The mere presence of a flame in a controlled environment, such as a candle, is perfectly acceptable, but when tasked with determining if there is cause for alarm solely using vision data, embedded AI models can struggle with false positives. Solomon Githu’s project aims to lower the rate of incorrect detections with a multi-input sensor fusion technique wherein image and temperature data points are used by a model to alert if there’s a potentially dangerous blaze.
Gathering both kinds of data is the Arduino TinyML Kit’s Nano 33 BLE Sense. Using the kit, Githu could capture a wide variety of images thanks to the OV7675 camera module and temperature information with the Nano 33 BLE Sense’s onboard HTS221 sensor. After exporting a large dataset of fire/fire-less samples alongside a range of ambient temperatures, he leveraged Google Colab to train the model before importing it into the Edge Impulse Studio. In here, the model’s memory footprint was further reduced to fit onto the Nano 33 BLE Sense.
The inferencing sketch polls the camera for a new frame, and once it has been resized, its frame data, along with a new sample from the temperature sensor, are merged and sent through the model which outputs either “fire” or “safe_environment”. As detailed in Githu’s project post, the system accurately classified several scenarios in which a flame combined with elevated temperatures resulted in a positive detection.
For children who experience certain developmental delays, specific types of physical therapies are often employed to assist them in improving their balance and motor skills/coordination. Ivan Hernandez, Juan Diego Zambrano, and Abdelrahman Farag were looking for a way to quantify the progress patients make while simultaneously presenting a gamified approach, so they developed a standalone node for equilibrium evaluation that could do both.
On the hardware side of things, an Arduino Nano BLE 33 Sense Rev2 is responsible for handling all of the incoming motion data from its onboard BMI270 six-axis IMU and BMM150 three-axis magnetometer. New readings are constantly taken, filtered, and fused together before being sent to an external device over Bluetooth Low Energy. The board was also connected to a buzzer and buttons for user inputs, as well as an RGB LED to get a real-time status.
The patient begins the session by first putting on the wearable and connecting to the accompanying therapist application. Next, a game starts in which the user must move their torso to guide an image of a shark over the image of a stationary fish within a time period — ultimately trying to get the highest score possible. Throughout all of this, a vision system synchronizes its readings with the IMU sensor readings for an ultra-detailed look at how the patient responds to the game over time.
A great deal of building maintenance expenses are the result of simple inaccessibility. Cleaning the windows are your house is a trivial chore, but cleaning the windows on a skyscraper is serious undertaking that needs specialized equipment and training. To make exterior wall tile inspection efficient and affordable, the GLEWBOT team turned to nature for inspiration.
GLEWBOT climbs up walls like a gecko and taps on tiles like a woodpecker to evaluate wall integrity. Like cleaning the windows on a skyscraper, the traditional inspection method requires specialized tools and skills. GLEWBOT can perform the same functions autonomously, dramatically reducing costs.
This robot has a two-part design that lets it scale walls in a manner similar to a climber using ascenders. One part grips, while the other releases. When the bottom part grips, the top part can extend to move up the wall. When the top part grips, the bottom part can retract to repeat the process. The robot grips the tile using suction cup feet connected to micro vacuum pumps and a linear actuator performs the extension/retraction. Each end has a motor that lets it rotate relative to the linear actuator, so the robot can turn.
The system is equipped with two Arduino boards. An Arduino Nano serves as central command and handles general functions, while an Arduino Nano 33 BLE Sense acts as an acoustic recognition module and controls the inspection tool. That tool is a hollow drum hammer that taps each tile and listens for the resulting echo. An audio classification model trained for this task will detect a questionable tile based on the sound it makes, so engineers can investigate further.
Soon after a police station opened near his house, Christopher Cooper noticed a substantial increase in the amount of emergency vehicle traffic and their associated noises even though local officials had promised that it would not be disruptive. But rather than write down every occurrence to track the volume of disturbances, he came up with a connected audio-classifying device that can automatically note the time and type of sound for later analysis.
