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Sofia d’Orey Advisors: Jorge Martins and Miguel Silva (IST), Hugh Herr and Dava Newman (MIT)

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Presentation on theme: "Sofia d’Orey Advisors: Jorge Martins and Miguel Silva (IST), Hugh Herr and Dava Newman (MIT)"— Presentation transcript:

1 Sofia d’Orey Advisors: Jorge Martins and Miguel Silva (IST), Hugh Herr and Dava Newman (MIT)

2 The total number of persons with paralysis, deformity or orthopedic impairments that use orthoses is expected to reach 7.3 million by the year 2020. American Academy of Orthotists and Prosthetists

3  Aim: Contribution to the development of a hybrid active ankle foot orthosis, with particular reference to:  Experimental data collection  Data analysis, e.g. inverse kinematics, inverse dynamics, EMG processing  Controller development, based on EMG and potentially IMU sensing schemes AFO Ankle Foot Orthoses PassiveActiveFES ISTMITELMETEC

4  Use of a human model for walking instead of the passive model originally developed;  Focus on FES has been assigned to Paulo Melo’s thesis

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15 Collected Data Labelled and pre-processed File format conversion, filtering and syncing Post- processing

16  Labelling of markers  Interpolation of small gaps, copy pattern in larger ones

17 Vicon OpenSim Coordinate Data Analog Data Force plate, EMG, FS Analog Data Force plate, EMG, FS C3D File Binary TRC File MOT File C3D Server SDK and ASCII Files

18 Scaling of generic model to match subject’s dimensions and mass

19 Use subject-specific scaled model and adjusted markers to track the motion Optimization: minimize the difference between the model and experimental marker positions by adjusting the model’s degrees of freedom (i.e. rotations, translations and joint angles)

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23 Stance Phase

24 Toe Off Heel Strike Swing Phase

25 Toe Off Heel Strike

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32  Inverse dynamics analysis for quantification of joint torques;  Analysis of EMG data collected for determination of muscle activation;  Development of an EMG-based controller for the orthosis.

33 Diana Young, Roedolph Opperman, Christopher Hainley, Avi Wolfson

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36  Time sequence of IMU and Vicon data acquisition Difference [ms] = imu_time + T4 + T2 - vicon_time + (5/Sample_rate)) 36

37  Features  Three inertial sensing components ▪ Accelerometer (+/- 10g) ▪ Gyroscope (1200 deg/sec) ▪ Magnetometer (+/- 1.2 gauss) ▪ 16 bits of resolution  Drawbacks  Limited sampling rate (1 – 155 Hz)  Microstrain software ▪ couldn’t acquire from all components

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41 41  Validation trials performed in Holodeck  IMU angles compared to Opensim angles

42 42  Close alignment in vertical direction (3 degree offset)  Time synchronization (horizontal alignment) very accurate (<10ms)


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