Searching for characteristic patterns in time series is a topic addressed for decades by the research community. Conventional subsequence matching techniques usually rely on the definition of a target template pattern and a searching method for detecting similar patterns. However, the intrinsic variability of time series introduces changes in patterns, either morphologically and temporally, making such techniques not as accurate as desired. Intending to improve segmentation performances, in this paper, we proposed a Mask-based Neural Network (NN) which is capable of extracting desired patterns of interest from long time series, without using any predefined template. The proposed NN has been validated, alongside a subsequence matching algorithm, in two datasets: clinical (electrocardiogram) and human activity (inertial sensors). Moreover, the reduced dimension of the data in the latter dataset led to the application of transfer learning and data augmentation techniques to reach model convergence. The results have shown the proposed model achieved better segmentation performances than the baseline one, in both domains, reaching average Precision and Recall scores of 99.0% and 97.5% (clinical domain), along with 77.0% and 71.4% (human activity domain), introducing Neural Networks and Transfer Learning as promising alternatives for pattern searching in time series.

As long as the COVID-19 pandemic is still active in most countries worldwide, rapid diagnostic continues to be crucial to mitigate the impact of seasonal infection waves. Commercialized rapid antigen self-tests proved they cannot handle the most demanding periods, lacking availability and leading to cost rises. Thus, developing a non-invasive, costless, and more decentralized technology capable of giving people feedback about the COVID-19 infection probability would fill these gaps. This paper explores a sound-based analysis of vocal and respiratory audio data to achieve that objective. This work presents a modular data-centric Machine Learning pipeline for COVID-19 identification from voice and respiratory audio samples. Signals are processed to extract and classify relevant segments that contain informative events, such as coughing or breathing. Temporal, amplitude, spectral, cepstral, and phonetic features are extracted from audio along with available metadata for COVID-19 identification. Audio augmentation and data balancing techniques are used to mitigate class disproportionality. The open-access Coswara and COVID-19 Sounds datasets were used to test the performance of the proposed architecture. Obtained sensitivity scores ranged from 60.00% to 80.00% in Coswara and from 51.43% to 77.14% in COVID-19 Sounds. Although previous works report higher accuracy on COVID-19 detection, this research focused on a data-centric approach by validating the quality of the samples, segmenting the speech events, and exploring interpretable features with physiological meaning. As the pandemic evolves, its lessons must endure, and pipelines such as the proposed one will help prepare new stages where quick and easy disease identification is essential.

Background/Objectives: Digital health applications (DHAs) promise to improve disease self-management, but adherence remains suboptimal. We aimed to explore self-monitoring practices of rheumatic and musculoskeletal diseases (RMD) patients. A web-survey was conducted over 7 months including RMD patients to study their self-monitoring practices and the potential of DHAs. Methods: Health, sociodemographic, and technology adherence indicators were retrieved for comparison. Regression analyses and unsupervised profiling were performed to investigate multiple patient profiles. Results: From 228 responses gathered, most reported willingness to use DHAs to monitor their condition (78% agreement), although the majority rarely/never tracked symptoms (64%), often due to stable condition or no perceived value (62%). Of those tracking regularly, 52% used non-digital means. Participants with regular self-monitoring practices were more open to use a self-monitoring app (OR = 0.8 [0.6, 0.9]; p = 0.008) and be embedded in multidisciplinary care (OR = 1.4 [1.1, 1.6]; p < 0.001), but showed worse health status (g = 0.4; p = 0.006). Cluster analyses revealed three distinct groups of reasons for not tracking regularly (${\chi }^2$ = 174.4; p < 0.001), two characterised by perceived low disease activity. Conclusions: Effective use of DHAs remains limited and non-digital means prevail in symptom monitoring. Findings suggest that better patient engagement strategies and passive monitoring should be adopted in early development stages of DHAs for better long-term disease self-care.

Rheumatic and Musculoskeletal Diseases (RMDs) are very common and can negatively impact patients' quality of life. The current care of patients with RMDs is episodic, based on a few yearly doctor visits, which may not provide an adequate picture of the patient's condition. Researchers have hypothesized that RMDs could be passively monitored using smartphones or sensors, however, there are no datasets to support this development. We introduce the COTIDIANA Dataset: a holistic, multimodal, multidimensional, and open-access resource that gathers data on mobility and physical activity, finger dexterity, and mental health, key dimensions affected by RMDs. We gathered smartphone and self-reported data from 31 patients and 28 age-matched controls, including inertial sensors, keyboard metrics, communication logs, and reference tests/scales. A preliminary analysis showed the potential for extracted metrics to predict RMD diagnosis and condition characteristics. Our dataset shall enable the community to create mobile and wearable-based solutions for patients with RMDs.

Background/Objectives: The interest in processing human speech and other human-generated audio signals as a diagnostic tool has increased due to the COVID-19 pandemic. The project OSCAR (vOice Screening of CoronA viRus) aimed to develop an algorithm to screen for COVID-19 using a dataset of Portuguese participants with voice recordings and clinical data. Methods: This cross-sectional study aimed to characterise the pattern of sounds produced by the vocal apparatus in patients with SARS-CoV-2 infection documented by a positive RT-PCR test, and to develop and validate a screening algorithm. In Phase II, the algorithm developed in Phase I was tested in a real-world setting. Results: In Phase I, after filtering, the training group consisted of 166 subjects who were effectively available to train the classification model (34.3% SARS-CoV-2 positive/65.7% SARS-CoV-2 negative). Phase II enrolled 58 participants (69.0% SARS-CoV-2 positive/31.0% SARS-CoV-2 negative). The final model achieved a sensitivity of 85%, a specificity of 88.9%, and an F1-score of 84.7%, suggesting voice screening algorithms as an attractive strategy for COVID-19 diagnosis. Conclusions: Our findings highlight the potential of a voice-based detection strategy as an alternative method for respiratory tract screening.

Progressive neurological conditions like Amyotrophic Lateral Sclerosis (ALS) require regular and detailed monitoring to track functional decline and support timely clinical decisions. Traditional in-clinic assessments, however, can be burdensome and infrequent, often missing daily symptom variability. Smartphones and wearables offer a promising alternative for remote data collection. We present HomeSenseALS, a smartphone-based digital phenotyping application developed for ALS patients and caregivers, using a user-centered design approach involving patients, caregivers, and clinicians. HomeSenseALS collects multimodal data, including self-reported data from the Revised Amyotrophic Lateral Sclerosis Functional Rating Scale (ALSFRS-R), speech and respiratory recordings, and human movement passive data. To assess its potential for tracking ALS progression, we conducted a cross-sectional in-clinic study with 27 ALS patients and a longitudinal at-home study with 11 ALS patients over 21 weeks. Self-reported ALSFRS-R scores showed a strong correlation with in-clinic assessments. High-level physical activity features, such as time spent indoors, were associated with gross motor function. Speech analysis differentiated patients with and without bulbar dysfunction and correlated with respiratory parameters. Home spirometry aligned with clinical respiratory measures. These findings support the feasibility of using HomeSenseALS for remote, multimodal monitoring of ALS progression, potentially reducing patient burden while enabling more continuous and granular tracking of functional decline.