Kevin Koch

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Title
Dr.
Last Name
Koch
First name
Kevin
Email
kevin.koch@unisg.ch
Phone
+41 71 224 7251

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    Increase of PEth after Single Consumption of Alcohol and Evaluation of a Volumetric DBS Filter Paper Device
    (2023-02)
    Stöth, Frederike
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    Koch, Kevin  
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    Bantle, Matthias
    ;
    Pütz, Philipp
    ;
    Wortmann, Felix  
    Direct alcohol biomarkers are of growing interest for the assessment of alcohol consumption, with particular interest in phosphatidylethanol (PEth) in recent years. PEth is only formed when alcohol is present in the body. However, there is no statement about how much the PEth concentration increases after single moderate alcohol consumption. This study was conducted to determine the increase in PEth concentrations after a single drinking event. Additionally, a new volumetric sampling device (volumetric dried blood spot cards (DBSV)) was evaluated, which was designed to simplify further sampling processes and to allow for easy self-sampling. Dried blood samples from 31 volunteers were collected before and after single alcohol consumption with a mean maximum breath alcohol concentration of 0.4 mg/L (range: 0.30–0.55 mg/L). PEth concentrations were determined after automated extraction by liquid chromatography-tandem mass spectrometry. PEth 16:0/18:1 and PEth 16:0/18:2 concentrations increased to an average of 45 ng/mL each in patients starting below 20 ng/mL (range: 25.0–57.0 ng/mL for PEth 16:0/18:1; range 26.8–62.3 ng/mL for PEth 16:0/18:2). PEth concentrations in patients starting above 20 ng/mL increased by a mean of 30 ng/mL (range: 6.2–71.3 ng/mL for PEth 16:0/18:1; range 8.8–65.3 ng/mL for PEth 16:0/18:2). In addition, the comparison of the new sampling device DBSV with a standard filter paper card (with volumetrically applied 20 µL of blood samples) yielded a close agreement for the determined PEth concentrations in 24 forensic samples and three external controls. Therefore, the sampling device DBSV proved to be suitable for the determination of PEth concentrations in blood.
    Type:journal article
    Journal:Journal of Analytical Toxicology
    URL:https://doi.org/10.1093/jat/bkad009
    DOI:10.1093/jat/bkad009
    URI:https://www.alexandria.unisg.ch/handle/20.500.14171/107775
    Scopus© Citations 7
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    Towards Non-intrusive Camera-Based Heart Rate Variability Estimation in the Car Under Naturalistic Condition
    (IEEE, 2022-07-15)
    Liu, Shu
    ;
    Koch, Kevin  
    ;
    Zhou, Zimu
    ;
    Maritsch, Martin
    ;
    He, Xiaoxi
    Type:journal article
    Journal:IEEE Internet of Things Journal
    Volume:09
    Issue:14
    DOI:10.1109/JIOT.2021.3131742
    URI:https://www.alexandria.unisg.ch/handle/20.500.14171/108487
    Scopus© Citations 18
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    Scopus© Citations 5
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    When Do Drivers Interact with In-Vehicle Well-being Interventions?
    (2021)
    Kevin Koch  
    ;
    Varun Mishra
    ;
    Shu Liu
    ;
    Thomas Berger
    ;
    Elgar Fleisch  
    <jats:p>Recent developments of novel in-vehicle interventions show the potential to transform the otherwise routine and mundane task of commuting into opportunities to improve the drivers' health and well-being. Prior research has explored the effectiveness of various in-vehicle interventions and has identified moments in which drivers could be interruptible to interventions. All the previous studies, however, were conducted in either simulated or constrained real-world driving scenarios on a pre-determined route. In this paper, we take a step forward and evaluate when drivers interact with in-vehicle interventions in unconstrained free-living conditions.</jats:p> <jats:p>To this end, we conducted a two-month longitudinal study with 10 participants, in which each participant was provided with a study car for their daily driving needs. We delivered two in-vehicle interventions - each aimed at improving affective well-being - and simultaneously recorded the participants' driving behavior. In our analysis, we found that several pre-trip characteristics (like trip length, traffic flow, and vehicle occupancy) and the pre-trip affective state of the participants had significant associations with whether the participants started an intervention or canceled a started intervention. Next, we found that several in-the-moment driving characteristics (like current road type, past average speed, and future brake behavior) showed significant associations with drivers' responsiveness to the intervention. Further, we identified several driving behaviors that "negated" the effectiveness of interventions and highlight the potential of using such "negative" driving characteristics to better inform intervention delivery. Finally, we compared trips with and without intervention and found that both interventions employed in our study did not have a negative effect on driving behavior. Based on our analyses, we provide solid recommendations on how to deliver interventions to maximize responsiveness and effectiveness and minimize the burden on the drivers.</jats:p>
    Type:journal article
    Journal:Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies
    Volume:5
    Issue:1
    DOI:10.1145/3448116
    URI:https://www.alexandria.unisg.ch/handle/20.500.14171/120358
    Scopus© Citations 22
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    When Do Drivers Interact with In-Vehicle Well-being Interventions? An Exploratory Analysis of a Longitudinal Study on Public Roads
    (Association for Computing Machinery, 2021)
    Koch, Kevin  
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    Mishra, Varun
    ;
    Liu, Shu
    ;
    Berger, Thomas
    ;
    Fleisch, Elgar  
    Type:journal article
    Journal:Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.