Categorizing each sound was done by leveraging Edge Impulse and an Arduino Nano 33 BLE Sense. After training a model and deploying it within a sketch, the Nano will continually listen for new noises through its onboard microphone, run an inference, and then output the label and confidence over UART serial. Reading this stream of data is an ESP32 Dev Kit, which displays every entry in a list on a useful GUI. The screen allows users to select rows, view more detailed information, and even modify the category if needed.
Going beyond the hardware aspect, Cooper’s project also includes a web server running on the ESP32 that can show the logs within a browser, and users can even connect an SD card to have automated file entries created. For more information about this project, you can read Cooper’s write-up here on Hackster.io.
In July 2023, Samuel Alexander set out to reduce the amount of trash that gets thrown out due to poor sorting practices at the recycling bin. His original design relied on an Arduino Nano 33 BLE Sense to capture audio through its onboard microphone and then perform edge audio classification with an embedded ML model to automatically separate materials based on the sound they make when tossed inside. But in this latest iteration, Alexander added several large improvements to help the concept scale much further.
Perhaps the most substantial modification, the bin now uses an Arduino Pro Portenta C33 in combination with an external Nicla Voice or Nano 33 BLE Sense to not only perform inferences to sort trash, but also send real-time data to a cloud endpoint. By utilizing the Arduino Cloud through the Portanta C33, each AI-enabled recycling bin can now report its current capacity for each type of waste and then send an alert when collection must occur.
While not as practical for household use, this integration could be incredibly effective for municipalities looking to create a network of bins that can be deployed in a city park environment or another public space.
Thanks to these upgrades, Alexander was able to submit his prototype for consideration in the 2023 Hackaday Prize competition where he was awarded the Protolabs manufacturing grant. To see more about this innovative project, you can check out its write-up here and watch Alexander’s detailed explanation video below.
For well over one hundred years, people have been constructing machines that dispense fortunes to those who ask at the insertion of a coin and the push of a button. In modern days, this has taken the form of mobile apps that can be far more expansive, albeit with a lack of physical interaction. Seeing an opportunity to use an embedded speech recognition model in this kind of application, the Electronic Cats team built the aptly named Fortune Cat just in time for some Halloween fun.
This small device, based on the Arduino Nano 33 BLE Sense, takes advantage of the onboard microphone to listen for words being spoken at the small cube. Performing the language processing is Cyberon’s Arduino Speech Recognition Engine, which was configured to listen for the wake phrase “Fortune Cat” and then later respond to “tell me my future” as its command phrase. After generating the model online, it was incorporated into the code via the DSpotter software development kit that lets the program check if either the wake or action phrase has been said and then act accordingly.
In this case, asking Fortune Cat for your future will present one of 20 random phrases that get displayed on the OLED mounted to the top of the 3D-printed enclosure. To read more about how the Electronic Cats crew created and configured Fortune Cat, you can check out their write-up here on Hackster.io or watch their video below!
When dealing with indoor climate controls, there are several variables to consider, such as the outside weather, people’s tolerance to hot or cold temperatures, and the desired level of energy savings. Windows can make this extra challenging, as they let in large amounts of light/heat and can create poorly insulated regions, which is why Jallson Suryo developed a prototype that aims to balance these needs automatically through edge AI techniques.
Suryo’s smart building ventilation system utilizes two separate boards, with an Arduino Nano 33 BLE Sense handling environmental sensor fusion and a Nicla Voice listening for certain ambient sounds. Rain and thunder noises were uploaded from an existing dataset, split and labeled accordingly, and then used to train a Syntiant audio classification model for the Nicla Voice’s NDP120 processor. Meanwhile, weather and ambient light data was gathered using the Nano’s onboard sensors and combined into time-series samples with labels for sunny/cloudy, humid, comfortable, and dry conditions.
