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Introduction: Human-centric artificial intelligence (HCAI) focuses on systems that support and collaborate with humans to achieve their goals. To better understand how collaboration develops in human-AI teaming, further exploration grounded in a theoretical model is needed. Tuckman's model describes how team development among humans evolves by transitioning through the stages of forming, storming, norming, performing, and adjourning. The purpose of this pilot study was to explore transitions between the first three stages in a collaborative task involving a human and a human-centric agent.

Method: The collaborative task was selected based on commonly performed tasks in a therapeutic healthcare context. It involved planning activities for the upcoming week to achieve health-related goals. A calendar application served as a tool for this task. This application embedded a collaborative agent designed to interact with humans following Tuckman's stages of team development. Eight participants completed the collaborative calendar planning task, followed by a semi-structured interview. Interviews were transcribed and analyzed using inductive content analysis.

Results: The results revealed that the participants initiated the storming stage in most cases (n = 7/8) and that the agent initiated the norming stage in most cases (n = 5/8). Additionally, three main categories emerged from the content analyses of the interviews related to participants' transition through team development stages: (i) participants' experiences of Tuckman's first three stages of team development; (ii) their reactions to the agent's behavior in the three stages; and (iii) factors important to the participants to team up with a collaborative agent.

Conclusion: Results suggest ways to further personalize the agent to contribute to human-agent teamwork. In addition, this study revealed the need to further examine the integration of explicit conflict management into human-agent collaboration for human-agent teamwork.

One of the crucial steps in assessing hemodynamic parameters using impedance cardiography (ICG) is the detection of the characteristic points in the dZ/dt ICG complex, especially the X point. The most often estimated parameters from the ICG complex are stroke volume and cardiac output, for which is required the left ventricular pre-ejection time. Unfortunately, for beat-to-beat calculations, the accuracy of detection is affected by the variability of the ICG complex subtypes. Thus, in this work, we aim to create a predictive model that can predict the missing points and decrease the previous work percentages of missing points to support the detection of ICG characteristic points and the extraction of hemodynamic parameters according to several existing subtypes. Thus, a time-series non-linear autoregressive model with exogenous inputs (NARX) feedback neural network approach was implemented to forecast the missing ICG points according to the different existing subtypes. The NARX was trained on two different datasets with an open-loop mode to ensure that the network is fed with correct feedback inputs. Once the training is satisfactory, the loop can be closed for multi-step prediction tests and simulation. The results show that we can predict the missing characteristic points in all the complexes with a success rate ranging between 75% and 88% in the evaluated datasets. Previously, without the NARX predictive model, the successful detection rate was 21%–30% for the same datasets. Thus, this work indicates a promising method and an accuracy increase in the detection of X, Y, O, and Z points for both datasets.

In impedance cardiography (ICG), the detection of dZ/dt signal (ICG) characteristic points, especially the X point, is a crucial step for the calculation of hemodynamic parameters such as stroke volume (SV) and cardiac output (CO). Unfortunately, for beat-to-beat calculations, the accuracy of the detection is affected by the variability of the ICG complex subtypes. Thus, in this work, automated classification of ICG complexes is proposed to support the detection of ICG characteristic points and the extraction of hemodynamic parameters according to several existing subtypes. A novel pattern recognition artificial neural network (PRANN) approach was implemented, and a divide-and-conquer strategy was used to identify the five different waveforms of the ICG complex waveform with output nodes no greater than 3. The PRANN was trained, tested and validated using a dataset from four volunteers from a measurement of eight electrodes. Once the training was satisfactory, the deployed network was validated on two other datasets that were completely different from the training dataset. As an additional performance validation of the PRANN, each dataset included four volunteers for a total of eight volunteers. The results show an average accuracy of 96% in classifying ICG complex subtypes with only a decrease in the accuracy to 83 and 80% on the validation datasets. This work indicates that the PRANN is a promising method for automated classification of ICG subtypes, facilitating the investigation of the extraction of hemodynamic parameters from beat-to-beat dZ/dt complexes.

Recording and processing physiological signal that give intrinsic characteristics information is one of scientific community needs. Electromyography (EMG) and Electrical Impedance Myography (EIMG) are both non-invasive approaches to measure and evaluate the muscle conditions and activity. Z-RPI device is a custom-made measurement device developed basically for ECG and ICG records. This paper presents the feasibility of acquiring surface EMG and EIMG signal of biceps and forearm muscle contractions using the Z-RPI device. The results obtained are acceptable, encouraging and converge to literature result. Thus, it shows that the Z-RPI device can be used for relatively several biomedical applications other than the ECG and ICG measurement. For supporting developers in research and engineering education.

Impedance cardiography (ICG) was discovered nearly half a century ago, being proposed as noninvasive monitoring method for estimation of several hemodynamics parameter. Despite of nearly 5 decades of clinical research and use there is still certain controversy about its performance when estimating Left Ventricular Ejection Time (LVET). This work present a comparison between using the different ICG subtype waveform and the ensemble averaged (EA) method to calculate the LVET. The ICG has been recorded from four volunteers, and the LVET parameter has been calculated using the two approaches. The result shows that each volunteer have different percentage of subtypes, and the mean relative error between the two approaches for estimation of LVET varied between 0.62 to 2.9% with an average mean absolute percentage error of 18,02% ranging between 13.82 to 18.42%.

