Tag Archives: Wearables

KeKePad is an ATmega32U4-powered wearables platform


KeKePad is a plug-and-play platform that replaces conductive thread with tiny connectors and thin cables.


Like most Makers, Michael Yang enjoyed using the Arduino Lilypad for his wearable and e-textile projects. However, he discovered that conductive thread has a few drawbacks: it is expensive, it has no insulation and its resistance is quite high. Plus, in order to achieve a tight connection, the wires need to be soldered (which means that it becomes rather difficult to remove if there are any mistakes).

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So, as any DIY spirited individual would do, he set out to solve this problem. The result? KeKePad, a new modular platform that’s 100% compatible with the Arduino LilyPad USB and can be programmed using the Arduino IDE. The board is based on the ATmega32U4 — the same chip that can be found at the heart of the wildly popular Adafruit FLORA — and features built-in USB support, so it can be easily connected to a PC. Like other wearable MCUs, the controller boasts a familiar round shape (which measures 50mm in diameter) along with 12 tiny three-pin Ke Connectors and 11 sew tab pins.

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What really sets the platform apart, though, is its unique wiring and connection method. The KeKePad entails a series of small sewable modules that link together via the Ke Connectors and special cables, or Ke Cables, with crimp terminals. This eliminates the frustration often associated with using conductive thread. With a diameter of only 0.32mm, the wire is extremely flexible, super thin and coated in Teflon.

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At the moment, there are approximately 20 different modules to choose from, including sensors for detecting light, UV, sound, barometric pressure, temperature, humidity, and acceleration, as well as actuator modules for things such as LEDs, MP3s, OLED displays and vibrating buzzers.

Intrigued? Head over to KeKePad’s Indiegogo campaign, where Yang and his team are currently seeking $2,000. Delivery is slated for April 2016.

This coat is heated by an Arduino


Odisseo is the winter jacket you wish you had…


A blast of bitter cold arctic air has brought the coldest temperatures in decades to some cities throughout the Northeast. As wind chills dip well below 0°F and bundling up in layers won’t do the trick, how great would it be to have a stylish jacket with a built-in heating unit to keep you warm?

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This is exactly what an Italian team of of physical computing students did back in 2014. Dubbed Odisseo, the Italian name for Odysseus, the coat is powered by an Arduino Uno (ATmega328) and comes with a complete set of IKEA-like instructions pinned to the inside flap.

The zipper activates a heating unit located inside the collar, while capacitive sensors detect when a wearer places his or her hands into their pockets to initiate additional warming.

Carv is a wearable that helps you ski better


This Atmel-powered system analyzes motion and pressure data to give skiers real-time feedback on how to improve.


Throughout the world, approximately 120 million people will hit the slopes each year. This doesn’t include the countless others who are dying to learn how to ski either. Whether a novice or professional or somewhere in between, how cool would it be to have a coach that could be right there with you trail after trail? Well, UK-based startup MotionMetrics has come up with the perfect solution.

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Meet Carv, a digital ski coach that combines a wearable device and a smartphone app with intuitive analysis algorithms to help you improve your technique. Inspired by Olympic technology, Carv gives you access to the feedback and knowledge that only elite skiers have had access to so far.

Carv itself is comprised of two parts. The first is a wearable that attaches to a ski boot, while the second is a ~1mm insert that’s actually placed inside the boot. All the data is then analyzed through an accompanying mobile app, which allows skiers to receive feedback on their performance either on-screen or in real-time, through earphones or heads-up displays. And for more sophisticated athletes, Carv can automatically synchronize video from your GoPro with the data, enabling instructors and coaches to scrutinize the footage alongside the overlaid information.

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The solution employs a series of sensors, hidden under the boot liners. Each sensor unit packs 48 independent pressure sensors, meant to pick up even the most minute changes in the pressure distribution, along with an accelerometer, a gyroscope and a magnetometer that provides Carv with information related to the motion and orientation of the skis. Communication is handled by Bluetooth Low Energy.

The sensor unit is powered and controlled by the boot-mounted Carv trackers, which serve as the brains of the system. These trackers are responsible for coordinating data collection, performing calculations for pressure and motion at high frequencies (220Hz), and overseeing wireless communication with the smartphone.

