6.4 Software development
6.4.3 Operations and main functions of the software
The data collected by the hardware were initially imported into the software. Measures in the different tests were then calculated based on the data. Finally, these measures were used in the models to identify individuals at high fall risk and then identify the possible causes.
The data import module was designed to input the data file of five inertial sensors, resample the data from the five sensors for time synchronization, and visualize and export the resampled data. The raw data from the hardware were 3D accelerations, 3D angular rates, 3D magnetization and 4D orientation represented by the quaternion. The open file function was used to combine all sensor data files as a unit and store the data in the database. Raw sensor data were recorded based on the same reference time (PC time), but there were some small shifts on time serials between different sensors. In addition, the sample rate appeared to be close to the number that we could set during data acquisition, but was not exactly the number that we expected. Consequently, the data resample function was utilized to identify the start and end points of all sensor time serials and then resample all data to the same frequency (100Hz).
After the data were imported and resampled, the resampled data could be plotted and exported in the software (Figure6.4). First, users had to load the file that they wanted to check or export by clicking the
’Load data file’ button. After selecting the files, the file names were shown in the interface. Then users needed to select the sensors and data type of the data. Once the sensors and data type were checked, the selected data were automatically plotted in a figure. If users intended to export the data file, they could simply click the ”export” button and choose the path or folder in which they wanted to store the data, which was saved in the CSV format.
FIGURE6.4: The interface of data visualization and export function.
After importing the data, the measure calculation module was designed to calculate inertial sensor based measures and input other necessary measures acquired from other methods, such as functional reach distance and muscular strength. According to measures in the tests, six sub-modules were used for calculating measures: sensory integration test measures, limits of stability test measures, sit-to-stand five times test measures, magnitude and gait measures of the TUG test, turning measures of the TUG test, and flexibility measures of motor function test. Figures6.5and6.6present the operations to calculate measures. In general, three steps were used to calculate measures: loading the data, identifying the feature points (E.g. the start and end points of functional reach), and calculating the data. For example, in the limits of the stability test measures (Figure6.5b), first the data were loaded and the file names were shown in the textboxes. The checkboxes located beside the textboxes were checked to plot the pelvis orientation and the start and end points of the functional reach. This procedure was done to examine whether the automatically determined start and end points of the functional reach were correct or not. In the figure, the start and end points were generated by our algorithms. If the points were not correct, users could point to the figure, find the correct one, note the start and end points in the textboxes, and click the
”Update” button to update the start and end points. Later, the start and end points would be renewed in
the figures. After the start and end points were identified, users could click the calculate measures button and the results were presented in the data table.
FIGURE6.5: The operations of calculating sensory integration measures (a); limits of stability measures (b); sit-to-stand five times measures (c).
FIGURE6.6: The operations of calculating magnitude and gait measures in timed up and go test (a);
turning measures in timed up and go test (b); flexibility measures in timed up and go test (c).
In addition to measures derived from inertial sensors, some other measures were not generated from inertial sensors, such as functional reach distance and information processing speed. These measures are necessary and needed to be manually input into the system for fall classification and evaluation.
Figure 6.7 shows the interface to input measures to the system. These measures included maximal muscular strengths in the motor function test, reaction test measures, short FES-I score, functional reach distance in forward, right and left directions, and additional basic information about the participant.
FIGURE6.7: The interface of inputting additional measures.
Figure6.8shows the interface of fall risk assessment functions, which contained fall classification and evaluation. In terms of the fall classification, users selected a fall classification model and then the classification results were shown in the textbox. In terms of the fall evaluation, if old people were detected to be at high fall risk from the results of the fall classification, the decision tree and the profile graph showed the possible causes of high fall risks. Our proposed method combined the results from the decision tree and profile graph to generate the overall results. For example, when the classification and regression model was chosen for a participant, the results showed that the participant’s probability of falling was 0.90, which indicated high fall risks. Then the ”Decision Tree” button was clicked to check the causes of high fall risks based on the CART model, showing a poor central nervous system and relatively high fear of falling (Figure 6.9). Clicking the ”Profile Graph” button allowed the user
to examine the causes of high fall risk using the profile assessment method (Figure 6.9). The results showed that a poor central nervous system was the factor and poor gait stability was a potential factor.
Finally, clicking the ”Combined Method” button revealed the overall results (Figure6.10). It showed that a poor central nervous system was the main cause of high fall risks, and in addition, relatively high fear of falling and poor gait stability were also indicated as potential factors that might cause high fall risks.
FIGURE6.8: The typical example of fall risk assessment.
FIGURE6.9: The typical example of identifying the causes of high fall risks: (a) the CART model; (b) profile assessment method.
FIGURE6.10: The typical example of identifying the causes of high fall risks using combined method.