    Volume:5
    Issue:1
    URL:https://doi.org/10.1145/3448116
    DOI:10.1145/3448116
    URI:https://www.alexandria.unisg.ch/handle/20.500.14171/111117
    Scopus© Citations 22
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    The Empathetic Car
    (2021)
    Shu Liu
    ;
    Kevin Koch  
    ;
    Zimu Zhou
    ;
    Simon Föll
    ;
    Xiaoxi He
    <jats:p>An empathetic car that is capable of reading the driver's emotions has been envisioned by many car manufacturers. Emotion inference enables in-vehicle applications to improve driver comfort, well-being, and safety. Available emotion inference approaches use physiological, facial, and speech-related data to infer emotions during driving trips. However, existing solutions have two major limitations: Relying on sensors that are not built into the vehicle restricts emotion inference to those people leveraging corresponding devices (e.g., smartwatches). Relying on modalities such as facial expressions and speech raises privacy concerns. By contrast, researchers in mobile health have been able to infer affective states (e.g., emotions) based on behavioral and contextual patterns decoded in available sensor streams, e.g., obtained by smartphones. We transfer this rationale to an in-vehicle setting by analyzing the feasibility of inferring driver emotions by passively interpreting the data streams of the control area network (CAN-bus) and the traffic context (inferred from the front-view camera). Therefore, our approach does not rely on particularly privacy-sensitive data streams such as the driver facial video or driver speech, but is built based on existing CAN-bus data and traffic information, which is available in current high-end or future vehicles. To assess our approach, we conducted a four-month field study on public roads covering a variety of uncontrolled daily driving activities. Hence, our results were generated beyond the confines of a laboratory environment. Ultimately, our proposed approach can accurately recognise drivers' emotions and achieve comparable performance as the medical-grade physiological sensor-based state-of-the-art baseline method.</jats:p>
    Type:journal article
    Journal:Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies
    Volume:5
    Issue:3
    DOI:10.1145/3478078
    URI:https://www.alexandria.unisg.ch/handle/20.500.14171/120356
    Scopus© Citations 36
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    Towards the Healing Car: Investigating the Potential of Psychotherapeutic In-vehicle Interventions
    (2020-06)
    Koch, Kevin  
    ;
    Liu, Shu
    ;
    Berger, Thomas
    ;
    Wortmann, Felix  
    The globally increasing prevalence and incident rates of mental diseases is one of the most serious public health challenges according to the World Health Organization. Today, treatment is based on professional therapies which require a high amount of financial resources and personnel effort, however IT-supported interventions in ubiquitous devices promise help and a new leverage beyond traditional therapies. We identify the car as a space for new treatments since drivers often have time and the environment in the automobile is highly controlled. In-vehicle information systems can reach people in their daily routine and could introduce innovative prevention measures. In this research in progress paper, we address the open question how the car can improve a driver’s affective state while driving. First, we thoroughly describe the design of a study we conducted to motivate other researchers for this topic. Second, we analyse 631 completed interventions collected in a 2-month field study with 10 drivers. First analyses indicate that we can positively influence the short-term affective state of drivers with at least one of ourintervention types. We provide first practical examples of how to reach the masses of everyday drivers.
