Human respiration rate (HRR) is an important physiological metric for diagnosing a variety of health conditions from stress levels to heart conditions. Estimation of HRR is well-studied in controlled terrestrial environments, yet robotic estimation of HRR as an indicator of diver stress in underwater for underwater human robot interaction (UHRI) scenarios is to our knowledge unexplored. We introduce a novel system for robotic estimation of HRR from underwater visual data by utilizing bubbles from exhalation cycles in scuba diving to time respiration rate. We introduce a fuzzy labeling system that utilizes audio information to label a diverse dataset of diver breathing data on which we compare four different methods for characterizing the presence of bubbles in images. Figure 1: Robotic estimation of diver respiration rate during Ultimately we show that our method is effective at estimating a closed-water evaluation of the proposed detection HRR by comparing the respiration rate output system. The Aqua autonomous underwater vehicle [8] is with human analysts.

Many deep learning-based computerized respiratory sound analysis methods have previously been developed. However, these studies focus on either lung sound only or tracheal sound only. The effectiveness of using a lung sound analysis algorithm on tracheal sound and vice versa has never been investigated. Furthermore, no one knows whether using lung and tracheal sounds together in training a respiratory sound analysis model is beneficial. In this study, we first constructed a tracheal sound database, HF_Tracheal_V1, containing 10448 15-s tracheal sound recordings, 21741 inhalation labels, 15858 exhalation labels, and 6414 continuous adventitious sound (CAS) labels. HF_Tracheal_V1 and our previously built lung sound database, HF_Lung_V2, were either combined (mixed set), used one after the other (domain adaptation), or used alone to train convolutional neural network bidirectional gate recurrent unit models for inhalation, exhalation, and CAS detection in lung and tracheal sounds. The results revealed that the models trained using lung sound alone performed poorly in tracheal sound analysis and vice versa. However, mixed set training or domain adaptation improved the performance for 1) inhalation and exhalation detection in lung sounds and 2) inhalation, exhalation, and CAS detection in tracheal sounds compared to positive controls (the models trained using lung sound alone and used in lung sound analysis and vice versa). In particular, the model trained on the mixed set had great flexibility to serve two purposes, lung and tracheal sound analyses, at the same time.

According to the World Health Organisation (WHO), 235 million people suffer from respiratory illnesses and four million people die annually due to air pollution. Regular lung health monitoring can lead to prognoses about deteriorating lung health conditions. This paper presents our system SpiroMask that retrofits a microphone in consumer-grade masks (N95 and cloth masks) for continuous lung health monitoring. We evaluate our approach on 48 participants (including 14 with lung health issues) and find that we can estimate parameters such as lung volume and respiration rate within the approved error range by the American Thoracic Society (ATS). Further, we show that our approach is robust to sensor placement inside the mask.

There is great promise and potential in artificial intelligence (AI), but if such technologies are built and trained by humans, are they capable of bias? Absolutely, says William Wang, the Duncan and Suzanne Mellichamp Chair in Artificial Intelligence and Designs at UC Santa Barbara, who will give the virtual talk "What is Responsible AI," at 4 p.m. Tuesday, Jan. 25, as part of the UCSB Library's Pacific Views speaker series (register here). "The key challenge for building AI and machine learning systems is that when such a system is trained on datasets with limited samples from history, they may gain knowledge from the protected variables (e.g., gender, race, income, etc.), and they are prone to produce biased outputs," said Wang, also director of UC Santa Barbara's Center for Responsible Machine Learning. "Sometimes these biases could lead to the'rich getting richer' phenomenon after the AI systems are deployed," he added. "That's why in addition to accuracy, it is important to conduct research in fair and responsible AI systems, including the definition of fairness, measurement, detection and mitigation of biases in AI systems."

A reliable, remote, and continuous real-time respiratory sound monitor with automated respiratory sound analysis ability is urgently required in many clinical scenarios-such as in monitoring disease progression of coronavirus disease 2019-to replace conventional auscultation with a handheld stethoscope. However, a robust computerized respiratory sound analysis algorithm has not yet been validated in practical applications. In this study, we developed a lung sound database (HF_Lung_V1) comprising 9,765 audio files of lung sounds (duration of 15 s each), 34,095 inhalation labels, 18,349 exhalation labels, 13,883 continuous adventitious sound (CAS) labels (comprising 8,457 wheeze labels, 686 stridor labels, and 4,740 rhonchi labels), and 15,606 discontinuous adventitious sound labels (all crackles). We conducted benchmark tests for long short-term memory (LSTM), gated recurrent unit (GRU), bidirectional LSTM (BiLSTM), bidirectional GRU (BiGRU), convolutional neural network (CNN)-LSTM, CNN-GRU, CNN-BiLSTM, and CNN-BiGRU models for breath phase detection and adventitious sound detection. We also conducted a performance comparison between the LSTM-based and GRU-based models, between unidirectional and bidirectional models, and between models with and without a CNN. The results revealed that these models exhibited adequate performance in lung sound analysis. The GRU-based models outperformed, in terms of F1 scores and areas under the receiver operating characteristic curves, the LSTM-based models in most of the defined tasks. Furthermore, all bidirectional models outperformed their unidirectional counterparts. Finally, the addition of a CNN improved the accuracy of lung sound analysis, especially in the CAS detection tasks.

For the next installment of the informal TechCrunch book club, we are reading the fourth story in Ted Chiang's Exhalation. The goal of this book club is to expand our minds to new worlds, ideas, and vistas, and The Lifecycle of Software Objects doesn't disappoint. Centered in a future world where virtual worlds and generalized AI have become commonplace, it's a fantastic example of speculative fiction that forces us to confront all kinds of fundamental questions. If you've missed the earlier parts in this book club series, be sure to check out: Some questions for the fifth story in the collection, Dacey's Patent Automatic Nanny, are included below. This is a much more sprawling story than the earlier short stories in Exhalation, with much more of a linear plot than the fractal koans we experienced before.

While many scifi authors write serial novels to make a living specializes the Chinese-American in short stories & novellas and publishes them infrequently. In the past 28 years, he's released 17 short stories and novellas (gq.com). The frugality seems to support the quality, Chiang collected a lot prestigious awards and his novella "Story of Your Live" was turned into the movie "Arrival". Recently Chiang published his second book: "Exhalation" ( amazon). This book is again a collection of short stories like the first book "Stories of Your Life and Others" from 2002.