transportation, and further exploration of their potential as an instrument to determine passenger comfort levels in dynamic multi-scenario settings is necessary⁸.

The absence of scenario-based distinction in data collection is a significant drawback of current ergonomic assessments of HSR seating. Conventional seat comfort research frequently uses static lab environments that do not accurately reflect real-world travel circumstances². But in a real-world HSR setting, passengers do a range of tasks like eating, working, relaxing, and entertaining themselves, all of which need different muscular load patterns and postural adjustments⁹. Researchers can identify biomechanical stressors unique to each usage context by combining EMG and SCR within a scenario-based framework. This enables a more accurate assessment of seat performance across various travel behaviors. Although this method has proven successful in domains like digital ergonomics and human-machine interaction, its application to transportation seating design is still in its infancy¹⁰. Furthermore, only a few studies have used multi-modal physiological and behavioral measurements to systematically compare muscle fatigue across various seating classes, even though few have investigated pressure distribution mapping¹¹.

The conceptual framework used here (Figure 1) shows that the development of high-speed rail, the experience of passengers, and urban agglomeration are interconnected in a complex manner. It is worth noting that this highlights how changing the lifestyle of passengers and city development pose new requirements for the design of HSR seats, with attention to human-machine interaction, travel behavior, and passenger comfort as the primary considerations. The HSR travel needs are also affected by the connectivity existing between the metropolitan clusters that determine the expectations of passengers as to long-term comfort and the ergonomic nature of the seats. The design of the traditional seats is also being challenged by the changing work patterns, lifestyle changes, including the more frequent use of mobile devices, and the changing work habits of people that require innovative ergonomic design. The results justify a more plastic, humanized way of designing seats, and this is in line with the objectives of the research.

Figure 1: Intercity high-speed rail research conceptual framework. The correlation between population, urban agglomeration, and high-speed rail (HSR) research is illustrated here. It focuses on the effect of urban growth and modern living on passenger transit patterns, consumption demand, and space requirements. Additionally, the diagram highlights the human-centered approach that links the processes of urbanization, mobility, and lifestyle changes by illustrating how HSR influences and responds to urban clusters and shifting passenger behaviors. Please click here to view a larger version of this figure.

To fill these gaps, this paper has methodically compared the physiological effects of various HSR seating arrangements in the business, first, and economy classes using EMG and SCR. The original feature of this research is the transitional nature of the scenario-based experimental design that makes it possible to discover the issues of muscular fatigue patterns peculiar to the various work, rest, dining, and entertainment postures. The research offers quantitative physiological information and involves actual passenger behavior to effectively give an ergonomic and human factors solution to the seating analysis.

The study provides evidence-based suggestions on lumbar adaptability, optimization of armrest table positions, and sit structures depending on cross-sectional variations in muscle loads and autonomic response reactions to various types of seats. In addition to providing new science on the ergonomics of long-term sitting in HSR, the current research also defines feasible design thinking about the future versions of HSR seats in cases where the emphasis of the passengers is on comfort, inclusivity, and flexibility. The research advances the old debate of ergonomic seating in transportation and offers guidance to high-speed train manufacturers on how to accommodate various passenger requirements in their seating category using the integration of biomechanical and real-life human practices.

Ethical approval was obtained from the Tsinghua University Science and Technology Ethics Committee Medical Subcommittee (Approval No.: THU01-20250087), and all participants provided written informed consent before joining the study.

The research determines physiological strain and muscle fatigue in the different high-speed rail (HSR) seating classes through a multi-modal ergonomic evaluation. This approach gives a more dynamic consideration compared to the traditional static measurements of muscular activity and physiological stress under diverse seating positions and traveling events through the combination of EMG and SCR in the framework of a scenario simulation. To measure the intensity of muscle activation and the progression of tiredness, two physiological evaluation metrics that are commonly used in ergonomic and biomechanical evaluations, such as median frequency (MF) and root mean square (RMS), were also included¹².