After deploying the board’s respective classification models, Suryo added some additional code that writes new I2C data from the Nicla Voice to the Nano that indicates if rain/thunderstorm sounds are present. If they are, the Nano can automatically close the window via servo motors while other environmental factors can set the position of the blinds. With this multi-sensor technique, a higher level of accuracy can be achieved for more precision control over a building’s windows, and thus attempt to lower the HVAC costs.
When playing a short game of basketball, few people enjoy having to consciously track their number of successful throws. Yet when it comes to automation, nearly all systems rely on infrared or visual proximity detection as a way to determine when a shot has gone through the basket versus missed. This is what inspired one team from the University of Ljubljan to create a small edge ML-powered device that can be suspended from the net with a pair of zip ties for real-time scorekeeping.
After collecting a total of 137 accelerometer samples via an Arduino Nano 33 BLE Sense and labeling them as either a miss, a score, or nothing within the Edge Impulse Studio, the team trained a classification model and reached an accuracy of 84.6% on real-world test data. Getting the classification results from the device to somewhere readable is handled by the Nano’s onboard BLE server. It provides two services, with the first for reporting the current battery level and the second for sending score data.
Once the firmware had been deployed, the last step involved building a mobile application to view the relevant information. The app allows users to connect to the basketball scoring device, check if any new data has been received, and then parse/display the new values onscreen.
Although a large percentage of our trash can be recycled, only a small percentage actually makes it to the proper facility due, in part, to being improperly sorted. So as an effort to help keep more of our trash out of landfills without the need for extra work, Samuel Alexander built a smart recycling bin that relies on machine learning to automatically classify the waste being thrown in and sort it into separate internal compartments.
Because the bin must know what trash is being tossed in, Alexander began this project by first constructing a minimal rig with an Arduino Nano 33 BLE Sense to capture sounds and send them to an Edge Impulse project. From here, the samples were split into 60 one-second samples for each rubbish type, including cans, paper, bottles, and random background noise. The model, once trained, was then deployed to the Nano as a custom Arduino library.
With the board now able to determine what type of garbage has been thrown in, Alexander got to work on the remaining portions of the smart bin. The base received a stepper motor which spins the four compartments to line up with a servo-actuated trap door while a LiPo battery pack provides power to everything for fully wireless operation.
Health tracking is a vital component to recovering after an injury or simply trying to improve one’s own fitness, and although accelerometer-based devices are decent at tracking general activity, they fail to accurately monitor specific areas of the body such as joint movement. This is why a team of researchers from the Singapore University of Technology and Design (SUTD) along with members of SingHealth Polyclinics designed a knitted wearable sensor for use on the knee.
Based on conductive fabric technology, the device utilizes a stitched pattern of conductive threads that change their resistance depending on the extent to which they are stretched. Once added to the garment, the team created a small pocket for storing an Arduino Nano 33 BLE Sense board whose job it is to continuously measure the voltage in the fabric via its ADC and output the results over Bluetooth® Low Energy with a response time of a mere 90 milliseconds.
Through their experiments of making subjects walk, jog, and climb stairs, the researchers were able to compare the electrical signals to actual joint movement in order to correlate the two and calibrate the sensor to translate voltages into degrees of motion. Because of the device having a resolution of just 0.12 degrees, it showed to be a promising candidate as both an effective activity tracker and a comfortable garment that can be worn for extended periods of time.
With an array of onboard sensors, Bluetooth® Low Energy connectivity, and the ability to perform edge AI tasks thanks to its nRF52840 SoC, the Arduino Nano 33 BLE Sense is a great choice for a wide variety of embedded applications. Further demonstrating this point, a group of students from the Introduction to Embedded Deep Learning course at Carnegie Mellon University have published the culmination of their studies through 10 excellent projects that each use the Tiny Machine Learning Kit and Edge Impulse ML platform.
Wrist-based human activity recognition
Traditional human activity tracking has relied on the use of smartwatches and phones to recognize certain exercises based on IMU data. However, few have achieved both continuous and low-power operation, which is why Omkar Savkur, Nicholas Toldalagi, and Kevin Xie explored training an embedded model on combined accelerometer and microphone data to distinguish between handwashing, brushing one’s teeth, and idling. Their project continuously runs inferencing on incoming data and then displays the action on both a screen and via two LEDs.
