## 7 Limitations and Future Research  
This study presents several limitations that should be considered when interpreting our results. The reported 99.1% accuracy represents optimal performance achieved under controlled laboratory conditions, while real-world deployment typically yields 94-97% accuracy due to environmental variations, user diversity, and hardware constraints. Our experiments were conducted with experienced operators and participants familiar with the gesture vocabulary, whereas novice users may experience reduced accuracy during initial system interaction. Additionally, the Jetson Nano platform occasionally experiences thermal throttling during extended operation, which can increase latency from the reported optimal 48.3 ms to 80-120 ms under demanding conditions. The training dataset, collected from participants in controlled environments, may not fully capture the diversity of real-world usage scenarios, potentially impacting performance generalization. Future research should address these limitations through comprehensive field studies validating performance claims under diverse real-world conditions. Development of adaptive algorithms could help maintain consistent performance despite environmental variations and user differences. We recognize the importance of transparent reporting and commit to presenting complete performance distributions rather than solely optimal results in subsequent publications. Independent third-party validation would strengthen the credibility of our performance claims, while long-term studies could investigate potential performance degradation over extended operational periods. Furthermore, expanding the dataset to include more diverse user populations and environmental conditions would improve the system's robustness and generalizability. These efforts will contribute to developing more reliable and universally applicable gesture recognition systems for safety-critical mobility applications. Conceptualization, [PERSON] and J.T. ; methodology, [PERSON]; validation, X.J., J.T. and J.C.; formal analysis, J.C.; investigation, J.C.; resources, J.T. ; data curation, [PERSON]; writing--original draft preparation, [PERSON]; writing--review and editing, [PERSON] and [PERSON]; visualization, [PERSON]; supervision, [PERSON]; project administration, [PERSON]; funding acquisition, [PERSON] All authors have read and agreed to the published version of the manuscript. This research was funded by the 2023 Ministry of Education Collaborative Education Project of China (230703711174142); the 2023 Zhanjiang Non-funded Science and Technology Tackling Plan Project (2023B01064); and the 2023 Lingnan Normal University Teaching Reform Project. Higher Education Teaching Reform Project of Guangdong Province: Official Letter No. YJ [2024]-30 of the Education Department. Data Availability Statement: Data available on request due to restrictions. The authors declare no conflicts of interest. * [PERSON] et al. (2025) [PERSON]; [PERSON]; [PERSON] Harnessing Tensor Decomposition for High-Dimensional Machine Learning. _Authorea Prepr._**2025**. Available online: [https://d197 for5662m48.cloudfront.net/documents/publicationsatus/253067/preprint_pdf/9e8d506/9212 ed9 accf96 fc42116 ld48.pdf](https://d197 for5662m48.cloudfront.net/documents/publicationsatus/253067/preprint_pdf/9e8d506/9212 ed9 accf96 fc42116 ld48.pdf) (accessed on 3 May 2025). * [PERSON] et al. (2023) [PERSON]; [PERSON]; [PERSON]; [PERSON] Parallel temporal feature selection based on improved attention mechanism for dynamic gesture recognition. _Complex Intell. Syst._**2023**, \(9\), 1377-1390. [CrossRef]
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