To the Editor: Ear and Hearing recently published a point of view paper on “Ecological validity, external validity and mundane realism in hearing science” (Beechey 2022) that references the 2020 Eriksholm workshop consensus paper on “The quest for ecological validity in hearing science: what it is, why it matters, and how to advance it” (Keidser et al. 2020). The consensus paper proposes an operational definition of ecological validity for use in hearing science and is the outcome of a three-day workshop on how to advance ecologically valid assessments of hearing and hearing devices. We thank the author of the point of view paper for his contribution to the discussion about the meaning of the term “ecological validity”. As his article suggests, there is an early original as well as a later and more recently popular usage of this term. The early definition proposed by Brunswik (1952,1955) refers to the correlation between cues received at the peripheral nervous system and the identity of distant objects or events in the environment, whereas the more popular definition, which has evolved from Brunswik’s original observations, construes ecological validity as a concept that examines to what extent the results of a research study are related to outcomes in situations occurring in everyday life [e.g., Schmuckler (2001); Neuhoff (2004); American Psychological Association, n.d.]. While the author makes an excellent case in favor of the first definition of ecological validity, the 2020 Eriksholm Workshop consensus paper builds on the more popular definition of the term, which is broadly considered a type of external validity. As a first point, we would like to take this opportunity to clarify that the working group behind the consensus paper was aware of Brunswik’s proposed definition and probably should have acknowledged its historical usage in the background review of the consensus paper. Our choice of following the more recently popular understanding of the term and proposing a definition that exists as a sub-category of external validity was twofold: (1) it captures a direction in which many hearing researchers have been heading for more than a decade, albeit without a comprehensive theoretical motivation and justification, and (2) it lends itself to an operational framework (like the one presented in the consensus paper) that we believe will assist hearing research in making long-awaited advancements in the field. While the two definitions of ecological validity focus on different aspects of experimental conditions, the concepts they stand for are not mutually exclusive, and although our adaptation of ecological validity is more consistent with current usage of the term, as long as authors clearly state in their articles the intended operationalized definition of ecological validity, either definition could be used. We should further like to take this opportunity to stress that the definition of ecological validity proposed by the working group refers to a type of external validity that examines how representative research outcomes are of the everyday listening situation(s) under investigation. In hearing science, such an investigation could address real-life hearing-related function, activity, or participation. This type of validity specifically targets defined situations occurring in everyday life, for example, a conversation between three people seated in a busy cabin on a moving train, while external validity more broadly examines whether research outcomes can be generalized to other contexts (Juni et al. 2001), for example, whether speech performance in static noise obtained with a group of university students translates to a broader population and to other background noises. In other words, our definition of ecological validity should not, as suggested in the point of view paper, be construed as implying that realism equates to any theoretical form of generalizability. The “external” validity we were concerned with is purely in the translation from an experimental condition to the (possibly singular) real-life condition it is intended to represent. Meanwhile, although the term “psychological realism” (as also discussed in the point of view paper) was not used in the consensus paper, it should be apparent from the consensus paper’s emphasis on variables beyond the perceptual, that this is the form of realism, which we sought to promote. While there is some emerging evidence that hearing research needs to implement more (psychologically) realistic listening environments, stimuli, and tasks [e.g., Carlile and Keidser (2020); Grimm et al. (2020); Hohmann et al. (2020); Orenois & Buchholz (2016)], we agree with the author of the point of view paper that realism itself is merely a potential means to an end, and that “ecological validity” is not to be equated with mundane realism. Finally, we welcome the author’s outline of the value of “Representative Design” in hearing research, and of how Ecological Momentary Assessment (EMA) is a step toward this approach. One of the biggest challenges for hearing researchers wishing to meaningfully emulate aspects of real-life listening in the laboratory is to gather knowledge of the physical and psychological variety, which has to be encompassed in a study design to claim a high level of ecological validity of the measured outcomes. Intuitively, such information must be informed by real-life behaviors and experiences that EMA studies may capture in an organized way. Although to achieve fully representative information from EMA studies there remain numerous challenges to overcome, such as preserving privacy; ensuring manageability of equipment; and reducing asynchrony between objective and subjective measures during data collection, as discussed in Holube et al. (2020). In summary, this discussion highlights the importance of thoughtful consideration to the research protocol and clear statements by researchers of the operational definitions of specialized terms such as ecological validity. ACKNOWLEDGMENT The views expressed in this letter are those of the authors and do not reflect the official policy of the Department of Defense, or U.S. Government (D.B.).

