Motion deficits, whether caused by stroke, spinal cord injury, diabetes, or neurological disease, have severe impacts on quality of life, and the technologies designed to minimize them sit at the intersection of engineering, neuroscience, and the social sciences. Rehabilitation is, at its core, a re-training process: a feedback loop that depends on observing movement and acting on it. That makes sensors, mechanisms, actuators, and control systems the essential ingredients, and it explains why virtually any modern technology can find a role in the field. Sequeira’s review frames this sprawling domain through taxonomies that classify devices by function, from robotic wheelchairs and smart walkers to wearable exoskeletons, and by the level of automation, the type of human interaction, and the technology readiness level of each system.

The taxonomy question is more than academic housekeeping. Structured classification schemes give clinicians and designers a common language for matching devices to patient needs, and the literature has proposed many of them, from walking support systems for the elderly to broader mobility assistance devices. For physical rehabilitation, the simplest schemes divide the field into exoskeletons, which wrap around the body, and end-effector devices, which interact with a limb at a single point. For mental and neurological rehabilitation, researchers have recognized the need for frameworks that link theoretical models of the brain to clinical practice, connecting stability, muscle performance, and neuromuscular coordination to the sensors and actuators that can measure and assist them.

One of the most striking technical insights in the review concerns kinematics, the mathematics of movement. The adult human body contains 206 bones and, from a robotics perspective, an enormous number of degrees of freedom, the independent directions in which joints can move. Modeling a full walking gait has required as many as 36 degrees of freedom, while studies of the lower limbs alone have used 18. Yet rehabilitation devices exploit the body’s kinematic redundancy to work with dramatically simpler mechanics. Typical lower-limb exoskeletons range from six to fourteen degrees of freedom, upper-limb systems from two to six, and a knee reeducation mechanism can be reduced to just five. The Robear nursing robot, designed to lift patients between a bed and a wheelchair, uses two human-like arms with force sensors on a mobile base, totaling fifteen degrees of freedom. Simpler kinematics, the review emphasizes, brings natural advantages in sensing, actuation, and control, and it is the key to keeping costs down.

Nowhere is that philosophy clearer than in the review’s first case study: a robotic cane. Building on a lineage of robotized canes that stretches from Lidar-equipped, three-wheeled designs for visually impaired users to a lightweight 1.5-kilogram model with an RGB-D camera and ultra-wideband positioning tags, Sequeira’s team built a device with a single degree of freedom, essentially an inverted pendulum mounted on a wheel. When a person walks with a traditional cane, the system alternates between two states: the cane swinging like a pendulum and the cane planted on the ground as a fixed support. A robot cane eliminates that switching entirely, because the wheel never leaves the ground. With only one control variable, the rotation of the wheel, the designer must trade off orientation error against linear displacement, adjusting the cane’s tilt and speed to match the user’s gait. A later version with a deformable wheel smoothed movement over slightly irregular surfaces without compromising stability, and rehabilitation sessions were conducted at two branches of the Portuguese healthcare system with informed, anonymized participants.

The human response was measured with a deliberately minimal questionnaire, a three-point Likert item plus an open question, designed to avoid survey fatigue among elderly participants. Half of the respondents reported a positive attitude toward the robot cane, a quarter were neutral, and a quarter negative. That modest but real acceptance, achieved outside the laboratory, is precisely what the review identifies as a distinctive feature: many published robotic canes have never been tested with actual people with locomotion limitations, and their bulk can make them impractical in cluttered domestic settings where rehabilitation actually happens.

