Having constant, reliable access to a working HVAC system is vital for our way of living, as they provide a steady supply of fresh, conditioned air. In an effort to decrease downtime and maintenance costs from failures, Yunior González and Danelis Guillan have developed a prototype device that aims to leverage edge machine learning to predict issues before they occur.
The duo went with a Nicla Sense ME due to its onboard accelerometer, and after collecting many readings from each of the three axes at a 10Hz sampling rate, they imported the data into Edge Impulse to create the model. This time, rather than using a classifier, they utilized a K-means clustering algorithm — which is great at detecting anomalous readings, such as a motor spinning erratically, compared to a steady baseline.
Once the Nicla Sense ME had detected an anomaly, it needed a way to send this data somewhere else and generate an alert. González and Guillan’s setup accomplishes the goal by connecting a Microchip AVR-IoT Cellular Mini board to the Sense ME along with a screen, and upon receiving a digital signal from the Sense ME, the AVR-IoT Cellular Mini logs a failure in an Azure Cosmos DB instance where it can be viewed later on a web app.
Arduino is ready to graduate its educational efforts in support of university-level STEM and R&D programs across the United States: this is where students come together to explore the solutions that will soon define their future, in terms of their personal careers and more importantly of their impact on the world.
Case in point: the groundbreaking partnership with the Ohio State University Buckeye Solar Racing Team, a student organization at the forefront of solar vehicle technology, committed to promoting sustainable transportation by designing, building, and racing solar-powered vehicles in national and international competitions. This collaboration will see the integration of advanced Arduino hardware into the team’s cutting-edge solar vehicles, enhancing driver displays, data transmission, and cockpit metric monitoring.
In particular, the team identified the Arduino Pro Portenta C33 as the best option for their car: “extremely low-powered, high-quality and reliable, it also has a CAN interface – which is how we will be getting data from our sensors,” team lead Vasilios Konstantacos shared.
We have also provided Arduino Student Kits for prototyping and, most importantly, accelerating the learning curve for new members. “Our goal is to rapidly equip our newcomers with vital skills, enabling them to contribute meaningfully to our team’s progress. Arduino’s hardware is a game-changer in this regard,” Vasilios stated. In addition, the team received Nicla Vision, Nicla Sense ME, and Nicla Voice modules to integrate essential sensors in the car, and more Portenta components to make their R&D process run faster (pun intended!): Portenta Breakout to speed up development on the Portenta C33, Portenta H7 to experiment with AI models for vehicle driving and testing, and Portenta Cat. M1/NB IoT GNSS Shield to connect the H7 to the car wirelessly, replacing walkie-talkie communication, and track the vehicle’s location.
Combining our beginner-friendly approach with the advanced features of the Arduino Pro range is the key to empower students like the members of the Buckeye Solar Racing Team to learn and develop truly innovative solutions with the support of a qualified industrial partner and high-performance technological products. In particular, the Arduino ecosystem offers a dual advantage in this case: components’ extreme ruggedness, essential for race vehicle operations, paired with the familiarity and ease of use of the Arduino IDE.
The partnership will empower Ohio State University students to experiment with microcontrollers and sensors in a high-performance setting, fostering a seamless, hands-on learning experience and supporting the institution’s dedication to providing unparalleled opportunities for real-world application of engineering and technology studies. Arduino’s renowned reliability and intuitive interface make it an ideal platform for students to develop solutions that are not only effective in the demanding environment of solar racing but also transferable to their future professional pursuits.
“We are thrilled to collaborate with the Ohio State University Buckeye Solar Racing Team,” commented Jason Strickland, Arduino’s Higher Education Sales Manager. “Our mission has always been to make technology accessible and foster innovation. Seeing our hardware contribute to advancing solar racing technology and education is a proud moment for Arduino.”
The traditional method for changing a diaper starts when someone smells or feels the that the diaper has been soiled, and while it isn’t the greatest process, removing the soiled diaper as soon as possible is important for avoiding rashes and infections. Justin Lutz has created an intelligent solution to this situation by designing a small device that alerts people over Bluetooth® when the diaper is ready to be changed.
Because a dirty diaper gives off volatile organic compounds (VOCs) and small particulates, Lutz realized he could use the Arduino Nicla Sense ME’s built-in BME688 sensor which can measure VOCs, temperature/humidity, and air quality. After gathering 29 minutes of gas and air quality measurements in the Edge impulse Studio for both clean and soiled diapers, he trained a classification model for 300 epochs, resulting in a model with 95% accuracy.
