Artificial neural networks (ANNs) constitute a class of flexible nonlinear models designed to mimic biological neural systems. In this entry, we introduce ANN using familiar econometric terminology and provide an overview of ANN modeling approach and its implementation methods. † Correspondence: Chung-Ming Kuan, Institute of Economics, Academia Sinica, 128 Academia Road, Sec. 2, Taipei 115, Taiwan; ckuan@econ.sinica.edu.tw. †† I would like to express my sincere gratitude to the editor, Professor Steven Durlauf, for his patience and constructive comments on early drafts of this entry. I also thank Shih-Hsun Hsu and Yu-Lieh Huang for very helpful suggestions. The remaining errors are all mine.

Artificial neural networks

Sensory abilities decline with age. More than 5% of the world's population, approximately 360 million people, have disabling hearing loss. In adults, disabling hearing loss is defined by thresholds greater than 40 dBHL in the better hearing ear.Hearing disability is an important issue in geriatric medicine because it is associated with numerous health issues, including accelerated cognitive decline, depression, increased risk of dementia, poorer balance, falls, hospitalizations, and early mortality. There are also social implications, such as reduced communication function, social isolation, loss of autonomy, impaired driving ability, and financial decline. Furthermore, the onset of hearing loss is gradual and subtle, first affecting the detection of high-pitched sounds and with difficulty understanding speech in noisy but not in quiet environments. Consequently, delays in recognizing and seeking help for hearing difficulties are common. Age-related hearing loss has no known cure, and technologies (hearing aids, cochlear implants, and assistive devices) improve thresholds but do not restore hearing to normal. Therefore, health care for persons with hearing loss and people within their communication circles requires education and counseling (e.g., increasing knowledge, changing attitudes, and reducing stigma), behavior change (e.g., adapting communication strategies), and environmental modifications (e.g., reducing noise). In this article, we consider the causes, consequences, and magnitude of hearing loss from a life-course perspective. We examine the concept of "hearing health," how to achieve it, and implications for policy and practice.

/pdf/aging-and-hearing-health-the-life-course-approach-17zclevy2r.pdf

Aging and Hearing Health: The Life-course Approach.

Over the past few decades, we have witnessed a dramatic rise in life expectancy owing to significant advances in medical science and technology, medicine as well as increased awareness about nutrition, education, and environmental and personal hygiene. Consequently, the elderly population in many countries are expected to rise rapidly in the coming years. A rapidly rising elderly demographics is expected to adversely affect the socioeconomic systems of many nations in terms of costs associated with their healthcare and wellbeing. In addition, diseases related to the cardiovascular system, eye, respiratory system, skin and mental health are widespread globally. However, most of these diseases can be avoided and/or properly managed through continuous monitoring. In order to enable continuous health monitoring as well as to serve growing healthcare needs; affordable, non-invasive and easy-to-use healthcare solutions are critical. The ever-increasing penetration of smartphones, coupled with embedded sensors and modern communication technologies, make it an attractive technology for enabling continuous and remote monitoring of an individual’s health and wellbeing with negligible additional costs. In this paper, we present a comprehensive review of the state-of-the-art research and developments in smartphone-sensor based healthcare technologies. A discussion on regulatory policies for medical devices and their implications in smartphone-based healthcare systems is presented. Finally, some future research perspectives and concerns regarding smartphone-based healthcare systems are described.

https://www.mdpi.com/1424-8220/19/9/2164/pdf

Smartphone Sensors for Health Monitoring and Diagnosis.

Airborne SAR data gathered by the NASA/JPL three-frequency, polarimetric, radar system in winter, spring, and summer over the Bonanza Creek Experimental Forest, near Fairbanks, AK, are compared to estimates of whole-tree aboveground dry biomass from 21 forest stands and two clear-cuts. While C-band radar backscatter shows little sensitivity to biomass, L- and P-band radar backscatter increase by more than 6 dB when biomass increases from 5 to 200 tons/ha. Using second-order polynomial regressions, biomass values are predicted from the radar at L- and P-band and compared to actual biomass values. At P-band HV-polarization, the error in predicted biomass is about 30% of the actual biomass. When HV-, HH-, and VV-polarization are used together in the regression, the error in predicted biomass is about 20%. Errors obtained using L-band data are a few percents larger. These errors are caused by uncertainties in actual stand biomass estimates, significant inner-stand spatial variations in biomass, unusual conditions of forest stands following natural disturbances, along with interactions of the radar signals with a complex three-dimensional structure of the canopy. Multiple incidence angle data reveal that the incidence angle /spl theta//sub i/ of the radar illumination is also a factor influencing the retrieval of biomass, even at HV-polarization, when /spl theta//sub i/>50/spl deg/ or /spl theta//sub i/ >

Radar estimates of aboveground biomass in boreal forests of interior Alaska

Driver behavior monitoring system as Intelligent Transportation Systems (ITS) have been widely exploited to reduce the traffic accidents risk. Most previous methods for monitoring the driver behavior are rely on computer vision techniques. Such methods suffer from violation of privacy and the possibility of spoofing. This paper presents a novel yet efficient deep learning method for analyzing the driver behavior. We have used the driving signals, including acceleration, gravity, throttle, speed, and Revolutions Per Minute (RPM) to recognize five types of driving styles, including normal, aggressive, distracted, drowsy, and drunk driving. To take the advantages of successful deep neural networks on images, we learn a 2D Convolutional Neural Network (CNN) on images constructed from driving signals based on recurrence plot technique. Experimental results confirm that the proposed method can efficiently detect the driver behavior.

