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Driver status monitoring systems are a vital component of smart cars in the future, especially in the era when an increasing amount of time is spent in the vehicle. The heart rate (HR) is one of the most important physiological signals of driver status. To infer HR of drivers, the mainstream of the existing research focused on capturing subtle heartbeat-induced vibration of the torso or leveraged photoplethysmography (PPG) that detects cardiac cycle-related blood volume changes in the microvascular. However, existing approaches rely on dedicated sensors that are expensive and cumbersome to be integrated or are vulnerable to ambient noise. Moreover, their performance on the detection of HR does not guarantee a reliable computation of the HR variability (HRV) measure, which is a more applicable metric for inferring mental and physiological status. The accurate computation HRV measure is based on the precise measurement of the beat-to-beat interval, which can only be accomplished by medical-grade devices that attach electrodes to the body. Considering these existing challenges, we proposed a facial expression-based HRV estimation solution. The rationale is to establish a link between facial expression and heartbeat since both are controlled by the autonomic nervous system. To solve this problem, we developed a tree-based probabilistic fusion neural network approach, which significantly improved HRV estimation performance compared to conventional random forest or neural network methods and the measurements from smartwatches. The proposed solution relies only on commodity camera with a lightweighted algorithm, facilitating its ubiquitous deployment in current and future vehicles. Our experiments are based on 3400 km of driving data from nine drivers collected in a naturalistic field study.

BACKGROUND Hypoglycemia is a serious complication in diabetes, it impairs cognitive and psychomotor function, and is linked to driving mishaps. In-vehicle voice assistants (VAs) have been designed to proactively deliver a warning of hypoglycemia while driving. However, proactive VAs can cause driving impairments through startling. Hence, we complement the voice warning from the VA with ambient light-emitting diodes (LED) and investigate the effect of this addition on emotional reaction. OBJECTIVE Designing an in-vehicle voice warning for hypoglycemia and assessing its effect on the emotional reaction and technology acceptance. METHODS We present two studies investigating the emotional reactions of drivers with diabetes to different hypoglycemia warning modalities. The same procedure was replicated in two settings: simulated and real driving. A quasi-experimental design, with two independent variables (blood glucose phase and warning modality) and one main dependent variable (emotional reaction), was implemented. The material and apparatus included intravenous catheters to manipulate blood glucose and a tablet with an app to simulate hypoglycemia warnings. The warnings had three possible modalities: Standard, Voice, and Voice + LED. Objective emotional reaction (arousal) was measured physiologically via skin conductance response (SCR). Subjective emotional reaction was measured with the Affective Slider (valence and arousal). Both emotional reaction measures were tested with a mixed-effect linear model. Secondary outcomes included self-reported measures of technology acceptance. RESULTS Our results showed that in the simulated-driving setting, the Voice + LED warning modality was preferred over other modalities. However, this advantage decreased in real-world driving because the LED was less visible outdoors. The Voice modality was more effective than the Standard warning modality in both simulated and real-world driving studies. The mixed model on self-reported emotional reaction yielded significant results. Self-reported arousal was higher during Decreasing blood glucose and Hypoglycemia for Voice and Voice + LED in the real-world driving study. In contrast, self-reported valence was influenced by blood glucose manipulation rather than the warning modality. The mixed model on SCR did not yield significant results. Participants consistently ranked the Voice and Voice + LED warning modalities superior to the Standard modality. CONCLUSIONS This paper proposes using the in-vehicle VA and ambient lighting system installed in a car to deliver a warning of hypoglycemia. It investigated how the warning modality affects emotional response and acceptance in virtual and real-world environments. The study provides insight into the potential of implementing VA-based health warnings in cars and contributes to a better understanding of the challenges and opportunities of in-vehicle VA-based health interventions.