In this paper we would like to clarify a essential question: "Why is impedance or dielectric spectroscopy sensitive to changes in glucose concentration in blood and why can this be done over a very broad frequency band, including microwaves?" Medical diagnostics would greatly benefit from an answer. Towards this, we want to summarize the underlying physics and base directions towards exploiting this direction. It is our impression that current worldview on the dielectric response of glucose in solution, as outlined below, will support the further evolution of practical noninvasive glucose monitoring solutions.

The detection and classification of vehicles by suitable monitoring systems is an integral part of Intelligent Transportation Systems (ITS). We report results of an ongoing research project on fine-grained vehicle classification based on images acquired from roadside and overhead based video cameras. In a previous work [1] a dataset of overall 100,000 sample images from 36 fine-grained vehicle classes has already been presented. These images were acquired from roadside based cameras and results for the classification accuracy obtained with state-of-the-art CNNs (convolutional neural networks) allowed to fulfil the challenging traffic norm TLS 8+1 A1. Here, in extension to this work, cameras in overhead perspective were used to avoid the problem of occlusion (i.e., a larger vehicle completely occluding a smaller one), which currently limits the roadside perspective to two-lane roads (with one camera per lane). Therefore, the original dataset was expanded with a new set of close to 100,000 images now taken in overhead perspective and representing the same 36 fine-grained vehicle classes. While keeping all model and hyperparameters identical (size of training and test set, resolution, CNN architecture, …) in overhead perspective a considerable drop in the classification accuracy was observed with respect to the roadside perspective. Analysis of the confusion matrix reveals that important details of the vehicles, which are essential for the distinction among certain classes, are not sufficiently well represented in the CNN in overhead position. These results seem to indicate, that standard CNNs come to their limits for the present task of fine-grained vehicle classification and other, part-based approaches are required to solve this problem.

Ecological validity is a relatively new concept in hearing science. It has been cited as relevant with increasing frequency in publications over the past 20 years, but without any formal conceptual basis or clear motive. The sixth Eriksholm Workshop was convened to develop a deeper understanding of the concept for the purpose of applying it in hearing research in a consistent and productive manner. Inspired by relevant debate within the field of psychology, and taking into account the World Health Organization’s International Classification of Functioning, Disability, and Health framework, the attendees at the workshop reached a consensus on the following definition: “In hearing science, ecological validity refers to the degree to which research findings reflect real-life hearing-related function, activity, or participation.” Four broad purposes for striving for greater ecological validity in hearing research were determined: A (Understanding) better understanding the role of hearing in everyday life; B (Development) supporting the development of improved procedures and interventions; C (Assessment) facilitating improved methods for assessing and predicting ability to accomplish real-world tasks; and D (Integration and Individualization) enabling more integrated and individualized care. Discussions considered the effects of variables and phenomena commonly present in hearing-related research on the level of ecological validity of outcomes, supported by examples from a few selected outcome domains and for different types of studies. Illustrated with examples, potential strategies were offered for promoting a high level of ecological validity in a study and for how to evaluate the level of ecological validity of a study. Areas in particular that could benefit from more research to advance ecological validity in hearing science include: (1) understanding the processes of hearing and communication in everyday listening situations, and specifically the factors that make listening difficult in everyday situations; (2) developing new test paradigms that include more than one person (e.g., to encompass the interactive nature of everyday communication) and that are integrative of other factors that interact with hearing in real-life function; (3) integrating new and emerging technologies (e.g., virtual reality) with established test methods; and (4) identifying the key variables and phenomena affecting the level of ecological validity to develop verifiable ways to increase ecological validity and derive a set of benchmarks to strive for.

