The Effect of EMG Features on the Classification of Swallowing Events and the Estimation of Fluid Intake Volume

Comprehensive assessment and treatment strategies for dysphagia in the elderly population: Current status and prospects

As the population ages, the prevalence of dysphagia among older adults is a growing concern. Age-related declines in physiological function, coupled with neurological disorders and structural changes in the pharynx associated with aging, can result in weakened tongue propulsion, a prolonged reaction time of the submental muscles, delayed closure of the laryngeal vestibule, and delayed opening of the upper esophageal sphincter (UES), increasing the risk of dysphagia. Dysphagia impacts the physical health of the elderly, leading to serious complications such as dehydration, aspiration pneumonia, malnutrition, and even life-threatening conditions, and it also detrimentally affects their psychological and social well-being. There is a significant correlation between frailty, sarcopenia, and dysphagia in the elderly population. Therefore, older adults should be screened for dysphagia to identify both frailty and sarcopenia. A reasonable diagnostic approach for dysphagia involves screening, clinical assessment, and instrumental diagnosis. In terms of treatment, multidisciplinary collaboration, rehabilitation training, and the utilization of new technologies are essential. Future research will continue to concentrate on these areas to enhance the diagnosis and treatment of dysphagia, with the ultimate aim of enhancing the quality of life of the elderly population.

A Cross-Day Analysis of EMG Features, Classifiers, and Regressors for Swallowing Events Detection and Fluid Intake Volume Estimation

Dehydration is a common problem among older adults. It can seriously affect their health and wellbeing and sometimes leads to death, given the diminution of thirst sensation as we age. It is, therefore, essential to keep older adults properly hydrated by monitoring their fluid intake and estimating how much they drink. This paper aims to investigate the effect of surface electromyography (sEMG) features on the detection of drinking events and estimation of the amount of water swallowed per sip. Eleven individuals took part in the study, with data collected over two days. We investigated the best combination of a pool of twenty-six time and frequency domain sEMG features using five classifiers and seven regressors. Results revealed an average F-score over two days of 77.5±1.35% in distinguishing the drinking events from non-drinking events using three global features and 85.5±1.00% using three subject-specific features. The average volume estimation RMSE was 6.83±0.14 mL using one single global feature and 6.34±0.12 mL using a single subject-specific feature. These promising results validate and encourage the potential use of sEMG as an essential factor for monitoring and estimating the amount of fluid intake.

Prediction of Voice Fundamental Frequency and Intensity from Surface Electromyographic Signals of the Face and Neck

Silent speech interfaces (SSIs) enable speech recognition and synthesis in the absence of an acoustic signal. Yet, the archetypal SSI fails to convey the expressive attributes of prosody such as pitch and loudness, leading to lexical ambiguities. The aim of this study was to determine the efficacy of using surface electromyography (sEMG) as an approach for predicting continuous acoustic estimates of prosody. Ten participants performed a series of vocal tasks including sustained vowels, phrases, and monologues while acoustic data was recorded simultaneously with sEMG activity from muscles of the face and neck. A battery of time-, frequency-, and cepstral-domain features extracted from the sEMG signals were used to train deep regression neural networks to predict fundamental frequency and intensity contours from the acoustic signals. We achieved an average accuracy of 0.01 ST and precision of 0.56 ST for the estimation of fundamental frequency, and an average accuracy of 0.21 dB SPL and precision of 3.25 dB SPL for the estimation of intensity. This work highlights the importance of using sEMG as an alternative means of detecting prosody and shows promise for improving SSIs in future development.

Development of Piezoelectric Silk Sensors Doped with Graphene for Biosensing by Near-Field Electrospinning

A novel piezoelectric fiber sensor based on polyvinylidene fluoride piezoelectric (PVDF) doped with graphene is presented. The near-field electrospinning technology was used for developing the sensor. The uniform experimental design method was introduced to determine the ranges of experimental parameters, including the applied voltage, the drum speed range, the graphene doping ratios from 0% to 11 wt% in PVDF solution, and the electrode gap. By experimental results, the conductivities of PVDF solutions with different doping ratios of graphene increased from 19.6 μS/cm to 115.8 μS/cm. Tapping tests were performed to measure the voltages and currents produced by the piezoelectric fibers. The maximum output voltage was 4.56 V at 5 wt% graphene doping ratio in PVDF fibers, which was 11.54 times that of the pure PVDF sensors. Moreover, mechanical properties of the proposed sensor were measured. Motion intention and swallowing test, such as saliva-swallowing and eating, were carried out. When the subject spoke normally, the output voltage of the sensor was between 0.2 and 0.4 V, approximately. Furthermore, when the subject drank water and ate food, the output voltage of the sensor was between 0.5 and 1 V, approximately. The proposed sensor could be used to detect signals of the human body and serve as a wearable device, allowing for more diagnosis and medical treatment.

On the Applications of EMG Sensors and Signals

The ability to execute limb motions derives from composite command signals (or efferent signals) that stem from the central nervous system through the highway of the spinal cord and peripheral nerves to the muscles that drive the joints [...].