The possibilities of AI in neurorehabilitation

IA en neurorehabilitación

The possibilities of AI in neurorehabilitation are an ever-expanding reality. Artificial intelligence has positioned itself as a transformative tool promising to optimize recovery and enhance function in patients with neurological disorders. This innovative approach is reshaping the landscape of neurorehabilitation, offering new hopes and opportunities for personalized and effective care.

A necessary definition to understand the use of AI in neurorehabilitation

Before delving into the power of AI in this field, it’s essential to understand what neurorehabilitation is. The World Health Organization defines it as an active process in which individuals who have suffered some neurological injury or disease seek to achieve the best possible overall recovery. In essence, this process encompasses physical, mental, and social development so that the patient can reintegrate into their daily life appropriately.

Certainly, neurorehabilitation has made significant progress since the mid-20th century when the focus shifted from considering the consequences of brain injuries as permanent. Now, this discipline focuses on the regenerative potential and dynamic reorganization of the brain even months or years after a brain injury. This paradigm shift drove translational research and the need to stimulate damaged brain networks in a controlled and intensive manner using various neuromodulation and neuronal repair tools. Hence, the relevance of incorporating technologies like AI in neurorehabilitation.

In this regard, the complexity of the process at hand requires specialists to identify impairments and guide the intrinsic plasticity phenomena of the nervous system to maximize recovery and prevent systemic and neurological complications. Additionally, the goal is to promote preserved capacities to achieve the highest possible physical, cognitive, and behavioral autonomy, thus favoring the patient’s social reintegration into their habitual environment.

New technologies and AI in neurorehabilitation

Undoubtedly, various technologies, in partnership with artificial intelligence, have found an important place in neurorehabilitation. Let’s analyze some of them:

  • One of the most significant advances is telerehabilitation, a telemedicine tool that enables remote patient rehabilitation. With the support of platforms with cameras and specific programs, therapists can interact with patients and guide both motor and cognitive rehabilitation. This not only offers the possibility of real-time work but also asynchronous monitoring with the patient’s regular tasks, providing remote evolutionary analysis and optimizing personalized care.
  • On the other hand, robotics also plays a fundamental role in neurorehabilitation. Robotic assistance devices can be divided into three categories: service robotics, non-portable assistance, and portable assistance. While service robotics provides assistance in activities of daily living for individuals with definitive disabilities, non-portable and portable assistance devices focus on rehabilitative and therapeutic work. Exoskeletons are a representative example of the latter category, demonstrating benefits in improving upper limb functionality.
  • Another technology on the rise in neurorehabilitation is virtual reality. This tool allows interaction with a realistic virtual environment, favoring the practice of specific functions. In fact, virtual reality has shown its ability to promote neuroplasticity and improve patient adherence to therapeutic goals, enriching the recovery process.
  • Finally, non-invasive neuromodulation techniques have gained ground in early neurorehabilitation. Repetitive transcranial magnetic stimulation and transcranial direct current stimulation are providing promising results in facilitating neuronal regeneration by activating the natural mechanisms of neuroplasticity.

Application of AI in neurorehabilitation

Artificial intelligence is playing a crucial role in neurorehabilitation by partnering with the aforementioned technologies. In this context, automation in therapeutic patient management is one of the most significant benefits that AI offers in this field. Thanks to the massive collection of biometric data during therapies, AI algorithms can classify and predict fictitious outcomes based on regression models. This capability improves the effectiveness of feedback and optimizes interfaces between the patient and machines, whether for progressive therapeutic benefits or to assist in definitive disabilities.

Furthermore, AI in neurorehabilitation is also having a positive impact on the diagnosis and prognosis of patients with neurological disorders. When applied in neuroimaging studies and neurophysiological tests, it can establish functional and temporal prognoses in complex scenarios, such as altered states of consciousness. AI can help define specific clinical subtypes and profiles, optimizing pharmacological and neurorehabilitative indications for each patient, increasing their effectiveness.

On the therapeutic front, AI has revolutionized how technological devices interact with patients during neurorehabilitation. By connecting AI to devices such as robots or exoskeletons, these machines can ‘learn’ from the data collected during therapy. This means that the machine can make decisions and progressively adjust the therapy plan for the functional and cognitive benefit of the patient. Moreover, AI facilitates the interconnection of diagnostic and therapeutic technological resources to establish an individualized therapeutic plan and monitor its progress, thus creating precision medicine for each patient.

AI in the areas of functional motor and neurocognitive application

Even in the area of functional motor application, wearable and non-wearable robots can greatly benefit from AI. By generating a progressive and individualized therapeutic plan, these devices can operate with minimal supervision from medical and therapeutic staff. In this case, specific sensors ensure patient safety during therapy by detecting incidents and preventing accidents. AI has also been crucial in dependency assistance devices (motorized wheelchairs, for example) mediated by brain-machine interfaces. This is essential for the system to learn brain patterns – through machine learning – corresponding to specific patient intentions.

In the neurocognitive application area, AI in neurorehabilitation is key to the long-term recovery of cognitive impairment after an acquired brain injury. Computational models based on AI record results and their evolution over time, allowing adaptive and individualized training for each patient. This frees therapists from routine tasks and enables more efficient therapy.

Inrobics, an example of AI application in rehabilitation

As seen, robotics and AI in neurorehabilitation have the potential to revolutionize how neurological diseases are treated. At Inrobics, we are committed to using these technologies to improve the lives of people with functional or neurological limitations.

At Inrobics, we use artificial intelligence and social robots to improve the quality of life for people with functional or neurological limitations. Our platform has been successfully tested in both group and individual therapies, helping many people regain their mobility and coordination.

Additionally, we use the humanoid robot Nao to provide patients with personalized care tailored to their needs. Nao can recognize patients, create narratives based on their preferences, and objectively monitor and measure the degree of movement of the user’s joints. This allows for precise, objective, and reliable data, generating reports for family members and therapists about the individual’s condition and progress. All of this is supported by artificial intelligence algorithms.

We invite you to learn more about Inrobics Rehab Clinic, a project designed, developed, and tested in Spain, integrating robotics and AI in neurorehabilitation. Contact us and request a free demonstration!

Picture of José Carlos González

José Carlos González

PhD in Computer Science and Technology from UC3M, with research focused on Artificial Intelligence and Robotics. Over 8 years of experience as a researcher leading IT architecture. Academic visitor at Carnegie Mellon University, USA, and the Karlsruhe Institute of Technology, Germany. Extensive experience with control architectures for autonomous robots, planning, and machine learning. Leads and manages software and IT architecture. Possesses substantial experience and leadership in technology and innovation. Committed to developing technologies that enhance people’s quality of life and make healthcare more accessible and efficient for everyone.