The detection and classification of vehicles by suitable monitoring systems is an integral part of Intelligent Transportation Systems (ITS). We report results on fine-grained vehicle classification based on video images obtained from roadside based cameras. A new dataset of more than 100'000 samples allowing for a total of 36 fine-grained vehicle categories is introduced and classification results based on convolutional neural networks (CNN) are presented. We show, that simple CNN architectures suitable for real-time applications lead to surprisingly good results. As a practical outcome applicable to ITS we illustrate that - to our knowledge - for the first time a vision system fulfils the challenging traffic norm TLS 8+1 A1.

With an ongoing shift from managing disease toward the inclusion of maintaining health and preventing disease, the world has seen the rise of increasingly sophisticated physiological monitoring and analytics. Innovations range from wearables, smartphone-based spot monitoring to highly complex noncontact, remote monitoring, utilizing different mechanisms. These tools empower the individual to better navigate their own health. They also generate powerful insights towards the detection of subclinical symptoms or processes via existing and novel digital biomarkers. In that context, a topic that is receiving increasing interest is the modulation of human physiology around an individual "baseline" in everyday life and the impact thereof on other sensorineural body functions such as hearing. More and more fully contextualized and truly long-term physiological data are becoming available that allows deeper insights into the response of the human body to our behavior, immediate environment and the understanding of how chronic conditions are evolving. Hearing loss often goes hand in hand with chronic conditions, such as diabetes, cognitive impairment, increased risk of fall, mental health, or cardiovascular risk factors. This inspires an interest to not only look at hearing impairment itself but to take a broader view, for example, to include contextualized vital signs. Interestingly, stress and its physiological implications have also been shown to be a relevant precursor to hearing loss and other chronic conditions. This article deduces the requirements for wearables and their ecosystems to detect relevant dynamics and connects that to the need for more ecologically valid data towards an integrated and more holistic mapping of hearing characteristics.

With diabetes set to become the number 3 killer in the Western hemisphere and proportionally growing in other parts of the world, the subject of noninvasive monitoring of glucose dynamics in blood remains a "hot" topic, with the involvement of many groups worldwide. There is a plethora of techniques involved in this academic push, but the so-called multisensor system with an impedance-based core seems to feature increasingly strongly. However, the symmetrical structure of the glucose molecule and its shielding by the smaller dipoles of water would suggest that this option should be less enticing. Yet there is enough phenomenological evidence to suggest that impedance-based methods are truly sensitive to the biophysical effects of glucose variations in the blood. We have been trying to answer this very fundamental conundrum: "Why is impedance or dielectric spectroscopy sensitive to glucose concentration changes in the blood and why can this be done over a very broad frequency band, including microwaves?" The vistas for medical diagnostics are very enticing. There have been a significant number of papers published that look seriously at this problem. In this review, we want to summarize this body of research and the underlying mechanisms and propose a perspective toward utilizing the phenomena. It is our impression that the current world view on the dielectric response of glucose in solution, as outlined below, will support the further evolution and implementation toward practical noninvasive glucose monitoring solutions.

Even if still at an early stage of development, non-invasive continuous glucose monitoring (NI-CGM) sensors represent a promising technology for optimizing diabetes therapy. Recent studies showed that the Multisensor provides useful information about glucose dynamics with a mean absolute relative difference (MARD) of 35.4% in a fully prospective setting. Here we propose a method that, exploiting the same Multisensor measurements, but in a retrospective setting, achieves a much better accuracy. Data acquired by the Multisensor during a long-term study are retrospectively processed following a two-step procedure. First, the raw data are transformed to a blood glucose (BG) estimate by a multiple linear regression model. Then, an enhancing module is applied in cascade to the regression model to improve the accuracy of the glucose estimation by retrofitting available BG references through a time-varying linear model. MARD between the retrospectively reconstructed BG time-series and reference values is 20%. Here, 94% of values fall in zone A or B of the Clarke Error Grid. The proposed algorithm achieved a level of accuracy that could make this device a potential complementary tool for diabetes management and also for guiding prediabetic or nondiabetic users through life-style changes.

