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Introduction Using data collected from hearing aid users’ own hearing aids could improve the customization of hearing aid processing for different users based on the auditory environments they encounter in daily life. Prior studies characterizing hearing aid users’ auditory environments have focused on mean sound pressure levels and proportions of environments based on classifications. In this study, we extend these approaches by introducing entropy to quantify the diversity of auditory environments hearing aid users encounter. Materials and Methods Participants from 4 groups (younger listeners with normal hearing and older listeners with hearing loss from an urban or rural area) wore research hearing aids and completed ecological momentary assessments on a smartphone for 1 week. The smartphone was programmed to sample the processing state (input sound pressure level and environment classification) of the hearing aids every 10 min and deliver an ecological momentary assessment every 40 min. Entropy values for sound pressure levels, environment classifications, and ecological momentary assessment responses were calculated for each participant to quantify the diversity of auditory environments encountered over the course of the week. Entropy values between groups were compared. Group differences in entropy were compared to prior work reporting differences in mean sound pressure levels and proportions of environment classifications. Group differences in entropy measured objectively from the hearing aid data were also compared to differences in entropy measured from the self-report ecological momentary assessment data. Results Auditory environment diversity, quantified using entropy from the hearing aid data, was significantly higher for younger listeners than older listeners. Entropy measured using ecological momentary assessment was also significantly higher for younger listeners than older listeners. Discussion Using entropy, we show that younger listeners experience a greater diversity of auditory environments than older listeners. Alignment of group entropy differences with differences in sound pressure levels and hearing aid feature activation previously reported, along with alignment with ecological momentary response entropy, suggests that entropy is a valid and useful metric. We conclude that entropy is a simple and intuitive way to measure auditory environment diversity using hearing aid data. 

Future cyber-physical systems will require higher capacity, meet more stringent real-time requirements, and adapt quickly to a broader range of network dynamics. However, the traditional approach of using fixed schedules to drive the operation of wireless networks has inherent limitations that make it unsuitable for these systems. As an alternative, we propose to replace schedules with domain-specific programs that coordinate the operation of the network. Our idea is that nodes in the network will run automatically generated programs that make informed decisions about flows at run time rather than using an a priori fixed schedule. We will sketch a domain-specific language that uses this additional flexibility to increase network capacity significantly. Furthermore, the constructed programs are also sufficiently simple to efficiently analyze key performance metrics such as flow response time and reliability. We conclude with future research directions. 

Automated cough detection has significant applications for the surveillance of diseases and supports medical decisions, as cough sounds can be a useful biomarker. However, the implementation and evaluation of robust cough detection models can be challenging due to the lack of real-world data. This paper introduces and makes available a collection of 2,883 coughs and 3,074 non-cough sounds recorded in clinic waiting rooms that we hope will become a baseline for this task. Using this dataset, we evaluate different convolutional network architectures for classifying short audio segments as cough or non-cough. An ensemble model of convolutional neuronal networks provides the most robust performance and has a ROC AUC of $98.1\%$. Equally important, we construct a cough counter that incorporates the ensemble model to compute the number of coughs per day. Then, a simple linear model estimates the number of visits in which the patients report cough symptoms from the cough counts. This simple regression model can predict the number of cough visits in the clinic with an absolute mean error of 4.26 cough visits per day. Using additional information about when patients are in the clinic helps a similar regression model reach a mean absolute error of 3.65 cough visits per day. These results demonstrate the feasibility of using cough detection as a biomarker for the spread of respiratory viruses within the community. 

Future Industrial Internet-of-Things (IIoT) systems will require wireless solutions to connect sensors, actuators, and controllers as part of high data rate feedback-control loops over real-time flows. A key challenge in such networks is to provide predictable performance and adaptability in response to link quality variations. We address this challenge by developing RECeiver ORiented Policies (Recorp), which leverages the stability of IIoT workloads by combining offline policy synthesis and run-time adaptation. Compared to schedules that service a single flow in a slot, Recorp policies share slots among multiple flows by assigning a coordinator and a list of flows that may be serviced in the same slot. At run-time, the coordinator will execute one of the flows depending on which flows the coordinator has already received. A salient feature of Recorp is that it provides predictable performance: a policy meets the end-to-end reliability and deadline of flows when the link quality exceeds a user-specified threshold. Experiments show that across IIoT workloads, policies provided a median increase of 50% to 142% in real-time capacity and a median decrease of 27% to 70% in worst-case latency when schedules and policies are configured to meet an end-to-end reliability of 99%. 

