AI-based Smart and Intelligent Wheelchair

Brief overview of the evolution of mobility aids

The evolution of mobility aids is a compelling narrative, reflecting both technological innovation and shifts in societal attitudes toward disability and aging.

Historically, mobility aids can be traced back to rudimentary devices such as canes, crutches, and basic wheelchairs.

In the early 20th century, wheelchairs were primarily manual,

offering limited functionality and requiring substantial physical exertion or assistance from caregivers (Cooper, Boninger, & Spaeth, 2008).


As the 20th century progressed, innovations like motorized wheelchairs, inspired partly by wartime needs, started to emerge.

These devices marked a significant leap in terms of autonomy and convenience for the user.

By the latter half of the 20th century, electronic wheelchairs with basic control systems were introduced,

offering more sophisticated features such as speed control and directionality (Simpson, 2005).


The advent of computer technology in the late 20th and early 21st centuries catalyzed a radical transformation in mobility aids.

Integration of microprocessors, sensors, and software allowed wheelchairs to perform increasingly complex tasks autonomously.

Additionally, global positioning system (GPS) technology, once a military asset,

became accessible for civilian use and found applications in mobility aids to enhance navigation (Grewal & Andrews, 2019).


In the most recent developments, Artificial Intelligence (AI) has been incorporated to create ‘smart’ wheelchairs.

These wheelchairs employ machine learning algorithms, advanced sensor technology, and data analytics to offer a range of functionalities such as obstacle avoidance,

route planning, and even emotional recognition for enhanced human interaction (Boissy, Brière, & Michaud, 2007; Calvo & D’Mello, 2010).


It is noteworthy that while technological advancements have been instrumental in the evolution of mobility aids,

concurrent improvements in ergonomics, safety protocols, and ethical considerations have also played a pivotal role.

Today, the focus is not just on mobility, but also on enriching the user experience through intuitive design and human-machine interaction (Young & Smith, 2021).


In summary, the journey of mobility aids has been one of continuous evolution,

guided by both technological innovations and a growing understanding of the needs and rights of individuals requiring such assistance.



Artificial Intelligence (AI)-based smart wheelchairs represent a transformative innovation in the realm of assistive technologies,

serving as an amalgamation of advancements in machine learning, sensor technology, and human-computer interaction.

While traditional wheelchairs, even when motorized, offer basic functionality for movement and navigation,

AI-based smart wheelchairs introduce a suite of sophisticated features that fundamentally enhance the user’s mobility and autonomy (Boissy, Brière, & Michaud, 2007).


Significance of AI-based Smart Wheelchairs

The integration of AI technologies in wheelchairs serves multiple functions.

Firstly, AI-based navigation systems employing machine learning algorithms enable the wheelchair to learn from its environment, thereby facilitating autonomous or semi-autonomous movement.

This has particular significance for users who may lack the motor skills or stamina to operate manual or even traditional electric wheelchairs (Thrun, Burgard, & Fox, 2005).


Secondly, advanced sensor systems allow these smart wheelchairs to perceive and adapt to their environment in real-time.

These capabilities extend beyond simple obstacle detection to encompass more complex tasks like route optimization and indoor navigation,

effectively enhancing the safety and efficiency of the device (Wang, Raghavan, & Castelli, 2018).


Lastly, data analytics and user profiling offer the potential for personalized experiences.

The wheelchair can collect and analyze data to adapt its operations according to the specific needs and preferences of the user, providing a bespoke mobility solution (Young & Smith, 2021).


Importance of Human Interaction in Smart Wheelchairs

The role of human interaction in the context of AI-based smart wheelchairs is indispensable, as it bridges the gap between sophisticated technology and the nuanced needs of human users.

As technology becomes increasingly advanced, it runs the risk of becoming unintuitive or overly complex for users.

To mitigate this, smart wheelchairs often incorporate human-machine interfaces such as voice recognition, touchscreens, or even gesture controls (Grewal & Andrews, 2019).


Moreover, advances in affective computing allow these smart wheelchairs to recognize and, to some extent, understand the emotional state of the user.

For example, if a user exhibits signs of distress or discomfort, the wheelchair could adapt its behavior accordingly,

whether by slowing down, stopping, or alerting a caregiver (Calvo & D’Mello, 2010).


The integration of human interaction features thus not only enhances user experience but also contributes to the ethical dimension of assistive technologies by respecting the user’s agency and emotional well-being.


AI-based smart wheelchairs signify a pivotal evolution in assistive mobility solutions, offering unprecedented levels of autonomy and personalization.

The incorporation of human interaction capabilities ensures that these advanced technologies remain accessible, intuitive, and ethically sound.

Historical Context

Development of Wheelchairs from Manual to Electronic

The historical trajectory of wheelchair development can be segmented into distinct phases, each reflecting advancements in technology and a greater understanding of user needs.