The research used comparable experimental controls and a neutral baseline (pre-test calibration). As a neutral physiological reference for normalization, participants recorded their resting EMG and SCR levels during a 10 min baseline calibration in a standard upright position before each session. Furthermore, business-, first-, and economy-class seat comparisons served as experimental controls, enabling the separation of seat-specific effects under the same scenario-based activities.

Experimental environment and equipment
For ecological validity, all measurements were done in actual high-speed rail (HSR) cabins. Figure 2 depicts the seating and environment of the business-class section, the ergonomic solutions, the cabin lighting, and the passenger seating solutions. These elements contribute to the general level of comfort and, in addition, influence such physiological reactions as SCR changes and EMG activity. The wearable sensor system in this research, described in Figure 3, was used in physiological monitoring. The apparatus used in the experiment consisted of:
Electromyography (EMG): Surface EMG sensors were attached to the lower back, neck, and shoulders to record muscular activity amplitude (µV) and median frequency changes (Hz), which are indicators of muscle fatigue.
Skin conductance response (SCR): A Shimmer3 GSR+ sensor was used to record electrodermal activity related to physical discomfort and stress at 50 Hz.
Data synchronization: Python-based software ensured real-time integration of the two types of data, EMG and SCR, in all experimental conditions.

Precise sensor placement: To achieve reproducibility and precise signal acquisition, surface electromyography (sEMG) electrodes have to be placed in line with the SENIAM guidelines¹³. The bipolar Ag/AgCl electrodes were positioned on the sternocleidomastoid (midpoint between the mastoid process and sternal notch), upper trapezius (midpoint between the C7 spinous process and the acromion), and erector spinae (3 cm lateral to the L3 spinous process) muscles so that the electrodes were positioned parallel to the muscle fibers with an inter-electrode distance of 20 mm. Each site was shaved, wiped with 70% isopropyl alcohol, and abraded lightly to ensure skin impedance was less than 5 Ω. The lateral epicondyle was covered with a reference electrode. The sensors were SCR sensors, the electrodes of which were fixed to the distal phalanges of the index and middle fingers of the non-dominant hand, with a 10 min pretest calibration, where EMG and SCR baseline reference values were determined. EMG sensors were attached to the lower back, neck, and shoulders to evaluate the amplitude of muscle activation (µV) and median frequency change (Hz), which is one of the signs of muscle fatigue.

Physiological data were collected using sensors for SCR and wireless sEMG systems for muscle activity measurement, both synchronized by a Python-based data acquisition software to ensure temporal precision. All devices were calibrated before each session to maintain signal accuracy and reliability.

Figure 2: Experimental environment of the cabin (business-class). The interior layout and ergonomic features of an HSR cabin, where the emphasis was put on the comfort of passengers and space planning in the cabin. It has reclining seats that are large with privacy screens, adjustable sitting controls, and adjustable lighting. Please click here to view a larger version of this figure.

Figure 3: Experimental setup for physiological monitoring. An HSR cabin interior layout is shown, and it emphasizes the custom passenger features, ambient lighting, and ergonomic seats. It focuses on comfort, making, and practicality, which include semi-privacy of seats, spacious legroom, and an adjustable reading light. Please click here to view a larger version of this figure.

Participants and experimental design
A total of 30 healthy adult participants (15 males and 15 females) were recruited using a stratified sampling approach to ensure equal representation across business, first-class, and economy seating conditions. Participants were included if they were adults between 18 and 40 years old, in good general health, and reported no history of musculoskeletal disorders affecting the neck, shoulders, or lower back. Individuals were required to have no diagnosed neurological or systemic diseases, be able to sit comfortably throughout the experiment, and agree to follow all procedures. Participants were excluded if they had recent or ongoing neck, shoulder, or lumbar pain; had undergone spinal or upper-body surgery; had skin allergies to Ag/AgCl electrodes; were pregnant; used medications affecting neuromuscular activity; or were unable to safely complete the protocol.

