Module1 - prep - Task Sensor data acquisition

To do this assignment you will need access to a phone running Android. If you don't have such a phone, find a WASP colleague that has and team up (group size max 2 persons).

Download the (free) application Sensor Fusion from Google Play. It has been developed at LiU. More information is available here: http://users.isy.liu.se/en/rt/fredrik/app/ Links to an external site.

Make sure you have read about sensor imperfection modeling in the preparatory material. You might also like to check this video out Links to an external site., illustrating the problem with long-time accuracy when using the IMU only (accelerometer + gyro. Adding a compass/magnetometer helps only slightly)

The assignment consists of a mandatory part and some voluntary extra challenges for the ambitious.

• [5p] Sensor error modeling: Log data when the phone is lying still. Write a small report  describing how to model observed variations in sensor output for (at least). Here are some suggestions on what the report could contain:
  
  • GPS (with good coverage). Plot histogram of position variations. Calculate  standard deviations in x,y and z. Are position errors normally distributed, are there many outliers? Any other interesting observations?
  • Accelerometer (try at least two different orientations of the phone). Calculate the norm of the accelerometer vector, does it correspond to the expected value of constant of gravity at your latitude? Plot histograms of the accelerometer signals. Are the variations normally distributed? Can discretization effects of the ADC converters be seen? Any other interesting observations?
  • Gyro (if available) Plot histograms and calculate standard deviations. Estimate the bias in the gyros. Are the errors normally distributed? Any other interesting observations?
    
  • Wifi RSSI (Try at least three different distances to the access point). Plot histograms and calculate standard deviations. Describe the variations. How accurate would you say the RSSI signal is as an estimator for the distance to the access point. Try both direct signal paths and situations with obstruction (e.g. walls).
    

The sensor error modeling report (pdf) should be submitted in Canvas before the deadline defined there. If you work in a pair you must register a group for the upload of the assignment. The group set is called "M1 Task Sensor data acquisition" and you should be able to add yourself to a group. This allows you to be able to upload the report only once and both people (if the group consist of 2) will get be given the same grade.

There are also the following voluntary challenges:

• [3p+3p] Zero-gravity challenge: Handin a pdf documenting the longest continuous zero-gravity period for your phone. [No cheating! All experiments are done on your own risk!  Zero gravity is defined as $\sqrt{a_x^2+a_y^2+a_z^2}<0.1ms^{-2}$. Please calculate and present the resulting time in seconds. Points will be allocated to the two best results].
• [5p] Tremor measuring app: Use the acceleration sensor in the phone to measure tremor when holding the phone as still as you can in your hand, e.g. extended from the body for 1 minute. Implement an algorithm to measure tremor (offline on logged data). (Hint: You will probably need to consider suitable high pass filtering of the signal.) Does the tremor increase over time? Try before and after physical activities or other interesting tests. Handin presentation and code+data.
  
• [5p] Activity detector: Describe and implement an activity detector. The algorithm should be able to discriminate between, standing still, walking and running. You don't have to implement the algorithm on the phone, you can work offline on collected data. Handin presentation and code+data.