Objective detection of a driver’s drowsiness is important for improving driving safety, and the most prominent indicator of drowsiness is changes in electroencephalographic (EEG) activity. Despite extensively documented behavioral differences between male and female drivers, previous studies have not differentiated drowsiness detection models based on drivers’ sex. Therefore, the overall aim of this study is to demonstrate that drowsiness detection can be improved with the use of drivers’ sex information, either as a feature or as separate sex-dependent datasets. Additionally, we aim to provide a reliable EEG-based sex classification model. The used dataset consists of 17 male and 17 female drivers which were evaluated during alert and drowsy sessions. Frequency-domain and recurrence quantification analysis EEG features were used. Four classification algorithms and three feature selection methods were applied to build the models. The accuracy of drowsiness detection based on sex-dependent datasets is 84% for male drivers and 88% for female drivers, which is 3% and 7% better, respectively, than the classification without information about driver’s sex (81%). The model for sex classification based on EEG achieved high accuracy: 97% correctly identified participants in alert sessions and 96% in drowsy sessions. All participants were correctly classified after the application of majority voting on five algorithm runs. The results suggest that sex-dependent datasets improve the accuracy of drowsiness models, which may be relevant to a variety of drowsiness detection systems currently being developed in the field.

Nanopore sequencing enables real-time, long-read analysis by processing raw signals as they are produced. A key step, segmentation of signals into events,

Nanopore sequencing enables real-time, long-read analysis by processing raw signals as they are produced. A key step, segmentation of signals into events, is typically handled algorithmically, struggling in noisy regions. We present Campolina, a first deep-learning frame-work for accurate segmentation of raw nanopore signals. Campolina uses a convolutional model to identify event boundaries and significantly outperforms the traditional Scrappie algorithm on R9.4.1 and R10.4.1 datasets. We introduce a comprehensive evaluation pipeline and show that Campolina aligns better with reference-guided ground-truth segmentation. We show that integrating Campolina segmentation into real-time frameworks, Sigmoni and RawHash2, improves their performance while maintaining time efficiency.

A neighborhood’s built environment can challenge the mobility of older mobility-challenged adults (due to knee osteoarthritis and falls), reducing their participation and quality of life. The Built Environment in Falls and arthrITis (BE-FIT) study aims to understand the neighborhood influence on the mobility, participation, and psychosocial health of older adults with knee osteoarthritis and/or falls. BE-FIT comprises four work packages (WPs). WP1 quantitatively explores relationships among environmental-, social-, and person-related factors and participation outcomes of its intended population. WP2 employs qualitative methods to comprehend the relationships among WP1’s variables. Via a combination of wearable sensor technology and qualitative geospatial methods, WP3 aims to characterize its population’s movement behavior, mobility, functional activity of daily living, and lived experiences of residing in a mature neighborhood. Finally, WP4 engages crucial stakeholders to co-develop evidence-based recommendations to inform public health, urban planning, and aging policies and implementation. BE-FIT could benefit societies with rising incidence of knee osteoarthritis and falls by improving neighborhoods and lives of older mobility-challenged residents.