## 5 Conclusions  
To conclude, the study underscores the value of electromyography (EMG) analysis in assessing ergonomic improvements for commuter cyclists through optimal bike-fitting positions. By comparing muscle activity in various cycling setups, the research demonstrates how individualised fitting--particularly in terms of saddle height and handlebar configuration--can enhance comfort and reduce muscle strain. The findings reveal that a tailored approach, which considers both subjective comfort and biomechanical data, significantly improves rider efficiency by redistributing muscle load across key stabilising and propulsion muscles. Specifically, the optimised P5 position, customised with input from both a comfort feedback sheet and professional fitting, showed reduced muscle activity in critical areas such as the tibialis anterior and vastus lateralis obliquus. These activity reductions may indicate a decreased energy cost, supporting the hypothesis that comfort-oriented bike fitting promotes efficient muscle engagement and lowers perceived exertion. The primary aim of the study was to validate whether EMG could reliably reflect ergonomic benefits, with results affirming EMG's utility in identifying muscle activation patterns that reflect improved comfort and performance. The research questions around the effectiveness of EMG in ergonomic analysis and the potential impact of cycling position on muscle activity patterns were addressed, showing that EMG, combined with Statistical Parametric Mapping (SPM), captures subtle shifts in muscle engagement due to optimised bike positioning. While these findings demonstrate the benefits of individualised fitting, conclusions about broader impacts on injury prevention or long-term health outcomes remain speculative. Although reduced muscle activation patterns may correlate with decreased musculoskeletal strain, further research is required to establish definitive links between these findings and reduced injury risk. The primary objective of validating EMG's utility in reflecting ergonomic benefits was achieved, with results affirming its capacity to capture subtle changes in muscle activation patterns resulting from optimised bike positioning. EMG, in combination with Statistical Parametric Mapping (SPM), proved effective in identifying muscle engagement shifts that align with improved comfort and efficiency. This study underscores the limitations of traditional fitting protocols, which may not fully accommodate individual anatomical differences and diverse bike geometries. While general guidelines offer a starting point, this research advocates for a data-driven approach to bike fitting that tailors adjustments to the specific needs of the cyclist. Such personalised strategies not only improve comfort and energy efficiency but also hold promise for promoting long-term musculoskeletal health by minimising asymmetrical muscle strain. Future studies should investigate the longitudinal effects of these fitting methods across diverse bicycle geometries and populations, particularly focusing on their impact on injury prevention and physical health. Integrating EMG and SPM analyses into routine bike-fitting practices could enable more precise and data-informed adjustments, making these tools valuable for cyclists across recreational, commuter, and competitive domains. In conclusion, the research validates EMG as a robust tool for ergonomic analysis in cycling, illustrating that personalised fitting strategies enhance rider comfort, redistribute muscle load, and improve overall performance. While further investigation is needed to confirm some projections, this study reinforces the value of individualised bike fitting in supporting sustained cycling enjoyment and long-term physical well-being. Conceptualization, S.W. ; methodology, S.W. ; investigation, S.W., A.K. and A.P. ; resources, S.W. ; data curation, S.W. and A.K. ; writing--original draft preparation, S.W. ; writing--review and editing, S.W., A.K. and A.P. ; supervision, S.W. ; project administration, S.W.