Based on his prior experience with the Nicla Sense ME’s BLE capabilities and MIT App Inventor, Lutz used the two to devise a small gadget that wirelessly connects to a phone app so it can send notifications when it’s time for a new diaper.
On June 26th-28th, the Arduino Pro team will be in Amsterdam for the tinyML EMEA Innovation Forum – one of the year’s major events for the world where AI models meet agile, low-power devices.
This is an exciting time for companies like Arduino and anyone interested in accelerating the adoption of tiny machine learning: technologies, products, and ideas are converging into a worldwide phenomenon with incredible potential – and countless applications already.
At the summit, our team will indeed present a selection of demos that leverage tinyML to create useful solutions in a variety of industries and contexts. For example, we will present:
A fan anomaly detection system based on the Nicla Sense ME. In this solution developed with SensiML, the Nicla module leverages its integrated accelerometer to constantly measure the vibrations generated by a computer fan. Thanks to a trained model, condition monitoring turns into anomaly detection – the system is able to determine whether the fan is on or off, notify users of any shocks, and even alert them if its super precise and efficient sensor detects sub-optimal airflow.
A vineyard pest monitoring system with the Nicla Vision and MKR WAN 1310. Machine vision works at the service of smart agriculture in this solution: even in the most remote field, a pheromone is used to attract insects inside a case lined with glue traps. The goal is not to capture all the insects, but to use a Nicla Vision module to take a snapshot of the captured bugs, recognize the ones that pose a real threat, and send updated data on how many specimens were found. New-generation farmers can thus schedule interventions against pests as soon as needed, before the insects get out of control and cause damage to the crops. Leveraging LoRa® connectivity, this application is both low-power and high-efficiency.
An energy monitoring-based anomaly detection solution for DC motors, with the Opta. This application developed with Edge Impulse leverages an Opta WiFi microPLC to easily implement industrial-level, real-time monitoring and fault detection – great to enable predictive maintenance, reducing downtime and overall costs. A Hall effect current sensor is attached in series with the supply line of the DC motor to acquire real-time data, which is then analyzed using ML algorithms to identify patterns and trends that might indicate faulty operation. The DC motor is expected to be in one of two statuses – ON or OFF – but different conditions can be simulated with the potentiometer. When unexpected electric consumption is shown, the Opta WiFi detects the anomaly and turns on a warning LED.
The Arduino Pro team is looking forward to meeting customers and partners in Amsterdam – championing open source, accessibility, and flexibility in industrial-grade solutions at the tinyML EMEA Innovation Forum!
On June 26th-28th, the Arduino Pro team will be in Amsterdam for the tinyML EMEA Innovation Forum – one of the year’s major events for the world where AI models meet agile, low-power devices.
This is an exciting time for companies like Arduino and anyone interested in accelerating the adoption of tiny machine learning: technologies, products, and ideas are converging into a worldwide phenomenon with incredible potential – and countless applications already.
At the summit, our team will indeed present a selection of demos that leverage tinyML to create useful solutions in a variety of industries and contexts. For example, we will present:
A fan anomaly detection system based on the Nicla Sense ME. In this solution developed with SensiML, the Nicla module leverages its integrated accelerometer to constantly measure the vibrations generated by a computer fan. Thanks to a trained model, condition monitoring turns into anomaly detection – the system is able to determine whether the fan is on or off, notify users of any shocks, and even alert them if its super precise and efficient sensor detects sub-optimal airflow.
A vineyard pest monitoring system with the Nicla Vision and MKR WAN 1310. Machine vision works at the service of smart agriculture in this solution: even in the most remote field, a pheromone is used to attract insects inside a case lined with glue traps. The goal is not to capture all the insects, but to use a Nicla Vision module to take a snapshot of the captured bugs, recognize the ones that pose a real threat, and send updated data on how many specimens were found. New-generation farmers can thus schedule interventions against pests as soon as needed, before the insects get out of control and cause damage to the crops. Leveraging LoRa® connectivity, this application is both low-power and high-efficiency.
An energy monitoring-based anomaly detection solution for DC motors, with the Opta. This application developed with Edge Impulse leverages an Opta WiFi microPLC to easily implement industrial-level, real-time monitoring and fault detection – great to enable predictive maintenance, reducing downtime and overall costs. A Hall effect current sensor is attached in series with the supply line of the DC motor to acquire real-time data, which is then analyzed using ML algorithms to identify patterns and trends that might indicate faulty operation. The DC motor is expected to be in one of two statuses – ON or OFF – but different conditions can be simulated with the potentiometer. When unexpected electric consumption is shown, the Opta WiFi detects the anomaly and turns on a warning LED.
