# An Audio-Based Method for Assessing Proper Usage of Dry Powder Inhalers
###### Abstract
Genuair is a multi-dose inhaler, giving the advantage to patients of carrying many doses wherever they go. Genuair is, also, reusable (in contrast with Diskus and Turbuhaler [21]), being cost effective over time. Moreover, [PERSON] et al. [22] tested the usability of the seven most used DPIs in COPD and Genuair ranked sixth in a scale of usability, indicating a high factor of errors to be performed during its usage. Mainly, Genuair was used for the purpose of this study because it was stated that it ranks in a high place in the Greek market of inhaler devices; thus, it is very common among respiratory patients. All the above attributes of Genuair triggered our research towards the device utilization and assessment. Detection and classification of sound events produced by the use of an inhaler is a field with emerging research as is shown by recent work [13; 23]. [PERSON] et al. [24] developed audio-signal processing methods to automatically detect inhaler events. An INhaler Compliance Assessment (INCA) device, which uses a microphone with sampling rate of 8 kHz and depth of 8 bits/sample, was used to obtain audio recordings of Diskus DPI use from 12 asthma patients. The study obtained 609 audio recordings. A set of features were employed to characterize the different inhaler events, such as the power of specific frequency bands, Mel-Frequency Cepstral Coefficients (MFCCs) and three types of sounds were classified using feature value decision thresholds: preparation (blister), inhalation, and exhalation. The algorithm achieved an accuracy of 92.1% for preparation detection, 91.7% for inhalation detection, and 93.7% for exhalation detection. [PERSON] et al. [25] developed an accurate method of detecting Metered Dose Inhaler (MDI) actuations using inhaler audio recordings (sampled at 44.1 kHz, with depth of 16 bits/sample) obtained from five healthy participants and 15 hospitalized asthma and COPD patients. There were 179 audio signals obtained using an attachable microphone fixed to the side of a pMDI. This method accurately distinguished actuations from other inhaler events using the continuous wavelet transform (by analyzing the high frequency power content of the signal). The method was trained on 20 and tested on 159 audio signals with an accuracy of 99.7%, sensitivity of 100%, and specificity of 99.4% at detecting pMDI actuations. [PERSON] et al. [26] investigated the use of convolution neural networks (CNNs) to detect pMDI actuation sounds. The authors used a microphone (connected to a smartphone) that was fixed onto the front side of a pMDI. The pMDI audio signals were acquired from five healthy participants who were asked to trigger the pMDI in open air and away from their mouth in different real-life noisy environments. This way they produced 400 recordings (200 of actuation and 200 of non-actuation) and an accuracy of 98% was reported at detecting pMDI actuation sounds. [PERSON] et al. [27] obtained pMDI audio data from five healthy participants used for the classification of four sound types or classes (inhaler actuation, inhalation, exhalation, background noise). The dataset was comprised of 280 samples per sound class giving a total of 1120 sound samples in the dataset (sampled at 4 kHz with depth of 4 bits/sample). In this study, it was reported that AdaBoost outperformed the alternative approaches leading to accuracies above 96% by using the Short Time Fourier Transform (STFT) as the feature extraction method. [PERSON] et al. [28] developed an energy efficient wireless audio-based method of remotely detecting pMDI events. In this study, a dataset from two healthy participants consisting of 500 actuations and 500 noise segments (dataset 1) along with 200 actuations, 200 inhalations, 200 exhalations, and 200 noise segments (dataset 2) were obtained. An accuracy of over 96% was reported for the two datasets.