# Systematic Review of Mecanum and Omni Wheel Technologies for Motor Impairments
## 4 Discussion
Across the literature, there is a strong emphasis on making control systems both intelligent--capable of automatically managing low-level mechanical complexity--and intuitive, allowing users to interact using whichever input method is most accessible to themwhether through subtle hand gestures, voice commands, or brain-computer interfaces. These advancements in control architecture and user interfaces significantly improve accessibility, enabling a broader spectrum of individuals--including those with severe motor impairments--to benefit from Mecanum and omni wheel-based mobility technologies. Energy efficiency is a technical constraint with major practical implications. Mecanum and omni systems require multi-motor configurations that increase energy consumption. As previously noted in Section 3.3, solutions like field-oriented control (FOC) [41] and power distribution optimization [43] have shown promising results in controlled settings. However, many studies neglected to assess energy consumption under variable terrain and usage scenarios. In future developments, efficiency must be considered not only at the motor level but also within the context of integrated systems, accounting for payload, control mode, and user behavior. Battery life, a limiting factor in telemedical or autonomous setups, must be extended through hardware optimization and software-level management of load distribution. The increasing presence of automation and intelligent systems is a key trend across reviewed studies. Building on recent implementations (Section 3.4), technologies such as SLAM [48], sensor fusion, and AI-driven decision-making are enabling wheelchairs to navigate autonomously, assist with transfers [46], and adapt to user intent. AI-driven autonomy can serve as a low-level navigation assistant supporting routine tasks, enabling users to focus on high-level decision-making while reducing cognitive and operational effort. These applications illustrate how AI can enhance real-time decision-making and personalize the driving experience for users with profound impairments. However, increasing autonomy also raises new challenges. For example, ensuring that AI-driven decisions are transparent and fail-safe in unpredictable environments is crucial for user trust and safety, yet this aspect has not been deeply addressed in the current literature. Developing explainable AI techniques (as described by [PERSON] et al. [54]) and robust fallback control mechanisms in smart wheelchairs can be a promising direction to tackle this concern. In the long run, integrating more advanced sensors (e.g., LiDAR or depth cameras) and machine learning algorithms could allow wheelchairs to autonomously avoid obstacles, map optimal paths, and even learn from the user's behavior to assist in navigation. Importantly, such autonomy features should augment the user's abilities without overriding the user's intent. The goal is for the AI to handle low-level details (like maintaining a safe distance from walls or adjusting speed for bumps) so that it handles operational complexity, reducing user burden while preserving user control over strategic decisions. Despite growing functionality, high cost and design trade-offs--previously reviewed in Section 3.5--continue to hinder a widespread adoption of omnidirectional wheelchairs. Few prototypes offer verticalization, as most prioritize compactness and maneuverability [36; 48; 49]. Standing mechanisms, while valuable for rehabilitation, increase weight and complexity, potentially limiting usability. Balancing sensor performance with affordability also poses challenges--early systems often relied on expensive arrays, but recent work explores lower-cost solutions like depth cameras [2]. Moreover, ergonomic and user-centered aspects remain underdeveloped. Many designs focus on technical data, overlooking comfort, adjustability, and ease of use. As [PERSON] et al. [2] note, end-users are rarely involved early in development, leading to innovations that may not align with real needs.