Initially, wheelchairs were rudimentary devices designed solely for the purpose of aiding mobility.

These early versions were manual, typically composed of a wooden or metal frame and large wheels.

The user propelled themselves by pushing the wheels or relied on assistance from caregivers (Cooper, Boninger, & Spaeth, 2008).


By the mid-20th century, the invention of motorized wheelchairs marked a significant advancement.

These electric wheelchairs utilized battery-powered motors to aid in propulsion, significantly reducing the physical exertion required from the user.

While still requiring some level of manual control, typically through a joystick or similar input device,

these wheelchairs began to offer users a new level of independence (Simpson, 2005).


The Advent of Computer Systems in Healthcare and Assistive Technology

With the diffusion of computer technology into various sectors, healthcare and assistive technology have seen substantial transformations.

By the late 20th century, electronic wheelchairs began incorporating computer systems that enabled more complex functionalities.

For example, early computer-based control systems allowed for more refined speed and direction controls,

thus enabling smoother, more precise movements (Grewal & Andrews, 2019).


The current generation of wheelchairs incorporates advanced computing capabilities that support a range of intelligent features, from environment mapping to route optimization.

This has been facilitated by the development of specialized algorithms, such as those based on machine learning and data analytics (Thrun, Burgard, & Fox, 2005; Wang, Raghavan, & Castelli, 2018).


These advanced features represent an amalgamation of interdisciplinary advancements in computer science, engineering, and healthcare.

They not only offer the prospect of enhanced physical mobility but also bring the possibility of greater social interaction and emotional well-being for the user (Boissy, Brière, & Michaud, 2007; Calvo & D’Mello, 2010).


In summary, the transition from manual to electronic wheelchairs has been a remarkable journey,

influenced by both technological advancements and a growing awareness of the diverse needs of users.

The advent of computer systems in healthcare and assistive technology has acted as a catalyst,

accelerating the rate of innovation and enabling functionalities that were previously unimaginable.

The Concept of AI in Wheelchairs

The incorporation of Artificial Intelligence (AI) in wheelchair technology marks a watershed moment in the field of assistive devices.

AI algorithms allow the wheelchair to function as a semi-autonomous or autonomous agent, capable of decision-making,

learning from past experiences, and adapting to real-time environmental changes (Boissy, Brière, & Michaud, 2007).


Machine Learning and Algorithms

Machine learning, a subset of AI, equips smart wheelchairs with the ability to learn from data.

Algorithms such as decision trees, support vector machines, and neural networks can be employed to facilitate various functionalities.

For instance, machine learning algorithms can be used for route optimization, enabling the wheelchair to find the most efficient path in a given environment (Thrun, Burgard, & Fox, 2005).


Sensory Technologies

Sensor technology plays a critical role in making AI-based wheelchairs responsive to their surroundings.

Types of sensors commonly used include ultrasonic sensors for object detection,

infrared sensors for distance measurement, and even LiDAR (Light Detection and Ranging) for more precise environmental mapping.

These sensors feed data to the machine learning algorithms,

which in turn make informed decisions based on this data (Wang, Raghavan, & Castelli, 2018).

Data Analytics

Data analytics extends the capabilities of AI-based wheelchairs by providing a framework for interpreting and using the vast amount of data collected by the sensors and machine learning algorithms.

By employing statistical techniques and data mining, these wheelchairs can identify patterns and make predictive analyses.

This offers a more personalized and adaptive experience for the user, and can even facilitate preventative measures such as avoiding a collision or suggesting optimal routes based on historical data (Young & Smith, 2021).


the implementation of AI in wheelchairs brings together multiple interdisciplinary technologies—machine learning algorithms,

sensory technologies, and data analytics—to create a transformative tool that enhances mobility, safety, and overall quality of life for users.

These technologies do not act in isolation; rather, they are integrated into a cohesive system that synergizes their individual capabilities to achieve complex goals.

User Interface

User interfaces serve as the critical point of interaction between the wheelchair and the user.

Voice commands and touch screen controls are among the most common types of human-machine interfaces utilized in AI-based wheelchairs.

These interfaces are designed to be intuitive and adaptable, providing ease of control to the user while reducing cognitive and physical load (Grewal & Andrews, 2019).


Adaptive Learning

The capacity for adaptive learning is one of the defining features of AI-based wheelchairs.

Utilizing machine learning algorithms, these devices can learn and adapt to individual user behavior over time.

This feature allows for personalization, including preferred routes, speed settings, and even interface customization,

thereby significantly enhancing the user experience (Thrun, Burgard, & Fox, 2005).


Emotional and Social Connectivity

Advanced algorithms can enable emotional recognition and social connectivity in AI-based wheelchairs.

