Psychophysiological Assessment of User's Cumbersome Feeling on Consumer Devices

2019 ◽  
Author(s):  
Junichiro Hayano ◽  
Yutaka Yoshida ◽  
Emi Yuda
Keyword(s):  
Psychophysiological Assessment ◽  
Consumer Devices

Feasibility of smartphone-based hearing-aid apps for patients with mild hearing loss: an exploratory pilot study (Preprint)

2020 ◽  
Author(s):  
Willy Nguyen ◽  
Miseung Koo ◽  
Seung Ha Oh ◽  
Jun Ho Lee ◽  
Moo Kyun Park
Keyword(s):  
Hearing Loss ◽  
Hearing Impairment ◽  
Hearing Aids ◽  
Clinical Performance ◽  
Hearing Aid ◽  
National Standards ◽  
Hearing In Noise ◽  
Mild Hearing Loss ◽  
Consumer Devices ◽  
Selection Of

BACKGROUND Underuse of hearing aids is caused by several factors, including the stigma associated with hearing disability, affordability, and lack of awareness of rising hearing impairment associated with the growing population. Thus, there is a significant opportunity for the development of direct-to-consumer devices. For the past few years, smartphone-based hearing-aid apps have become more numerous and diverse, but few studies have investigated them. OBJECTIVE This study aimed to elucidate the electroacoustic characteristics and potential user benefits of a selection of currently available hearing-aid apps. METHODS We investigated the apps based on hearing-aid control standards (American National Standards Institute) using measurement procedures from previous studies. We categorized the apps and excluded those we considered inefficient. We investigated a selection of user-friendly, low-end apps, EarMachine and Sound Amplifier, with warble-tone audiometry, word recognition testing in unaided and aided conditions, and hearing-in-noise test in quiet and noise-front conditions in a group of users with mild hearing impairment (n = 7) as a pilot for a future long-term investigation. Results from the apps were compared with those of a conventional hearing aid. RESULTS Five of 14 apps were considered unusable based on low scores in several metrics, while the others varied across the range of electroacoustic measurements. The apps that we considered “high end” that provided lower processing latencies and audiogram-based fitting algorithms were superior overall. The clinical performance of the listeners tended to be better when using hearing aid, while the low end hearing-aid apps had limited benefits on the users. CONCLUSIONS Some apps showed the potential to benefit users with limited cases of minimal or mild hearing loss if the inconvenience of relatively poor electroacoustic performance did not outweigh the benefits of amplification.

scholarly journals Virtualizing AI at the Distributed Edge Towards Intelligent IoT Applications

2021 ◽  
Vol 10 (1) ◽  
pp. 13
Author(s):  
Claudia Campolo ◽  
Giacomo Genovese ◽  
Antonio Iera ◽  
Antonella Molinaro
Keyword(s):  
Resource Constraints ◽  
Smart Cities ◽  
Traditional Approach ◽  
Low Cost ◽  
Iot Applications ◽  
Software And Hardware ◽  
Iot Devices ◽  
Consumer Devices ◽  
Hardware Platforms ◽  
Smart Factories

Several Internet of Things (IoT) applications are booming which rely on advanced artificial intelligence (AI) and, in particular, machine learning (ML) algorithms to assist the users and make decisions on their behalf in a large variety of contexts, such as smart homes, smart cities, smart factories. Although the traditional approach is to deploy such compute-intensive algorithms into the centralized cloud, the recent proliferation of low-cost, AI-powered microcontrollers and consumer devices paves the way for having the intelligence pervasively spread along the cloud-to-things continuum. The take off of such a promising vision may be hurdled by the resource constraints of IoT devices and by the heterogeneity of (mostly proprietary) AI-embedded software and hardware platforms. In this paper, we propose a solution for the AI distributed deployment at the deep edge, which lays its foundation in the IoT virtualization concept. We design a virtualization layer hosted at the network edge that is in charge of the semantic description of AI-embedded IoT devices, and, hence, it can expose as well as augment their cognitive capabilities in order to feed intelligent IoT applications. The proposal has been mainly devised with the twofold aim of (i) relieving the pressure on constrained devices that are solicited by multiple parties interested in accessing their generated data and inference, and (ii) and targeting interoperability among AI-powered platforms. A Proof-of-Concept (PoC) is provided to showcase the viability and advantages of the proposed solution.

Design, Reliability and Manufacturing Readiness for MEMS Microphone

2011 ◽  
Author(s):  
Ganesh Iyer ◽  
Wei Li ◽  
Lavanya Gopalakrishnan
Keyword(s):  
Electrical Signal ◽  
Consumer Products ◽  
Smart Phones ◽  
System Level ◽  
Design Considerations ◽  
Final Assembly ◽  
Mems Microphone ◽  
Robust System ◽  
Manufacturing Readiness ◽  
Consumer Devices

Microphone is a critical component for seamless communication converting an acoustic signal (vocal) to an electrical signal. Traditionally Electrets Condenser Microphones (ECM) have been the primary proponent of audio component in many consumer products. With functionally rich consumer devices (example smart phones, etc) there is a growing trend to look at components with higher functionality but a smaller form factor. Microelectronic Mechanical Systems (MEMS) microphone is seen as a possible replacement to ECM due to its significant reduction in form fit with additional functionality. The paper is an effort to illustrate steps that can be considered while designing MEMS microphone in a system. This includes Design considerations, Reliability tests, Manufacturing challenges and Readiness to ensure higher yield during the final assembly. Manufacturing issues (Top 5) and guideline presented in the paper are not just to increase the assembly yield (system level), but also to increase an awareness upfront to the design phase to help create a robust system/product.

Future Directions in Surface Electromyography

Biofeedback ◽  
2010 ◽  
Vol 38 (2) ◽  
pp. 78-82 ◽  
Author(s):  
John G. Arena
Keyword(s):  
Surface Electromyography ◽  
Ambulatory Monitoring ◽  
Future Directions ◽  
The Past ◽  
Psychophysiological Assessment ◽  
The Future ◽  
The Stability

Abstract The use of surface electromyography (SEMG) has increased exponentially in the past four decades. SEMG is one of the most widespread measures employed today in psychophysiological assessment and one of three primary biofeedback modalities. This article briefly outlines three areas that the author believes are important for SEMG to address if it is to continue to flourish in the future: applications in telehealth, the use of telemetry and ambulatory monitoring, and studies on the stability or reliability of surface electromyography.

scholarly journals Sleep and BMI: Do (Fitbit) bands aid?

F1000Research ◽  
2018 ◽  
Vol 7 ◽  
pp. 511 ◽  
Author(s):  
Laura McDonald ◽  
Faisal Mehmud ◽  
Sreeram V. Ramagopalan
Keyword(s):  
Body Mass Index ◽  
Body Mass ◽  
Biomedical Research ◽  
Chinese Population ◽  
Scientific Research ◽  
Negatively Associated ◽  
Significant Potential ◽  
Consumer Devices

Recent studies have used mainstream consumer devices (Fitbit) to assess sleep objectively and test the well documented association between sleep and body mass index (BMI). In order to further investigate the applicability of Fitbit data for biomedical research across the globe, we analysed openly available Fitbit data from a largely Chinese population. We found that after adjusting for age, gender, race, and average number of steps taken per day, average hours of sleep per day was negatively associated with BMI (p=0.02), further demonstrating the significant potential for wearables in international scientific research.

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