The final sample consisted of 30 healthy adults (15 males and 15 females), all free of neurological and musculoskeletal impairments. The sample covered a typical range of body sizes relevant for commercial aircraft seating evaluations, and all participants were right-hand dominant to ensure consistency in electromyography measurements.

A crossover experimental design was adopted to randomly assign participants to different seating configurations so that each individual experienced business, first-class, and economy seats. During the in-seat evaluation, each participant completed four functional scenarios of equal duration within a 1-hour session. These scenarios reflect common in-flight behaviors, including resting in a relaxed seated posture, eating or drinking using the tray table, working on a laptop or tablet, and engaging in entertainment activities such as watching videos on a phone or tablet. This design allowed participants to be exposed to a range of typical cabin environments and in-flight activities, enabling controlled comparison of muscle activation patterns across conditions.

The summary of the scenarios and the frequent occurrence of passengers in each testing scenario is summarized in Figure 4.

Scenario standardization: Every practical scenario was standardized with clear instructions and posture standards to guarantee uniformity across participants and reduce confounding variables. Participants in the resting scenario reclined with their arms resting on armrests and their feet flat on the ground. In the dining setting, participants completed a standardized eating task while tray tables were raised to a consistent height of 45 cm above the seat base. In the working setting, participants executed a consistent typing job while keeping their backs in contact with lumbar support, while laptops or tablets were positioned on the tray table at a fixed height and angle. In the entertainment scenario, participants were told to keep their heads and shoulders in a neutral position and to hold their devices at a constant viewing distance. In order to guarantee adherence, researchers kept an eye on posture during each scenario and made adjustments as necessary. To acquaint participants with the standardized positions and tasks, a brief practice session was held before data collection.

Figure 4: Experiment conditions classification of scenarios. The passenger activities in HSR settings include working as in an office, eating, entertaining, sleeping, and moving around. It attracts attention to diverse activities that travelers engage in during traveling, which exemplifies the multi-purpose and multiple uses of the seating space. Please click here to view a larger version of this figure.

Physiological metrics for muscle fatigue evaluation
To enhance the assessment of muscle fatigue, we employed root mean square (RMS) and MF as the primary physiological indicators. RMS quantifies the entire electrical activity of the muscle and gives an estimate of muscle exertion, whereas MF represents fatigue-induced spectrum alterations in the EMG signal⁸. The following equations served as the foundation for these computations:

    (1)

In Eq. (1), T represents the total duration, and EMG² is the recorded EMG signal at time t. Higher RMS values indicate greater muscle exertion, while lower values suggest reduced muscle strain⁸.

   (2)

In Eq. (2), p(f) represents the power spectral density of the EMG signal. A downward shift in MF over time is indicative of increasing muscle fatigue¹⁴.

For a thorough assessment of muscle tension and physiological stress responses under various seating situations, SCR data were analyzed in conjunction with EMG. Muscle fatigue and physiological stress were measured using SCR and surface electromyography (sEMG). Electrodes were positioned on the sternocleidomastoid (neck), trapezius (shoulder), and erector spinae (lumbar) for sEMG. The raw signals underwent full-wave rectification to change negative values to positive after being band-pass filtered (20-450 Hz) to eliminate motion artefacts and low-frequency noise. The signal was smoothed with a 200 ms moving average window. The intensity of muscular activation was measured using Root Mean Square (RMS), and the progression of tiredness was identified by extracting median frequency (MF) from the power spectral density.

The autonomic changes were slow and were isolated together by recording SCR signals at a frequency of 50 Hz and low pass filtering at a frequency of 2 Hz. The tonic baseline was marked by the computation of Skin Conductance Level (SCL), and instantaneous stress reactions were recorded by removing the magnitude and delay of the phasic SCR peaks. EMG and SCR measurements were synchronized in real-time, and statistical tests, including Pearson correlations, one-way ANOVA, and repeated-measures ANOVA, were conducted to prove the correlation between physiological parameters and subjective ratings of comfort. To measure seating ergonomics in different HSR classes in different activity contexts, the pipeline ensures that the raw physiological data is converted into reliable, understandable measurements.

