Schlagwort: wearables

  • Machine learning makes fabric buttons practical

    Machine learning makes fabric buttons practical

    Reading Time: 2 minutes

    The entire tech industry is desperate for a practical wearable HMI (Human Machine Interface) right now. The most newsworthy devices at CES this year were the Rabbit R1 and the Humane AI Pin, both of which are attempts to streamline wearable interfaces with and for AI. Both have numerous drawbacks, as do most other approaches. What the world really needs is an affordable, practical, and unobtrusive solution, and North Carolina State University researchers may have found the answer in machine learning-optimized fabric buttons.

    It is, of course, possible to adhere a conventional button to fabric. But by making the button itself from fabric, these researchers have improved comfort, lowered costs, and introduced a lot more flexibility — both literally and metaphorically. These are triboelectric touch sensors, which detect the amount of force exerted on them by measuring the energy between two layers of opposite charges.

    But there is a problem with this approach: the measured values vary dramatically based on usage, environmental conditions, manufacturing tolerances, and physical wear. The fabric button on one shirt sleeve may present completely different readings than another. If this were a simple binary button, it wouldn’t be as challenging of an issue. But the whole point of this sensor type is to provide a one-dimensional scale corresponding to the pressure exerted, so consistency is important.

    Because achieving physical consistency isn’t practical, the team turned to machine learning. A TensorFlow Lite for Microcontrollers machine learning model, running on an Arduino Nano ESP32 board, interprets the readings from the sensors. It is then able to differentiate between several interactions: single clicks, double clicks, triple clicks, single slides, double slides, and long presses.

    Even if the exact readings change between sensors (or the same sensor over time), the patterns are still recognizable to the machine learning model. This would make it practical to integrate fabric buttons into inexpensive garments and users could interact with their devices through those interfaces.

    The researchers demonstrated the concept with mobile apps and even a game. More details can be found in their paper here.

    Image credit: Y. Chen et al.

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  • Building the OG smartwatch from Inspector Gadget

    Building the OG smartwatch from Inspector Gadget

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    We recently showed you Becky Stern’s recreation of the “computer book” carried by Penny in the Inspector Gadget cartoon, but Stern didn’t stop there. She also built a replica of Penny’s most iconic gadget: her watch. Penny was a trendsetter and rocked that decades before the Apple Watch hit the market. Stern’s replica looks just like the cartoon version and even has some of the same features.

    The centerpiece of this project is an Arduino Nicla Voice board. The Arduino team designed that board specifically for speech recognition on the edge, which made it perfect for recognizing Penny’s signature “come in, Brain!” voice command. Stern used Edge Impulse to train an AI to recognize that phrase as a wake word. When the Nicla Voice board hears that, it changes the image on the smart watch screen to a new picture of Brain the dog.

    The Nicla Vision board and an Adafruit 1.69″ color IPS TFT screen fit inside a 3D-printed enclosure modeled on Penny’s watch from the cartoon. That even has a clever 3D-printed watch band with links connected by lengths of fresh filament. Power comes from a small lithium battery that also fits inside the enclosure.

    This watch and Stern’s computer book will both be part of an Inspector Gadget display put on by Digi-Key at Maker Faire Rome, so you can see it in person if you attend.

    [youtube https://www.youtube.com/watch?v=Yd74FYTvGX8?feature=oembed&w=500&h=281]

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  • This AI system helps visually impaired people locate dining utensils

    This AI system helps visually impaired people locate dining utensils

    Reading Time: 2 minutes

    People with visual impairments also enjoy going out to a restaurant for a nice meal, which is why it is common for wait staff to place the salt and pepper shakes in a consistent fashion: salt on the right and pepper on the left. That helps visually impaired diners quickly find the spice they’re looking for and a similar arrangement works for utensils. But what about after the diner sets down a utensil in the middle of a meal? The ForkLocator is an AI system that can help them locate the utensil again.

