Design of intelligent monitoring of loneliness in the elderly using a serverless architecture with real-time communication API.

BACKGROUND: Loneliness and social isolation are recognized as critical public health issues. Older people are at greater risk of loneliness and social isolation as they deal with things like living alone, loss of family or friends, chronic illness, and hearing loss. Loneliness increases a person's risk of premature death from all causes, including dementia, heart disease, and stroke. To address these issues, the inclusion of technological platforms and the use of commercial monitoring devices are vastly increasing in healthcare and elderly care. OBJECTIVE: The objective of this study is to design and develop a loneliness monitor serverless architecture to obtain real-time data from commercial activity wristbands through an Application Programming Interface. METHODS: For the design and development of the architecture, the Amazon Web Services platform has been used. To monitor loneliness, the Fitbit Charge 5 bracelet was selected. Through the web Application Programming Interface offered by the AWS Lambda service, the data is obtained and stored in AWS services with an automated frequency thanks to the event bridge. RESULTS: In the pilot stage in which the system is, it is showing great possibilities in the ease of collecting data and programming the sampling frequency. Once the request is made, the data is automatically analyzed to monitor loneliness. CONCLUSION: The proposed architecture shows great potential for easy data collection, analysis, security, personalization, real-time inference, and scalability of sensors and actuators in the future. It has powerful benefits to apply in the health sector and reduces cases of depression and loneliness.

Harmonic to noise ratio improvement in oesophageal speech.

BACKGROUND: This manuscript presents oesophageal speech enhancement. Patients who have undergone a laryngectomy as a result of larynx cancer, that is, laryngectomees, have communication problems. Due to removal of the larynx, oesophageal speech has extremely low intelligibility. OBJECTIVE: Thus, it is necessary to process acoustical parameters, such as Harmonic to Noise Ratio (HNR) in order to increase intelligibility. METHODS: The research focused on oesophageal Spanish /a/ phoneme improvement. In order to enhance oesophageal speech two techniques were applied: Kalman filtering and an algorithm which stabilizes the vocal tract poles. Speech enhancement was measured using the MDVP tool. The oesophageal voice database was compiled with the help of the local association of laryngectomees. RESULTS: The results show an average improvement of 4.2 dB in the HNR. Statistically, differences on average between the original and processed voices, (p < 0.001) for HNR parameter were proven to be significant and we therefore conclude that voice quality was improved due to evidence of a higher HNR on average. CONCLUSIONS: As a conclusion, the study confirms oesophageal voice enhancement since speech parameters are closer to the normal average range. Subjectively, the oesophageal breathing noise is reduced substantially, as is reflected in the MOS test.