Data collection and processing
Before data collection, a 10 min baseline calibration was performed to ensure signal accuracy. Each participant then completed a fixed 60 min activity phase corresponding to the predefined experimental conditions. Fair exposure to different seating configurations was ensured through random seat assignment. After the trial, semi-structured interviews and subjective discomfort ratings were collected using a 1-10 Likert-type scale based on the classical methodology introduced by Likert¹⁵, providing qualitative data on perceived comfort.

To have more accurate measurements of muscle tension, the EMG differential values in all scenarios were calculated prior to and after each scenario. A reduced difference in EMG will mean more relaxed sitting and less muscle strain.

Statistical analysis and processing
Data preprocessing: Band-pass filtering (20-450 Hz), full-wave rectification, and a 200 ms moving average window were applied to extract meaningful signal trends. Raw EMG and SCR signals were first preprocessed to ensure data quality and comparability across participants. EMG signals were band-pass filtered between 20 and 450 Hz, rectified, and smoothed using a root mean square (RMS) window of 100 ms. SCR data were low pass filtered at 5 Hz and normalized relative to individual baseline recordings obtained during the pre-test calibration. Motion artifacts and outliers were removed through manual inspection and automated thresholding. The preprocessed data were then averaged over each scenario for subsequent statistical analysis.

Statistical analysis: ANOVA Comparisons: A one-way ANOVA was used to compare EMG amplitudes across seating classes, while a repeated-measures ANOVA assessed muscle fatigue trends over time. SCR Analysis: Signals were low pass filtered at 2 Hz, and peak SCR event rates (peaks per min) were compared using ANOVA. Correlation Analysis: Pearson correlation tests examined the relationships between EMG, SCR, and subjective discomfort ratings.

Qualitative analysis
The potential disparities in biomechanical strain and user experience are thematically analyzed by examining the relationship between subjective impressions and physiological results. This combination of methods provides a comprehensive, data-based approach for evaluating HSR seating ergonomics and an evidence-based platform for adaptive seat design enhancement.

The results indicate high disparities in autonomic nervous activities, muscle fatigue, and overall comfort among seating classes of HSR. To provide a comprehensive picture of the impact of seat design on the physiological and psychological well-being of passengers during long flights, SCR, EMG, and scenario-based behavioral observations are integrated. These results confirm the ergonomic hypotheses presented in the introduction by supporting the theory that seat class and scenario-dependent posture have a significant influence on muscular fatigue and stress responses.

The research is an integration of scenario-based research, EMG, and SCR in order to critically test the comfort of high-speed train seats. Whereas SCR measures autonomic stress and arousal, EMG measures muscular fatigability in the lumbar, shoulder, and neck areas. To establish the relationship between physiological responses and posture and seat design, and to offer an overall evaluation of the ergonomic activity and the comfort of the passengers across the seating classes, these data are reconciled during four realistic travel conditions: resting, eating, working, and entertainment.

EMG differences between pre- and post-journey across seating classes
EMG measurement of muscle activity before and after the journey shows significant differences in the accumulation of muscular fatigue in the shoulder, lumbar, and neck areas depending on the sitting class (Figure 5). First-class and economy-class seats exhibit the least amount of shoulder muscle fatigue, whereas business-class seats exhibit the most. A significant effect of seating class on shoulder EMG changes is confirmed by a one-way ANOVA (F (2, 27) = 5.72, p = 0.009). Post-hoc Tukey's HSD shows a statistically significant difference between business and first-class (p = 0.015), but not between business and economy (p = 0.081). A contrasting trend is observed in the lumbar region, where first-class seats exhibit the least amount of fatigue and economy-class seats show the highest (Figure 5). The ANOVA results show a near-significant difference (F (2, 27) = 3.98, p = 0.052), suggesting that while economy-class lumbar support may contribute to pain, there is no statistically significant difference between business and economy. Economy-class seats generate the most EMG activation in the neck region, whereas first-class and business-class seats produce the least (Figure 5). There is no significant difference between first and economy class (p = 0.074), but there is a significant difference between economy and business class (p = 0.021), according to post-hoc comparisons. A statistically significant effect is revealed by a one-way ANOVA (F (2,27) = 4.61, p = 0.028).