Aim To develop a non-invasive hypoglycemia detection approach using smartwatch data. Research Design and Methods We prospectively collected data from two wrist-worn wearables (Garmin vivoactive 4S; Empatica E4) and continuous glucose monitoring values in adults with diabetes mellitus on insulin treatment. Using this data, we developed a machine learning (ML) approach to detect hypoglycemia ( Results Twenty-two individuals were included in the final analysis (age 54.5±15.2y, HbA1c 6.9±0.6%, 16 male). Hypoglycemia was detected with an area under the receiver operating characteristic curve of 0.76±0.07 solely based on wearable data. Feature analysis revealed that the ML model associated increased heart rate, decreased heart rate variability, and increased tonic electrodermal activity with hypoglycemia. Conclusions Our approach may allow for non-invasive hypoglycemia detection using wearables in people with diabetes and thus complement existing methods for hypoglycemia detection and warning.

Excessive alcohol consumption causes disability and death. Digital interventions are promising means to promote behavioral change and thus prevent alcohol-related harm, especially in critical moments such as driving. This requires real-time information on a person's blood alcohol concentration (BAC). Here, we develop an in-vehicle machine learning system to predict critical BAC levels. Our system leverages driver monitoring cameras mandated in numerous countries worldwide. We evaluate our system with n = 30 participants in an interventional simulator study. Our system reliably detects driving under any alcohol influence (area under the receiver operating characteristic curve [AUROC] 0.88) and driving above the WHO recommended limit of 0.05 g/dL BAC (AUROC 0.79). Model inspection reveals reliance on pathophysiological effects associated with alcohol consumption. To our knowledge, we are the first to rigorously evaluate the use of driver monitoring cameras for detecting drunk driving. Our results highlight the potential of driver monitoring cameras and enable next-generation drunk driver interaction preventing alcohol-related harm., CHI '23: Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems, ISBN:978-1-4503-9421-5

BACKGROUND Hypoglycemia is a frequent and acute complication in type-1 diabetes mellitus (T1DM) and is associated with a higher risk of car mishaps. Currently, hypoglycemia can be detected and signaled through flash glucose monitoring (FGM) or continuous glucose monitoring (CGM) devices. These devices, however, require manual and visual interaction, removing the focus of attention from the driving task. Hypoglycemia is known to cause a decrease in attention, challenging the safety of using such devices behind the wheel. Here, we present an investigation of hands- and distraction-free technology: an in–vehicle voice–assistant–based warning. OBJECTIVE Designing and assessing a voice–assistant–based health warning for hypoglycemia while driving and addressing the limitations of the current warning solutions. METHODS We developed and assessed the warning in three studies, where participants received the warning while driving. In all studies, we measured participants’ self-reported technology readiness, perception of the warning, and compliance behavior (whether they stopped the car and their reaction time), and assessed any room for improvement through participants’ feedback. In Study 0, 10 healthy participants drove in a simulator and assessed the feasibility of using a voice assistant to deliver a warning. In Study 1, 18 participants with T1DM drove in a simulator and assessed the revised version of the warning. In Study 2, 20 participants with T1DM undergoing hypoglycemia assessed a further revised version of the warning while driving in a real car on a test track. In all studies, we also measured self-reported technology readiness, and acceptance of the warning, and assessed compliance behavior and reaction time in response to the warning. RESULTS In all studies, 100% of participants complied with the warning. In Study 0, healthy participants perceived the warning as usable and useful, and their feedback suggested reducing speech rate and increasing driver-assistant interaction. In Study 1, participants with T1DM reported good perception and their feedback suggested the warning to be less instructive. In Study 2, we observed moderate perception (lower than in Study 1), and participants’ feedback revealed the warning was too overloading. CONCLUSIONS To the best of the authors’ knowledge, this is the first study investigating the feasibility of an in–vehicle voice–assistant–based warning for hypoglycemia while driving. Drivers find such an implementation useful and effective, although individuals with T1DM preferred a simple and direct voice warning, rather than a conversational one. This may reflect the utility and unfamiliarity of proactive behavior in voice assistants. We anticipate this research to be a starting point for the combination of driver-state warnings and for voice–assistant–based health support, and to be a guide for the design of such a combination. CLINICALTRIAL ClinicalTrials.gov NCT04035993, NCT04569630