BACKGROUND:Extensive past work showed that noninvasive continuous glucose monitoring with a wearable Multisensor device worn on the upper arm provides useful information about glucose trends to improve diabetes therapy in controlled and semicontrolled conditions.METHODS:To test previous findings also in uncontrolled in-clinic and outpatient conditions, a long-term study has been conducted to collect Multisensor and reference glucose data in a population of 20 type 1 diabetes subjects. A total of 1072 study days were collected and a fully on-line compatible algorithmic routine linking Multisensor data to glucose applied to estimate glucose trends noninvasively. The operation of a digital log book, daily semiautomated data transfer and at least 10 daily SMBG values were requested from the patient.RESULTS:Results showed that the Multisensor is capable of indicating glucose trends. It can do so in 9 out of 10 cases either correctly or with one level of discrepancy. This means that in 90% of all cases the Multisensor shows the glucose dynamic to rapidly increase or at least increase.CONCLUSIONS:The Multisensor and the algorithmic routine used in controlled conditions can track glucose trends in all patients, also in uncontrolled conditions. Training of the patient proved to be essential. The workload imposed on patients was significant and should be reduced in the next step with further automation. The feature of glucose trend indication was welcomed and very much appreciated by patients; this value creation makes a strong case for the justification of wearing a wearable.

Background: Extensive past work showed that noninvasive continuous glucose monitoring with a wearable multisensor device worn on the upper arm provides useful information about glucose trends to improve diabetes therapy in controlled and semicontrolled conditions. Method: To test previous findings also in uncontrolled conditions, a long term at home study has been organized to collect multisensor and reference glucose data in a population of 20 type 1 diabetes subjects. A total of 1072 study days were collected and a fully on-line compatible algorithmic routine linking multisensor data to glucose applied to estimate glucose levels noninvasively. Results: The algorithm used here calculates glucose values from sensor data and adds a constant obtained by a daily calibration. It provides point inaccuracy measured by a MARD of 35.4 mg/dL on test data. This is higher than current state-of-the-art minimally invasive devices, but still 86.9% of glucose rate points fall within the zone AR+BR. Conclusions: The multisensor device and the algorithmic routine used earlier in controlled conditions tracks glucose changes also in uncontrolled conditions, although with lower accuracy. The examination of learning curves suggests that obtaining more data would not improve the results. Therefore, further efforts would focus on the development of more complex algorithmic routines able to compensate for environmental and physiological confounders better.

BACKGROUND:We study here the influence of different patients and the influence of different devices with the same patients on the signals and modeling of data from measurements from a noninvasive Multisensor glucose monitoring system in patients with type 1 diabetes. The Multisensor includes several sensors for biophysical monitoring of skin and underlying tissue integrated on a single substrate.METHOD:Two Multisensors were worn simultaneously, 1 on the upper left and 1 on the upper right arm by 4 patients during 16 study visits. Glucose was administered orally to induce 2 consecutive hyperglycemic excursions. For the analysis, global (valid for a population of patients), personal (tailored to a specific patient), and device-specific multiple linear regression models were derived.RESULTS:We find that adjustments of the model to the patients improves the performance of the glucose estimation with an MARD of 17.8% for personalized model versus a MARD of 21.1% for the global model. At the same time the effect of the measurement side is negligible. The device can equally well measure on the left or right arm. We also see that devices are equal in the linear modeling. Thus hardware calibration of the sensors is seen to be sufficient to eliminate interdevice differences in the measured signals.CONCLUSIONS:We demonstrate that the hardware of the 2 devices worn on the left and right arms are consistent yielding similar measured signals and thus glucose estimation results with a global model. The 2 devices also return similar values of glucose errors. These errors are mainly due to nonstationarities in the measured signals that are not solved by the linear model, thus suggesting for more sophisticated modeling approaches.

