Wearable Sensing

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*

      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.

  • Wearable Sensing
  • Last Updated On: 
    Thu, 11/08/2018 - 10:34
    Citation Author(s): 
    David St-Onge and Ulysse Côté-Allard and Giovanni Beltrame

    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.

  • Wearable Sensing
  • Last Updated On: 
    Thu, 11/08/2018 - 10:34
    Citation Author(s): 
    Drew Bresnahan

    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.

  • Wearable Sensing
  • Last Updated On: 
    Thu, 11/08/2018 - 10:34
    Citation Author(s): 
    Mohd Nazrin Muhammad, Zoran Salcic, Kevin I-Kai Wang

    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.

  • Wearable Sensing
  • Last Updated On: 
    Thu, 11/08/2018 - 10:34
    Citation Author(s): 
    Luyao Chen, Ben Milligan, Tony Qu, Luxsumi Jeevananthan, George Shaker, Safieddin Safavi-Naeini

    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

  • Communications
  • Last Updated On: 
    Thu, 11/08/2018 - 10:34
    Citation Author(s): 
    Mohamed Haseeb, Ramviyas Parasuraman

     

  • Energy
  • Last Updated On: 
    Thu, 11/08/2018 - 10:34
    Citation Author(s): 
    Bassani Giulia, Filippeschi Alessandro, Ruffaldi Emanuele

    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). 

  • Communications
  • Last Updated On: 
    Thu, 11/08/2018 - 10:34
    Citation Author(s): 
    Walid Gomaa, Reda Elbasiony

    The TST FB4FD dataset contains data acquired through a pair of smart shoes. The smart shoes are specifically designed for fall detection purposes and are equipped respectively with 3 Force Sensing Resistors (FSRs) and an inertial unit.  More specifically, the dataset consists of 32 different falls and 8 activities of daily living (ADLs) performed by 17 healthy subjects aged between 21 and 55 years, for a total of 544 falls and 136 ADLs sequences .

  • Wearable Sensing
  • Last Updated On: 
    Thu, 11/08/2018 - 10:34
    Citation Author(s): 
    Susanna Spinsante, Ennio Gambi, Laura Montanini, Davide Perla, Antonio Del Campo

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