    This is a wearable device meant for people with visual impairments. It uses object recognition and haptic cues to help the user locate their fork. The current prototype, built by Revoxdyna, only works with forks. But it would be possible to expand the system to work with the full range of utensils. Haptic cues come from four servo motors, which prod the user’s arm to indicate the direction in which they should move their hand to find the fork.

    The user’s smartphone performs the object recognition and should be worn or positioned in such a way that its camera faces the table. The smartphone app looks for the plate, the fork, and the user’s hand. It then calculates a vector from the hand to the fork and tells an Arduino board to actuate the servo motors corresponding to that direction. Those servos and the Arduino attach to a 3D-printed frame that straps to the user’s upper arm.

    A lot more development is necessary before a system like the ForkLocator would be ready for the consumer market, but the accessibility benefits are something to applaud.

    [youtube https://www.youtube.com/watch?v=_TgC0KYyzwI?feature=oembed&w=500&h=281]

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  • The Emotion Aid is a wearable device that communicates its user’s emotions

    The Emotion Aid is a wearable device that communicates its user’s emotions

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    Many people (especially those with autism spectrum disorder) have difficulty communicating with others around them. That is always a challenge, but becomes particularly noticeable when one cannot convey their emotions through body language. If someone can’t show that they’re not in the mood to talk, that may lead to confusing interactions. To help people express their emotions, University of Stuttgart students Clara Blum and Mohammad Jafari came up with this wearable device that makes them obvious.

    The aptly named Emotion Aid sits on the user’s shoulders like a small backpack. The prototype was designed to attach to a bra, but it could be tweaked to be worn by those who don’t use bras. It has two functions: detecting the user’s emotions and communicating those emotions. It uses an array of different sensors to detect biometric indicators, such as temperature, pulse, and sweat, to try and determine the user’s emotional state. It then conveys that emotional state to the surrounding world with an actuated fan-like apparatus.

    An Arduino Uno Rev3 handles these functions. Input comes from a capacitive moisture sensor, a temperature sensor, and a pulse sensor. The Arduino actuates the fan mechanism using a small hobby servo motor. Power comes from a 9V battery. The assembly process is highly dependent on the way the device is to be worn, but the write-up illustrates how to attach the various sensors to a bra. There are many possible variations, so the creators of the Emotion Aid encourage people to experiment with the idea.

    You can read more about the Emotion Aid, which was developed by Blum and Jafari as part of the University of Stuttgart’s ITECH master’s program, here on Instructables.

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  • Fingertip force control aids in sports and musical training

    Fingertip force control aids in sports and musical training

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    A great number of activities require the precise application of force with the fingertips. When playing a guitar, for example, you must exert the proper amount of force to push a string against the fret board. Training is difficult, because new guitarists don’t know how much force to apply. This wearable system controls fingertip force to help users learn how to perform new activities.

    Developed by NTT Corporation researchers, the system needs two parts to enable fingertip force control: stimulation and feedback. EMS (electronic muscle stimulation) handles the former by pulsing a small amount of electric current through the user’s muscles, forcing them to contract. That is commonplace technology today, with uses ranging from legitimate medical therapy to more homeopathic remedies. For feedback, the system utilizes bioacoustic technology (a transducer and piezoelectric sensor) to determine the amount of force applied by a user’s finger.

    An Arduino Uno Rev3 board paired with a function generator gives the system precise control over the EMS unit, allowing it to adjust muscle stimulation as necessary. It does so in real-time in response to fingertip force estimated by a machine-learning regression model. An expert in the activity could use the system to train it on the proper amount of force for an action, then the system could provide the amount of stimulation necessary for a new student to replicate the expert’s force. With practice, the student would gain a feel for the force and then could perform the activity on their own without the aid of the system.

    [youtube https://www.youtube.com/watch?v=7T8IF5Lo65c?feature=oembed&w=500&h=281]

    Additional details on the project can be found in the researchers’ paper here.