Categorizing trash with sound
In some circumstances, such as smart cities or home recycling, knowing what types of materials are being thrown away can provide a valuable datapoint for waste management systems. Students Jacky Wang and Gordonson Yan created their project, called SBTrashCat, to recognize trash types by the sounds they make when being thrown into a bin. Currently, the model can three different kinds, along with background noise and human voices to eliminate false positives.
Distributed edge machine learning
The abundance of Internet of Things (IoT) devices has meant an explosion of computational power and the amount of data needing to be processed before it can become useful. Because a single low-cost edge device does not possess enough power on its own for some tasks, Jong-Ik Park, Chad Taylor, and Anudeep Bolimera have designed a system where each device runs its own “slice” of an embedded model in order to make better use of available resources.
Predictive maintenance for electric motors
Motors within an industrial setting require constant smooth and efficient operation in order to ensure consistent uptime, and recognizing when one is failing often necessitates manual inspection before a problem can be discovered. By taking advantage of deep learning techniques and an IMU/camera combination, Abhishek Basrithaya and Yuyang Xu developed a project that could accurately identify motor failure at the edge.
Estimating inventory in real-time with computer vision
Warehouses greatly rely on having up-to-date information about the locations of products, inventory counts, and incoming/outgoing items. From these constraints, Netra Trivedi, Rishi Pachipulusu, and Cathy Tungyun collaborated to gather a dataset of 221 images labeled with the percentage of space remaining on the shelf. This enables the Nano 33 BLE Sense to use an attached camera to calculate empty shelf space in real-time.
Dog movement tracking
Fitness trackers such as the FitBit and Apple Watch have revolutionized personal health tracking, but what about our pets? Ajith Potluri, Eion Tyacke, and Parker Crain addressed this hole in the market by building a dog collar that uses the Nano’s IMU to recognize daily activities and send the results to a smartphone via Bluetooth. This means the dog’s owner has the ability to get an overview of their pet’s day-to-day activity levels across weeks or months.
Intelligent bird feeding system
Owners of backyards everywhere encounter the same problem: “How do I keep the squirrels away from a birdfeeder while also allowing birds?” Eric Wu, Harry Rosmann, and Blaine Huey worked together on a Nano 33 BLE Sense-powered system that employs a camera module to identify if the animal at the feeder is a bird or a squirrel. If it is the latter, an alarm is played from a buzzer. Otherwise, the bird’s species is determined through another model and an image is saved to an SD card for future viewing.
Improving one’s exercise form
Exercise, while being essential to a healthy lifestyle, must also be done correctly in order to avoid accidental injuries or chronic pain later on, and maintain proper form is an easy way to facilitate this. By using both computer vision on an NVIDIA Jetson Nano and anomaly detection via an IMU on a Nano 33 BLE Sense, Addesh Bhargava, Varun Jain, and Rohan Paranjape built a project that was more accurate than typical approaches to squatting form detection.
Whether it is a library, conference room, school classroom, or some other public space, we all require peace and quiet to work sometimes, but achieving it can be a challenge. After wondering if loudmouths could be automatically asked to be silent via some kind of sensor-driven system, Bas op ten Berg, the founder of BotBerg, chose to build one using just a few components.
His smart shusher is based on the Arduino Nano 33 BLE Sense board and its built-in MP34DT05 MEMS microphone element. By reading in the pulse-density modulation (PDM) value from the output pin, he could easily convert it into the sound pressure, and thus the ambient noise level. Setting the noise threshold is done by carefully adjusting a connected potentiometer that gets read multiple times per second from an analog input pin. When the set threshold has been exceeded for a predetermined amount of time, a DFRobot MP3 player module is triggered and plays a sound file containing the phrase “Pssst, silence please! Silence please!” which is sure to grab anyone’s interest. All of the parts are housed within an equally attention-grabbing 3D printed lower face so that it appears to be speaking.
