An environment-adaptive management algorithm for hearing-support devices incorporating listening situation and noise type classifiers.

In order to provide more consistent sound intelligibility for the hearing-impaired person, regardless of environment, it is necessary to adjust the setting of the hearing-support (HS) device to accommodate various environmental circumstances. In this study, a fully automatic HS device management algorithm that can adapt to various environmental situations is proposed; it is composed of a listening-situation classifier, a noise-type classifier, an adaptive noise-reduction algorithm, and a management algorithm that can selectively turn on/off one or more of the three basic algorithms-beamforming, noise-reduction, and feedback cancellation-and can also adjust internal gains and parameters of the wide-dynamic-range compression (WDRC) and noise-reduction (NR) algorithms in accordance with variations in environmental situations. Experimental results demonstrated that the implemented algorithms can classify both listening situation and ambient noise type situations with high accuracies (92.8-96.4% and 90.9-99.4%, respectively), and the gains and parameters of the WDRC and NR algorithms were successfully adjusted according to variations in environmental situation. The average values of signal-to-noise ratio (SNR), frequency-weighted segmental SNR, Perceptual Evaluation of Speech Quality, and mean opinion test scores of 10 normal-hearing volunteers of the adaptive multiband spectral subtraction (MBSS) algorithm were improved by 1.74 dB, 2.11 dB, 0.49, and 0.68, respectively, compared to the conventional fixed-parameter MBSS algorithm. These results indicate that the proposed environment-adaptive management algorithm can be applied to HS devices to improve sound intelligibility for hearing-impaired individuals in various acoustic environments.

K-EmoPhone: A Mobile and Wearable Dataset with In-Situ Emotion, Stress, and Attention Labels.

With the popularization of low-cost mobile and wearable sensors, several studies have used them to track and analyze mental well-being, productivity, and behavioral patterns. However, there is still a lack of open datasets collected in real-world contexts with affective and cognitive state labels such as emotion, stress, and attention; the lack of such datasets limits research advances in affective computing and human-computer interaction. This study presents K-EmoPhone, a real-world multimodal dataset collected from 77 students over seven days. This dataset contains (1) continuous probing of peripheral physiological signals and mobility data measured by commercial off-the-shelf devices, (2) context and interaction data collected from individuals' smartphones, and (3) 5,582 self-reported affect states, including emotions, stress, attention, and task disturbance, acquired by the experience sampling method. We anticipate the dataset will contribute to advancements in affective computing, emotion intelligence technologies, and attention management based on mobile and wearable sensor data.

Beyond Pathogen Filtration: Possibility of Smart Masks as Wearable Devices for Personal and Group Health and Safety Management.

Face masks are an important way to combat the COVID-19 pandemic. However, the prolonged pandemic has revealed confounding problems with the current face masks, including not only the spread of the disease but also concurrent psychological, social, and economic complications. As face masks have been worn for a long time, people have been interested in expanding the purpose of masks from protection to comfort and health, leading to the release of various "smart" mask products around the world. To envision how the smart masks will be extended, this paper reviewed 25 smart masks (12 from commercial products and 13 from academic prototypes) that emerged after the pandemic. While most smart masks presented in the market focus on resolving problems with user breathing discomfort, which arise from prolonged use, academic prototypes were designed for not only sensing COVID-19 but also general health monitoring aspects. Further, we investigated several specific sensors that can be incorporated into the mask for expanding biophysical features. On a larger scale, we discussed the architecture and possible applications with the help of connected smart masks. Namely, beyond a personal sensing application, a group or community sensing application may share an aggregate version of information with the broader population. In addition, this kind of collaborative sensing will also address the challenges of individual sensing, such as reliability and coverage. Lastly, we identified possible service application fields and further considerations for actual use. Along with daily-life health monitoring, smart masks may function as a general respiratory health tool for sports training, in an emergency room or ambulatory setting, as protection for industry workers and firefighters, and for soldier safety and survivability. For further considerations, we investigated design aspects in terms of sensor reliability and reproducibility, ergonomic design for user acceptance, and privacy-aware data-handling. Overall, we aim to explore new possibilities by examining the latest research, sensor technologies, and application platform perspectives for smart masks as one of the promising wearable devices. By integrating biomarkers of respiration symptoms, a smart mask can be a truly cutting-edge device that expands further knowledge on health monitoring to reach the next level of wearables.

Trends in Smart Helmets With Multimodal Sensing for Health and Safety: Scoping Review.

BACKGROUND: As a form of the Internet of Things (IoT)-gateways, a smart helmet is one of the core devices that offers distinct functionalities. The development of smart helmets connected to IoT infrastructure helps promote connected health and safety in various fields. In this regard, we present a comprehensive analysis of smart helmet technology and its main characteristics and applications for health and safety. OBJECTIVE: This paper reviews the trends in smart helmet technology and provides an overview of the current and future potential deployments of such technology, the development of smart helmets for continuous monitoring of the health status of users, and the surrounding environmental conditions. The research questions were as follows: What are the main purposes and domains of smart helmets for health and safety? How have researchers realized key features and with what types of sensors? METHODS: We selected studies cited in electronic databases such as Google Scholar, Web of Science, ScienceDirect, and EBSCO on smart helmets through a keyword search from January 2010 to December 2021. In total, 1268 papers were identified (Web of Science: 87/1268, 6.86%; EBSCO: 149/1268, 11.75%; ScienceDirect: 248/1268, 19.55%; and Google Scholar: 784/1268, 61.82%), and the number of final studies included after PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) study selection was 57. We also performed a self-assessment of the reviewed articles to determine the quality of the paper. The scoring was based on five criteria: test environment, prototype quality, feasibility test, sensor calibration, and versatility. RESULTS: Smart helmet research has been considered in industry, sports, first responder, and health tracking scenarios for health and safety purposes. Among 57 studies, most studies with prototype development were industrial applications (18/57, 32%), and the 2 most frequent studies including simulation were industry (23/57, 40%) and sports (23/57, 40%) applications. From our assessment-scoring result, studies tended to focus on sensor calibration results (2.3 out of 3), while the lowest part was a feasibility test (1.6 out of 3). Further classification of the purpose of smart helmets yielded 4 major categories, including activity, physiological and environmental (hazard) risk sensing, as well as risk event alerting. CONCLUSIONS: A summary of existing smart helmet systems is presented with a review of the sensor features used in the prototyping demonstrations. Overall, we aimed to explore new possibilities by examining the latest research, sensor technologies, and application platform perspectives for smart helmets as promising wearable devices. The barriers to users, challenges in the development of smart helmets, and future opportunities for health and safety applications are also discussed. In conclusion, this paper presents the current status of smart helmet technology, main issues, and prospects for future smart helmet with the objective of making the smart helmet concept a reality.