Pattern Recognition of EMG Signals by Machine Learning for the Control of a Manipulator Robot.

Human Machine Interfaces (HMI) principles are for the development of interfaces for assistance or support systems in physiotherapy or rehabilitation processes. One of the main problems is the degree of customization when applying some rehabilitation therapy or when adapting an assistance system to the individual characteristics of the users. To solve this inconvenience, it is proposed to implement a database of surface Electromyography (sEMG) of a channel in healthy individuals for pattern recognition through Neural Networks of contraction in the muscular region of the biceps brachii. Each movement is labeled using the One-Hot Encoding technique, which activates a state machine to control the position of an anthropomorphic manipulator robot and validate the response time of the designed HMI. Preliminary results show that the learning curve decreases when customizing the interface. The developed system uses muscle contraction to direct the position of the end effector of a virtual robot. The classification of Electromyography (EMG) signals is obtained to generate trajectories in real time by designing a test platform in LabVIEW.

Methodology for the Development of Augmented Reality Applications: MeDARA. Drone Flight Case Study.

Industry 4.0 involves various areas of engineering such as advanced robotics, Internet of Things, simulation, and augmented reality, which are focused on the development of smart factories. The present work presents the design and application of the methodology for the development of augmented reality applications (MeDARA) using a concrete, pictorial, and abstract approach with the intention of promoting the knowledge, skills, and attitudes of the students within the conceptual framework of educational mechatronics (EMCF). The flight of a drone is presented as a case study, where the concrete level involves the manipulation of the drone in a simulation; the graphic level requires the elaboration of an experiential storyboard that shows the scenes of the student's interaction with the drone in the concrete level; and finally, the abstract level involves the planning of user stories and acceptance criteria, the computer design of the drone, the mock-ups of the application, the coding in Unity and Android Studio, and its integration to perform unit and acceptance tests. Finally, evidence of the tests is shown to demonstrate the results of the application of the MeDARA.

Evaluation of the population dynamics of microalgae isolated from the state of Chiapas, Mexico with respect to the nutritional quality of water.

As Chiapas state, México, counts on an extensive hydrography with diverse nutrimental and climatic characteristics, it therefore allows isolating and identifying microalgae with bioenergetics potential. For this purpose, samples from 8 locations were collected, corresponding to 6 rivers, a wastewater and a springwater. The isolation of microalgae was developed for 4 weeks with 12:12 light/dark cycles. We demonstrated that the most efficient means for the isolation of microalgae of the hydrographic areas evaluated was the medium BG11 with 80.53% effectiveness. Of the microalgal consortium identified, 90% are composed of microalgae belonging to the class Chlorophycear. It was shown that another factor favouring the richness of morphotypes identified in the Santo Domingo River is associated with adequate concentrations of macroelements such as nitrates, nitrites, ammonium, phosphorus, sodium, potassium, magnesium and calcium at concentrations of 0.03 mg/l, 0.0006 mg/l, 0.08 mg/l, 0.03 mg/l, 62.93 mg/l, 5.46 mg/l, 34.52 mg/l and 48.78 mg/l respectively and microelements such as copper, zinc, iron, andmanganese at concentrations less than 0.2 mg/l in all microelements. The identified morphotypes, according to literature, have lipid contents ranging from 2 to 90%; this is of biotechnological importance for the production of biodiesel.