Beyond this local, offline functionality, op ten Berg offers other ways to expand the project, including ideas such as BLE connectivity, extra sounds/lights, or even switching on a relay. More details can be found on his website.
The art of making pottery has existed for tens of thousands of years, and the materials used have stayed relatively similar as nearly all items were made from clay which was left to harden either from the sun or via a kiln. But for those who wish to do only a little sculpting with little regard for the finished product, such as Guillermo Perez Guillen had the idea to employ a cornstarch-based material instead for reduced costs. Beyond merely using one’s hands to shape the “clay”, he also upgraded his 3D pottery machine project with new controls, patterns, and more.
Just like in the first version, this second iteration of the clay sculpting machine relies on an old CD-ROM drive to both spin the platter — handled by an Arduino Nano 33 BLE Sense, and move the toolhead along a single axis. But unlike the previous version, this one introduces far more automation and control. An Arduino Mega 2560 receives inputs from a 4×4 matrix keypad for homing, positioning the toolhead, or running a predefined pattern. A stepper motor, driven by an L298N, moves the gantry left and right while a servo motor can raise or lower the stylus.
With this combination of moveable axes and the ability to creates patterns automatically, this improved system is capable of producing very creative works of cornstarch-based pottery. Guillen hopes that this project could find its way into classrooms as kit for STEM education, helping students intuitively learn how to make 2D figures such as circles, squares and triangles, or even 3D figures like cylinders and cubes. More information about Guillen’s machine can be found here on Hackaday.io.
You’ve heard about the many different snake oil concoctions shilled by con men over the centuries, but did you know that inventors created a variety of machines for similar purposes? The most well-known example is probably the belt vibrator, which purported to induce weight loss. In Argentina during the 1930s, Juan Baigorri Velar claimed to have constructed a functioning rainmaking machine. To pay homage, Roni Bandini used an Arduino to create a replica of the legendary Argentinian rainmaking machine.
Velar’s rainmaking machine almost certainly didn’t work and was either an outright hoax, or the result of misguided optimism masking coincidence. Velar supposedly demonstrated the machine and it was reported as successful at the time, but he never published details about the machine or its operating principles. It was never proven under scientific conditions and no modern experts believe that it could actually summon rainfall. Even today, weather manipulation is very controversial and difficult to perform.
Because details about the original machine are so lacking, Bandini had a lot of freedom for his recreation. But he did try to keep it as accurate as possible, with the notable exception of the radioactive material — including that could be dangerous. The primary component here is an Arduino Nano 33 BLE Sense development board. Other components include a relay, a Peltier cooling cell, an electromagnet, and an analog meter.
In reality, this rainmaking machine isn’t really doing anything except monitoring barometric pressure (through the Arduino’s onboard sensor) and cooling the surface of the Peltier cell. But it sure looks the part. Bandini did a fabulous job with the enclosure, controls, and overall design aesthetic, which looks like something cobbled together by a mad scientist in the 1930s.
In care facilities and hospitals, being able to tell when beds are occupied or free is vital knowledge for the staff, as they can move onto other tasks more quickly with up-to-date information. Adam Milton-Barker’s hospital bed occupancy detection system aims to accomplish this goal by combining embedded machine learning models and connected hardware for gathering real-time data.
Milton-Barker’s first step was to create a new Edge Impulse project and add several samples of himself either getting into bed for an occupied status or standing up to indicate a vacancy by taking continuous measurements from a Nano 33 BLE Sense’s built-in accelerometer, gyroscope, and magnetometer. Once passed through a spectral analysis block, the resulting nine-channel data was used to train a classification model that could accurately detect when a person either gets in or out of bed, or for a lack of general activity.
The resulting model was exported as an Arduino library and added to a custom sketch that fuses the readings from each of the sensors’ three axes and passes it through the same spectral analysis block and the now-trained model to receive an inference.