Emerging Industrial Internet-of-Things systems require wireless solutions to connect sensors, actuators, and controllers as part of high data rate feedback-control loops over real-time flows. A key challenge is to provide predictable performance and agility in response to fluctuations in link quality, variable workloads, and topology changes. We propose WARP to address this challenge. WARP uses programs to specify a network’s behavior and includes a synthesis procedure to automatically generate such programs from a high-level specification of the system’s workload and topology. WARP has three unique features: (1) WARP uses a domain-specific language to specify stateful programs that include conditional statements to control when a flow’s packets are transmitted. The execution paths of programs depend on the pattern of packet losses observed at runtime, thereby enabling WARP to readily adapt to packet losses due to short-term variations in link quality. (2) Our synthesis technique uses heuristics to improve network performance by considering multiple packet loss patterns and associated execution paths when determining the transmissions performed by nodes. Furthermore, the generated programs ensure that the likelihood of a flow delivering its packets by its deadline exceeds a user-specified threshold. (3) WARP can adapt to workload and topology changes without explicitly reconstructing a network’s program based on the observation that nodes can independently synthesize the same program when they share the same workload and topology information. Simulations show that WARP improves network throughput for data collection, dissemination, and mixed workloads on two realistic topologies. Testbed experiments show that WARP reduces the time to add new flows by 5 times over a state-of-the-art centralized control plane and guarantees the real-time and reliability of all flows. 

The self-fitting Bose SoundControl™ hearing aid is the first of its kind to gain FDA clearance. In the self-fitting process, the Bose Hear app uses the Bose CustomTune™ interface for mapping to a wide range of target gain profiles, derived from a hearing loss database. This article compares the population coverage—or the percentage of people who would be able to find a frequency gain profile similar to a NAL-NL2 prescription fit—of SoundControl to other self-fitting amplification devices which typically feature only 1 to 4 preset gain profiles. 

Abstract Background Ecological momentary assessment (EMA) is a methodology involving repeated surveys to collect in situ data that describe respondents' current or recent experiences and related contexts in their natural environments. Audiology literature investigating the test-retest reliability of EMA is scarce. Purpose This article examines the test-retest reliability of EMA in measuring the characteristics of listening contexts and listening experiences. Research Design An observational study. Study Sample Fifty-one older adults with hearing loss. Data Collection and Analysis The study was part of a larger study that examined the effect of hearing aid technologies. The larger study had four trial conditions and outcome was measured using a smartphone-based EMA system. After completing the four trial conditions, participants repeated one of the conditions to examine the EMA test-retest reliability. The EMA surveys contained questions that assessed listening context characteristics including talker familiarity, talker location, and noise location, as well as listening experiences including speech understanding, listening effort, loudness satisfaction, and hearing aid satisfaction. The data from multiple EMA surveys collected by each participant were aggregated in each of the test and retest conditions. Test-retest correlation on the aggregated data was then calculated for each EMA survey question to determine the reliability of EMA. Results At the group level, listening context characteristics and listening experience did not change between the test and retest conditions. The test-retest correlation varied across the EMA questions, with the highest being the questions that assessed talker location (median r = 1.0), reverberation (r = 0.89), and speech understanding (r = 0.85), and the lowest being the items that quantified noise location (median r = 0.63), talker familiarity (r = 0.46), listening effort (r = 0.61), loudness satisfaction (r = 0.60), and hearing aid satisfaction (r = 0.61). Conclusion Several EMA questions yielded appropriate test-retest reliability results. The lower test-retest correlations for some EMA survey questions were likely due to fewer surveys completed by participants and poorly designed questions. Therefore, the present study stresses the importance of using validated questions in EMA. With sufficient numbers of surveys completed by respondents and with appropriately designed survey questions, EMA could have reasonable test-retest reliability in audiology research. 

Abstract Background Ecological momentary assessment (EMA) is a methodology involving repeated surveys to collect in-situ self-reports that describe respondents' current or recent experiences. Audiology literature comparing in-situ and retrospective self-reports is scarce. Purpose To compare the sensitivity of in-situ and retrospective self-reports in detecting the outcome difference between hearing aid technologies, and to determine the association between in-situ and retrospective self-reports. Research Design An observational study. Study Sample Thirty-nine older adults with hearing loss. Data Collection and Analysis The study was part of a larger clinical trial that compared the outcomes of a prototype hearing aid (denoted as HA1) and a commercially available device (HA2). In each trial condition, participants wore hearing aids for 4 weeks. Outcomes were measured using EMA and retrospective questionnaires. To ensure that the outcome data could be directly compared, the Glasgow Hearing Aid Benefit Profile was administered as an in-situ self-report (denoted as EMA-GHABP) and as a retrospective questionnaire (retro-GHABP). Linear mixed models were used to determine if the EMA- and retro-GHABP could detect the outcome difference between HA1 and HA2. Correlation analyses were used to examine the association between EMA- and retro-GHABP. Results For the EMA-GHABP, HA2 had significantly higher (better) scores than HA1 in the GHABP subscales of benefit, residual disability, and satisfaction (p = 0.029–0.0015). In contrast, the difference in the retro-GHABP score between HA1 and HA2 was significant only in the satisfaction subscale (p = 0.0004). The correlations between the EMA- and retro-GHABP were significant in all subscales (p = 0.0004 to <0.0001). The strength of the association ranged from weak to moderate (r = 0.28–0.58). Finally, the exit interview indicated that 29 participants (74.4%) preferred HA2 over HA1. Conclusion The study suggests that in-situ self-reports collected using EMA could have a higher sensitivity than retrospective questionnaires. Therefore, EMA is worth considering in clinical trials that aim to compare the outcomes of different hearing aid technologies. The weak to moderate association between in-situ and retrospective self-reports suggests that these two types of measures assess different aspects of hearing aid outcomes.