Healthcare Education and Labeling for Cardiopulmonary Sounds, HEALSound, is an interactive platform designed to enhance the identification and labeling of adventitious cardiopulmonary sounds through gamification. HEALSound enables users, particularly medical professionals, to engage in exercises that improve their knowledge of abnormal heart and lung sounds while simultaneously contributing to the labeling of raw audio data. By incorporating real-time feedback and progress tracking, the app promotes continuous learning and provides a valuable crowdsourced resource for building high-quality labeled datasets over time. This dual-purpose platform not only aids in medical education but also contributes to the advancement of machine learning models for sound classification in healthcare. The system demonstrates a new potential for significant impact in educational and clinical environments by seamlessly integrating learning with data collection, ensuring scalability and the continuous improvement of cardiopulmonary sound databases.

Advances in digital stethoscopes and wearable health sensors now support real-time cardiopulmonary monitoring and AI-driven diagnostic tools. However, the extensive manual labeling required for large datasets of respiratory sounds presents a significant barrier, traditionally dependent on expert input. To address this, we introduce HEALSound, a mobile application designed to blend educational training with crowdsourced data labeling. By engaging users in interactive auscultation exercises and providing immediate feedback, HEALSound promotes skill development while generating high-quality labeled data through weighted consensus methods. Experimental results highlight the app’s dual effectiveness: enhancing diagnostic learning for users and accelerating the development of machine-learning models through enriched datasets, thus addressing critical challenges in AI-based health diagnostics.

Data-retention flip-flops (DR-FFs) efficiently maintain data during sleep mode, and retain state during transitions between active and sleep mode. This brief proposes an ultralow power DR-FF design with an improved autonomous data-retention (ADR) latch operating with a supply voltage range down to near/subthreshold, achieving a sleep mode leakage power of 12.2 pW,$1.4\times $–$3.8\times $less than the prior CMOS DR-FFs. Our proposed DR-FFs consume the lowest active mode switching efficiency of 36.5 fJ/step,$1.2\times $–$4\times $less than the prior works, and a comparable transition efficiency of 1.9 fJ/step. Furthermore, our proposed DR-FFs require minimal control signals, logic gates, and switches, significantly reducing design complexity, and avoiding the drawbacks of nonvolatile data retention FFs (NV-FFs).

This work introduces an innovative wearable health-care system that offers personalized cardiopulmonary monitoring by continuously capturing a variety of physiological signals. Users can engage with the system to view their own data in real-time, thereby gaining a nuanced understanding of its multi-modal sensing capabilities and the potential for remote health monitoring.

Continuous physiological sound monitoring is crucial for the prevention, diagnosis, and treatment of various diseases like cardiopulmonary and gastrointestinal conditions. Wearable healthcare sensors have emerged as a potent solution, streamlining the capture, storage, transmission, and analysis of individualized physiological sounds. However, challenges exist including large data volumes, limited hardware computational capabilities, and constrained transmission bit rates. To address these issues, we propose a physiological sound compression system using compressive sensing with self-adaptive compression ratio across sound types to implement physiological sound compression and Optimized Discrete Cosine Transform (ODCT) reconstruction to reduce loss in effective bands. Evaluated on SPRSound and PhysioNet 2016, our approach attains correlation coefficients of 0.863 and 0.883 for respiratory and cardiac sounds, with -3.14 dB and -1.84 dB signal-to-noise ratio loss at 3.5 and 3.0 compression ratios. Implemented on a custom healthcare sensor, our approach optimizes bit rate to 1.73× and power consumption to 0.82× compared to the uncompressed system.