The second case study attacks one of diabetes most devastating complications. Diabetic Foot, characterized by poor circulation in the feet and frequent ulcerations that can lead to amputation, affects an estimated 18.6 million people each year, against a backdrop of roughly 3.4 million diabetes-related deaths in 2024. Standard monitoring relies on visual inspection, sensitivity tests, pedal pulse detection, and pressure analysis, all dependent on the experience of the technician. The review describes an automated alternative developed in the DFAA project at a Portuguese hospital: four images of each patient’s feet are fed to a ResNet50 convolutional neural network trained on a dataset of 3,255 images labeled with the help of generative AI, achieving 80.5 percent agreement with human experts across 87 participants. The system produced F1-scores of 76.51 percent, 72.08 percent, and 89.74 percent for low, medium, and high risk respectively, and a Grad-CAM activation map shows clinicians exactly which regions of the image drove each prediction, a basic but visually intuitive form of explainability. Thermography adds another low-cost signal, since people with peripheral arterial disease tend to have cooler lower limbs, and AI analysis of thermal images has reported accuracies between 85 and 100 percent for detecting ulcers in prior studies.

The third demonstration turns to the brain. Electroencephalograms, the squiggly traces recorded from the scalp, are notoriously difficult to read, and neurologists’ visual analysis is strongly shaped by experience. Research has shown that even normal-looking EEGs can hide epileptic traits such as high-frequency oscillations, spikes, and altered brain connectivity, with machine learning approaches to biomarker detection reporting accuracies between 86 and 99 percent. Sequeira’s team took a radically simple approach: a three-layer convolutional network fed with tiny 28-by-28-pixel images sampled directly from the EEG curves, mimicking the way human readers scan the traces across frequency bands for sharp peaks and asymmetries. Trained on 35 abnormal and 85 normal EEGs, the system achieved a 77 percent median accuracy across 909 runs, with precision of 0.9 and recall of 0.7, and a low interquartile range indicating consistent performance. For a recommendation system that flags recordings for expert review, the authors argue, that is an interesting result, and the misclassified samples illustrate a genuine clinical truth: epileptic traits can hide in apparently normal recordings.

Perhaps the most sobering part of the review is its discussion of why such technologies struggle to reach patients. Performance measured in controlled laboratory settings routinely overstates real-world effectiveness, where clinicians may record only a subset of required data, images may be taken from uninformative angles or under poor lighting, and no one is available to supervise acquisition. Poor data quality, the paper argues, should be treated as a genuine characteristic of the real world rather than as noise. The novelty factor compounds the problem: acceptance of a new device is often high at first and then declines, biasing short experiments. Clinicians have reported low acceptance of rehabilitation robots despite good research indicators, citing safety perceptions, training, and maintenance concerns, while physical therapists have described missed expectations with wearable knee rehabilitation technology. Even pricing psychology works against cheap devices, since low prices can induce a perception of low quality, and consumers tend to weight negative properties more heavily than positive ones. Socio-cultural context matters too, with acceptance differing across studies conducted in different environments.

The review’s conclusions are pragmatic rather than utopian. Despite the staggering kinematic complexity of the human body, very simple mechanics can yield genuinely useful rehabilitation systems, and AI techniques have a clear role in helping technicians analyze data and cope with the variability of human bodies and health conditions. The real challenge, Sequeira argues, lies in developing agile experimental protocols that distinguish early medical viability studies from certification experiments, so that innovation can diffuse through healthcare systems as fast as it does through consumer technology. In ageing societies and struggling economies alike, the devices that matter most may not be the most sophisticated ones, but the cheapest ones that actually work in the messy, uncontrolled environments where people live and recover.

Subject of Research: Low-cost robotics and artificial intelligence for rehabilitation and health monitoring

Article Title: Low-cost robotics and AI technologies for rehabilitation and health monitoring

Article References: Sequeira, J. S. (2026). Low-cost robotics and AI technologies for rehabilitation and health monitoring. Frontiers in Robotics and AI, 13, Article 1919797. https://doi.org/10.3389/frobt.2026.1919797

Image Credits: AI Generated

DOI: 10.3389/frobt.2026.1919797

Keywords: rehabilitation robotics, low-cost devices, artificial intelligence, robotic cane, diabetic foot, EEG, machine learning, exoskeletons, wearable sensors, health monitoring, human factors, kinematics