General Discussion

In this section, problems and discussion points regarding measuring breathing with Airleviate and how breathing is classified in the program will be covered.

7.5.1 Measurement

Mr. Bulsink had explained in his interview (Section 4.5.1) that the sampling frequency of the Airleviate was approximately on the lower side of 8 Hz. This has implications on the filtering of the data as the bandpass filter has an input which is the sampling frequency of the signal. This means the filter did not filter only filter all of the noise but possibly some of the frequencies at the edges of the band (0.2 and 0.7). This does not affect the experiment too much considering that 0.2 Hz amounts to 12 breaths per minute and 0.7 Hz amounts to 42 breaths per minute; neither of which could have been present during the data collection as the participants were only seated or standing.

The Airleviate, containing the accelerometer, is worn on the abdominal band in all the recordings made with it. This means that the accelerometer was not able to capture upper body movement, therefore the accelerometer data is not fully descriptive of the user’s movement. The user may be twisting the upper part of their torso, or moving their arms around and the accelerometer could not pick up on it. So the motion labels “Stationary” and “Moving” need to be considered from the context of sitting or standing in one place versus walking around.

The participants mentioned that there was a difference in performing diaphragmatic breathing when they were sitting and standing. When standing, it was easier to expand the abdomen outward while breathing in, however when sitting it was slightly more difficult and abdomen did not expand as far outward. For the calibration routines in future iterations of the program, a distinction should be made between the two.

The participants all mention that they were very conscious of their breathing during the training data recording sessions. Similar to the experience of the author, they felt they were exaggerating the motions while recording the training data and paying explicit attention to it, as opposed to paying less attention to their breathing while recording the breathing session data. This will change in the future when users become more accustomed to breathing diaphragmatically and do not need to exaggerate the motions in order to confirm that they are performing the right type of breathing.

A complaint that many participants had was that the thoracic band slipped a fair bit during the long recording session. This problem can be attributed to the shape of the chest for most participants and how it induces slippage, however for one participant there was a different problem. The participant had an exceptionally long torso, causing the linking cable between the two bands to fully stretch. The cable was unable to link the two bands without pulling the thoracic band downward. There was relatively less slippage experienced with the abdominal band. Slippage can be fixed by fixing the thoracic band in place using skin-friendly adhesive tape or using bandages.

There was also the problem that the thoracic band was slightly too large for some of the participants with narrower chests, which also encouraged slippage even though it was tightened using clothes pegs. There is no real fix for the issue of a participant with a long torso other than getting a longer linking cable. The material of the bands that the current prototype Airleviate can be replaced with something less stretchable.

7.5.2 Classification

A snapshot of the breathing (represented by a pair of raw band values, their filtered values and their contribution to their sum) was provided to the classifier recorded every 8th of a second and a prediction was provided by the classifier. This method identified DBMs, however there are other methods to approach classifying breathing signals. Samples were grouped by the minute they belonged to; within this however, another step could be taken to find the samples belonging to individual breaths

(inhalation and exhalation). This may rule out errors due to users pausing between breaths and gives a better view of the continuous breathing signal. The area under the curve of the sum signal for each breath, and the average abdominal contribution to the sum signal during the inhalation and exhalation could be given as an additional inputs to the classifier. Samples would then be grouped by breath within a given minute. A point for future work would be to explore the difference in results between a classifier

that looks at individual samples and one that looks at samples in the context of a breath with additional parameters relating to the breath, for example the area under the curve of the sum signal.

Diaphragmatic breathing was classified, as shown in Image 5.5, by sample. The total number of samples labeled “diaphragmatic” were tallied. Another way this could have been done is to only consider adjacent samples that are labeled “diaphragmatic”. This would be a stricter definition of diaphragmatic than the simple tallying method currently implemented.

Repetitive breathing is calculated based on the breathing frequency and does not consider the form of type of breathing of the previous minutes. True repetitive breathing would distinguish between chest breathing and diaphragmatic breathing. Even if the breathing frequency of a minute falls within one standard deviation of the buffer’s breathing frequency, if the type of breathing changes from chest to diaphragmatic or vice versa, then the breathing in that minute is not considered repetitive.

While investigating and comparing the accuracies of various classifiers in Section 6.2, it was found that the SVM classified both breathing data and accelerometer data with the highest accuracy. However each classifier had various parameters that they could be initialized with. For example the number of hidden layers could be declared when initializing the Neural Network. A point for future work would be to investigate how varying different parameters of the classifiers affects their prediction accuracy. It is possible that a higher prediction accuracy can be obtained through this process.

The methodology used in this program is as follows: the classifiers are trained on the respective participant’s training data for whom the breathing session data is being processed. Dr. Kamilaris mentioned the “transfer knowledge” aspect of the program and whether breathing data can be generalized. The author had not attempted to process a participants’ breathing session data with classifiers trained on another’s training data. The author is under the impression that breathing is very personal and although there are definitely some similarities between how participants perform

diaphragmatic breathing, they each have their own slight variations that would lower the quality of the processing if used on another participants data. Also, there can be cases where an individual has an abnormal chest to abdominal cross sectional area ratio. In this case, the relative contribution from the abdomen may be slightly less compared to a user with a close to 1:1 ratio. This is a point for future work. Instead of appending labels to the end of each sample, a dictionary could have been used to store all the values. A dictionary in python is a data structure where objects can be addressed via set labels instead of positions in the array, allowing the position to be arbitrary. The labels would look like: “R1_value”, “R2_value”, “X_axis” etc. This would also make the coding process easier as values would not need to