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Discover the work of researcher Marcelo Fernandes from the Federal University of Rio Grande do Norte (UFRN)

The intersection of Artificial Intelligence (AI), the Internet of Things (IoT), and Edge Computing is reshaping industry and healthcare. The work of researcher Marcelo Fernandes, from the Federal University of Rio Grande do Norte (UFRN), who has been working with AI for 25 years, explores this integration. He is currently involved in three thematic areas within INCT ICoNIoT: Industrial IoT, Edge Computing, and Healthcare.

Marcelo Fernandes

Marcelo explains that one of the major obstacles to deploying machine learning (ML) on IoT devices is model size. Although ML models for IoT are not language models and are smaller than Large Language Models (LLMs), they can still be too large to fit on resource-constrained devices. The proposed solution involves compressed models. In collaboration with Professor H. T. Kung from Harvard University, Marcelo Fernandes developed two innovative aware compression techniques. The compression is considered “aware” because the model not only learns to perform its task but also learns to compress itself. This occurs during training, where the model is forced to use fewer bits and to self-prune by removing nodes based on their importance. Other techniques reduce model size after training, but this often leads to a drop in accuracy.

The two techniques proposed by the researchers are:

• Iterative Aware Compression Based on Quantization Followed by Pruning
• Iterative Aware Compression Based on Pruning Followed by Quantization

The researchers’ first publication on this topic appeared in 2021 (doi: 10.1109/IJCNN52387.2021.9534430). Since then, Fernandes has been testing these techniques with a focus on various applications. They are now being applied to IoT devices for the first time—this direction began when Marcelo Fernandes started working with ICoNIoT. He is currently supervising a PhD student, Mateus Golbarg, and a Master’s student, Vitor Fidelis Freitas, at UFRN, who are also working within this

The initiative aims to encourage bicycle use in São Paulo by rewarding active users

Bike SP Project arose from the need to make urban mobility more efficient, inclusive, and environmentally sustainable. Its main idea is to encourage more people to use bicycles as a means of transportation in São Paulo, choosing them for daily commutes between home and work, school, university, and other routine destinations. To strengthen this incentive, the project provides cyclists with credits that can be used in public transportation (buses, trains, and the metro).

Researcher Fabio Kon, a member of ICoNIoT from IME-USP, is part of the team coordinating the project. He explained in detail how the initiative works and the technologies involved.

The core idea is to use an application (initially developed for Android) to track users’ bicycle trips and understand their behavior, generating insights that can inform the development of public policies. The project is not limited to producing data for a specific policy area; rather, it aims to serve as a general framework demonstrating how scientific research can support public policy for improving urban mobility in large cities.

How does it work?

During each trip, the app collects the user’s location every 30 seconds. Data from the phone’s accelerometer are used to verify whether the trip is actually being made by bicycle, which is determined through AI algorithms. In addition, GPS coordinates are essential: each segment of the trip has its average speed analyzed to confirm that the user is indeed cycling.

It is crucial for the system to verify that trips are genuinely made by bicycle, as the collected data determine the user’s compensation. Each week, a reward value per kilometer is randomly defined. At the end of the week, the system calculates the total distance traveled and multiplies it by the reward value to determine the amount each user receives.

Not all bicycle trips qualify. The project focuses on collecting data from essential daily trips, such as commuting between home and work, school, university, or public transport terminals. Users register these key locations, which are converted into latitude and longitude coordinates, and the system calculates the distances between them.

One of the project’s main goals is to understand how much financial incentive is needed to effectively encourage people to adopt cycling for essential daily travel. The reward values will be proposed based on these analyses.

Generated Data

The data collected by the Bike SP system will form a large dataset, which will be analyzed by a multidisciplinary team of statisticians, economists, and computer scientists.

The application is capable of collecting detailed data for each city block traveled by users. This enables the system to generate unprecedented insights into urban mobility in São Paulo. The resulting dataset will be unique and directly useful for city planning, particularly for expanding cycling infrastructure. Currently, São Paulo has around 740 km of bike lanes and paths, and the goal is to triple this network within ten years.

