Logistic robots are useful to move goods into facilities, but normally these types of robots require special infrastructure or a controlled environment to operate correctly. In this project, simultaneous localization and mapping (SLAM) is used to achieve autonomous and semi-autonomous navigation capable of collision avoidance. Moreover, the information from cameras and a LIDAR sensor is used to detect when a human is present (or not) in the scene, as well as, to track the person using AI. This functionality can be used to help a human operator to handle and transport heavy loads from one place to another in a non-controlled environment.

Around the world, the health sector is affected by the overgrowth of the population in proportion to the number of doctors and nurses that exist per person, which is why the patient does not normally receive quality care from health sector workers. Moreover, nowadays due to human-to-human contact, the medical staff is exposed to dangerous diseases such as COVID-19, thus creating the need to develop medical assistant robotic systems that can help relieve these situations. The purpose of this project is to develop a voice bot that can provide pre-diagnosis to patients quickly and efficiently while protecting our human resources in hospitals.

Spinal cord injury (SCI) affects over 378,000 individuals each year. Incomplete tetraplegia is the most common symptom of SCI, and restoration of the use of hand and arm is the highest priority between tetraplegics. This work shows the development of a system for rehabilitation of the upper limb for people with spinal cord injury through Functional Electrical Stimulation (FES). The proposed system uses AI-based algorithms to recognize hand-arm movements from a therapist and replicates these movements into the patient through electrical stimulation. The functional prototype presented in this work demonstrates the feasibility of the system to be used in rehabilitation for the recovery of upper limb movement.

Online media content – whether it is educational, entertainment, social, or marketing content – can be highly optimized to improve the experience of the consumer if the content design takes into consideration the consumer’s preferences and emotions. A novel approach to measure a person’s preferences and emotions is by monitoring biosignals, such as brain signals, eye movement or facial expression, and calculate the level of visual and cognitive attention when the person observes the media content. With this in mind, in this project, we developed MIRAISense, a biosignal analysis platform that analyzes three types of data (EEG, Eyegaze, FaceEncoding) to see the reaction when a person observes video commercials or online classes. The software then calculates the cognitive engagement, auditory engagement, visual engagement and creates a representation to highlight the important segments of the video.

Unmanned vehicles have been advancing year after year, from UAV’s better known as drones to different ground vehicles mainly with movements based on wheels and even aquatic vehicles, which can be submerged to significant depths. Different hybrids of these have been created, mainly submersible drones, which can move underwater with the help of their propellers. This type of vehicle can be used for rescue work, mainly due to its size and versatility, allowing each one to operate in their respective environment with few limitations. However, it is important to emphasize that no type of hybrid has been created that can be deployed in the 3 environments (Air, Earth, and Water), which is why this project aims to develop a functional robot that can fly, walk and move in the water.

We live in a world that revolutionizes at the speed of light, thus bringing us new unknowns about how new technologies impact our daily basis, in the case of neurotechnologies it is possible to see that they will give us answers to many of the challenges that different industries face today, however, the development of a technology with such an impact also requires a great responsibility which throws us questions on how to deal with ethical, legal, social impact as well as safety issues. This is why prestigious institutes such as The Institute of Electrical and Electronics Engineers (IEEE) have developed proposals including the guideline “STANDARDS ROADMAP: NEUROTECHNOLOGIES FOR BRAIN-MACHINE INTERFACING”. In this project, we had the opportunity to analyze this document with the help of experts of international stature in order to obtain a broad vision that helps us to make recommendations that lead to the early adoption of both neurotechnologies and standards in the near future.

The education industry is currently evolving and nowadays online learning has begun to acquire greater interest than traditional face-to-face classes. Nonetheless, there are several issues that slow down the process to make online education an efficient and competitive learning approach. On one side, pre-recorded video lessons do not take into consideration the visual or auditory attention of the student and cannot adapt to the student’s learning capabilities. On the other side, during live lessons, teachers do not have the ability to know if the students are paying attention. Therefore, there is a need to develop systems that can optimize the learning experience by monitoring the student’s attention to personalize the educational content or provide feedback to the human teacher. One way to achieve this is through neurotechnology that can monitor brain activity. In this work, we present WAVEX , a wearable device that aims to solve the future landscape of effective online education. WAVEX is an EEG headset adapted with a VR headset, that can monitor the brain activity of the user while attending an online class.

Bioprinting is an emerging technology that will revolutionize the manufacturing of human tissue and organs. Currently, bioprinting has two main issues: it is an expensive technology and has a lack of versatility. Previous works have presented Handheld Bio Dispensers or 3D bioprinters, but only as a single-mode use, not as a dual mechanism that covers both needs. The main objective of this project is to create an extrusion-based dual bioprinting system that can be implemented as a build-in extruder in a 3D conventional printer and as a handheld dispenser device for manual use for covering both needs.