The Arduino Pro team is looking forward to meeting customers and partners in Amsterdam – championing open source, accessibility, and flexibility in industrial-grade solutions at the tinyML EMEA Innovation Forum!
Personal safety is a growing concern in a variety of settings: from high-risk jobs where HSE managers must guarantee workers’ security to the increasingly common work and study choices that drive family and friends far apart, sometimes leading to more isolated lives. In all of these situations, having a system capable of sensing and automatically contacting help in case of emergency can not only give people peace of mind, but save lives.
A particularly interesting case – as the world population ages – regards the increasing number of elderly people who are still healthy enough to be independent yet must also accept the fact their bodies are becoming weaker and their bones more fragile. This specific target is more prone to falls, which can result in fractures, head injuries, and other serious accidents that can severely impact the quality of life. Detecting falls early can allow for prompt medical attention and prevent serious consequences. Additionally, detecting falls can help identify underlying health issues or environmental factors that may be contributing to accidents, allowing for appropriate interventions to be put in place to avoid future falls.
A variety of person-down systems and fall detection methods exist, ranging from threshold-based algorithms to traditional machine-learning applications. The biggest challenge they all share is they suffer from high false-positive triggers. In other words, they cause unnecessary alarm and distress to both the seniors and their caregivers, resulting in unwarranted actions.
Our solution
A tiny but mighty deployment device: Nicla Sense ME
For its project, Aizip selected the Nicla Sense ME: a compact module integrating multiple cutting-edge sensors from Bosch Sensortec, enabling sensor fusion applications directly at the edge. Additionally, the module houses an Arm® Cortex®-M4 microcontroller (nRF52832) leveraging Bluetooth® 4.2. Aizip’s neural network model fits right in with the remaining resources of the microcontroller, thanks to its compact footprint. The result? A small and lightweight device that can be clipped onto one’s belt and worn all day without hassle, able to monitor health parameters and immediately alert assistance in case of fall, with near-zero latency and full respect for privacy.
A more accurate fall detection algorithm
Aizip’s fall detection solution integrates a neural network algorithm with sensor fusion to greatly enhance detection accuracy, while also being lightweight enough it can run in real time on a microcontroller. The neural network within the microcontroller continuously processes sensor readings from the accelerometer (BHI260AP) and the pressure sensor (BMP390). Upon detecting a fall, the device sends an alarm via Bluetooth and activates an on-board LED. In order to minimize frequent false alarms that could significantly affect user experience, the neural network is optimized to differentiate real falls from abrupt movements such as jumping, sprinting, and quickly sitting down. The neural network-based algorithm excels at capturing subtle features in inputs, leading to a substantial reduction in false alarm rates compared to threshold-based approaches or traditional machine learning algorithms.
Typical neural networks offer superior performances but also pose additional challenges, when deploying them onto resource-constrained microcontroller devices, due to the extensive computing and memory resources required. The simultaneous need for Bluetooth connectivity and sensor fusion further compounds this issue. However, Aizip’s proprietary efficient neural network architecture makes this solution stand out because it minimizes resource requirements while maintaining high accuracy. The neural network is quantized to 8bit and deployed onto the microcontroller using Aizip’s automated design tool. The implemented model achieves a 94% fall detection accuracy and a <0.1% false positive rate, all while utilizing less than 3KB of RAM. A perfect fit for the low-consumption Nicla Sense ME!
Solving it with Arduino Pro
Now let’s explore how we could put all of this together and what we would need for deployment both in terms of hardware and software stack. The Arduino Pro ecosystem is the latest generation of Arduino solutions bringing users the simplicity of integration and scalable, secure, professionally supported services.
When personal safety is a concern, smart wearables that leverage AI can help. And processing the data required to monitor health conditions and prevent falls doesn’t have to come at the expense of comfort or privacy: thanks to extremely efficient models like Aizip’s and compact yet high-performance modules like Arduino Pro’s Nicla Sense ME, you can create a discreet and reliable solution able to immediately call for help when needed (and only when needed).
Hazardous pollution in the form of excess CO2, nitrogen dioxide, microscopic particulates, and volatile organic compounds has become a growing concern, especially in developing countries where access to cleaner technologies might not be available or widely adopted. Krazye Karthik’s Environmental Sense Mask (ES-Mask) focuses on bringing attention to these harmful compounds by displaying ambient air quality measurements in real-time.