Figure 5: EMG pre- and post-journey differences in seating classes (RMS, 0.5). Comparison of muscle activity among Economy-, Business-, and First-class passengers during entertainment, dining, and office-work scenarios. The red circles highlight key task-related actions such as eating, operating a laptop or handheld device, and viewing digital screens. The red arrows indicate that First-class passengers are able to perform these activities with lower muscular effort and greater comfort compared with the other seating classes. Please click here to view a larger version of this figure.

Business-class seats offer the best neck support, followed by economy-class and first-class seats, according to EMG RMS, a measure of total muscular effort and prolonged contraction (Figure 5). There is a substantial main effect of seating class (F(2, 27) = 6.12, p = 0.005), and post-hoc tests reveal that business and first-class seating differ significantly (p = 0.011). First-class chairs are the most comfortable in the lumbar area, with economy and business-class seats coming in second and third. The statistical test, however, fails to reach significance (F(2, 27) = 2.83, p = 0.079), indicating that although a trend is present, the differences between seat classes do not demonstrate a clear influence. With a moderately significant ANOVA result (F (2, 27) = 4.34, p = 0.032) and a verified significant difference between first-class and economy-class (p = 0.018), first-class seats provide the best level of comfort for the shoulder area, followed by business-class and economy-class.

EMG variations across functional scenarios
Muscle activation levels clearly vary depending on the activity setting (Figure 6). In all three locations, economy-class seats exhibit the highest levels of muscle tiredness during rest and entertainment, followed by business-class and first-class seats. A significant main impact of seating class is confirmed by a repeated-measures ANOVA (F (2, 27) = 5.42, p = 0.012), and post-hoc comparisons reveal significant differences between first-class and economy seats (p = 0.008). The eating situation, on the other hand, shows a reversed tendency, with business-class seats showing the lowest EMG activity, followed by first-class and economy-class seats. Significant differences between business and economy-class seats are confirmed by post-hoc tests (p = 0.004), and a one-way ANOVA shows a strong effect (F (2, 27) = 6.81, p = 0.003). Because first-class seats cause the least amount of muscular tiredness, followed by business-class and economy-class seats, the office work scenario further demonstrates the significance of seat design. Although first-class seats seem to offer a more ergonomic work configuration, the ANOVA test did not uncover a significant effect (F (2, 27) = 2.46, p = 0.101), suggesting that the variance is not robust enough to be statistically significant.

Figure 6: Cross-scenario muscle load analysis (RMS and MF Variations; MF, Hz). An EMG median frequency (MF) analysis of the shoulder and neck, as well as the waist muscles, when the seat is of different classes (Economy, Business, and First-class) and various activities (eating, entertainment, office work) are performed. The lower plots depict the changes in EMG signals with time in the course of every task, and the bar charts show the magnitude of muscle activity. As the results demonstrate, compared to Economy and Business seats, the First-class seats are more ergonomic and do not press the muscles as much. Please click here to view a larger version of this figure.

SCR and emotional variability
The psychological variations in passenger experiences between seating classes are further revealed by the SCR results (Figure 7). A 15 min increase in emotional reactivity is observed at the start of the trip in first-class seating, likely due to social interactions and environmental changes. Transient emotional swings are indicated by a significant time effect, F (1, 29) = 6.87, p = 0.013, according to a within-subjects ANOVA. According to behavioral indicators, carrying luggage and using the lavatory cause negative emotional spikes, while eating is linked to good reactions. With an average of 4.91 µS for men and 1.85 µS for women, the mean Skin Conductance Level (SCL) results show that male passengers are more emotionally variable than female passengers.