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As high-tech of a platform Carv may be, you’ll barely notice that it’s even there. Inspired from current snow sports wear accessories such as customized insoles and boot warmers, the smart insert is super thin and can be slipped in without affecting the way you ski. What’s more, the Carv tracker can be quickly and easily clipped and unclipped whenever it needs to be charged (via USB).

“The idea for Carv began when I was looking at how recording and analysis of data can help people do things better during my PhD. It was an academic problem that got out of hand,” founder Jamie Grant explains. “Coming from a physics background, I was particularly interested in the telemetry side of things – looking at how you can measure movement. Then, whilst studying for a PhD in financial statistics, I worked on the data analysis side – what can you actually do with those measurements once you’ve recorded them? As a keen skier myself, I soon started applying this to my experience on the slopes.”

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Grant and his team had a secured a place at HAX, an exclusive hardware accelerator in Shenzhen. With this mentorship, the MotionMetrics crew was not just able to bring their idea to life, but to further develop their unique pressure and motion sensing system that can now measure metrics like weight distribution. Ultimately, this development helps users spot common mistakes such as leaning too heavily on a turn, an action that can slow the skier down, or even worse, cause cranes.

Sound like the 24/7 coach you’ve been looking for? Race over to Carv’s Kickstrater campaign, where the team of PhD students from the University of Imperial College London is currently seeking $50,000. Delivery is expected to get underway in November 2016, just in time for next year’s snow season.

High school student creates a smart wearable for Parkinson’s patients


OneRing monitors motor distortions and generates patient reports.


After school activities for the average high school student typically entails sports practices, music lessons and homework; but creating a smart medical device for a disease that affects 10 million people seems unlikely. That’s not the case for Cupertino High School sophomore Utkarsh Tandon. Tandon is the founder of OneRing, an intelligent tool for monitoring Parkinson’s disease.

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OneRing is a wearable that captures movement data from a patient, algorithmically identifies Parkinson’s tremor patterns and classifies the severity. Tandon first became interested in studying the disease when he watched a video of Muhammad Ali, who has Parkinson’s, light the Olympic torch in 1996. After volunteering at a local Parkinson’s institute, the 15-year-old decided to build a company that focuses on improving the lives of those affected by this movement disorder. He began working on signal processing and machine learning algorithms, before evolving the concept and founded OneRing.

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OneRing quantifies Parkinson’s disease movements and its mobile app leverages the data collected to generate smart patient reports that physicians can use to better prescribe medication. At the core of the device is its machine learning technology. The OneRing has been trained to model various Parkinson’s motor patterns such as dyskinesia, bradykinesia and tremors. A Bluetooth module encased inside the 3D-printed plastic ring allows it to communicate with its accompanying iOS app to provide time-stamped analytics about the patient’s movement severity during the day.

The ring itself currently comes in three sizes, each varying in diameter: 18mm, 19mm and 20 mm. Tandon and his team hope to develop a “one-size-fits-all” piece in the near future.

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With this Kickstarter campaign, Tandon hopes to deploy OneRing to a local Parkinson’s institute where the device can be used in exams and sent home with patients. Ultimately he wants to bring OneRing to patients all around the world in hopes of suppressing the condition’s rapid progression. Interested in the cause? Head over to the OneRing project page, where Tandon and his team have already doubled their pledged goal of $1,500.

Wearable sweat sensors provide real-time analysis of the body


UC Berkeley engineers have developed new wearable sensors that can measure skin temperature, as well as glucose, lactate, sodium and potassium in sweat.


As it turns out, future wearable devices may not be as interested in your activities, as they are the sweat produced during them. That’s because engineers at UC Berkeley have developed a flexible sensor system capable of measuring metabolites and electrolytes in sweat and sending the results to a smartphone in real-time.

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According to researchers, these bendable plastic patches can be easily implemented into bands for the wrist and head, and provide early warnings to health problems such as fatigue and dangerously high temperatures.

“Human sweat contains physiologically rich information, thus making it an attractive body fluid for non-invasive wearable sensors,” explained Ali Javey, a UC Berkeley professor of electrical engineering and computer sciences.