Aim: To develop a non-invasive machine learning (ML) approach to detect hypoglycemia during real car driving based on driving (CAN) , and eye and head motion (EHM) data. Methods: We logged CAN and EHM data in 21 subjects with type 1 diabetes (18 male, 41 ± yrs, A1c 6.8 ± 0.7 % [51 ± 7 mmol/mol]) during driving in eu- (EU) and hypoglycemia (< 3.0 mmol/L, HYPO) . Participants drove in a car (Volkswagen Touran) supervised by a driving instructor on a closed test-track. Using CAN and EHM data, we built ML models to predict the probability of the driver being in HYPO. To make our approach applicable to different generations of cars, we present 3 ML models: first, a model combining CAN+EHM, representing the modern car with integrated camera. Second, a CAN model using driving data only, since modern cars are not generally equipped with EHM tracking. Third, anticipating that autonomous driving will limit the role of CAN data in the future, we tested a model solely based on EHM. Results: Mean BG in EU and HYPO was 6.3 ± 0.8 mmol/L and 2.5 ± 0.5 mmol/L (p< 0.001) , respectively. The model CAN+EHM achieved an area under the receiver operating characteristic curve of 0.88 ± 0.05, sensitivity of 0.70 ± 0.30, and specificity of 0.83 ± 0.in detecting HYPO. Further results are in Fig. 1. Conclusion: We propose ML-based approaches to non-invasively detect HYPO from driver behavior, applicable to contemporary cars and anticipating developments in automotive technology. Disclosure V.Lehmann: None. E.Fleisch: None. T.Kowatsch: Advisory Panel; Pathmate Technologies AG, Research Support; CSS Insurance, Stock/Shareholder; Pathmate Technologies AG. S.N.Lagger: None. M.Laimer: None. F.Wortmann: None. C.Stettler: None. T.Zueger: None. M.Maritsch: None. M.Notter: None. S.Schallmoser: None. C.Bérubé: None. C.Albrecht: None. M.Kraus: None. S.Feuerriegel: None. Funding Swiss National Science Foundation (SNF CRSII5_183569) , Swiss Diabetes Foundation, Diabetes Center Berne, Automobile Club Switzerland (ACS) , Federal Department of Defence, Civil Protection and Sport (DDPS) and Department of Research of the University Hospital Berne

BACKGROUND To provide effective care for inpatients with COVID-19, clinical practitioners need systems that monitor patient health and subsequently allow for risk scoring. Existing approaches for risk scoring in patients with COVID-19 focus primarily on intensive care units (ICUs) with specialized medical measurement devices but not on hospital general wards. OBJECTIVE In this paper, we aim to develop a risk score for inpatients with COVID-19 in general wards based on consumer-grade wearables (smartwatches). METHODS Patients wore consumer-grade wearables to record physiological measurements, such as the heart rate (HR), heart rate variability (HRV), and respiration frequency (RF). Based on Bayesian survival analysis, we validated the association between these measurements and patient outcomes (ie, discharge or ICU admission). To build our risk score, we generated a low-dimensional representation of the physiological features. Subsequently, a pooled ordinal regression with time-dependent covariates inferred the probability of either hospital discharge or ICU admission. We evaluated the predictive performance of our developed system for risk scoring in a single-center, prospective study based on 40 inpatients with COVID-19 in a general ward of a tertiary referral center in Switzerland. RESULTS First, Bayesian survival analysis showed that physiological measurements from consumer-grade wearables are significantly associated with patient outcomes (ie, discharge or ICU admission). Second, our risk score achieved a time-dependent area under the receiver operating characteristic curve (AUROC) of 0.73-0.90 based on leave-one-subject-out cross-validation. CONCLUSIONS Our results demonstrate the effectiveness of consumer-grade wearables for risk scoring in inpatients with COVID-19. Due to their low cost and ease of use, consumer-grade wearables could enable a scalable monitoring system. CLINICALTRIAL Clinicaltrials.gov NCT04357834; https://www.clinicaltrials.gov/ct2/show/NCT04357834

