Vibration of Babies in Cargo Bikes and Strollers

This repository houses the analysis and paper that investigates the vibrations experienced by babies (0-9 months old) when carried in cargo bicycles and strollers.

Preliminary results can be viewed at:

https://mechmotum.github.io/baby-vibration/

License

The paper content (text, figures, etc.) and the generated paper itself are licensed under the Creative Commons BY-4.0 license. The computer source code is licensed under the MIT license. The data is available as public domain with no copyright restrictions. If you make use of the copyrighted material use a full citation to the paper to acknowledge the use and include the relevant license(s).

Build Instructions

Create a config.yml file in the repository directory with these contents:

data-directory: /path/to/Trillingen-Project_Dec2024

or on Windows:

data-directory: C:\path\to\Trillingen-Project_Dec2024

Do not commit the configuration file to Git.

The Trillingen-Project_Dec2024 is currently a private mountable directory share on TU Delft network. The data will be shared in a public repository in the future.

Install conda then create and activate the environment:

conda env create -f baby-vibration-env.yml
conda activate baby-vibration

Usage

Run a sample processing of a single session:

python src/data.py

Or name a specific session and trial and show sample plots:

python src/data.py -s session020 -t klinkers -r 200 --plot

Process sessions 0 through 3 for specific signal at sample rate 200:

python src/process.py 0 3 SeatBotacc_ver 200

Process all sessions in data/sessions.yml with:

python src/run.py

Data Description

We mount five Shimmer IMUs on the vehicle that collect data simultaneously during different trials.

Scenario

We test a combination of vehicle, baby seat, baby dummy, road surface (including shock), and speed. A unique combination of these factors is called a "scenario".

Session

A "session" is a continuous collection of data from a set of Shimmer IMUs which contains one or more scenarios. The IMU's internal clocks are time synchronized on the base station, attached to the vehicle, and each IMU is started. After a time period of collecting data of possibly multiple trials and calibrations the IMUs are stopped. Each IMU produces a single acquisition CSV file for that session.

Trial

A trial is defined as a continuous time segment selected from a session that represents a specific scenario. The trial may be a static calibration period, a time synchronization motion period, or a constant speed period of vehicle motion.

Trial Segment

We split trials into shorter segments to have an average trial segment duration of about 20 seconds.

Shimmer IMU names:

• BotTrike: a sensor placed underneath the baby seat on the frame structure of the vehicle
• FrontWHeel : attached to the axle of the front wheel (closest possible point to the ground)
• RearWheel : attached to the rotating rear wheel (used for speed measurements)
• SeatBot : attached to the seat just under the baby's buttocks
• SeatHead : attached to the seat just under the back of the baby's head

Vehicle name:

• bugaboo: Bugaboo Fox 5 stroller
• greenmachine: an old-style (~70's) cot for very young infants
• maxicosi: Maxi-Cosi Street Plus stroller
• oldrusty: an old-style (~70's) seat for older infants
• trike: Keiler cargo tricycle (tadpole wheel arrangement with some extra mass added to represent propulsion motor and battery)
• urbanarrow: Urban Arrow cargo electric bicycle
• yoyo: Stokke BABYZEN YOYO 0+ stroller

The Shimmer IMUs are set to +/- 16 g and +/- 2000 deg/s. The values are recorded to 16 bit floating point precision other than the time stamp which is a 16 bit positive integer. The IMUs are placed in the base station and their clocks are synchronized with each other. This means we assume that the time stamp values represents the same real time value in each IMU. The following column order is consistent among the files.

