gyro | line tool for creating , updating , and maintaining cloud | Infrastructure Automation library

I don't have an example code that exactly works for your case. But this approach can help (based on past experience),

1. Decide and formulate the states X, control inputs U, outputs, prediction and observation equations.
2. Implement/ reuse some implementation of Kalman Filter. Here is a Simulink based implementation for reference.
3. Set the measurement noise and prediction error variances. It may require some fine tuning later.
4. Verify that the KF works against some reference. If you have another way to measure velocity, check the KF velocity against it.

States could be a array containing

1. Linear velocities [Vx, Vy, Vz]
2. Angular velocities [omega_x, omega_y, omega_z]
3. Bias in gyroscope. This bias is largely constant but can change with temperature and other factors. Accelerometer measurements will be used by KF to correct for gyro bias.
4. Bias in Accelerometer. This bias is largely constant but can change with temperature and other factors. Camera velocity will be used by KF to correct for accel bias.
5. Orientation (Euler angles or quaternion)

Control inputs need not be the actual commands that are being sent to your actuators. In this case, control inputs can be the net force or net acceleration which is,

Accelerometer data (Which is specific force) + Acceleration due to gravity

Prediction equations predict the states for next time step based on current states and control inputs.

This MathWorks documentation has a good reference for prediction equations relevant to IMU.

Relates measurements with states.

Accel data is already used in prediction. Ignore it here.

Gyro data is [gx, gy, gz] = [omega_x + gyro_bias_x, ....] + errors

One way to handle magnetometer is to obtain yaw angle from it - arctan(y/x) and then use the yaw_mag as measurement.

Camera data is [vx_cam, vy_cam, vz_cam] = [Vx, Vy, Vx] + errors

Finally append all the rows and bring it to Y=C*X + noise form.

Y denotes the measurements from different sensors and X represents the states.

Y would be [gx, gy, gz, yaw_mag, vx,cam, vy_cam, vz_cam] in this case.

Disclaimer: I am a MathWorks employee and links are shared from MathWorks documentation.

There is something wrong with how you use rows, columns and index in add_subplot. I hope this here helps:

The easiest is to use the signature API and use signature names for inputs/outputs

You should find a signature defined if you used the v2 TFLite Converter.

Example that prints which signatures defined is below

It's not so very obvious on how to use these libraries (but that's complex math, so this is actually expected...)

You can print the contents of a matrix like so:

Construct the individual DataFrames of authors and genres and join to the original df:

You said it yourself; "it does not necessarily follow that the user performed this movement phrase perfectly." The feature set that your model extracts from the phrases are not necessarily good candidates for evaluating the quality of very short movements (sub-actions, if you will), unless your model is trained to keep the consistency within these very short movements.

You could address this concern in the loss function. And the way you can accomplish that pretty much completely depends on your dataset. You have mentioned that you have a high quality dataset so I assume that you might have enough granularity in your data to measure the quality of your sub-actions. These measurements could be integrated into the general loss function as an auxiliary loss so that your model can be optimized towards prioritizing the quality of sub-actions.

Here are some studies (1)(2) that explore similar possibilities for Crowd Density Estimation task.

If I understood you need to connect to stdout from a long running process.

see if this works for your scenario using IO.popen:

The "yaw" of a quaternion generally means q_yaw in a quaternion formed by q_roll * q_pitch * q_yaw. So that quaternion without its yaw would be q_roll * q_pitch. If you have the pitch and roll values at hand, the easiest thing to do is just to reconstruct the quaternion while ignoring q_yaw.

However, if we are really dealing with a completely arbitrary quaternion, we'll have to get from q_roll * q_pitch * q_yaw to q_roll * q_pitch.

We can do it by appending the opposite transformation at the end of the equation: q_roll * q_pitch * q_yaw * conj(q_yaw). q_yaw * conj(q_yaw) is guaranteed to be the identity quaternion as long as we are only dealing with normalized quaternions. And since we are dealing with rotations, that's a safe-enough assumption.

In other words, removing the "Yaw" of a quaternion would involve:

1. Find the yaw of the quaternion
2. Multiply the quaternion by the conjugate of that.

So we need to find the yaw of the quaternion, which is how much the forward vector is rotated around the up axis by that quaternion.

The simplest way to do that is to just try it out, and measure the result:

1. Transform a reference forward vector (on the ground plane) by the quaternion
2. Take that and project it back on the ground plane.
3. Get the angle between this projection and the reference vector.
4. Form a "Yaw" quaternion with that angle around the Up axis.

Putting all this together, and assuming you are using a Y=up system of coordinates, it would look roughly like this:

The override keyword in Kotlin suggests that the class is inheriting a function from a super class or interface. The Android documentation seems to be missing a pretty important step which is having your activity class implement the SensorEventListener interface.

To do this change your MainActivity declaration to something like this: