Search for: All records

ObjectivesMicrointeraction-based Ecological Momentary Assessment (micro-EMA) is a smartwatch-based tool that delivers single-question surveys, enabling respondents to quickly report their real-time experiences. The objectives of the two studies presented here were to evaluate micro-EMA's psychometric characteristics and feasibility across three response formats (2-point, 5-point, and 10-point scales) for adults with hearing loss. DesignIn the first study, thirty-two participants completed a dual-task experiment aimed at assessing the construct validity, responsiveness, intrusiveness, and test-retest reliability of micro-EMA across the three response formats. Participants listened to sentences at five signal-to-noise ratios (SNRs) ranging from −3 to 9 dB relative to the SNR for 50% speech understanding, answered the question “Hearing well?” on smartwatches, and repeated the sentences. In the second study, twenty-one participants wore smartwatches over 6 days. Every 15 min, participants were prompted to answer the question “Hearing well?” using one of the three response formats for 2 days. Participants provided feedback on their experience with micro-EMA. ResultsIn the dual-task experiment, participants reported improved hearing performance in micro-EMA as SNRs and speech recognition scores increased across all three response formats, supporting the tool's construct validity. Statistical models indicated that the 5-point and 10-point scales yielded larger relative changes between SNRs, suggesting higher responsiveness, compared to the 2-point scale. Participants completed surveys significantly faster with the 2-point scale, indicating lower intrusiveness, compared to the 5-point and 10-point scales. Correlation analysis revealed that over two visits 1 week apart, the 2-point scale had the poorest test-retest reliability, while the 5-point scale had the highest. In the field trial, participants completed 79.6% of the prompted surveys, with each participant averaging 42.9 surveys per day. Although participants experienced interruptions due to frequent prompts, annoyance and distraction levels were low. Most participants preferred the 5-point scale. ConclusionsThe dual-task experiment suggested that micro-EMA using the 5-point scale demonstrated superior psychometric characteristics compared to the 2-point and 10-point scales at the tested SNRs. The field trial further supported its feasibility for evaluating hearing performance in adults with hearing loss. Additional research is needed to explore the potential applications of micro-EMA in audiology research. 

Adults with mild-to-moderate hearing loss can use over-the-counter hearing aids to treat their hearing loss at a fraction of traditional hearing care costs. These products incorporate self-fitting methods that allow end-users to configure their hearing aids without the help of an audiologist. A self-fitting method helps users configure the gain-frequency responses that control the amplification for each frequency band of the incoming sound. This paper considers how to guide the design of self-fitting methods by evaluating certain aspects of their design using computational tools before performing user studies. Most existing fitting methods provide various user interfaces to allow users to select a configuration from a predetermined set of presets. Accordingly, it is essential for the presets to meet the hearing needs of a large fraction of users who suffer from varying degrees of hearing loss and have unique hearing preferences. To this end, we propose a novel metric for evaluating the effectiveness of preset-based approaches by computing their population coverage. The population coverage estimates the fraction of users for which a self-fitting method can find a configuration they prefer. A unique aspect of our approach is a probabilistic model that captures how a user's unique preferences differ from other users with similar hearing loss. Next, we propose methods for building preset-based and slider-based self-fitting methods that maximize the population coverage. Simulation results demonstrate that the proposed algorithms can effectively select a small number of presets that provide higher population coverage than clustering-based approaches. Moreover, we may use our algorithms to configure the number of increments of slider-based methods. We expect that the computational tools presented in this article will help reduce the cost of developing new self-fitting methods by allowing researchers to evaluate population coverage before performing user studies. 

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.