The detection of the characteristic points of the complex of the impedance cardiography (ICG) is a crucial step for the calculation of hemodynamical parameters such as left ventricular ejection time, stroke volume and cardiac output. Extracting the characteristic points from the dZ/dt ICG signal is usually affected by the variability of the ICG complex and assembling average is the method of choice to smooth out such variability. To avoid the use of assembling average that might filter out information relevant for the hemodynamic assessment requires extracting the characteristics points from the different subtypes of the ICG complex. Thus, as a first step to automatize the extraction parameters, the aim of this work is to detect automatically the kind of dZ/dt complex present in the ICG signal. To do so artificial neural networks have been designed with two different configurations for pattern matching (PRANN) and tested to identify the 6 different ICG complex subtypes. One of the con figurations implements a 6-classes classifier and the other implemented the divide and conquer approach classifying in two stages. The data sets used in the training, validation and testing process of the PRANNs includes a matrix of 1 s windows of the ICG complexes from the 60 s long recordings of dZ/dt signal for each of the 4 healthy male volunteers. A total of 240 s. As a result, the divide and conquer approach improve the overall classification obtained with the one stage approach on +26% reaching and average classification ration of 82%.

Cardiovascular activity is one of the primordial physiological phenomena of human life. However, it is subject to many pathologies due to various factors such as: lifestyle, age, diabetes, cholesterol or heredity. As a consequence, there are different kinds of examination and methods used to enable the diagnostic. In this paper cardiovascular parameters are calculated due to the acquisition of simultaneous electrocardiography (ECG) and photoplethysmography (PPG) signals and the high limb peripheral arterial stiffness is evaluated by the calculation of the Pulse Wave Velocity (PWV) parameter. Those measurements are performed by two different low-cost systems based on Arduino board. The acquired waveforms form each device prototype present a satisfactory result, and the cardiovascular parameters are in the range of the value found in the literature. The developed prototype devices could easily be used for engineering educational purposes, hobby or first steps research.

Impedance cardiography (ICG) is a noninvasive method for monitoring cardiac dynamics using electrical bioimpedance (EBI) measurements. Since its appearance more than 40 years ago, ICG has been used for assessing hemodynamic parameters. This paper presents a measurement system based on two System on Chip (SoC) solutions and Raspberry PI, implementing both a full three-lead ECG recorder and an impedance cardiographer, for educational and research development purposes. Raspberry PI is a platform supporting Do-It-Yourself project and education applications across the world. The development is part of Biosignal PI, an open hardware platform focusing in quick prototyping of physiological measurement instrumentation. The SoC used for sensing cardiac biopotential is the ADAS1000, and for the EBI measurement is the AD5933. The recordings were wirelessly transmitted through Bluetooth to a PC, where the waveforms were displayed, and hemodynamic parameters such as heart rate, stroke volume, ejection time and cardiac output were extracted from the ICG and ECG recordings. These results show how Raspberry PI can be used for quick prototyping using relatively widely available and affordable components, for supporting developers in research and engineering education. The design and development documents will be available on www.BiosignalPI.com, for open access under a Non Commercial-Share A like 4.0 International License.

Impedance cardiography (ICG) is a noninvasive method for monitoring mechanical function of the heart with the use of electrical bioimpedance measurements. This paper presents the feasibility of recording an ICG signal simultaneously with electrocardiogram signal (ECG) using the same electrodes for both measurements, for a total of five electrodes rather than eight electrodes. The device used is the Z-RPI. The results present good performance and show waveforms presenting high similarity with the different signals reported using different electrodes for acquisition; the heart rate values were calculated and they present accurate evaluation between the ECG and ICG heart rates. The hemodynamics and cardiac parameter results present similitude with the physiological parameters for healthy people reported in the literature. The possibility of reducing number of electrodes used for ICG measurement is an encouraging step to enabling wearable and personal health monitoring solutions.

The quality of an impedance cardiography (ICG) signal critically impacts the calculation of hemodynamic parameters. These calculations depend solely on the identification of ICG characteristic points on the ABEXYOZ complex. Unfortunately, contrary to the relatively constant morphology of the PQRST complex in electrocardiography, the waveform morphology of ICG data is far from stationary, which causes difficulties in the accuracy of the automated detection of characteristic ICG points. This study evaluated ICG recordings obtained from 10 volunteers. The results indicate that there are several different waveforms for the ABEXYOZ complex; there are up to five clearly distinct waveforms for the ABEXYOZ complex in addition to those that are typically reported. The differences between waveform types increased the difficulty of detecting ICG points. To accurately detect all ICG points, the ABEXYOZ complex should be analyzed according to the corresponding waveform type.