• S_SENSORNAME_Timestamp_Unix_CAL : milliseconds since epoch
• S_SENSORNAME_Accel_WR_X_CAL: m/s/s
• S_SENSORNAME_Accel_WR_Y_CAL: m/s/s
• S_SENSORNAME_Accel_WR_Z_CAL: m/s/s
• S_SENSORNAME_Gyro_X_CAL: deg/s
• S_SENSORNAME_Gyro_Y_CAL: deg/s
• S_SENSORNAME_Gyro_Z_CAL: deg/s

Data Processing

1. Load each acquisition file into a Pandas sparse data frame with the time stamp as the index.
2. Combine all sensor data frames from a single session into a single data frame. These can be up to 2 Gb in size. NaNs are used to represent mismatches in the sample times.
3. Extract the trial start/stop times for trials from the manually created CSV files for each session.
4. Use a period of no motion, "static", in the session to find the direction of gravity in all sensors assuming that one axis of each sensor is aligned with the lateral axis of the vehicle.
5. Down sample the time series from ~900 Hz to 400 Hz.
6. Set any values greater than +/-16 g or +/-2000 deg/s to those maximum values, as the sensors are not valid at higher values.
7. Low pass filter the time series at 120 Hz (ISO 2631-1 recommended 1.5*80 Hz) with a 2nd Order zero-lag Butterworth filter.
8. Calculate linear speed of the vehicle using wheel radius and rear wheel rate gyro. Calculate the mean speed and standard deviation per trial.
9. Calculate the vibration dose value (VDV) from the unfiltered time series.
10. Calculate the crest factor from unweighted maximum and unweighted RMS.
11. Calculate the frequency spectrum of the buttocks sensor's vertical acceleration component for health assessment and magnitude of acceleration for comfort assessment.
12. Apply the ISO 2631-1 spectrum weights for health and comfort assessments.
13. Smooth the frequency spectrums with low pass filter.
14. Calculate the root mean square (RMS) from the weighted spectrums.
15. Calculate the peak frequency and peak amplitude from the spectrum. Calculate the bandwidth containing 80% of the spectrum area.

Final data table should have these columns:

• Trial ID
• Vehicle [bugaboo|yoyo|maxicosi|urbanarrow|keiler|greenmachine|oldrusty]
• Vehicle Type [stroller|bicycle]
• Seat Type [cot|seat]
• Baby Age [month] [0|3|9]
• Baby Mass [kg] [3.48|5.9|8.9]
• Surface [aula|stoeptegels|tarmac|klinkers|pave]
• Duration [s]
• Mean of Speed [m/s]
• Standard Deviation of Speed [m/s]
• Speed Category [5 kph|12 kph|20 kph|25 kph]
• Peak Frequency [Hz]
• Peak Spectrum Amplitude [m/s/s]
• 80% Bandwidth [Hz]
• SENSOR_N lateral acceleration RMS [m/s/s]
• SENSOR_N longitudinal acceleration RMS [m/s/s]
• SENSOR_N vertical acceleration RMS [m/s/s]
• SENSOR_N acceleration magnitude RMS [m/s/s]
• SENSOR_N pitch angular rate RMS [deg/s]
• SENSOR_N yaw angular rate RMS [deg/s]
• SENSOR_N roll angular rate RMS [deg/s]
• SENSOR_N angular rate magnitude RMS [deg/s]
• SENSOR_N ISO filtered lateral acceleration RMS [m/s/s]
• SENSOR_N ISO filtered longitudinal acceleration RMS [m/s/s]
• SENSOR_N ISO filtered vertical acceleration RMS [m/s/s]
• SENSOR_N ISO filtered acceleration magnitude RMS [m/s/s]
• SENSOR_N ISO filtered pitch angular rate RMS [deg/s]
• SENSOR_N ISO filtered yaw angular rate RMS [deg/s]
• SENSOR_N ISO filtered roll angular rate RMS [deg/s]
• SENSOR_N ISO filtered angular rate magnitude RMS [deg/s]
• SENSOR_N lateral acceleration VDV [m/s/s]
• SENSOR_N longitudinal acceleration VDV [m/s/s]
• SENSOR_N vertical acceleration VDV [m/s/s]
• SENSOR_N acceleration magnitude VDV [m/s/s]
• SENSOR_N pitch angular rate VDV [deg/s]
• SENSOR_N yaw angular rate VDV [deg/s]
• SENSOR_N roll angular rate VDV [deg/s]
• SENSOR_N angular rate magnitude VDV [deg/s]

Resources

• Partial implementation of vibration comfort filters: https://github.com/tobias-bettinger/comfpy
• NFFT implementations: https://github.com/jakevdp/nfft & https://github.com/pyNFFT/pyNFFT