Figure 7: Alternation of skin conductance response (SCR) in seating classes (SCL, µS). The electrodermal activity (EDA) analysis provides phasic (rapid response) and tonic (slow-varying) aspects over time. Peaks, onsets, and half-decay points represent SCRs linked to emotional or physiological arousal. Though the bottom panels depict temporary stress or engagement variation, which depicts changes in levels of user arousal at different times during the period of monitoring, the top panel depicts general patterns of the signals. Please click here to view a larger version of this figure.

Subjective discomfort ratings and validation of SCR
In addition to physiological data, subjective discomfort ratings were collected using a 1-10 Likert-type scale. Participants reported higher discomfort during the working and eating scenarios and the lowest discomfort during resting, a pattern that closely mirrored the SCR trends across seating classes. Scenarios and seat types associated with elevated SCR values also received higher discomfort scores, whereas conditions with lower autonomic activation were rated as more comfortable. This alignment between subjective ratings and SCR responses validates the use of SCR as a reliable indicator of autonomic stress and perceived discomfort in long-duration seating.

Speed-related variability and speed-control protocol
To minimize speed-related variability in task performance, all participants followed a standardized speed-control protocol in which each functional scenario was performed for a fixed duration, and transitions were verbally prompted by the investigator. Analysis of task timing confirmed minimal inter-participant variability, indicating that pacing was consistent and not a confounding factor. Therefore, the observed SCR and EMG differences are attributable to seat design and task demands rather than inconsistencies in activity speed.

Overall comfort across functional scenarios
Seat comfort during various tasks is demonstrated through an integrated analysis of EMG, SCR, and behavioral observations (Figure 8). First-class male passengers are the most comfortable during leisure and amusement, followed by business-class and economy-class passengers. The use of handheld devices, which causes severe muscle strain in business-class reclining postures due to insufficient arm support, is the main discomfort issue found in qualitative analysis. While first-class female passengers suffer due to enormous headrests and insufficient lumbar support, economy-class armrests are essential for reducing pain.

Figure 8: Comfort evaluation (Percentage) scenario-based (integrated). The behavior of passengers who sit in Economy, Business, and First-class seats with regards to entertainment, dining, and office work. The red circles indicate key interaction spaces such as displays, tables, and devices. The first-class passengers were better ergonomically and included in all activities, which is reflected by the arrows that demonstrate the inclination to higher comfort and efficiency in the higher classes. Please click here to view a larger version of this figure.

Business-class seats have the most ergonomic tray table placement during dining, which reduces muscular activation and increases comfort. First-class and economy-class seats come next. A substantial seating class impact (F (2, 27) = 6.29, p = 0.004) is confirmed by the one-way ANOVA, and there are significant post-hoc differences between business and economy classes (p = 0.001). Lastly, first-class seating is considered the most pleasant in the office work setting, followed by business-class and economy-class. The conclusion that first-class tray tables are best positioned for laptop use is supported by a repeated-measures ANOVA that reveals a significant main effect of seating class F (2, 27) = 5.12, p = 0.014.

As the findings indicated, first-class seats were the most optimal in terms of overall ergonomic performance, which is the highest comfort and postural fit in the working environment. The limited head and arm support of business-class chairs had increased neck and shoulder fatigue during reclining and entertainment tasks, although they had better lumbar support, which lowered lower-back strain significantly. Economy-class seats were the most prone to cumulative muscle fatigue and stress response because of their low adjustability and limited space, although these seats ensured safe lumbar and neck positioning. In general, it is possible to note that the design of the seat and the type of activity influenced the seating comfort dynamically.

Data availability:
Due to restrictions imposed by the ethics approval of this study (Tsinghua University Science and Technology Ethics Committee, Approval No. THU01-20250087), the raw EMG and SCR data containing identifiable physiological signatures cannot be made publicly available. De-identified summary data and all analysis data are included here. Additional materials may be obtained from the corresponding author upon reasonable request and subject to institutional approval.