This chapter describes a biomedical application of dielectric spectroscopy for glucose detection from skin measurements. The chapter begins with a brief overview of diabetes as a disease and its long- and short-term effects on the human body. We describe the physiological effects of glucose changes. This is given to highlight the biophysical changes that are associated with the disease and how dielectric spectroscopy can be utilized to monitor the impact of such changes. Then, requirements that measurement sensors need to meet are reviewed. This includes details of frequency range and sensor characteristic geometry required in order to permit non-invasive continuous glucose monitoring using such dielectric-based sensor systems. Finally, we describe a roadmap to future developments for such a system.

In diabetes research, non-invasive continuous glucose monitoring (NI-CGM) devices represent a new and appealing frontier. In the last years, some multi-sensor devices for NI-CGM have been proposed, which exploit several sensors measuring phenomena of different nature, not only for measuring glucose related signals, but also signals reflecting some possible perturbing processes (temperature, blood perfusion). Estimation of glucose levels is then obtained combining these signals through a mathematical model which requires an initial calibration step exploiting one reference blood glucose (RBG) sample. Even if promising results have been obtained, especially in hospitalized volunteers, at present the temporal accuracy of NI-CGM sensors may suffer because of environmental and physiological interferences. The aim of this work is to develop a general methodology, based on Monte Carlo (MC) simulation, to assess the robustness of the calibration step used by NI-CGM devices against these disturbances. The proposed methodology is illustrated considering two examples: the first concerns the possible detrimental influence of sweat events, while the second deals with calibration scheduling. For implementing both examples, 45 datasets collected by the Solianis Multisensor system are considered. In the first example, the MC methodology suggests that no further calibration adjustments are needed after the occurrence of sweat events, because the "Multisensor+model" system is able to deal with the disturbance. The second case study shows how to identify the best time interval to update the model's calibration for improving the accuracy of the estimated glucose. The methodology proposed in this work is of general applicability and can be helpful in making those incremental steps in NI-CGM devices development needed to further improve their performance.

Continuous glucose monitoring (CGM) by suitable portable sensors plays a central role in the treatment of diabetes, a disease currently affecting more than 350 million people worldwide. Noninvasive CGM (NI-CGM), in particular, is appealing for reasons related to patient comfort (no needles are used) but challenging. NI-CGM prototypes exploiting multisensor approaches have been recently proposed to deal with physiological and environmental disturbances. In these prototypes, signals measured noninvasively (e.g., skin impedance, temperature, optical skin properties, etc.) are combined through a static multivariate linear model for estimating glucose levels. In this work, by exploiting a dataset of 45 experimental sessions acquired in diabetic subjects, we show that regularisation-based techniques for the identification of the model, such as the least absolute shrinkage and selection operator (better known as LASSO), Ridge regression, and Elastic-Net regression, improve the accuracy of glucose estimates with respect to techniques, such as partial least squares regression, previously used in the literature. More specifically, the Elastic-Net model (i.e., the model identified using a combination of and norms) has the best results, according to the metrics widely accepted in the diabetes community. This model represents an important incremental step toward the development of NI-CGM devices effectively usable by patients.

In this review, we present an overview of the state of the art concerning the fundamental properties of electrode polarization (EP) of interest in the measurement of high conductivity samples and its implications for both dielectric (DS) and impedance spectroscopy (IS). Initially a detailed description of what constitutes EP is provided and the problems that it induces. Then, we review some of the more popular models that have been used to describe the physical phenomena behind the formation of the ionic double layer. Following this we shall enumerate the common strategies used historically to correct its influence on the measured signals in DS or in IS. Finally we also review recent attempts to employ fractal electrodes to bypass the effects of EP and to offer some physical explanation as to the limitations of their use.