DOI: 10.18742/28528115.v1
title: Electromyogram and Sound of Swallowing Events

description: Dehydration is a prevalent and serious problem among older adults. As people age, the body’s ability to conserve water decreases, and the sensation of thirst diminishes. This reduced sense of thirst makes it easy for older adults to become dehydrated without realizing it. Monitoring fluid intake is therefore essential for maintaining proper hydration for overall health. The main objectives of this study are to investigate whether transfer learning can enhance classification performance compared to less complex CNN models in detecting swallowing and drinking types, to compare the performance of EMG and sounds in detecting swallowing from non-swallowing events, and to explore if the downsampling will affect the classification performance. In the methodology, two transfer learning models ResNet-18 and ResNet-9 along with proposed convolutional neural network (CNN) architectures (from one to nine layers) were trained on spectrogram images derived from swallowing and non-swallowing signals collected via surface electromyography (sEMG) and a microphone. Eleven individuals (eight females and three males) participated in the study. The results showed that the ensemble F-score for classifying swallowing from non-swallowing events using ResNet was 85% and 95% using the EMG and Microphone signals, respectively. <b>Experimental Procedure:</b>The experiment consisted of a 90-minute session for two types of signals, EMG and acoustic signals, which were captured concurrently during the experiment. Two Delsys Trigno sEMG sensors (Natick, MA, USA) were utilised to capture sEMG data. The sEMG signals were analogue filtered between 10 and 850 Hz and sampled at 2.2 kHz. One EMG sensor was placed on one side of the sternohyoid muscles’ belly, part of the infrahyoid group, chosen for their superficial location, and the microphone was placed on the other side. The use of two EMG sensors was informed by our previous studies. To capture the acoustic signals of the swallowing data, we used the RØDE SmartLav+ Smartphone Lavalier Microphone. Acoustic signals were analogue filtered between 100 and 10000 Hz and sampled at 44.1 kHz. The microphone was placed on the right side of the sternohyoid muscles’ belly, part of the infrahyoid group. Participants were seated comfortably, and their neck area was cleaned with alcohol wipes. The sensor’s placement was determined by palpating the relevant swallowing muscles. After proper anatomical positioning of the sensors, participants were instructed to perform nine different tasks in a random order for each session. The first task required the participants to talk while recording. The second task involved coughing, while the third and fourth tasks involved swallowing saliva and solid food. Participants were given chocolate chip cookies and instructed to take one bite at a time for the solid food task. Tasks five through nine involved swallowing water from a cup in single sips, with the water volume increasing by 5 mL for each subsequent task, starting at 5 mL for the fifth task and reaching 25 mL by the ninth task. A needleless syringe with markings was used to ensure accurate measurement. Participants followed verbal instructions to perform these tasks, and their actions were recorded for analysis and further evaluation.