Planet Arduino

Spectroscopy is a field of study that utilizes the measurement of electromagnetic radiation (often visible light) as it reflects off of or passes through a substance. It can, for instance, help researchers determine the composition of a material, as that composition influences how the material reflects light. Spectroscopy is also used in medicine, but traditionally requires that patients visit a lab. To enable long-term spectroscopic analysis, a team of engineers built a wearable spectroscopy sensor called Lumos.

Lumos comes in two forms: a smartwatch-like wearable wristband and a fingertip model that resembles the pulse oximeters that nurses put on your finger when you go in for a checkup. The latter is meant for use in doctor’s offices and labs, but the former was designed for patients to wear as they go about their daily lives. It would continue to collect spectroscopic data as they do, which could provide valuable insight. Such long-term data collection would help physicians observe how conditions progress or to see conditions that don’t present consistently.

The engineers chose an A7341 spectral sensor for Lumos because it is compact, but still has a large sensing range. An Arduino Nano 33 IoT development board provides power to the A7341, receives the data from the A7341 through an I2C connection, and then sends the data to a base station via WiFi. Power comes from a 400mAh lithium-ion battery, which lasts for around five hours before it needs recharging. That’s five hours of spectroscopic data to analyze — far more than can be gathered using traditional in-lab instruments.

Image credit: Watson and Kendel et al.

The post Lumos finally enables wearable spectroscopy research appeared first on Arduino Blog.

It’s 15 years since LilyPad — learn what’s new and make your first iron-on circuit!

The post Make an Iron-on Textile Circuit and Learn All About Wearables appeared first on Make: DIY Projects and Ideas for Makers.

It’s 15 years since LilyPad — learn what’s new and make your first iron-on circuit!

The post Make an Iron-on Textile Circuit and Learn All About Wearables appeared first on Make: DIY Projects and Ideas for Makers.

Getting in your daily exercise is vital to living a healthy life and having proper form when squatting can go a long way towards achieving that goal without causing joint pain from doing them incorrectly. The Squats Counter is a device worn around the thigh that utilizes machine learning and TensorFlow Lite to automatically track the user’s form and count how many squats have been performed. 

Creator Manas Pange started his project by flashing the tf4micro-moition-kit code to a Nano 33 BLE Sense, which features an onboard three-axis accelerometer. From there, he opened the Tiny Motion Trainer Experiment by Google that connects to the Arduino over Bluetooth and captures many successive samples of motion. After gathering enough proper and improper form samples, Manas trained, tested, and deployed the resulting model to the board.

Every time a proper squad is performed, the counter ticks down by one until it reaches a predefined goal.

For more details about the Squats Counter, which was recently named a winner in the TensorFlow Lite for Microcontroller Challenge, you can view its GitHub repository here. 

The post This tinyML device counts your squats while you focus on your form appeared first on Arduino Blog.

Wearable displays are nothing new, but many of them lack that all-important “fun” element. That’s why OlivierZ over on Instructables created Wilson the IoT Hat. The smart hat contains a large 232mm by 22mm flexible LED strip on its front that prominently shows rainbow text across a 71×7 LED matrix. The whole thing runs on a single 9V battery, which powers an Arduino Nano, HC-05 Bluetooth module, and LED matrix. All of these components are nicely tucked away within the top of the hat to prevent wearers from seeing unsightly wires. 

Olivier wrote a simple app the connects to the HC-05 module with a single press of a button. Users are then able to type out a message and send it to the device where the letters scroll across the display with various effects applied. If people are sending undesirable messages repeatedly, there’s a blacklist function that enables blocking the problematic user(s). 

Wilson the IoT Hat is a great showcase of just how enjoyable creating interactive wearables can be. More details on the project and its accompanying app can be found in Olivier’s write-up here. 

The post Use your smartphone to control Wilson the IoT Hat appeared first on Arduino Blog.

Epilepsy can be a very terrifying and dangerous condition, as sufferers often experience seizures that can result in a lack of motor control and even consciousness, which is why one team of developers wanted to do something about it. They came up with a simple yet clever way to detect when someone is having a convulsive seizure and then send out an alert to a trusted person. The aptly named Epilet (Epilepsy + bracelet) system uses a Nano 33 BLE Sense along with its onboard accelerometer to continually read data and infer if the sensor is picking up unusual activity. 

