Semi-autonomous vehicles as a cognitive assistive device for older adults

Losing the capacity to drive due to age-related cognitive decline can have a detrimental impact on the daily life functioning of older adults living alone and in remote areas. Semi-autonomous vehicles (SAVs) could have the potential to preserve driving independence of this population with high health needs. This paper explores if SAVs could be used as a cognitive assistive device for older aging drivers with cognitive challenges. We illustrate the impact of age-related changes of cognitive functions on driving capacity. Furthermore, following an overview on the current state of SAVs, we propose a model for connecting cognitive health needs of older drivers to SAVs. The model demonstrates the connections between cognitive changes experienced by aging drivers, their impact on actual driving, car sensors' features, and vehicle automation. Finally, we present challenges that should be considered when using the constantly changing smart vehicle technology, adapting it to aging drivers and vice versa. This paper sheds light on age-related cognitive characteristics that should be considered when developing future SAVs manufacturing policies which may potentially help decrease the impact of cognitive change on older adult drivers

Novel Coronavirus Cough Database: NoCoCoDa

The current pandemic associated with the novel coronavirus (COVID-19) presents a new area of research with its own set of challenges. Creating unobtrusive remote monitoring tools for medical professionals that may aid in diagnosis, monitoring and contact tracing could lead to more efficient and accurate treatments, especially in this time of physical distancing. Audio based sensing methods can address this by measuring the frequency, severity and characteristics of the COVID-19 cough. However, the feasibility of accumulating coughs directly from patients is low in the short term. This article introduces a novel database (NoCoCoDa), which contains COVID-19 cough events obtained through public media intervi

A perceptual performance measure for adaptive echo cancellers in packet-based telephony

This paper investigates performance measures of adaptive echo cancellers for packet-based telephony. It is shown that steady-state echo return loss enhancement (ERLE) does not accurately reflect perceived echo canceller convergence when background noise is present. An upper bound is derived for the maximum perceivable ERLE achievable in practice, and an algorithm is introduced for calculating ERLE that incorporates these masking effects based on a perceptual hearing model. Simulation and informal listening test results show a clear correspondence between the new performance measure and the perceptual upper bound induced by background noise

Reduced-delay mixing of compressed speech signals for VoIP and cellular telephony

This paper introduces techniques for reducing the computational complexity of re-encoding mixed speech using LPC-based vocoders, with application to conferencing over VoIP and cellular telephony networks. Our methods exploit a priori knowledge of the LPC synthesis filter coefficients, codebook gains and pitch periods for the compressed source speech signals. Experimental results with a modified G.729 vocoder show an average spectral distortion of 1.61 dB compared to reference mixed signals, 1.34 dB less than that introduced by the full encoder. Furthermore, the proposed techniques introduce no additional decrease in speech quality measured using ITU P.862 (PESQ) and informal MOS tests