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  • Emoband strokes and squeezes your wrist

    Emoband strokes and squeezes your wrist

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    Modern consumer devices are fantastic at providing visual and auditory stimulation, but they fail to excite any of the other senses. At most, we get some tactile sensation in the form of haptic feedback. But those course vibrations do little more than provide an indication that something is happening, which is why researchers look for alternatives. Developed by a team of City University of Hong Kong researchers, Emoband provides a new kind of tactile feedback in the form of stroking and squeezing of the user’s wrist.

    Emoband looks a bit like an over-sized smartwatch with three bands. Two of those bands are just normal straps that secure the device to the user’s wrist. The third band, in the middle, can be made of several different materials. It attaches to two spools on the device, which can reel in or out the material. If both reel in the band, then it will squeeze the user’s wrist. If one reels in while the other reels out, then the band strokes the user’s wrist. Depending on the material, those sensations may elicit different emotional responses from the user.

    The prototype Emoband unit uses an Arduino Mega 2560 board to control two servo motors that turn the spools for the material band. A laptop communicates with the Arduino through serial, telling it how to move the band to mirror the onscreen content. Two load cells provide feedback on the amount of squeezing pressure. The prototype device’s frame and spools were 3D-printed.

    [youtube https://www.youtube.com/watch?v=UcP6W2LHZfg?feature=oembed&w=500&h=281]

    In the future, it could be possible to integrate this functionality into the smartwatches that people already wear—if the general public decided that they want this kind of tactile feedback. Initial testing showed the users certainly noticed the feedback, but it isn’t clear if they thought it was worthwhile or practical. More details on the project can be found in the researchers’ paper here.

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  • This GIGA R1 WiFi-powered wearable detects falls using a Transformer model

    This GIGA R1 WiFi-powered wearable detects falls using a Transformer model

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    For those aged 65 and over, falls can be one of the most serious health concerns they face either due to lower mobility or decreasing overall coordination. Recognizing this issue, Naveen Kumar set out to produce a wearable fall-detecting device that aims to increase the speed at which this occurs by utilizing a Transformer-based model rather than a more traditional recurrent neural network (RNN) model.

    Because this project needed to be both fast and consume only small amounts of current, Kumar went with the new Arduino GIGA R1 WiFi due to its STM32H74XI dual-core Arm CPU, onboard WiFi/Bluetooth®, and ability to interface with a wide variety of sensors. After connecting an ADXL345 three-axis accelerometer, he realized that collecting many hours of samples by hand would be far too time consuming, so instead, he downloaded the SisFall dataset, ran a Python script to parse the sample data into an Edge Impulse-compatible format, and then uploaded the resulting JSON files into a new project. Once completed, he used the API to split each sample into four-second segments and then used the Keras block edit feature to build a reduced-sized Transformer model.

    The result after training was a 202KB large model that could accurately determine if a fall occurred 96% of the time. Deployment was then as simple as using the Arduino library feature within a sketch to run an inference and display the result via an LED, though future iterations could leverage the GIGA R1 WiFi’s connectivity to send out alert notifications if an accident is detected. More information can be found here in Kumar’s write-up.

    [youtube https://www.youtube.com/watch?v=wPJF7lJrIWw?feature=oembed&w=500&h=281]

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  • Making jackets smarter by letting them smell

    Making jackets smarter by letting them smell

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    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.

    For more details, you can check out their proof of concept on the Arduino Project Hub.

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  • Detecting falls by embedding ML into clothing

    Detecting falls by embedding ML into clothing

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    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 2 smartwatch to the system which automatically calls an emergency number if the wearer fails to intervene. For more details, you can check out his Edge Impulse docs page here. Although only a proof of concept, this K-Way project 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.

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  • Lumos finally enables wearable spectroscopy research

    Lumos finally enables wearable spectroscopy research

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    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.

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  • The Smart-Badge recognizes kitchen activities with its suite of sensors

    The Smart-Badge recognizes kitchen activities with its suite of sensors

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    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.

    For more details on the project, you can read the team’s paper here.

    Image credit: Liu and Suh et al.

