## 1 Introduction  
The evolution of adaptive mobility devices has increasingly incorporated intelligent interaction systems to address safety concerns inherent in dynamic play environments. Traditional scooter designs often neglect the integration of real-time gesture recognition, creating potential risks during high-speed maneuvers or crowded usage scenarios [1]. Recent advancements in heterogeneous sensor arrays, particularly millimeter-wave radar and time-of-flight (TOF) infrared systems, have demonstrated potential for robust motion tracking in adult-centric applications, yet their adaptation to specialized mobility systems remains underexplored [2]. Multi-user gesture interaction systems face unique challenges due to the unpredictable kinematics of user limb movements and the necessity for near-instantaneous safety responses [3]. Conventional vision-based methods struggle with occlusion and lighting variability, while wearable sensors introduce ergonomic constraints unsuitable for free-play activities [4]. This has spurred interest in contactless technologies capable of resolving fine-grained motion patterns without impeding natural movement. Algorithmic innovations in spatiotemporal modeling have begun addressing these limitations. Graph convolutional networks (GCNs) have shown promise in encoding skeletal joint correlations, while transformer architectures enable long-range temporal dependency capture [5; 6]. However, existing frameworks lack mechanisms to distinguish intentional commands from spontaneous motions in dynamic environments, a critical requirement for safety assurance [7]. The integration of such systems with mechanical platforms introduces additional complexity. Reconfigurable chassis designs, while enabling multifunctional use cases, create dynamic safety envelopes that demand adaptive control strategies [8]. Prior research in human-machine interfaces has largely focused on static industrial environments, overlooking the kinematic variability inherent in transformable mobility devices [9]. This study addresses these gaps through three synergistic contributions:  
1. Complementary Sensor Fusion Insights: Our dual-modality approach combining 60 GHz radar micro-Doppler analysis (capturing velocity dynamics \(\geq\) 2.3 m/s) with TOF skeletal tracking (providing 3D joint positions) achieves a breakthrough 0.08% false-positive rate--a 76% reduction compared to single-modality LSTM (0.34%). This synergy reveals that radar's velocity signatures effectively filter ambient motions while TOF's spatial precision ensures accurate gesture classification, particularly crucial under high-vibration conditions (80 km/h) where conventional vision systems fail. 2. Adaptive Design-Performance Trade-off Discovery: Our IoT-enabled reconfigurable chassis demonstrates that mechanical adaptability need not compromise performance--achieving 37% material reduction while improving safety reliability by 93% over fixed-design systems. The key insight is that topology-aware modular design, when coupled with real-time sensor feedback, enables dynamic optimization of both structural efficiency and operational safety across different usage modes. 3. Distributed Learning Robustness Finding: Our federated learning experiments across 32 edge nodes reveal that maintaining <5% accuracy variance in heterogeneous environments requires addressing feature distribution shifts through manifold alignment (reducing KL divergence by 68%). This finding demonstrates that edge-cloud collaboration can achieve industrial-grade consistency (99.1% accuracy) without centralized data aggregation, enabling privacy-preserving deployment at scale. Theoretical foundations from hybrid system verification [10] and Riemannian manifold learning [11; 12] inform the safety assurance framework, ensuring compliance with mobility device safety standards.