This study proposes a multimodal, scenario-based ergonomic evaluation framework that integrates electromyography (EMG), skin conductance response (SCR), and contextual behavioral analysis to examine high-speed rail (HSR) seating comfort. Unlike traditional laboratory-based seating studies that focus primarily on static posture or pressure distribution, the present work incorporates dynamic travel behaviors -- such as dining, entertainment, resting, and work -- allowing the assessment of real-time muscular demands and autonomic arousal across different seating classes. This approach aligns with emerging literature emphasizing the importance of ecological validity and multi-sensor ergonomics in transportation research^16,17. The findings demonstrate that seat comfort cannot be predicted solely by spatial layout or backrest angle; rather, it is shaped by interactions among activity type, postural transitions, and upper-body support. In particular, the heightened neck and shoulder fatigue observed in business-class reclined conditions underscores the need to reconsider assumptions about the ergonomic superiority of high-end seating designs^18,19.

This research advances the scientific field by providing evidence that dynamic, activity-specific assessments reveal undetectable patterns in conventional single-posture evaluations. The combined EMG-SCR method captures simultaneous musculoskeletal and autonomic stress responses, complementing earlier studies that examined only one physiological dimension. Our results show that entertainment and workstation tasks produce substantial increases in upper-body muscle activation and stress fluctuations in both economy and business-class seats, challenging earlier claims that increased space alone improves comfort. Moreover, the study supports the integration of real-time sensing into adaptive seating systems, contributing to ongoing research on intelligent transportation ergonomics. Similar approaches using pressure mapping, EMG, and reinforcement learning have demonstrated potential for automated lumbar and thigh support adjustment in vehicular environments. The present findings strengthen the scientific argument for physiologically informed, user-centered seat design.

The study does, however, present several limitations that should be addressed in future work. The sample size (n = 30) limits the generalizability across diverse anthropometric and demographic populations. Additionally, the absence of whole-body vibration (WBV) input -- which is known to accelerate lumbar fatigue and alter neuromuscular stabilization in transport settings -- reduces ecological completeness. SCR, although useful for tracking autonomic arousal, does not directly quantify cognitive fatigue or emotional valence; incorporating heart rate variability (HRV), EEG, or gaze metrics would strengthen psychophysiological interpretation. Finally, although scenario-based behavioral notes were collected, cross-modal synchronization among EMG, SCR, and posture was not implemented, limiting the depth of integrative analysis.

Alternative approaches could enrich future investigations of the hypothesis. Longitudinal field studies on actual HSR journeys would allow researchers to capture cumulative fatigue, musculoskeletal adaptation, and psychosocial influences across multi-hour travel periods. Simulated WBV exposure platforms could experimentally isolate vibration-posture interactions, while wearable inertial sensors and advanced motion capture systems may provide finer-grained postural analytics. Data-fusion algorithms -- such as feature-level multimodal integration or model-level ensemble learning -- could generate unified comfort indices that consolidate muscular, autonomic, and behavioral dimensions. Such methods have proven effective in vehicle ergonomics, aviation fatigue monitoring, and seated cognitive workload prediction.

The methodological contributions of this study have important implications for several research and applied domains. Transportation ergonomics can adopt this multimodal analytical framework to redesign seats, tray tables, and headrests to support activity-dependent biomechanics rather than prioritizing static comfort. Intelligent seating design -- leveraging real-time EMG and pressure signals -- may enable personalized adaptive support systems capable of reducing fatigue during prolonged travel. The findings also offer value for automotive seating, aircraft cabin design, and occupational ergonomics, where dynamic postures influence neck and lumbar musculoskeletal load. More broadly, the validated use of SCR for detecting micro-fluctuations in stress during activity transitions contributes to expanding psychophysiological monitoring in mobility research.

Future work should aim to create integrated, adaptive ergonomic systems that employ multimodal sensor data, machine learning, and predictive modeling to enhance passenger well-being. Smart HSR seating could dynamically modify lumbar support, headrest angles, tray positioning, or recline based on real-time physiological feedback. Further, incorporating EEG, HRV, and eye tracking may enable comprehensive assessments of cognitive fatigue and attentional engagement during travel. Longitudinal research should also investigate repetitive travel exposure, vibration-posture interactions, and population diversity. Ultimately, this study's multimodal, scenario-based approach lays foundational groundwork for next-generation intelligent seating environments that actively mitigate fatigue, improve comfort, and enhance the long-term health of passengers.

