Wearable sensor

This dataset contains leg joint kinematics, kinetics, and EMG activity from an experimental protocol approved by the Institutional Review Board at the University of Texas at Dallas. Ten able-bodied subjects walked at steady speeds and inclines on a Bertec instrumented treadmill for one minute per trial. Each subject walked at every combination of the speeds 0.8 m/s, 1.0 m/s, and 1.2 m/s and inclines from -10 degrees to +10 degrees at 2.5 degree increments, for a total of 27 trials.

  • Biomedical and Health Sciences
  • Last Updated On: 
    Wed, 10/31/2018 - 15:15

    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)

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

    Previous neuroimaging research has been traditionally confined to strict laboratory environments due to the limits of technology. Only recently have more studies emerged exploring the use of mobile brain imaging outside the laboratory. This study uses electroencephalography (EEG) and signal processing techniques to provide new opportunities for studying mobile subjects moving outside of the laboratory and in real world settings. The purpose of this study was to document the current viability of using high density EEG for mobile brain imaging both indoors and outdoors.

  • Neuroscience
  • Last Updated On: 
    Sat, 06/16/2018 - 23:16

    The image displays four segments of gestures from our dataset.

    (a) The video sequence of rotating the wrist down and up as a signal for starting a new gesture.

    (b)–(d) Three gestures samples (the triangle, letter b, and letter Z) taken from three different subjects at three different scenes (sitting at a desk, standing indoors, and standing outdoors.). The trajectory of each gesture canbe recognized from the movement of the background objects.

  • Wearable Sensing
  • Last Updated On: 
    Thu, 11/08/2018 - 10:34
    Citation Author(s): 
    Feiyu Chen, Honghao Lv, Zhibo Pang, Junhui Zhang, Huayong Yang, Geng Yang*