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IT-S0C Lab. Chonnam National Univeristy

EPS Sensors & Hand Tracking/Gesture Recognition. IT-S0C Lab. Chonnam National Univeristy. Contents. System Summary EPIC Sensor: Circuits & Principles Source Code Analysis Using Labview NUI-Based Data Preprocessing NUI-Based Gesture Recognition Algorithm. Chapter 1. System Summary.

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IT-S0C Lab. Chonnam National Univeristy

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  1. EPS Sensors & Hand Tracking/Gesture Recognition IT-S0C Lab. Chonnam National Univeristy

  2. Contents System Summary EPIC Sensor: Circuits & Principles Source Code Analysis Using Labview NUI-Based Data Preprocessing NUI-Based Gesture Recognition Algorithm

  3. Chapter 1 System Summary

  4. System Summary Human body (Dielectric area) outsidecharge ↑ ⇒ outsde voltage ↑ Sensor probe area EPIC sensor Input : Dielectric permittivity bet. human and sensor Electrostatic Field : permittivity of dielectric material deposited on EPS sensor Electric field surrounding EPIC sensors

  5. Chapter 2 EPIC Sensor: Circuits & Principle

  6. EPIC Sensor: Circuit Analysis(1/2) Electric Potential Integrated Circuit EPIC sensor: data detection principle ■ Variation in static-electric charge Once objects with different dielectric constants) enter E-field, static-electric charges change ■ Disturbance in E-field Utilize the principle that disturbance in E-filed occurs due to the movement of human body whose role is like big container with polarity ■ Detect surrounding E-field variation ■ Non-directional & metal-shut off Contact mode: bio-electric signal (ECG, EMG, EOG, etc.) measurement Non-contact mode: Measure variation in surrounding E-field Single-ended mode : measure electric potential Differential mode : measure difference bet. two sensors - Main detected signal: power line noise EPIC sensor input stage Bootstrapping : Corner frequency control(input resistance) Guarding : Gain control(input impedence) Basic block circuit diagram of EPIC sensor Contact mode & Non-contact mode

  7. EPIC Sensor: Circuit Analysis(2/2) Input stage of a EPIC sensor

  8. Simulation for verification Simulation1. Simulation2.

  9. Simulation for verificaton • Non Inverting OP-AMP • Kirichhoff’s current Law on node x • Vx (virtual short voltage) = Vi(input voltage). - Virtual short: Input node voltage feedback AMP equals to voltage of inverting node voltage Non-inverting OP-AMP

  10. Simulation for Verification • Input bias current • Current for OP-AMPoperation • Feed back loop gain becomes to be different from open loop gain, but it generate Offset • In order to solve “offset” problem, resistance, Rin, is connected to outside ofOP-AMP Input stage of EPIC sensor

  11. Simulation for Verification • Simulation Result(1/2) • Frequency: 1000Hz • C_ext : 250pF, C_in : 15pF, Rin : 20GΩ, Av = 50(PS25401) Cext = 250pF, Freq = 0~100kHz RG1/RG2 = 52, RG1 = 1GΩ Cext = 250pF, Freq = 1000 Hz RG1/RG2 = 52, RG1 = 0.01~100GΩ

  12. 이론 검증을 위한 시뮬레이션 • 시뮬레이션 결과(2/2) • 주파수 : 1000Hz • C_ext : 250pF, C_in : 15pF, Rin : 20GΩ, Av = 10(PS25405) Cext = 250pF, Freq = 0~100kHz RG1/RG2 = 9.6, RG1 = 1GΩ Cext = 250pF, Freq = 1000 Hz RG1/RG2 = 9.6, RG1 = 0.01~100GΩ

  13. EPIC Detection Principle& Analysis(1/2) Change in static electric charges Simulation result SensorElectrode Real output from EPIC Electrometer Amplifier Skin Air Condition: PC, CIB grounding &surrounding E-field0.1713V/m

  14. EPIC Sensor Detection Principle & Analysis(2/2) Disturbance in E-field PS25201 EPIC voltage due to movement of a target PS25201 EPIC voltage standard deviation according to distance of a moving target Condition: PC, CIB grounding &surrounding E-field0.1713V/m

  15. Chapter 3 Source Code Analysis using Labview and C++ Compatibility Method

  16. Source code anlaysis using Labview tool(1/2) • DAQ (Data Acquisition) Process • DAQ hardware : Measure and generate electric signals • In general, 10 AD conversion per period is desirable • For example, set sampling rate to more 100KS/s if 10KHz is measured AI : Analog signal measurement AO : Analog signal generation DI : Digital signal measurement DO : Digital signal generation CI : Pulse edge no. measurement CO : Pulse signal generation Computer based measurement system (DAQ hardware equipped

  17. Source Code Analysis Using Labview tool(2/2) • DAQ (Data Acquisition) Process • Measure continuous signal measurement using hardware timing • Channel generation • Analog input> voltage • Timing setting • Sample mode> continuous sample • Data reading • analog> Multiplexed channels> multiplexed samples> 1D Wfm(waveform) Generate the channel Setting the timing Start Read the data Stop Clear the data Error handle

  18. C++ 호환 방법 • C++ tool 을 이용한 프로그래밍을 위한 준비 사항 • 드라이버 설정 • 펌웨어 업데이트 • 해더 파일과 라이브러리 파일 설정 “NIDAQmxBase.h”, “NIDAQmxBase.lib” • DAQ 과정에 따른 DAQmxErrChk API 함수 사용 DAQmxBaseCreateTask / DAQmxBaseCreateAIVoltageChan / DAQmxBaseCfgSampClkTiming / DAQmxBaseCfgInputBuffer / DAQmxBaseStartTask / DAQmxBaseReadAnalogF64 / DAQmxBaseGetExtendedErrorInfo / DAQmxBaseStopTask

  19. Chapter 4 NUI Based Data Preprocessing

  20. IIR Low Pass Filter Source Codes IIR LPF 2st 계수 설정

  21. Data Preprocessing Process(1/2) 정전기 잡음 목표물의 움직임으로 발생된 신호 [ extracted signalsin case that static electricity occurs] [ Variation in frequency domainaccording to movement] [ signal variation caused by static electricity]

  22. Data Preprocessing Process(2/2) [Data preprocessing result in case of a fixed target] [ Data preprocessing result in case of a moved target]

  23. Chapter 5 NUI Based Gesture Recognition Algorithm

  24. NUI Gesture Recognition BasedDTW Algorithm IIR Low-pass filter 10Hz 2st order(AC) Non-Contact Electrometer Sensor Data Analog voltage(AC) True Generation of Electrostatic charge? True Calibration enabled ? Data preprocessing False Substitution toprevious data buffers False Extracting maximum Value of data buffers Digital voltage(DC) Timer1 enable Extracting differential signals Store of voltage Calibration processing Kalman filter False Timer1 = 3sec? False Velocity > Th_vel ? True True ExtractingVNH and Th_vel Timer2 enable Calibration enable Store of voltage False Timer2 = 1sec? True Training data Data normalization DTW Runtimerecognizer Classification Argmin(class) Event [Flow diagram of the proposed DTW ]

  25. NUI Gesture Recognition BasedDTW Algorithm • DTW(Dynamic Time Warping) algorithm Algorithm measuring similarity between two data in terms of time change Comparing simultaneously by searching optimal non-linear mapping function 구현된 Warping 구간의 전역 상수 제한 범위

  26. Warping Path Type

  27. THANK YOU FOR YOUR ATTENTION Any Questions ?

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