OS node that uses hidden markov models to classify a human's gait phase as swing or stance based on IMU data, foot pressure sensors, infrared and proximity data requires ROS indigo and the pomegranate library. Included are the data for 3 subjects, with 5 intervals per subject where:

• Interval 1(and 2 for subject1 (not used)): normal walking
• Interval 2: low foot clearance
• Interval 3: small step width
• Interval 4: excessive trunk sway
• Interval 5: all of the previous combined in a random manner

(Code not documented is deprecated/not used)

./launch/record.sh \<output_bagfile_name\> \<duration_in_minutes\>

Run:

For Single subject:

  roslaunch bag2mat.launch prefix:=\<path_to_folder/subject_name_recordingnumber\> file=/<path_to_parameter_file/>

The parameter file contains the topics for each imu, see /scripts/new_bags/sub1.yaml

For all subejcts:

  ./launch/bag2matnewbags.sh

This will create a number of .mat files for each input file containing each component recorded.

To annotate the data file with mocap data is required in a format similar to the files found in /scripts/<subject_number>.zip, that are in the follwoing format:

COMmovement: X-anterior-posterior trunk sway; Y-medio-lateral trunk sway.

Annotate a single subject:

rosrun gait_hmm_ros auto_annotate.py _prefix:=new_bags/subject1_1 _matfile:=new_bags/subject_one/subject01_normal

Annotate every subject

./launch/annotatenewbags.sh

This will create a file named like your input file with the addition of "_labels" in the end. It will contain the gait phase label for every timestamp created in the previous cript.

Create different sets of data containing or excluding the sensors you want to use, based on /scripts/new_bags/<subject>_full_data.mat created with the previous scripts.

./batch_create_combos_\<sub1,sub2,sub3,gyro\>

Uses the /launch/create_data.launch script which accepts the following parameters:

• batch_train: 0/1 whether to train classifier in batches or with single inputs
• batch_test: 0/1 whether to test classifier in batches or with single inputs
• use_quat: 0/1 whether to use the quaternion data when training the classifier
• use_gyro: 0/1 whether to use the gyroscope data when training the classifier
• use_accel: 0/1 whether to use the accelerometer data when training the classifier
• use_com: 0/1 whether to use the compass data when training the classifier
• use_ir: 0/1 whether to use the IR data when training the classifier
• use_prox: 0/1 whether to use the proximity data when training the classifier
• use_fsr: 0/1 whether to use the FSR data when training the classifier
• rf: topic for foot IMU, empty to not use
• rl: topic for lower leg IMU, empty to not use
• rul: topic for upper leg IMU, empty to not use
• m: topic for lower back IMU, empty to not use

Train Single Classifier

rosrun gait_hmm_ros create_single_classifier.py _folds:=\<number of training folds\> _norm:=\<1 or 0, use normalized data or not\>
_datafile:=\<first component of datafile to use se /launch/create_single_classifiers_1.sh for the format\>

Train Multpile Classifiers:

./launch/create_single_classifiers_(1-4).sh

The classifiers will be saved as pickled python objects in /scripts/trained_classifiers/

Receives input from IMUs and other devices and uses an ANFIS to classify the type of gait of the user (normal gait, excessive trunk sway, small step width, low foot clearance). Requires Matlab's Fuzzy Logic Toolbox, the 3Space IMU python API, numpy, and scipy. Tested in ROS Indigo

Sample bag files are located in scripts/new_bags/subject[number]_[trial].bag

• record.sh: waits 5 seconds and the starts recording available IMU frames

  sh record.sh [filename] [duration-minutes]

• batch_create_combos_x.sh: create different datasets using subsets of the available measurements

• create_single_classifier_x.sh: create different classifiers using subsets of the available measurements

• align.launch: used to align mocap files with data from rosbags

  roslaunch align.launch prefix:=[datafile] mocap:=[mocap_file]

• bag2mat.launch: used to create new matfiles from rosbags

  roslaunch bag2mat.launch param_file:=[param file] prefix:=[file name]

  param files contain the topic names for the different imus as "upper_leg_topic lower_leg_topic foot_topic"

• auto_annotate.launch: annotates a matfile using a given mocap file

  roslaunch auto_annotate.launch prefix:=[data file] prefix:=[mocap file]

• create_data.launch: creates training files using subsets of the available sensor data, edit the launch file to change, 1 means uses, 0 means don't use. Creates normalized and non-normalized datasets and prints out their results in /new_bags. Data is saved as .mat files or pickles. Also save are the label for each reading, and trunk sway, step length and width for mocap data.

  A complete data file has the following format:

  [upper_leg_quaternion_x][upper_leg_quaternion_y][upper_leg_quaternion_z][upper_leg_quaternion_w][upper_leg_accelerometer_x] [upper_leg_accelerometer_y][upper_leg_accelerometer_z][upper_leg_gyroscope_x][upper_leg_gyroscope_y][upper_leg_gyroscope_z] [upper_leg_magnetometer_x][upper_leg_magnetometer_y][upper_leg_magnetometer_z] [lower_leg_quaternion_x][lower_leg_quaternion_y][lower_leg_quaternion_z][lower_leg_quaternion_w][lower_leg_accelerometer_x] [lower_leg_accelerometer_y][lower_leg_accelerometer_z][lower_leg_gyroscope_x][lower_leg_gyroscope_y][lower_leg_gyroscope_z] [lower_leg_magnetometer_x][lower_leg_magnetometer_y][lower_leg_magnetometer_z] [foot_quaternion_x][foot_quaternion_y][foot_quaternion_z][foot_quaternion_w][foot_accelerometer_x] [foot_accelerometer_y][foot_accelerometer_z][foot_gyroscope_x][foot_gyroscope_y][foot_gyroscope_z] [foot_magnetometer_x][foot_magnetometer_y][foot_magnetometer_z] [FSR reading][IR reading][Proximity Reading]

• align_mocap.py: Used to align mocap data and rosbag recordings, use with align.launch
• arduino_listener.py: Used to read arduino data and republish it as a ROS message
• auto_annotate.py: Annotates a matfile using a mocap output file (see auto_annotate.launch)
• fsr_annotate_2.py: annotates data using just the FSR input
• create_data_combos.py: creates matfiles using subset of the available measurements (see create_data.launch)

• bigOlClassifierScript2: creates and evaluates all possible classifiers
• create_anfis_list: reads available files in ~/full_data and creates a list of classifiers to test
• build_anfis_classifier_loop.m: for every classifier in the list created with create_anfis_list, build and evaluate the classifier
• evaluate_anfis_classifier_loop.m: just evaluates the classifier and saves the results
• create_and_evaluate_classifier.m: creates a classifier based on a given workspace, evaluates the classifier and saves the results
• create_anfis_classifier.m: splits data into test and training data, builds a classifier and saves the workspace
• plots_and_stats.m: loads a built classifier, evaluates it, and creates a graph of its performance

HMM classifiers, can be used for reference

• create_single_classifier.py
• create_distributed_classifier.py
• dis_class_2.py
• distributed_classifier_2.py
• test_classifiers.py
• test_classifiers.distributed.py
• create_anfis_classifier_phase.m
• create_anfis_classifier_normal.m

• fsr_annotate.py: see fsr_annotate_2.py