Background: Due to the high rate of asymptomatic patients, the impact of postprandial hypoglycemia after bariatric surgery (PBH) on daily activities such as driving remains controversial. We sought to assess driving performance in patients with PBH. Methods: In a random single-blind crossover study, 10 active drivers with PBH (2 males, age 38±15years, BMI 27.2±4.6kg/m2, gastric bypass 5±2years ago) ingested 75g glucose (GLU) to induce hypoglycemia or aspartame (ASP). A simulator was driven during 10min before and at 20 (D1), 80 (D2), 125 (D3) and 140min (D4) after GLU/ASP, reflective of expected blood glucose (BG) increase (D1), decrease (D2), and hypoglycemia (D3 and D4). Seven driving features (traffic violations and high accelerations) were integrated in a Bayesian hierarchical regression model to assess altered driving performance in GLU vs. ASP. Results: Mean peak and nadir BG after GLU were 182±24 and 47±14mg/dl, respectively. BG was stable after ASP (85±4mg/dl). Driving performance differed between GLU and ASP at times of hypoglycemia (Figure 1), particularly D4, whereas no differences were found for D1 and D2. The posterior probability for impaired driving after GLU was 16% in D1, 82% for D2, 81% for D3 and 98% for D4. Conclusion: Our findings suggest impaired driving performance during PBH. Disclosure A. Tripyla: None. V. Lehmann: None. D. Herzig: None. J. Meier: None. N. Banholzer: None. M. Maritsch: None. S. Feuerriegel: None. F. Wortmann: None. L. Bally: None. Funding Swiss National Science Foundation (PCEGP3_186978, CRSII5_183569, PCEGP1_186932); Dexcom, Inc. (OUS-2020-014)

Aim: To non-invasively detect hypoglycemia in individuals with type 1 diabetes (T1D) based on gaze behavior while driving. Methods: Controlled hypoglycemia was induced in 19 individuals (12 males, age 32 ± 7.1 yrs) with T1D (HbA1c 7.1 ± 0.6% [54 ± 6 mmol/mol]) using an adapted hypoglycemic clamp protocol. Gaze and blood glucose (BG) data were gathered while driving in a simulator during three 18 min sessions: session 1 (BG 90-144 mg/dL), session 2 (BG declining from 72 to 45 mg/dL), and session 3 (BG 36-45 mg/dL). A gradient-boosting machine learning (ML) model was built for hypoglycemia (BG Results: Mean venous BG was 105.4 ± 11.4 mg/dL during session 1, declined from 61.4 ± 6.1 mg/dL to 47.2 ± 8.5 mg/dL during session 2, and was 42.7 ± 4.1 mg/dL during session 3, respectively. Gaze analysis provided 29,968 data samples (1,577.5 ± 52 per subject, 10,041 euglycemia, 19,927 hypoglycemia). Overall, ML achieved an area under the receiver-operating-characteristics curve of 0.83 ± 0.09 for hypoglycemia detection with leave-one-subject-out cross-validation. Conclusion: ML-based gaze analysis shows high accuracy in non-invasive hypoglycemia detection while driving. Our approach offers promising potential in various settings where cameras are available. Disclosure V. Lehmann: None. E. Fleisch: Research Support; Self; CSS Health Insurance, Switzerland, Stock/Shareholder; Self; Pathmate-Technologies AG, Switzerland. F. Wortmann: None. C. Stettler: None. M. Maritsch: None. T. Zueger: None. A. Marxer: None. C. Berube: None. M. Kraus: None. C. Albrecht: None. S. Feuerriegel: None. T. Kowatsch: Advisory Panel; Self; Pathmate Technologies AG, Switzerland and Germany, Stock/Shareholder; Self; Pathmate Technologies AG, Switzerland and Germany. Funding Swiss National Science Foundation (SNF CRSII5_183569); Insel Group; Diabetes Center Berne

Postbariatric hypoglycaemia (PBH) is an increasingly recognized complication of bariatric surgery, but its effect on daily functioning remains unclear. In this randomized, single-blind, crossover trial we assessed driving performance in patients with PBH. Ten active drivers with PBH (eight females, age 38.2 ± 14.7 years, body mass index 27.2 ± 4.6 kg/m

CHI EA '20: Extended Abstracts of the 2020 CHI Conference on Human Factors in Computing Systems, ISBN:978-1-4503-6819-3