By revealing where cyclists travel and the challenges they face, the dataset can support decisions about where to expand bike lanes. Today, many cycling paths in São Paulo function like isolated “islands.” Because they are disconnected, cyclists are often forced to leave the bike lane and rejoin it later, which increases their exposure to traffic and reduces safety. Due to these safety concerns, women—who tend to be more cautious in traffic—are significantly underrepresented among cyclists in the city.

Technologies Involved

The Bike SP system communicates with the SPTrans system, informing the municipal system of the amount to be paid to each cyclist. In addition, the system integrates with several other applications.

For example, the TomTom API calculates distances between registered locations, ensuring routes are suitable for bicycles. Crashlytics is used to report errors in the app during usage, allowing system managers to address issues. Firebase Cloud Messaging (FCM) enables notifications to be sent simultaneously to all users. AppCheck helps prevent fraudulent trip registrations by verifying, through cryptographic methods, that the app in use is authentic, checking its digital signature.

Artificial Intelligence

Artificial intelligence is used in the project to determine whether a journey was actually made by bike: machine learning is applied to analyse the mobile phone’s accelerometer. In addition, an analytics dashboard is currently being developed to help the project team make sense of the 30,000 journeys. It will feature a dashboard for the organised visualisation of this data. In this analysis system, clustering techniques will be applied to detect patterns and group users into categories – for example, those most affected by the incentive will be identified, making it possible to understand for whom (which user profile) specifically the public policy will be developed.

Working with sensory effects and multimedia experiences for 2D screens or immersive environments—such as those requiring virtual reality headsets—is part of the daily routine of researcher Débora Muchaluat Saade, from the Fluminense Federal University (UFF). She coordinates the Digital Health research line at INCT ICoNIoT.

The Digital World Meets the Physical

Muchaluat Saade develops relaxation environments based on immersive virtual settings. These resources are designed for individuals with neurodivergent conditions, such as people with Autism Spectrum Disorder (ASD). They can be integrated with the physical world through IoT devices that enable this connection.

Within this multisensory approach, for example, a drum system was developed that integrates virtual and physical dimensions, combining instrumented drumsticks (physical) with VR headsets for 3D immersion. The movements of the user—playing a virtual drum—generate haptic feedback, meaning that the person feels vibrations in their fingers as if the drumsticks were striking a real instrument. Another project, currently under development in partnership with Professor Marcelo Fernandes (UFRN) and other ICoNIoT institutions, aims to create a piano that operates in a similar way.

Socially assistive robotics is also a highly promising field for therapies such as emotional regulation. These therapies are applied to patients who have difficulty understanding others’ emotions and expressing their own. Robots enable interactive dialogues with children in these conditions, encouraging communication and helping them recognize emotions, including their own.

For elderly individuals, socially assistive robots can be used to stimulate cognition. Memory, for example, can be enhanced through games similar to “Simon Says.” By controlling smart lights, the robot presents sequences of colored lights, while the user must observe and recall the sequence.

Researcher Débora Muchaluat Saade and her team are committed to developing applications that are not only technologically effective but also accessible and low-cost. It is equally important that these technologies contribute to creating open spaces for public experimentation.

Assistive Robotics and Inclusion

Another application of assistive technologies highlighted by Muchaluat Saade is the use of socially assistive robots to promote the inclusion of girls and women in STEM fields. This approach was implemented in the project Include <meninas.uff>, a partner of the Brazilian Computer Society’s “Meninas Digitais” program. It is an extension project at UFF that currently involves 15 volunteers and has had over 50 collaborators throughout its history.

One branch of the project focuses on teaching computing concepts to girls from public schools in Niterói. As part of this initiative, a hackathon was organized using the design thinking methodology to encourage participants to identify a problem and propose a solution.

The challenge proposed was for student teams to imagine a socially assistive robot in a school setting and explore ways it could be useful. The students learned to program using the robot’s language—through a simulator also developed by the UFF team—during a six-week activity. Each week, participants attended sessions at the university, progressing through the stages of the design thinking process. Throughout the program, they developed solutions by programming the robot, and at the end, each team executed its program on the robot.

One of the ideas that emerged from this experience was to use the robot as a tool for teaching computational thinking in schools. This is now a key competency in Brazil’s National Common Core Curriculum (BNCC), which emphasizes the importance of skills such as understanding, analyzing, defining, modeling, solving, comparing, and automating problems and their solutions in a systematic and methodical way through the development of algorithms.