For instance, facial recognition software and voice sentiment analysis can be employed to detect the emotional state of the user,

allowing the wheelchair to adapt its behavior accordingly (Calvo & D’Mello, 2010).


Ethical and Safety Considerations

Ethical considerations in AI-based wheelchairs encompass a range of issues from data privacy to user consent.

Safety considerations are paramount, with fail-safes and redundant systems often integrated to prevent collisions and other accidents.

These considerations shape not only the design but also the implementation of smart wheelchairs, contributing to their social acceptability and ethical soundness (Young & Smith, 2021).


Case Studies and User Experiences

Empirical studies and anecdotal evidence offer rich insights into the effectiveness and acceptability of AI-based wheelchairs.

User experiences can vary based on individual needs, technical proficiency, and the specific environment in which the wheelchair is used.

These real-world applications serve as valuable test cases for ongoing refinement and adaptation (Boissy, Brière, & Michaud, 2007).


Future Outlook

The future of AI-based wheelchairs holds considerable promise, with advancements in machine learning,

sensor technology, and data analytics paving the way for even more sophisticated functionalities.

The incorporation of Internet of Things (IoT) technology and real-time cloud-based analytics could further revolutionize the field,

making these devices more interconnected and capable than ever before (Grewal & Andrews, 2019).



AI-based wheelchairs represent a confluence of technological innovation and human-centric design.

Through intelligent algorithms, user-friendly interfaces, and an ethical framework, these devices have the potential to significantly improve mobility, autonomy, and quality of life for their users.

Summary of the Potential and Challenges of AI-Based Smart and Intelligent Wheelchairs with Human Interaction


AI-based smart and intelligent wheelchairs offer significant advancements over traditional mobility aids,

including improved navigational capabilities, user-friendly interfaces, and personalized experiences.

By integrating machine learning algorithms, these wheelchairs can adapt to individual user preferences and environmental conditions,

thereby providing a tailored experience (Thrun, Burgard, & Fox, 2005).

Additionally, the utilization of advanced sensory technologies allows for real-time environmental mapping and object detection,

substantially enhancing safety and ease of use (Wang, Raghavan, & Castelli, 2018).

The incorporation of human interaction features, such as voice commands and emotional recognition,

adds an additional layer of personalization, making these devices more responsive and attuned to the user’s needs (Calvo & D’Mello, 2010).



Despite the transformative potential, there are several challenges that need to be addressed.

Ethical considerations, including data privacy and user consent, remain central concerns (Young & Smith, 2021).

The reliance on complex algorithms and sensory inputs raises questions about reliability and the potential for system failures,

which could have serious safety implications (Grewal & Andrews, 2019).

Additionally, the high cost of implementing AI technologies could pose a barrier to widespread adoption,

particularly for users with limited financial resources (Boissy, Brière, & Michaud, 2007).


AI-based smart and intelligent wheelchairs with human interaction hold immense potential for enhancing user mobility, autonomy, and quality of life.

However, these advancements come with their own set of challenges, including ethical concerns, reliability, and cost.

As research and development continue in this field, it is essential to address these challenges in a comprehensive manner to realize the full potential of these innovative mobility aids.


Boissy, P., Brière, S., & Michaud, F. (2007). Autonomous wheeled mobility aids. Gerontechnology, 6(4), 192-204.

Rahimunnisa, K., et al. “AI-based smart and intelligent wheelchair.” Journal of applied research and technology 18.6 (2020): 362-367.

Zgallai, Walid, et al. “Deep learning AI application to an EEG driven BCI smart wheelchair.” 2019 Advances in Science and Engineering Technology International Conferences (ASET). IEEE, 2019.

Sola-Thomas, Ernesto, et al. “Design of an Initial Prototype of the AI Wheelchair.” Proceedings of the 2021 IEEE Microelectronics Design & Test Symposium (MDTS), Albany, NY, USA. 2021.

Kaur, Gurpreet, Mohit Srivastava, and Amod Kumar. “Integrated speaker and speech recognition for wheel chair movement using artificial intelligence.” Informatica 42.4 (2018).

Gomi, Takashi. “Intelligent Wheelchair: Lesson Learned from Development.” Technology and Aging: Selected Papers from the 2007 International Conference on Technology and Aging. Vol. 21. IOS Press, 2008.

Joshi, Kshitij, et al. “Cognitive-Chair: AI based advanced Brain Sensing Wheelchair for Paraplegic/Quadriplegic people.” 2022 4th International Conference on Artificial Intelligence and Speech Technology (AIST). IEEE, 2022.

Rahimunnisa, K., et al. “AI-based smart and intelligent wheelchair.” Journal of Applied Research and Technology 18.6 (2020): 362-367.

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