In his project write-up, Milton-Barker speculates that this solution could be further extended by leveraging the Arduino’s onboard LEDs, Bluetooth connectivity, and recognizing more motions.
The damage and destruction caused by structure fires to both people and the property itself is immense, which is why accurate and reliable fire detection systems are a must-have. As Nekhil R. notes in his write-up, the current rule-based algorithms and simple sensor configurations can lead to reduced accuracy, thus showing a need for more robust systems.
This led Nekhil to devise a solution that leverages sensor fusion and machine learning to make better predictions about the presence of flames. His project began with collecting environmental data consisting of temperature, humidity, and pressure from his Arduino Nano 33 BLE Sense’s onboard sensor suite. He also labeled each sample either Fire or No Fire using the Edge Impulse Studio, which was used to generate spectral features from the three time-series sensor values. This information was then passed along to a Keras neural network that had been configured to perform classification, resulting in an overall accuracy of 92.86% when run on real world test samples.
Confident in his now-trained model, Nekhil deployed his model as an Arduino library back to the Nano 33 BLE Sense. The Nano sends a message over its UART pins to an awaiting ESP8266-01 board when a fire has been detected. And in turn, the ESP8266 triggers an IFTTT webhook to alert the user via an email.
If you would like to learn more about the construction of this fire recognition system, plenty of details can be found on the project page.
Released in 2015, the Apple Pencil is a technology-packed stylus that allows users to write on iPad screens with variations in pressure and angle — all while communicating with very low latencies. Nekhil Ravi and Shebin Jose Jacob of Coders Café were inspired by this piece of handheld tech to come up with their own pencil concept, except this one wouldn’t need a screen in order to function.
The pair’s writing utensil relies on recognizing certain gestures as letters, and once one has been detected, outputs the result over USB or Bluetooth® to the host device. They started by first gathering many samples of different letters and how they correlate to the change in motion on the Arduino Nano 33 BLE Sense’s built-in accelerometer. From here, they designed an impulse in the Edge Impulse Studio to extract spectral features from the time series accelerometer data and pass it to a classification Keras neural network. The resulting model could accurately determine the correct letter from each gesture, making it suitable for deployment back to the Nano 33 BLE Sense.
Before testing their new inferencing code on the hardware, a simple 3D-printed case was designed to fit around the board to look like the real Apple Pencil. Additionally, the team made a simple website that could receive data from the board over BLE and display the corresponding letter within the browser window. To see more about this project, you can watch their video below!
Nearly every manufacturer uses a machine at some point in their process, and each of those machines is almost guaranteed to contain at least one motor. In order to maintain uptime and efficiency, these motors must always work correctly, as even a small breakdown can lead to disastrous effects. Predictive maintenance aims to achieve this goal while also not going overboard in trying to prevent them entirely by combining sensors with predictive techniques that can schedule maintenance when a failure is probable.
Shebin Jose Jacob’s solution utilizes the Arduino Nano 33 BLE Sense, along with its built-in microphone, to capture audio and predict when a motor is about to fail. He achieved this by first creating a new Edge Impulse project and gathering samples for four classes of sound: OK, anomaly 1, and anomaly 2, as well as general background noise. After designing an impulse and training a classification model on the samples, he was able to achieve an impressive accuracy of about 95% on the test samples.
The final step involved deploying the model as firmware for the Arduino, which would allow it to classify sounds in real-time by continuously reading from the microphone. Whenever an anomaly is detected, a red LED at the top illuminates.
Having something broken into and/or destroyed is an act that most people hope to avoid altogether or at least catch the perpetrator in the act when it does occur. And as Nekhil R. notes in his project write-up, traditional deterrence/detection methods often fail, meaning that a newer type of solution was necessary.