Driver behavior detection and classification using deep convolutional neural networks

In this paper the various driving style analysis solutions are investigated. An in-depth investigation is performed to identify the relevant machine learning and artificial intelligence algorithms utilised in current driver behaviour and driving style analysis systems. This review therefore serves as a trove of information, and will inform the specialist and the student regarding the current state of the art in driver style analysis systems, the application of these systems and the underlying artificial intelligence algorithms applied to these applications. The aim of the investigation is to evaluate the possibilities for unique driver identification utilizing the approaches identified in other driver behaviour studies. It was found that Fuzzy Logic inference systems, Hidden Markov Models and Support Vector Machines consist of promising capabilities to address unique driver identification algorithms if model complexity can be reduced.

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A Review of Intelligent Driving Style Analysis Systems and Related Artificial Intelligence Algorithms.

Objective: To determine if a smartphone application could be used as a calibrated screening audiometer with real-time noise monitoring for school screening using automated test sequences. Design: The investigation comprised three studies. Study 1 evaluated calibration accuracy across four Samsung S5301 smartphones (Android v4.0.4) using commercial Sennheiser HD202 headphones. Study 2 involved referencing smartphone microphone sensitivity to narrowband noise intensity as measured in octave bands by a sound-level meter between 30 and 75 dB SPL (5 dB increments). Study 3 compared screening outcomes of smartphone based and conventional hearing screening. Study sample: Study 2: 15 normal-hearing subjects (age range, 18–22 years; all female). Study 3: 162 children (324 ears) aged 5 to 7 years. Results: Smartphone calibration at 20, 30, and 40 dB was within 1 dB of recommended reference equivalent thresholds levels. Microphone calibration for noise monitoring had maximum variability across phones of 0.9,...

/pdf/smartphone-hearing-screening-with-integrated-quality-control-174l6220gs.pdf

Smartphone hearing screening with integrated quality control and data management

The financial assistance of the National Research Foundation (NRF) towards this research is hereby acknowledged.

/pdf/smartphone-hearing-screening-in-mhealth-assisted-community-1fj4emyuv6.pdf

Smartphone hearing screening in mHealth assisted community-based primary care

Objectives The study aimed to determine the validity of a smartphone hearing screening technology (hearScreen™) compared with conventional screening audiometry in terms of (1) sensitivity and specificity, (2) referral rate, and (3) test time. Design One thousand and seventy school-age children in grades 1 to 3 (8 ± 1.1 average years) were recruited from five public schools. Children were screened twice, once using conventional audiometry and once with the smartphone hearing screening. Screening was conducted in a counterbalanced sequence, alternating initial screen between conventional or smartphone hearing screening. Results No statistically significant difference in performance between techniques was noted, with smartphone screening demonstrating equivalent sensitivity (75.0%) and specificity (98.5%) to conventional screening audiometry. While referral rates were lower with the smartphone screening (3.2 vs. 4.6%), it was not significantly different (p > 0.05). Smartphone screening (hearScreen™) was 12.3% faster than conventional screening. Conclusion Smartphone hearing screening using the hearScreen™ application is accurate and time efficient.

/pdf/clinical-validity-of-hearscreen-smartphone-hearing-screening-tgsige0796.pdf

Clinical Validity of hearScreen™ Smartphone Hearing Screening for School Children.

Objective: The objective of this study was to develop and validate a smartphone-based digits-in-noise hearing test for South African English. Design: Single digits (0–9) were recorded and spoken by a first language English female speaker. Level corrections were applied to create a set of homogeneous digits with steep speech recognition functions. A smartphone application was created to utilize 120 digit-triplets in noise as test material. An adaptive test procedure determined the speech reception threshold (SRT). Experiments were performed to determine headphones effects on the SRT and to establish normative data. Study sample: Participants consisted of 40 normal-hearing subjects with thresholds ≤15 dB across the frequency spectrum (250–8000 Hz) and 186 subjects with normal-hearing in both ears, or normal-hearing in the better ear. Results: The results show steep speech recognition functions with a slope of 20%/dB for digit-triplets presented in noise using the smartphone application. The results ...

/pdf/development-and-validation-of-a-smartphone-based-digits-in-3xuk8tn1k9.pdf

Hermanus Carel Myburgh

Papers

A Review of Intelligent Driving Style Analysis Systems and Related Artificial Intelligence Algorithms.

Smartphone hearing screening with integrated quality control and data management

Smartphone hearing screening in mHealth assisted community-based primary care

Clinical Validity of hearScreen™ Smartphone Hearing Screening for School Children.

Development and validation of a smartphone-based digits-in-noise hearing test in South African English.