Conclusion:
In the research, the behavioral observations of the scenarios are carried out with the use of EMG and SCR to offer a thorough ergonomic reading of business, first-class, and economy-class seats on HSR. Through measurement of muscular fatigue, mental stress, and body adjustment among the various travel tasks, e.g., resting, dining, working, and entertainment, this research will offer new information regarding seat design issues and possible ways of improving current rail systems. The results show that posture-specific interactions and ergonomic support systems, in addition to class hierarchy, are an important factor in determining sitting comfort²⁰. Chairs used in business classes are better at lumbar relaxation, whereas the neck and upper limbs are more strained as they lack adequate reclining cushions when operating handheld devices. The first-class seats are not the most ideal working environment, as they are not flexible enough to accommodate the various body proportions, thus causing discomfort in the head and the lumbar area. The economy-class chairs surprisingly are able to provide continuous support, particularly in the lumbar and neck area, though they are a little small. Nevertheless, they possess the greatest amount of the built-up muscular load based on the inflexibility of their sitting systems and limited elasticity^21,22. The functional scenario analysis focuses on the effect of activity-based postural variations on stress and muscle fatigue. Unsupported reclining positions also considerably augment the upper-body muscular load during times of rest and amusement, especially when seated in business class, and when handheld devices are used without an armrest, they contribute to the worsening of the neck pain²³. The seating arrangements of the first and economy classes create extra pressure on the shoulders and forearms because the tray height is not aligned, and business-class dining is the most comfortable one since the tray table is properly placed. Working in accordance with the work situation, first-class chairs are the most ergonomic; however, business-class and economy-class seats must have forward-leaning variations, which gradually add tension to the muscles.

Psychologically, SCR data has indicated that passengers in the economy class have the highest cognitive load throughout the trip, and those in the business class show stable moods except when they are undergoing entertainment. The first-class seats are the least emotional and most balanced, although the comfort of the passengers depends on their interaction with the environment. In general, the present research suggests that the design of good seats should be based on the dynamic behavior of the passenger, rather than relying solely on ergonomic models based on posture^24,25. The discrepancies between perceived and actual comfort levels indicate the necessity of flexible sitting options, including an adjustable lumbar system, movable headrests, and tray tables, which have to be placed in the most convenient position. These results prove that the combination of human factors engineering, biomechanical assessment, and passenger-centered design could help improve the ergonomics of HSR seats and increase long-term travel comfort to accommodate the diverse needs of passengers. The research's conclusions show that adaptive, activity-responsive ergonomics must replace preset comfort classes in future HSR seating design. Dynamic headrests, flexible tray tables, and adjustable lumbar and neck supports should all be incorporated into seats to allow different positions for eating, working, and resting. Intelligent modification to reduce muscular fatigue and stress can be made by incorporating real-time physiological monitoring (EMG and SCR feedback)²⁶. User-centred flexibility should be given top priority by designers in order to accommodate a variety of body types and travel habits²⁷. During lengthy rail trips, this evidence-based strategy improves overall passenger well-being, encourages sustained comfort, and lessens cumulative musculoskeletal strain. The current research's analysis of muscle activation intensity and fatigue progression did not take train speed variations into account¹³. To separate the ergonomic impacts of posture and activity from outside vibration interference, experiments were carried out in controlled laboratory settings. However, whole-body vibration (WBV), which impacts fatigue and muscle co-contraction, can be influenced by train speed¹⁵. This limitation is recognized, and in order to investigate the connection between train speed, vibration frequency, and muscle activation, future studies will use dynamic vibration or in situ measurements. A more thorough understanding of fatigue progression in actual high-speed rail situations will result from this integration, which will also enhance ecological validity.