BackgroundA direct blow to the knee is one way to injure the anterior cruciate ligament (ACL), e.g., during a football or traffic accident. Robot-assisted therapy (RAT) rehabilitation, simulating regular walking, improves walking and balance abilities, and extensor strength after ACL reconstruction. However, there is a need to perform RAT during other phases of ACL injury rehabilitation before attempting an advanced exercise such as walking. This paper aims to propose a myoelectric control (MEC) algorithm for a robot-assisted rehabilitation system, “Nukawa”, to assist knee movement during these types of exercises, i.e., such as in active-assisted extension exercises.MethodsSurface electromyography (sEMG) signal processing algorithm was developed to detect the motion intention of the knee joint. The sEMG signal processing algorithm and the movement control algorithm, reported by the authors in a previous publication, were joined together as a hardware-in-the-loop simulation to create and test the MEC algorithm, instead of using the actual robot.Experiments and resultsAn experimental protocol was conducted with 17 healthy subjects to acquire sEMG signals and their lower limb kinematics during 12 ACL rehabilitation exercises. The proposed motion intention algorithm detected the orientation of the intention 100% of the times for the extension and flexion exercises. Also, it detected in 94% and 59% of the cases the intensity of the movement intention in a comparable way to the maximum voluntary contraction (MVC) during extension exercises and flexion exercises, respectively. The maximum position mean absolute error was , , and for the hip, knee, and ankle joints, respectively.ConclusionsThe MEC algorithm detected the intensity of the movement intention, approximately, in a comparable way to the MVC and the orientation. Moreover, it requires no prior training or additional torque sensors. Also, it controls the speed of the knee joint of Nukawa to assist the knee movement, i.e., such as in active-assisted extension exercises.

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Este artículo fue aprobado para publicación en el v68n3 de la Revista de la Facultad de Medicina teniendo en cuenta los conceptos de los pares evaluadores y los cambios realizados por los autores según estos conceptos. Por lo tanto, se publica la versión preliminar del artículo para su consulta y citación provisional, pero debe aclararse que esta puede diferir del documento final, ya que no ha completado las etapas finales del proceso editorial (corrección de estilo, traducción y diagramación) y solo los títulos, datos de autores, palabras clave y resúmenes corresponden a la versión final del artículo.Esta versión puede consultarse, descargarse y citarse según se indique a continuación, pero debe recordarse que el documento final (PDF, HTML y XML) puede ser diferente. Cómo citar:Portela MA, Sánchez-Romero JI, Pérez VZ, Betancur MJ. [Estimación de par basada en electromiografía de superficie: potencial herramienta para la rehabilitación de rodilla]. Rev. Fac. Med. 2020;68(3): In press -2020. English. doi: MJ. Torque estimation based on surface electromyography: potential tool for knee rehabilitation. Rev. Fac. Med. 2020;68(3): In press -2020. English. doi: http://dx.doi.org/10.15446/ revfacmed.v68n3.75214. Revista de la Facultad de MedicinaIn press publication Rev. Fac. Med.

Abstract-This paper presents the implementation of a Microelectrode Array Platform for registering until 60 channels of cellular bio-potentials in real-time. This electrophysiological system was set by using home-made and commercial elements acquired from Multichannel Systems (Germany). Some of them are, the planar microelectrode arrays, the housing system, and the amplifier. Additionally, we used a data-acquisition card (NI USB-6225, from National Instruments) for the digitalization of the signals. We developed the monitoring and acquisition software through a high-level programming environment (LabView, National Instruments). The system has additional synchronization features, which allow it to interact with external devices. For instance, an electrical stimulator, an optical video camera, etc. The result is a completely modular electrophysiological system, which allows monitoring the bio potentials from living cells cultured onto the planar microelectrodes. The data registered by the acquisition card can be visualized and stored on the computer by a friendly graphic user interface.