Wearable Sensing

AoT for Smart Society provides solutions of industry 4.0 standards in which contains custom-built multisensory wearable suit with cloud connectivity interfaced Artificial Intelligent techniques and Machine Learning algorithms in order to detect, to monitor and to analyze biofeedback control and visualization during human daily activities.

 

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  • Sensors
  • Last Updated On: 
    Thu, 02/28/2019 - 05:26

    The dataset comprises motion sensor data of 19 daily and sports activities each performed by 8 subjects in their own style for 5 minutes. Five Xsens MTx units are used on the torso, arms, and legs.

    211 views
  • Sensors
  • Last Updated On: 
    Fri, 02/15/2019 - 08:57

    VideoSupplement "SEM-assisted (LVEM-assisted) isopotential mapping of dielectric charging of the nonwoven fabric structures using Sobel–Feldman operator (Sobel filter)" for our article in russian journal (translated in English). 

    56 views
  • Standards Research Data
  • Last Updated On: 
    Mon, 12/31/2018 - 14:35

    Microfluidic Lab-on-a-dish (3D printing).

     

    O.V. Gradov group, INEPCP RAS, 2017-2018.

    https://vimeo.com/273508513

     

    Acknowledgements:

    40 views
  • Image Fusion
  • Last Updated On: 
    Thu, 11/08/2018 - 10:34

    Wearable Inertial Measurement Units (IMU) measuring acceleration, earth magnetic field and gyroscopic measurements can be considered for capturing human skeletal postures in real time. This dataset contains IMU readings (accelerometer, magnetometer and gyroscope) for common shoulder exercises: extension- flexion and abduction-adduction and simultaneously measures VICON readings and Kinect readings.

    683 views
  • Wearable Sensing
  • Last Updated On: 
    Thu, 11/08/2018 - 10:34

    Orientation tracking of a moving object has a widevariety of applications, including but not limited to military,surgical aid, navigation systems, mobile robots, gaming, virtualreality, and gesture recognition. In this article, a novel algorithmis presented to automatically track and quantify change ofdirection (COD) incident angles or heading angles (i.e. turningangles) of a moving athlete using the inertial sensor signals froma microtechnology unit (an inertia measurement unit (IMU))commonly used in elite sport.

    89 views
  • Sensors
  • Last Updated On: 
    Thu, 11/08/2018 - 10:34

    This dataset is a highly versatile and precisely annotated large-scale dataset of smartphone sensor data for multimodal locomotion and transportation analytics of mobile users.

    The dataset comprises 7 months of measurements, collected from all sensors of 4 smartphones carried at typical body locations, including the images of a body-worn camera, while 3 participants used 8 different modes of transportation in the southeast of the United Kingdom, including in London.

    519 views
  • Computational Intelligence
  • Last Updated On: 
    Thu, 11/08/2018 - 10:34

    This dataset consists of raw EEG data from 48 subjects who participated in a multitasking workload experiment utilizing the SIMKAP multitasking test. The subjects’ brain activity at rest was also recorded before the test and is included as well. The Emotiv EPOC device, with sampling frequency of 128Hz and 14 channels was used to obtain the data, with 2.5 minutes of EEG recording for each case. Subjects were also asked to rate their perceived mental workload after each stage on a rating scale of 1 to 9 and the ratings are provided in a separate file.

    640 views
  • Biophysiological Signals
  • Last Updated On: 
    Tue, 07/10/2018 - 02:19

    The electronic system has been design to know the position human body. Of this way the system use a three axis accelerometer to detect five common positions (i) ventral decubitus, (ii) right lateral decubitus, (iii) left lateral decubitus, (iv) supine decubitus and (v) seated.  The sensor data was acquire with ten diferrents persons, their each positions was  how they felt confortable. The accelerometer acquire data from  3 axis possible (X,Y,Z)

    428 views
  • Health
  • Last Updated On: 
    Sat, 06/16/2018 - 22:57

    Expansion of wireless body area networks (WBANs) applications such as health-care, m-banking, and others  has lead to vulnerability of privacy and personal data. An effective and unobtrusive natural method of authentication is therefore a necessity in such applications. Accelerometer-based gait recognition has become an attractive solution, however, continuous sampling of accelerometer data reduces the battery life of wearables. This paper investigates the usage of received signal strength indicator (RSSI) as a source of gait recognition.

    248 views
  • Communications
  • Last Updated On: 
    Thu, 11/08/2018 - 10:34

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