    Boards:Nano 33 BLE Sense
    Categories:Arduino

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  • An UNO Mini Limited Edition necklace is a must-have accessory for Arduino lovers!

    An UNO Mini Limited Edition necklace is a must-have accessory for Arduino lovers!

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    Arduino Team — June 11th, 2022

    When Katie Dumont of element14 Presents received her Arduino UNO Mini Limited Edition, she was concerned that it would end up like most of her other pieces of hardware — either stored somewhere safely in its box or on a shelf for display. But because she wanted it to avoid this fate, her other idea was to feature it prominently within an amusing wearable.

    For her project, a series of LEDs would be the main output as their color and animation can be changed dynamically. In addition to the lights, the necklace was planned to include its own LiPo battery pack for maximum mobility, although it would not feature any user inputs so that space could be saved. Each of these components were carefully laid out in FreeCAD and had a case constructed around them, which exposes the side of the pendant so that the LEDs can emit a faint glow onto the shirt material below, whereas the UNO Mini is front and center.

    The device’s code is based on the preexisting Adafruit NeoPixel example, as it contains the typical rainbow and solid color modes. Because the top pins of the Uno Mini are exposed, connecting one of three digital inputs pins to ground will make the board enter a specific color pattern, otherwise it shows a default rainbow one.

    To see more about how Dumont built this fun pendant, be sure to watch her e14 Presents video!

    [youtube https://www.youtube.com/watch?v=FQIIQ1V3MxQ?feature=oembed&w=500&h=281]

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  • GetFit is a DIY fitness tracker based on the Nano 33 BLE Sense

    GetFit is a DIY fitness tracker based on the Nano 33 BLE Sense

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    Arduino Team — June 3rd, 2022

    When it comes to fitness tracking, the average consumer would most likely reach for a smartwatch or similar wearable band. These all work by using their internal accelerometers and gyroscopes to sense motion and detect when a certain action, such as stepping or lifting a weight, has been completed. But to further simplify the process by eliminating the need to select a workout before starting an exercise routine, Nekhil and Shebin Jacob have worked together to come up with the GetFit fitness tracker.

    The GetFit is a battery-powered device that uses machine learning to detect not only when an action has been done, but also what kind of workout is being performed. They achieved this by gathering plenty of samples from a Nano 33 BLE Sense’s onboard accelerometer and training a Keras model with the help of the Edge Impulse Studio. It can accurately identify between arm circles, pushups, squats, and anything else in the future while also disregarding anomalous data.

    The Arduino sends the now-recognized motion to a connected smartphone over Bluetooth® Low Energy where it’s then used to calculate the number of calories burned and display weekly activity levels. Best of all, this data is tied to an account in a Firebase database for easy transferability.

    [youtube https://www.youtube.com/watch?v=BUWZJuIqC2I?feature=oembed&w=500&h=281]

    To read more about GetFit, you can read the duo’s write-up here on Hackster.io.

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  • Introvention is a wearable device that can help diagnose movement disorders early

    Introvention is a wearable device that can help diagnose movement disorders early

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    Arduino Team — May 17th, 2022

    Conditions such as Parkinson’s disease and essential tremors often present themselves as uncontrollable movements or spasms, especially near the hands. By recognizing when these troubling symptoms appear, earlier treatments can be provided and improve the prognosis for the patient compared to later detection. Nick Bild had the idea to create a small wearable band called “Introvention” that could sense when smaller tremors occur in hopes of catching them sooner.

    An Arduino Nano 33 IoT was used to both capture the data and send it to a web server since it contains an onboard accelerometer and has WiFi support. At first, Bild collected many samples of typical activities using the Edge Impulse Studio and fed them into a K-means clustering algorithm which detects when a movement is outside of the “normal” range. Once deployed to the Arduino, the edge machine learning model can run entirely on the board without the need for an external service.

    If anomalous movements are detected by the model, a web request gets sent to a custom web API running on the Flask framework where it’s then stored in a database. A dashboard shows a chart that plots the number of events over time for easily seeing trends.