Unlike other glass breaking sensors, Nekhil’s project relies on a single, inexpensive Arduino Nano 33 BLE Sense and its onboard digital microphone to record audio, classify it, and then alert a property owner over WiFi via an ESP8266-01 board. The dataset used to train the machine learning model came from two sources: the Microsoft Scalable Noisy Speech Dataset for background noise, and breaking glass recorded on the device itself. Both of these were added to an Edge Impulse project via the Studio and split into two-second samples before being processed by a Mel-filterbank Energy (MFE) algorithm.
The resulting model, trained using 200 training cycles and slight noise additions, resulted in an impressive 92% accuracy, with some glass breaking samples being misclassified as mere noise. This was then exported to the Nano 33 BLE Sense as a library for use in a sketch that continually classifies incoming sounds and sends an email with the help of IFTTT if breaking glass is detected.
A large number of diseases involve coughing as one of their primary symptoms, but none are quite as concerning as chronic obstructive pulmonary disease (COPD), which causes airflow blockages and other breathing problems in those afflicted by it. Consistently monitoring the frequency and intensity of coughing is vital for tracking how well the disease is being treated, yet current solutions are impractical outside of a hospital setting.
Eivind Holt had the idea to use an Arduino Nano 33 BLE Sense running a custom tinyML model to automatically classify sounds as either a cough or non-cough and report them to a cloud service. Once a total of 647 audio samples had been collected, Eivind trained a Keras neural networking using Edge Impulse that could correctly identify the sound about 99% of the time. The program he wrote for the Nano creates a custom BLE service with a single cough counting characteristic that is incremented for each detection.
Getting the number of coughs from the local device to the cloud for later analysis and display was accomplished by using the nRF Android app to receive BLE data and transmit it to the nRF Cloud. Meanwhile, a pair of 500mAh batteries were connected and everything was placed into a 3D-printed case that could easily sit near a person’s neck.
As the frequency and intensity of droughts around the world continues to increase, being able to reduce our water usage is vital for maintaining already strained freshwater resources. And according to the EPA, leaving a faucet running, whether intentionally or by accident for just five minutes can consume over ten gallons of water. However, Naveen has leveraged the power of machine learning to build a device that can automatically detect running faucets and send alerts over a cellular network in response.
The hardware for this project is primarily centered around a Blues Wireless Notecard for cellular connectivity, a Blues Wireless Notecarrier-B as its breakout board, and a machine learning-capable microcontroller in the form of an Arduino Nano 33 BLE Sense. Beyond merely having a 32-bit Arm Cortex-M4 processor and 1MB of flash storage, its built-in microphone can be used to easily capture audio data. In this project, Naveen uploaded a dataset containing 15 minutes of either faucet noises or background noise into the Edge Impulse Studio before training a 1D convolutional neural network, which achieved an accuracy of 99.2%.
From here, a new Twilio route was created that allows the Blues Wireless Notecard to generate SMS messages by sending an API request. Now whenever a faucet has been classified as running for too long, the Nano 33 BLE Sense can transmit a simple command over I2C to the Notecard and alert the recipient.
We all strive to maintain healthier lifestyles, yet the kitchen is often the most challenging environment by far due to it containing a wide range of foods and beverages. The Smart-Badge project, created by a team of researchers from the German Research Centre for Artificial Intelligence (DFKI), aims to track just how many times we reach for the refrigerator door or drink water using machine learning and a suite of environmental sensors.
The wearable device itself is comprised of a single PCB that houses a pair of microcontrollers, an NXP iMXRT1062 for quickly gathering complex data, and an Arduino Nano 33 BLE Sense for collecting more basic samples. Whether it’s the digital gas sensor, the accelerometer, an IR thermal array, or an air pressure sensor, each reading is compiled into a single stream which updates at 6Hz and can either be stored locally on an SD card or sent via Bluetooth® to a phone.
After having 10 volunteers perform various tasks around a mock kitchen while wearing the Smart-Badge and then labeling each activity, the researchers were able to collect a sizable dataset. The 791 total data channels were fed through several layers of a neural network that could ultimately classify activities with 92.4% accuracy.
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