Wearable Sensing

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.

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

    The impact of high curvature bending on thin film transistor(TFT) performance is of interest for flexible electronics. Bending influences TFT performance in two ways. First due to mechanical stress and second due to the pure geometric effect of converting a planar architecture to a cylindrical one. Experiments to simultaneously create and yet distinguish these two effects are difficult. Analytical models are required to identify the individual impact of stress and geometry. The goal of this work is to identify the purely geometrical impact on TFT characteristics.

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

    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.

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

      In recent years, researchers have explored gesture-based interfaces to control robots in non-traditional ways. These interfaces require the ability to track the body movements of the user in 3D. Deploying mo-cap systems for tracking tends to be costly, intrusive, and requires a clear line of sight, making them ill-adapted for applications that need fast deployment, such as artistic performance and emergency response.

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

    These .s2p files contain the S-parameters measured between two on-neck antennas for multiple test subjects acting out four activites. Each files is one trial of measurement, containing 20 seconds of data sampled at 200 Hz.

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

    The file contains raw data collected from 9 pedestrians. Three of them walked in Track 1, another three walked in Track 2 and the last three walked in Track 3. All the pedestrians ended their walks at the starting point. Track 1 and Track 3 cover a distance of 150.3m. While, the Track covers a distance of 111.4m.

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

    An energy harvester for a smart contact lens that monitors the glucose level of a user is developed and demonstrated. The energy harvester captures a smartphone’s 2G cellular emission, and rectifies it into DC power to operate on-lens microelectronics for glucose detection and wireless data transmission. The energy harvester can reach a maximum Ra- dio Frequency (RF) to Direct Current (DC) power conversion efficiency of 47%. An electrically realistic human eye model is designed and fabricated using 3D printing technologies to assist in various measurements of the proposed energy harvester.

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

    The dataset is an extensive collection of labeled high-frequency Wi-Fi Radio Signal Strength (RSS) measurements corresponding to multiple hand gestures made near a smartphone under different spatial and data traffic scenarios. We open source the software code and an Android app (Winiff) to create this dataset, which is available at Github (https://github.com/mohaseeb/wisture). The dataset is created using an artificial traffic induction (between the phone and the access point) approach to enable useful and meaningful RSS value

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

     

    123 views
  • Energy
  • Last Updated On: 
    Thu, 11/08/2018 - 10:34

    Recognition of human activities is one of the most promising research areas in artificial intelligence. This has come along with the technological advancement in sensing technologies as well as the high demand for applications that are mobile, context-aware, and real-time. We have used a smart watch (Apple iWatch) to collect sensory data for 14 ADL activities (Activities of Daily Living). 

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

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