In order to get values for the air quality index (AQI), CO2, volatile organic compounds (VOCs), and temperature/humidity, Karthik selected the Nicla Sense ME due to its onboard Bosch BME688 sensor module. In addition to providing this data over Bluetooth® Low Energy, the Nicla Sense ME also sends it over I2C to a MKR WiFi 1010 which is responsible for parsing the data. Once done, a comment is generated for the current AQI ranging from “excellent” to “hazardous.” This reading is displayed on an attached OLED screen and a ring of 24 NeoPixel LEDs are illuminated according to the level of dangerous pollutants.
Beyond the microcontroller and sensor components, Karthik added a 5V fan to a mask along with a few air filters to help increase the cleanliness of the air he was breathing. Last of all, he built a mobile app that grabs the data via BLE and shows it in an organized format.
The art of cave exploration, or spelunking, can get its practitioners far closer to nature and the land we inhabit, but it also comes with a host of potential dangers. Some include extreme environmental conditions, lack of oxygen/toxic gases, and simply having their path closed off due to rock falls. Seeing these problems, Rifqi Abdillah decided to create the Sajac Project based on the Nicla Sense ME attached to a K-Way jacket with the aim of assisting cavers.
Because the Nicla Sense ME contains a combination of motion, pressure, and gas sensors onboard, Abdillah used it to gather raw data about the wearer’s surroundings by continuously taking readings and then transmitting the values over BLE to a mobile device. Each sensor fusion sample was then added to the Edge Impulse Studio and labeled with either “safe,” “bad,” or “danger” depending on how harmful the conditions would be. Finally, a Keras classification model was trained and deployed back to the Nicla as an Arduino library, which is used in conjunction with an OLED screen to show the classification result.
With the model now outputting the sensor readings and if they are safe or unsafe, Abdillah went one step further and developed an app to display them in real-time on a Seeed Studio Wio Terminal. Built in MIT’s App Inventor, it allows the user to select the current status as shown by the Nicla and have it appear on the Terminal’s screen. Fellow cavers are able to be notified in an emergency via a connected LoRa radio that can transmit an alert message.
Few things are worse than going to exercise, coming back home, and then realizing that you have been nose blind the entire time to your own odor. In order to detect the potential stench before anyone else does, Luke Berndt and his daughter, Elena, teamed up to create the Smelling Fresh, Feeling Fresh! project.
Their idea was to take a Nicla Sense ME board along with one of K-Way’s jackets as part of our recent collaboration and use it to recognize when the outerwear developed a foul smell. Data was gathered using already stinky clothes from dirty laundry bins and trash, with the BME688 four-in-one gas sensor picking up the slight differences in CO2, humidity, and volatile organic compounds (VOCs) between clean and smelly samples. All of the data was then uploaded to the Edge Impulse Studio and used to train a model, and after a few more rounds of gathering more data, it was finally accurate enough to deploy.
The original plan involved sending an alert over Bluetooth® Low Energy to an accompanying phone app and displaying the message to the user, but this proved too difficult because of low-memory issues. So instead, the duo simply made the code illuminate the RGB either red, yellow, or green to indicate the current air cleanliness.
Bone density, strength, and coordination all decrease as we age, and this fact can lead to some serious consequences in the form of slips, falls, and other accidents. In Finland, falling is the most common type of accidental death among those age 65 and over, amounting to around 1,200 per year. But Thomas Vikstrom hopes to decrease this number by detecting falls the moment they occur through the use of the Arduino Nicla Sense ME’s accelerometer together with a K-Way jacket and a smartwatch.
At first, Vikstrom tried to gather and label data for all kinds of activities, including sitting, walking, running, driving, etc., but later realized anomaly detection would be much better suited for this application. After collecting around 80 seconds of data with Edge Impulse Studio, he trained an anomaly detection model to detect when any out-of-the-ordinary events occur. The model was then deployed to the Nicla Sense ME by integrating the inferencing code with a BLE service that outputs a positive value when a fall is detected, as well as illuminating the onboard LED.
To receive this information, Vikstrom added a Bangle.js smartwatch to the system which automatically calls an emergency number if the wearer fails to intervene. For more information about this project, you can check out his Edge Impulse docs page here. Although only a proof of concept, this K-Way demonstrates how tinyML-powered outerwear can be used to detect falls, and together with cellular network devices send for help in case the user is immobile.
Planet Arduino is, or at the moment is wishing to become, an aggregation of public weblogs from around the world written by people who develop, play, think on Arduino platform and his son. The opinions expressed in those weblogs and hence this aggregation are those of the original authors. Entries on this page are owned by their authors. We do not edit, endorse or vouch for the contents of individual posts. For more information about Arduino please visit www.arduino.cc
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