    To read more about Bild’s project, check out its write-up here on Hackster.io.

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  • Arduino device uses tinyML to help wearers recover from shoulder injury

    Arduino device uses tinyML to help wearers recover from shoulder injury

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    Arduino Team — March 15th, 2022

    Shoulder injuries can be quite complex and require months of careful physical therapy to overcome, which is what led to Roni Bandini to build a tinyML-powered wearable that monitors a patient’s rotator cuff movements to aid in the recovery process. His system is designed around a Nano 33 BLE Sense and its onboard accelerometer that measures both the types and frequencies of certain shoulder motions. 

    After 3D printing a small case to house the Arduino along with a battery pack and an OLED display, Bandini created a new project using the Edge Impulse Studio. The impulse takes in time-series three-axis accelerometer data, runs it through a spectral analysis block, and then infers the current movement being performed by the wearer. 

    Once switched on, the system initializes a set of three movement counts to zero: right, left, and up, while the last type, idle, is not counted. Then throughout the day, the patient is encouraged to perform various exercises in order to fill up the bars completely. The eventual goal is to make steady progress towards having a recovered rotator cuff joint with a larger range of motion than immediately after the injury.

    Bandini’s video explaining this shoulder recovery system can be viewed below, and the project’s design files/code can be found here on Hackster.io.

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  • Arduino device uses tinyML to help wearers recover from shoulder injury

    Arduino device uses tinyML to help wearers recover from shoulder injury

    Reading Time: 2 minutes

    Arduino Team — March 15th, 2022

    Shoulder injuries can be quite complex and require months of careful physical therapy to overcome, which is what led to Roni Bandini to build a tinyML-powered wearable that monitors a patient’s rotator cuff movements to aid in the recovery process. His system is designed around a Nano 33 BLE Sense and its onboard accelerometer that measures both the types and frequencies of certain shoulder motions. 

    After 3D printing a small case to house the Arduino along with a battery pack and an OLED display, Bandini created a new project using the Edge Impulse Studio. The impulse takes in time-series three-axis accelerometer data, runs it through a spectral analysis block, and then infers the current movement being performed by the wearer. 

    Once switched on, the system initializes a set of three movement counts to zero: right, left, and up, while the last type, idle, is not counted. Then throughout the day, the patient is encouraged to perform various exercises in order to fill up the bars completely. The eventual goal is to make steady progress towards having a recovered rotator cuff joint with a larger range of motion than immediately after the injury.

    Bandini’s video explaining this shoulder recovery system can be viewed below, and the project’s design files/code can be found here on Hackster.io.

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  • This tinyML device counts your squats while you focus on your form

    This tinyML device counts your squats while you focus on your form

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    Arduino Team — October 22nd, 2021

    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. 

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  • Use your smartphone to control Wilson the IoT hat

    Use your smartphone to control Wilson the IoT hat

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    Arduino Team — June 21st, 2021

    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 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. 

    [youtube https://www.youtube.com/watch?v=2e4FJNInXrw?feature=oembed&w=500&h=281]

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  • Epilet is a tinyML-powered bracelet for detecting epileptic seizures

    Epilet is a tinyML-powered bracelet for detecting epileptic seizures

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    Arduino Team — June 8th, 2021

    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.

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  • Woven fabric becomes on-skin wearable interface

    Woven fabric becomes on-skin wearable interface

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    Woven fabric becomes on-skin wearable interface

    Arduino Team — October 14th, 2020

    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)

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    Website: LINK

  • Student designs his own pair of smart glasses with a transparent OLED display and Arduino Nano Every

    Student designs his own pair of smart glasses with a transparent OLED display and Arduino Nano Every

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    Student designs his own pair of smart glasses with a transparent OLED display and Arduino Nano Every

    Arduino Team — July 24th, 2020

    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.

    [youtube https://www.youtube.com/watch?v=UJfCBOXrqYc?feature=oembed&w=500&h=281]

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    Website: LINK