Schlagwort: Wearable Technology

  • 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.

    The post The Emotion Aid is a wearable device that communicates its user’s emotions appeared first on Arduino Blog.

    Website: LINK

  • 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.

    The post Detecting falls by embedding ML into clothing appeared first on Arduino Blog.

    Website: LINK

  • 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.

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

    Website: LINK

  • Arduino and K-Way, with the support of Edge Impulse, team up for a new idea of smart clothing

    Arduino and K-Way, with the support of Edge Impulse, team up for a new idea of smart clothing

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    Imagine the possibilities generated by integrating advanced AI and powerful sensors in to one of the most iconic outdoors jackets with a heritage that’s more than 50 years old. You could start sensing and interacting with the surroundings like never before.

    This is what we created here at Arduino: enclosing the Nicla Sense ME, the new sensory brain from Arduino, into the K-WAY jacket, powered with Edge Impulse AI, to sense the external world and imagine a new way to conceive smart clothing.

    The Nicla Sense ME is beautifully nestled in a custom silicone mold, attached to the iconic coloured zipper of the K-WAY jacket to help you program, monitor, and work with some of the most relevant environmental data that matters to you most. 

    The Nicla Sense ME on the K-WAY jacket recognizes in real-time whenever the air you’re breathing is polluted, can indicate changing weather conditions, and it communicates with you through a LED on the board or even a smart phone app.

    And what would you do with the same technology?
    If this question is intriguing to you, get ready and pitch your idea. Arduino, with the support of Edge Impulse, will select the best pitch ideas and send over a jacket and a Nicla Sense ME for developing your ideas and make them come true!

    The call for developers will officially open on October 18, be sure you won’t miss it!

    Website: LINK

  • 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 TeamMay 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.

    Website: LINK

  • 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 TeamJune 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.

    Website: LINK

  • GymSoles ensure correct form and posture during your workout

    GymSoles ensure correct form and posture during your workout

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    GymSoles ensure correct form and posture during your workout

    Arduino TeamMay 16th, 2019

    While you can get a very good workout on your own, it’s ideal if you have someone else watching over your form. This, of course, isn’t always practical, so researchers at the University of Auckland’s Augmented Human Lab have prototyped a wearable system called GymSoles to help. 

    GymSoles consists of a pressure-sensitive insole that is used to determine a foot’s center of pressure, and thus infer whether or not the participant is keeping the weights in the proper position relative to his or her body—perfect for exercises like squats and deadlifts. 

    Feedback is provided visually as well as through tactile feedback via eight vibrating motors, allowing participants to modify technique without having to focus on a screen. A computer is used to control the device using an Arduino Uno with motor drivers and an I2C multiplexer.

    The correct execution of exercises, such as squats and dead-lifts, is essential to prevent various bodily injuries. Existing solutions either rely on expensive motion tracking or multiple Inertial Measurement Units (IMU) systems require an extensive set-up and individual calibration. This paper introduces a proof of concept, GymSoles, an insole prototype that provides feedback on the Centre of Pressure (CoP) at the feet to assist users with maintaining the correct body posture, while performing squats and dead-lifts. GymSoles was evaluated with 13 users in three conditions: 1) no feedback, 2) vibrotactile feedback, and 3) visual feedback. It has shown that solely providing feedback on the current CoP, results in a significantly improved body posture.

    [youtube https://www.youtube.com/watch?v=9UHuFJm2nb4?feature=oembed&w=500&h=281]

    Website: LINK