TAILOR: Patient-specific hybrid wearable systems for walking rehabilitation

Personalization of robotic exoskeletons for walking assistance to the end user is still a challenge for this technology. The TAILOR project aims to create hybrid and modular robotic-neuroprosthetic wearable systems that can be adapted to an individual patient. For that, a User-Centered Design approach and predictive dynamic simulation tools are conceived to support the design of the hybrid assistive technology.Peer ReviewedObjectius de Desenvolupament Sostenible::3 - Salut i BenestarPostprint (published version

El proyecto TAILOR: sistemas para la asistencia de la marcha

En las últimas décadas se ha constatado que la alteración de la capacidad de andar es una de las consecuencias más prevalentes, debido por un lado a la creciente incidencia de patologías de origen neurológico –ictus, daño cerebral, lesión medular, diferentes formas de esclerosis, etc.– así como al envejecimiento de la población, dado que una mayor longevidad conlleva, entre otras consecuencias, un aumento de la fragilidad y por tanto afectación de la capacidad de andarPeer ReviewedPostprint (published version

Modelling neuromusculoskeletal response to functional electrical stimulation

A model for functional electrical stimulation with hysteretic muscle recruitment is used to reproduce experimental tibialis anterior stimulation to control ankle dorsiflexion. The subject-specific parameters of the muscle recruitment model where identified from experimental data by solving a nonlinear least-squares problemPeer ReviewedObjectius de Desenvolupament Sostenible::3 - Salut i BenestarPostprint (published version

Desarrollo de sistemas modulares robóticos y neuroprotésicos personalizables para la asistencia de la marcha patológica a través del diseño centrado en el usuario: Proyecto TAILOR

Currently available wereable exoskeletons and neuroprosthetic gait assistive devices have controverted efficacy and low penetration into rehabilitation centers because they are generic solutions that do not consider the individual requirements and characteristics of each patient. TAILOR project proposes a new approach to the design of customizable neurorobotic systems, based on robotic exoskeletons (WR) and modular neuroprosthetics (NP) to provide subject-specific solutions. These two technologies are integrated with a closed loop hybrid controller. We are using a User-Centered Design approach for the design and development of these technologies to effectively introduce the requirements and needs of patients and clinical staff. Currently, we are making modifications to the WR to integrate it to the NP, which has already been developedPeer ReviewedObjectius de Desenvolupament Sostenible::3 - Salut i BenestarPostprint (author's final draft

Adaptation Strategies for Personalized Gait Neuroprosthetics

Personalization of gait neuroprosthetics is paramount to ensure their efficacy for users, who experience severe limitations in mobility without an assistive device. Our goal is to develop assistive devices that collaborate with and are tailored to their users, while allowing them to use as much of their existing capabilities as possible. Currently, personalization of devices is challenging, and technological advances are required to achieve this goal. Therefore, this paper presents an overview of challenges and research directions regarding an interface with the peripheral nervous system, an interface with the central nervous system, and the requirements of interface computing architectures. The interface should be modular and adaptable, such that it can provide assistance where it is needed. Novel data processing technology should be developed to allow for real-time processing while accounting for signal variations in the human. Personalized biomechanical models and simulation techniques should be developed to predict assisted walking motions and interactions between the user and the device. Furthermore, the advantages of interfacing with both the brain and the spinal cord or the periphery should be further explored. Technological advances of interface computing architecture should focus on learning on the chip to achieve further personalization. Furthermore, energy consumption should be low to allow for longer use of the neuroprosthesis. In-memory processing combined with resistive random access memory is a promising technology for both. This paper discusses the aforementioned aspects to highlight new directions for future research in gait neuroprosthetics.Peer ReviewedPostprint (published version