Linking Conversation Analysis and Motion Capturing: How to robustly track multiple participants?

Pitsch K, Brüning B-A, Schnier C, Dierker H, Wachsmuth S. Linking Conversation Analysis and Motion Capturing: How to robustly track multiple participants? In: Kipp M, Martin J-C, Paggio P, Heylen D, eds. Proceedings of the LREC Workshop on Multimodal Corpora: Advances in Capturing, Coding and Analyzing Multimodality (MMC 2010). 2010: 63-69.If we want to model the dynamic and contingent nature of human social interaction (e.g. for the design of human robot interaction), analysis and description of natural interaction is required that combines different methodologies and research tools (qualitative/quantitative; manual/automated). In this paper, we pinpoint the requirements and technical challenges for constituting and managing multimodal corpora that arise when linking Conversation Analysis with novel 3D motion capture technologies: i.e. to robustly track multiple participants over an extended period of time. We present and evaluate a solution to by-pass the limits of the current standard Vicon system (using rigid bodies) and ways of mapping the obtained coordinates to a human skeleton model (inverse kinematics) and to export the data into a format that is supported by standard annotation tools (such as ANVIL)

Linking Conversation Analysis and Motion Capturing: “How to robustly track multiple participants

If we want to model the dynamic and contingent nature of human social interaction (e.g. for the design of human-robot-interaction), analysis and description of natural interaction is required that combines different methodologies and research tools (qualitative/quantitative; manual/automated). In this paper, we pinpoint the requirements and technical challenges for constituting and managing multimodal corpora that arise when linking Conversation Analysis with novel 3D motion capture technologies: i.e. to robustly track multiple participants over an extended period of time. We present and evaluate a solution to by-pass the limits of the current standard Vicon system (using rigid bodies) and ways of mapping the obtained coordinates to a human skeleton model (inverse kinematics) and to export the data into a format that is supported by standard annotation tools (such as ANVIL). 1. Introduction: Detectin

Risk Factors for Carbapenem-Resistant Enterobacterales Clinical Treatment Failure

The Centers for Disease Control and Prevention (CDC) categorized carbapenem-resistant Enterobacterales (CRE) infections as an urgent health care threat requiring public attention and research. Certain patients with CRE infections may be at higher risk for poor clinical outcomes than others. Evidence on risk or protective factors for CRE infections are warranted in order to determine the most at-risk populations, especially with newer beta-lactam/beta-lactamase inhibitor (BL/BLI) antibiotics available to treat CRE. We aimed to identify specific variables involved in CRE treatment that are associated with clinical failure (either 30-day mortality, 30-day microbiologic recurrence, or clinical worsening/failure to improve throughout antibiotic treatment). We conducted a retrospective, observational cohort study of hospitalized patients with CRE infection sampled from 2010 to 2020 at two medical systems in Detroit, Michigan. Patients were included if they were ≥18 years old and culture positive for an organism in the Enterobacterales order causing clinical infection with in vitro resistance by Clinical and Laboratory Standards Institute (CLSI) breakpoints to at least one carbapenem. Overall, there were 140 confirmed CRE infections of which 39% had clinical failure. The most common infection sources were respiratory (38%), urinary (20%), intra-abdominal (16%), and primary bacteremia (14%). A multivariable logistic regression model was developed to identify statistically significant associated predictors with clinical failure, and they included Sequential Organ Failure Assessment (SOFA) score (adjusted odds ratio [aOR], 1.18; 95% confidence interval [CI], 1.06 to 1.32), chronic dialysis (aOR, 5.86; 95% CI, 1.51-22.7), and Klebsiella pneumoniae in index culture (aOR, 3.09; 95% CI, 1.28 to 7.47). Further research on CRE infections is needed to identify best practices to promote treatment success. IMPORTANCE This work compares carbapenem-resistant Enterobacterales (CRE) infections using patient, clinical, and treatment variables to understand which characteristics are associated with the highest risk of clinical failure. Knowing which risk factors are associated with CRE infection failure can provide clinicians better prognostic and targeted interventions. Research can also further investigate why certain risk factors cause more clinical failure and can help develop treatment strategies to mitigate associated risk factors

Comodulation Masking Release Determined in the Mouse (Mus musculus) using a Flanking-band Paradigm

Comodulation masking release (CMR) has been attributed to auditory processing within one auditory channel (within-channel cues) and/or across several auditory channels (across-channel cues). The present flanking-band (FB) experiment—using a 25-Hz-wide on-frequency noise masker (OFM) centered at the signal frequency of 10 kHz and a single 25-Hz-wide noise FB—was designed to separate the amount of CMR due to within- and across-channel cues and to investigate the role of temporal cues on the size of within-channel CMR. The results demonstrated within-channel CMR in the Naval Medical Research Institute mouse, while no unambiguous evidence could be found for CMR occurring due to across-channel processing (i.e., “true CMR”). The amount of within-channel CMR was dependent on the frequency separation between the FB and the OFM. CMR increased from 4 to 6 dB for a frequency separation of 1 kHz to 18 dB for a frequency separation of 100 Hz. The large increase for a frequency separation of 100 Hz is likely to be due to the exploitation of changes in the temporal pattern of the stimulus upon the addition of the signal. Temporal interaction between both masker bands results in modulations with a large depth at a modulation frequency equal to the beating rate. Adding a signal to the maskers reduces the depth of the modulation. The auditory system of mice might be able to use the change in modulation depth at a beating frequency of 100 Hz as a cue for signal detection, while being unable to detect changes in modulation depth at high modulation frequencies. These results are consistent with other experiments and model predictions for CMR in humans which suggested that the main contribution to the CMR effect stems from processing of within-channel cues