The Epilet was configured to leverage machine learning for seizure detection, trained using data captured from its accelerometer within Edge Impulse’s Studio. The team collected 30 samples each of both normal, everyday activities and seizures. From this, they trained a model that is able to correctly classify a seizure 97.8% of the time.

In addition to the physical device itself is an accompanying mobile app that handles the communication. When it receives seizure activity that lasts for at least 10 seconds from the Nano 33 BLE Sense, the app sends an SMS message to a contact of the user’s choice. The Epilet has a lot of potential to help people suffering from epilepsy, and it will be exciting to see what other features get added to it in the future.

The post Epilet is a tinyML-powered bracelet for detecting epileptic seizures appeared first on Arduino Blog.

Researchers at Cornell University’s Hybrid Body Lab have been pursuing a novel woven interface that attaches to the user’s skin. Their aptly named WovenSkin integrates electronics into a fabric pattern, including capacitive sensing materials, shape-memory alloys (SMAs), and thermochromic materials to allow for both input and output functionality.

The “second skin” is connected to Arduino Mini, small LiPo battery, and a capacitive touch controller, enabling it to perform tasks such as transforming the woven output from a visible “8” to “9” after being touched just after 0:40 in the video below. Bluetooth can also be implemented for phone or laptop interactions.

The Hybrid Body Lab team’s full research paper is available here if you’d like to delve deeper into the WovenSkin project.

Weaving as a craft possesses the structural, textural, aesthetic, and cultural expressiveness for creating a diversity of soft, wearable forms that are capable of technological integration. In this project, we extend the woven practice for crafting on-skin interfaces, exploring the potential to “weave a second skin.” Weaving incorporates circuitry in the textile structure, which, when extended to on-skin interface fabrication, allows for electrical connections between layers while maintaining a slim form. Weaving also supports multi-materials integration in the structure itself, offering richer materiality for on-skin devices. We present the results of extensive design experiments that form a design space for adapting weaving for on-skin interface fabrication. We introduce a fabrication approach leveraging the skin-friendly material of PVA, which enables on-skin adherence, and a series of case studies illustrating the functional and design potential of the approach. To understand the feasibility of on-skin wear, we conducted a user study on device wearability. To understand the expressiveness of the design space, we conducted a workshop study in which textiles practitioners created woven on-skin interfaces. We draw insights from this to understand the potential of adapting weaving for crafting on-skin interfaces.

Images: Hybrid Body Lab (CC BY-NC-SA 4.0)

For his school science fair, Mars Kapadia decided to take things up a notch and create his own pair of smart glasses.

The wearable device, which went on to place in the state competition, uses a transparent OLED display to show info from Retro Watch software running on an Android phone. They’re controlled by an Arduino Nano Every with an HC-05 Bluetooth module to communicate with the mobile app. Power is provided via a LiPo battery.

One unusual feature is that the darkened lenses can be flipped down for sun protection in outdoor environments, then up to allow easy viewing in darker areas. Kapadia demonstrates how his glasses work, plus discusses the technology used in the video below.

Hand movements have long been used as a computer interface method, but as reported here, the MemGlove from a team of MIT CSAIL researchers takes things several steps further. This augmented glove can sense hand poses and how it’s applying pressure to an object.

The wearable uses a novel arrangement of 16 electrodes to detect hand position based on resistance, and six fluid filled tubes that transmit pressure depending on how an item is gripped.

An Arduino Due is used to sense these interactions, which pass information on to a computer for processing. Pose verification is accomplished with a Leap Motion sensor. By training neural networks with TensorFlow, the glove is able to identify various hand poses, as well as distinguish between 30 different household things that are grasped.

More details on the MemGlove can be found in the researchers’ paper here.

Typing with your thumbs on a smartphone has become an everyday activity for many, but what if you could enter text by simply tapping on your index fingers? With BiTipText, that may soon be a reality. 

The researchers’ prototype consists of an interactive skin overlay made out of flexible PCB material, allowing an Arduino Uno and MPR121 sensor chip to read capacitive signals from both digits. 

In testing, users were able to enter text at over 23 WPM, with a 0.03% uncorrected error rate. Notably, the two-handed implementation means that software can determine not only the position of presses, but the sequence of left/right inputs to help with word interpretation.

More details on the bimanual text input method can be found in the team’s paper here.