The prototype consists of five sensors and a flexible circuit with (what would appear to be an Atmel) MCU and a Bluetooth transceiver. This board measures the concentration of various chemicals in sweat and skin temperature, calibrates the information and then sends it over to its accompanying mobile app.

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To test their proof-of-concept, the engineers put the device and more than two dozen volunteers through various indoor and outdoor exercises, such as riding stationary bikes and running trails. In doing so, the team kept tabs on sodium, potassium, glucose and lactate. Monitoring electrolytes like sodium and potassium may help track conditions,  and can ultimately be utilized to assess a user’s state of health.

“When studying the effects of exercise on human physiology, we typically take blood samples. With this non-invasive technology, someday it may be possible to know what’s going on physiologically without needle sticks or attaching little, disposable cups on you,” added physiologist George Brooks, a UC Berkeley professor of integrative biology.

Intrigued? Learn all about the wearable sweat sensor here, or watch the team’s video below!

‘Sup Brow? Send a message to your friend by making a muscle


Text a friend by lifting your eyebrow using a MyoWare muscle sensor and an Adafruit Bluefruit Feather board. 


In today’s world, there are all kinds of ways to message one another. There’s texting, emailing, Skyping, Snapchatting, and countless other forms of communication. But what if you could send a message to your friend by simply raising your eyebrow?

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This was something Adafruit’s Becky Stern and Kate Hartman wanted to make a reality in their recent wearables project, ’Sup Brows. To bring this idea to life, the duo employed a MyoWare muscle sensor along with a Feather Bluefruit 32U4 LE (ATmega32U4) microcontroller to transmit a signal through the phone to Adafruit IO and then IFTTT to trigger an SMS.

“It’s really neat to use non-verbal communication like facial expressions as an interface for electronics,” Hartman explains.

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As cool of a project as this may be, ‘Sup Brows is simply the beginning. Since it’s connected to IFTTT, the possibilities of what you can accomplish by creating a recipe and just raising your eyebrows are endless. Similarly, Stern and Hartman note that it can also be hooked up to a variety of other muscles to have activities prompted by other facial expressions, gestures and actions.

So whether it’s booking an appointment with your cosmetic surgeon when your Botox wears off or getting yourself out of a date with a butt dial, everything is fair game. Intrigued? Head over to Adafruit’s tutorial page to get started.

 

Is your smartwatch stealing your passwords?


A computer science student has demonstrated that software running on a smartwatch could be used to record a user’s passwords and PINs.


Unless you eschew modern technology altogether (such as reading websites), chances are that data on you is being collected. Smartphones are capable enough data sponges, but smartwatches have the potential to extend this reach even further. According to Tony Beltramelli’s master’s thesis for the IT University of Copenhagen, the sensors on the Sony SmartWatch 3 (and likely many other present and future watches) are so accurate that they can be used to sense what button you press on a 12-segment keypad with “above-average” precision.

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As seen in the video below, it appears that this ability comes from the user actually moving their hand from button to button. The wearable’s built-in accelerometer and gyroscope can sense these motions and then feed that information into a recurrent neural network. Using a deep learning algorithm, Beltramelli is able to sift through all the “noisy data” and detect patterns for various events, such as when the user moves and taps their finger on a touchscreen to unlock a PIN-protected phone or when the user enters a code on an ATM’s keypad.

Interestingly, as reported in section 6.3 of the thesis, the device did a better job of “touchlogging” — recording virtual keystrokes on a touchscreen — at 73% acuracy, versus “keylogging” — where a physical keyboard is used for input — at 59% accuracy. The touchscreen used was larger in this experiment than the keypad, apparently leading to this discrepancy.

“By their very nature of being wearable, these devices, however, provide a new pervasive attack surface threatening users privacy, among others,” Beltramelli explains. “The goal of this work is to raise awareness about the potential risks related to motion sensors built-in wearable devices and to demonstrate abuse opportunities leveraged by advanced neural network architectures.”

As you can imagine, there are still a few limitations that make this type of approach with a smartwatch impractical as an attack against specific targets. For starters, it only works if the person is using the arm that the gadget is on. So, if you have a watch and are concerned about spying, you can simply strap it onto your less dominant wrist. Or alternatively, you could make a habit of typing with three fingers on numeric keypads.