    Type:journal article
    Journal:ECIS 2020 Proceedings
    URL:https://aisel.aisnet.org/ecis2020_rip/8/
    URI:https://www.alexandria.unisg.ch/handle/20.500.14171/112075
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    The Costs of Traffic Accident Hotspots
    (IEEE, 2019-10)
    Gahr, Bernhard  
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    Caves, Katherine
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    Wen, Junhan
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    Koch, Kevin  
    ;
    Liu, Shu
    Despite efforts to reduce them, traffic accidents continue to increase and bypass reduction targets. The costs of traffic accidents are enormous, killing 1.35 million people every year and costing 3% of most countries' GDP. Recent research aims to target interventions at high-accident-density locations, called accident hotspots. New methods and technologies can systematically identify hotspots, but it remains unclear whether hotspots contribute to accident costs as well as volume. This paper investigates the monetary and human costs of accident hotspots. We analyze a dataset of all accidents from 2011 - 2017 in Switzerland. We identify hotspots, then analyze their contributions to traffic accident costs. We find that hotspot accidents are not different in monetary costliness or injury rates from non-hotspot accidents, so hotspots drive costs along with accident volume. However, hotspot accidents are less fatal, so hotspot targeting might not be best for fatalities. If hotspots are reduced to normal road conditions, total monetary costs can be reduced by up to 5% per year as a theoretical upper bound. Targeting the top 10% most frequent, costly, injurious, or deadly hotspots yeilds different results for different cost types, with accident number and monetary cost targets creating the highest reductions overall.
    Type:journal article
    Journal:2019 IEEE Intelligent Transportation Systems Conference (ITSC)
    Volume:22
    URL:https://ieeexplore.ieee.org/abstract/document/8916851
    DOI:10.1109/ITSC.2019.8916851
    URI:https://www.alexandria.unisg.ch/handle/20.500.14171/98163
    Scopus© Citations 4
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    Driver Identification via the Steering Wheel
    (2019-09-09)
    Gahr, Bernhard  
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    Liu, Shu
    ;
    Koch, Kevin  
    ;
    Barata, Filipe
    ;
    Dahlinger, André  
    Driver identification has emerged as a vital research field, where both practitioners and researchers investigate the potential of driver identification to enable a personalized driving experience. Within recent years, a selection of studies have reported that individuals could be perfectly identified based on their driving behavior under controlled conditions. However, research investigating the potential of driver identification under naturalistic conditions claim accuracies only marginally higher than random guess. The paper at hand provides a comprehensive summary of the recent work, highlighting the main discrepancies in the design of the machine learning approaches, primarily the window length parameter that was considered. Key findings further indicate that the longitudinal vehicle control information is particularly useful for driver identification, leaving the research gap on the extent to which the lateral vehicle control can be used for reliable identification. Building upon existing work, we provide a novel approach for the design of the window length parameter that provides evidence that reliable driver identification can be achieved with data limited to the steering wheel only. The results and insights in this paper are based on data collected from the largest naturalistic driving study conducted in this field. Overall, a neural network based on GRUs was found to provide better identification performance than traditional methods, increasing the prediction accuracy from under 15\% to over 65\% for 15 drivers. When leveraging the full field study dataset, comprising 72 drivers, the accuracy of identification prediction of the approach improved a random guess approach by a factor of 25.
    Type:journal article
    Journal:arXiv
    URL:https://arxiv.org/abs/1909.03953
    URI:https://www.alexandria.unisg.ch/handle/20.500.14171/98245
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    Brake Maneuver Prediction–An Inference Leveraging RNN Focus on Sensor Confidence
    (IEEE, 2019-11-28)
    Liu, Shu
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    Koch, Kevin  
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    Gahr, Bernhard  
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    Wortmann, Felix  
    In recent years, driver behavior analysis has led to countless driver assistance systems. In these systems, earlier detection of a driver’s maneuver intentions offers opportunities to improve driving experience and safety. Especially brake maneuvers are of fundamental importance because they are directly related to the avoidance of potential hazards.Current state-of-the-art brake assistance systems rely on the release speed of accelerator pedal as an indicator whether a brake event is planned. However, this simple and practical algorithm, fails to capture the overall movement pattern of accelerator pedal behaviors and cannot utilize rich information from different vehicle sensors.To address this issue, we propose a novel recurrent neural network architecture for the purpose of brake maneuver prediction. The proposed method exploits the advantages of multiple sensors. Unlike conventional practices where all signals are aggregated to a single neural network, we leverage the confidence of each sensor. We evaluate our approach based on a dataset of 44 drivers, comprising around 500 hours of naturalistic driving data. The evaluation results show that the proposed algorithm outperforms baseline method by large margin.
    Type:journal article
    Issue:21
    URL:https://ieeexplore.ieee.org/abstract/document/8917405
    DOI:10.1109/ITSC.2019.8917405
    URI:https://www.alexandria.unisg.ch/handle/20.500.14171/97999
    Scopus© Citations 9