A Selfpowered Alwayson Visionbased Wakeup Detector For Wearable Gesture User Interfaces

• doi: 10.1109/ASSCC.2017.8240262
• title: A self-powered always-on vision-based wake-up detector for wearable gesture user interfaces
• publisher: IEEE
• isbn: 978-1-5386-3179-9
• issn:
• rank: 1277
• access_type: LOCKED
• content_type: Conferences
• abstract: Hand gesture recognition is one of the secure natural user interface (NUI) mechanisms on wearable devices since it does not reveal user's intention in public domain unlike the speech recognition. However, its energy dissipation is very demanding as it requires compute-intensive machine vision processing. Recently, wake-up detectors have been proposed to improve the energy-efficiency of always-on sensing nature of the NUI systems by switching off the main functional blocks while just keeping the wake-up detector alive during idle time. However, vision-based wake-up detectors still require power-consuming vision processing so we propose a self-powered vision wake-up detector to alleviate burdens on limited battery and thus facilitate always-on wake-up detection for the wearable gesture UIs. Our work has four key features to realize the self-powered wake-up detection; 1) imaging-harvesting dual-mode CMOS image sensor (CIS) with 0.6V 3T pixels, 2) subthreshold SRAM with disturb-free 0.3V 10T bitcells, 3) hand detection engine with modified Haar-like filters invariant to skin-colors, and 4) on-die switched capacitor DC-DC converter for lightweight system design. Thanks to the features combined, this work achieves selfpowered operation of always-on vision wake-up detection.
• article_number: 8240262
• pdf_url: https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8240262
• html_url: https://ieeexplore.ieee.org/document/8240262/
• abstract_url: https://ieeexplore.ieee.org/document/8240262/
• publication_title: 2017 IEEE Asian Solid-State Circuits Conference (A-SSCC)
• conference_location: Seoul, Korea (South)
• conference_dates: 6-8 Nov. 2017
• publication_number: 8226344
• is_number: 8240197
• publication_year: 2017
• publication_date: 6-8 Nov. 2017
• start_page: 245
• end_page: 248
• citing_paper_count: 2
• citing_patent_count: 0
• download_count: 657
• insert_date: 20171228
• index_terms:
  • ieee_terms:
    • Detectors
    • Engines
    • Random access memory
    • DC-DC power converters
    • Gesture recognition
    • Capacitors
    • Imaging
  • author_terms:
    • wake-up detector
    • gesture recognition
    • energy-harvesting
    • dual-mode CMOS image sensor
    • subthreshold SRAM
  • dynamic_index_terms:
    • Visual Processing
    • Skin Color
    • Complexion
    • Skin Tone
    • Wearable Devices
    • Wearable Technology
    • Speech Recognition
    • Speech Understanding
    • Image Sensor
    • Camera Sensor
    • Dcdc Converter
    • Gesture Recognition
    • Natural Interface
    • Natural User Interface
    • Switched Capacitor
    • Hand Gesture Recognition
    • Power Consumption
    • Pixel Intensity
    • System Architecture
    • Analog-to-digital Converter
    • Analog-to-digital
    • Digital-to-analog Converter
    • Supply Voltage
    • Maximum Power Point Tracking
    • MPPT
    • Power Stage
    • Low-voltage Operation
    • Regeneration Buffer
    • Framebuffer
    • Display Memory
    • Read Buffer
    • Analog Domain
• isbn_formats:
  • format: Print on Demand(PoD) ISBN, value: 978-1-5386-3179-9, isbnType: New-2005
  • format: Electronic ISBN, value: 978-1-5386-3178-2, isbnType: New-2005
• authors:
  • Author Name: Suhwan Cho
    Affiliation: Department of Computer Science and Engineering, Chungnam National University, Daejeon, Korea
    Author URL: https://ieeexplore.ieee.org/author/37086115484
    ID: 37086115484
    Order: 1
    Author Affiliations:
    • Department of Computer Science and Engineering, Chungnam National University, Daejeon, Korea
  • Author Name: Seongrim Choi
    Affiliation: Department of Computer Science and Engineering, Chungnam National University, Daejeon, Korea
    Author URL: https://ieeexplore.ieee.org/author/37086113883
    ID: 37086113883
    Order: 2
    Author Affiliations:
    • Department of Computer Science and Engineering, Chungnam National University, Daejeon, Korea
  • Author Name: Junsik Woo
    Affiliation: Department of Computer Science and Engineering, Chungnam National University, Daejeon, Korea
    Author URL: https://ieeexplore.ieee.org/author/37086271224
    ID: 37086271224
    Order: 3
    Author Affiliations:
    • Department of Computer Science and Engineering, Chungnam National University, Daejeon, Korea
  • Author Name: Ara Kim
    Affiliation: Department of Computer Science and Engineering, Chungnam National University, Daejeon, Korea
    Author URL: https://ieeexplore.ieee.org/author/37086109847
    ID: 37086109847
    Order: 4
    Author Affiliations:
    • Department of Computer Science and Engineering, Chungnam National University, Daejeon, Korea
  • Author Name: Byeong-Gyu Nam
    Affiliation: Department of Computer Science and Engineering, Chungnam National University, Daejeon, Korea
    Author URL: https://ieeexplore.ieee.org/author/37290590700
    ID: 37290590700
    Order: 5
    Author Affiliations:
    • Department of Computer Science and Engineering, Chungnam National University, Daejeon, Korea

Image Sensor

• sensor_type: CMOS
• resolution: 160 x 120
• dynamic_range: not specified
• pixel_size: not specified
• dark_current: not specified

Optical Data

• focal_length: not specified
• aperture: not specified
• field_of_view: not specified
• distortion: not specified

Performance Metrics

• power_consumption: 26 PW (picoWatts)

Applications & Benefits

• cell_imaging: used in gesture recognition for wearable devices
• benefits: enhances user interface in wearable smart devices with low power consumption and high efficiency

Supporting Organizations

• supported_by: IC Design Education Center (IDEC), Korea

Manuscript Details

• publication_date: 6-8 Nov. 2017

Relevancy Score

• score: 9
• missing_fields:
  • fill_factor
  • quantum_efficiency
  • readout_speed
  • temporal_noise
  • fixed-pattern_noise
  • microlenses
  • on-chip_colours_filters
  • global_shutter
  • rolling_shutter
  • backside_illumination

Processed JSON Filename

request_379f0d67-6ceb-4cbe-93e3-833e06d6e295-a_selfpowered_alwayson_visionbased_wakeup_detector_for_wearable_gesture_user_interfaces.json