Background and Objective: Researchers use wearable sensing data and machine learning (ML) models to predict various health and behavioral outcomes. However, sensor data from commercial wearables are prone to noise, missing, or artifacts. Even with the recent interest in deploying commercial wearables for long-term studies, there does not exist a standardized way to process the raw sensor data and researchers often use highly specific functions to preprocess, clean, normalize, and compute features. This leads to a lack of uniformity and reproducibility across different studies, making it difficult to compare results. To overcome these issues, we present FLIRT: A Feature Generation Toolkit for Wearable Data; it is an open-source Python package that focuses on processing physiological data specifically from commercial wearables with all its challenges from data cleaning to feature extraction. Methods: FLIRT leverages a variety of state-of-the-art algorithms (e.g., particle filters, ML-based artifact detection) to ensure a robust preprocessing of physiological data from wearables. In a subsequent step, FLIRT utilizes a sliding-window approach and calculates a feature vector of more than 100 dimensions – a basis for a wide variety of ML algorithms. Results: We evaluated FLIRT on the publicly available WESAD dataset, which focuses on stress detection with an Empatica E4 wearable. Preprocessing the data with FLIRT ensures that unintended noise and artifacts are appropriately filtered. In the classification task, FLIRT outperforms the preprocessing baseline of the original WESAD paper. Conclusion: FLIRT provides functionalities beyond existing packages that can address unmet needs in physiological data processing and feature generation: (a) integrated handling of common wearable file formats (e.g., Empatica E4 archives), (b) robust preprocessing, and (c) standardized feature generation that ensures reproducibility of results. Nevertheless, while FLIRT comes with a default configuration to accommodate most situations, it offers a highly configurable interface for all of its implemented algorithms to account for specific needs., Computer Methods and Programs in Biomedicine, 212, ISSN:0169-2607, ISSN:1872-7565

This book presents an in-depth description of the Arrowhead Framework and how it fosters interoperability between IoT devices at service level, specifically addressing application. The Arrowhead Fr ...

The need for new technology High level topics in today’s society are sustainability, flexibility, efficiency, and competitiveness. These in turn are driven by big societal questions like environmen ...

Global competition exhibits a strong pressure on organizations to optimize production processes and increase production flexibility. For example, to improve maintenance, repair and overhaul (MRO) processes of industrial equipment, an Internet-based data exchange system and backend workflow processing is required. Thereby an equipment vendor gathers field intelligence from equipment on customer premises worldwide. With the acquired data, smart services can be offered. Here, equipment maintenance tasks can be anticipated and thus on-premise equipment maintenance can be scheduled proactively. However, related work has focused mainly on two aspects. On the one hand, how to enable Internet-connectivity for legacy equipment has been investigated. One the other hand, the secure data acquisition systems have been presented. To address both aspects, Broker-based systems have been proposed. But related work does not address possible Broker architectures or the backend processing of data. Thus, in this work we investigate Broker- and backend-related aspects. First, we discuss topic structuring and access restriction. Second, we elaborate on different Broker placement architectures. Finally, we discuss and evaluate the design and implementation of an overall system and detail two concrete backend workflows.

Increasing the efficiency of production and manufacturing processes is a key goal of initiatives like Industry 4.0. Within the context of the European research project ARROWHEAD, we enable and secure smart maintenance services. An overall goal is to proactively predict and optimize the Maintenance, Repair and Operations (MRO) processes carried out by a device maintainer, for industrial devices deployed at the customer. Therefore it is necessary to centrally acquire maintenance relevant equipment status data from remotely located devices over the Internet. Consequently, security and privacy issues arise from connecting devices to the Internet, and sending data from customer sites to the maintainer's back-end. In this paper we consider an exemplary automotive use case with an AVL Particle Counter (APC) as device. The APC transmits its status information by means of a fingerprint via the publish-subscribe protocol Message Queue Telemetry Transport (MQTT) to an MQTT Information Broker in the remotely located AVL back-end. In a threat analysis we focus on the MQTT routing information asset and identify two elementary security goals in regard to client authentication. Consequently we propose a system architecture incorporating a hardware security controller that processes the Transport Layer Security (TLS) client authentication step. We validate the feasibility of the concept by means of a prototype implementation. Experimental results indicate that no significant performance impact is imposed by the hardware security element. The security evaluation confirms the advanced security of our system, which we believe lays the foundation for security and privacy in future smart service infrastructures.