Human trust in machines is critical for effective human-machine interaction in virtual reality (VR). Prior work defined a three-layered framework of such trust but also indicated two deficiencies. Firstly, there is an absence of a model with metrics spanning all layers to objectively capture fluctuations of the trust (trust dynamics) in real time. Secondly, there is an inadequate consideration of human and machine reliability for the trust. Herein, this study proposed a trust model by defining metrics for all the layers and evaluated this model by considering human and machine reliability. Using objective and subjective data, the evaluation was based on two VR use-cases. The outcomes of the evaluation confirmed the pertinence of the model to capture trust dynamics in the presence of human and machine reliability. The objective data was notably more sensitive to capturing trust dynamics than the subjective counterpart. The model could enable designing trustworthy and adaptive VR.

Measuring cognitive workload (CWL) is crucial for dynamic task reallocation (i.e., adaptation) between a human and a machine in a human-machine system (HMS). A conventional measurement of the CWL is based on subjectively reported scores about the six factors of the NASA Task Load Index (NASA-TLX) questionnaire. The questionnaire cannot however capture real-time fluctuations of the factors for an objective quantification. Additionally, each of the factors is associated with distinct activities and can be influenced by individual characteristics and/or task contexts. Such HMS adaptation should thus consider the objective quantification of each factor. So far, the quantification remains largely unexplored, while existing studies reveal a potential use of an electroencephalography (EEG) in measuring the CWL levels (e.g., high, medium, and low). Herein, we presented a pioneering study to propose EEG features for quantifying the factors. The pertinence of the features was demonstrated by their strong correlations with the scores of the factors across three distinct cases of visuomotor tasks. The pertinence is the stepping stone toward factor-based interventions in enabling HMS adaptation.

Haptic perception of object shape is crucial for humans to interact with machines in human-machine systems (HMS). This perception is prone to disruptions arising from the human and/or machine sides of HMS. An unexplored topic is cognitive processes of the perception. Herein, this study examined the feasibility of measuring the cognitive processes within a virtual environment (i.e., an HMS). Non-invasive electroencephalography was employed to record brain activity of human participants during a task, which was perturbed by disruptions from the human and machine sides. The cognitive processes were measured by using an engagement ratio (ER) and an attention ratio (AR) as physiological metrics, besides behavioral metrics. The results of the study confirmed the feasibility of ER and AR to measure the processes and, in turn, opens an avenue towards elucidating the processes for improving HMS.

Brain machine interfaces (BMI) are becoming increasingly prevalent in diverse applications including motor rehabilitation, virtual reality training, etc. Two critical aspects of an effective BMI are machine reliability and cognitive workload (CWL). Previous studies have reported a notable effect of machine reliability on the 6 factors of the CWL. However, it remains unclear whether this effect can be detected in cognitive processes. Electroencephalography (EEG) is a widely used technique to explore cognitive processes by recording brain activities as signals. Therefore, we utilized the event-related spectral power (ERSP) feature of EEG signals to determine the cognitive processes regarding the effect of machine reliability. The results revealed that machine reliability affected the CWL factor of performance which was reflected in the$y$band activities of the right prefrontal cortex. The findings indicate the potential of cognitive processes in detecting the effect of machine reliability. The detection could pave the way for designing adaptive BMI to balance the machine reliability and the CWL.

The Underwater sensor network (UWSN), also known as Marine Sensor Network (MSN), is gaining increasing attention due to its applications in the monitoring of the marine environment and assisting Marine Intelligent Transportation Systems (MITS). Such systems provide in-vehicle assistance services (i.e., traffic monitoring and driver alerts) by gathering transportation and environmental information. Though very promising, there are several barriers to developing energy-efficient communication protocols for heterogeneous MSN, including selecting optimal routing paths twinned with the lifetime of these sensor nodes along the path, which are restricted due to the limited energy storage capacity. Hereby, the selection of an optimal route path also necessitates harvesting and management of the sensor nodes’ energy. To facilitate this, the current work presents REER-H, a Reliable Energy Efficient Routing protocol with Harvesting for cluster-based MSN capable of multi-source energy harvesting and an incorporated energy management technique. Incorporating three separate layers of the protocol stack, namely, network, MAC, and physical layers, REER-H uses its proposed adaptive scheduling technique to support collision-free data transmission by assigning adaptive time slots based on demand and data load. Also, the proposed integrated energy harvesting and management solves the energy hole problem and enhances the overall network lifetime. In comparison to the existing cooperative and cluster-based energy-efficient routing protocols for underwater maritime communication, the simulated results using Network Simulator-3 (NS3) reveal that the proposed scheme remarkably enhances the overall network performance in terms of packet delivery ratio, throughput, lifetime energy consumption, and end-to-end delay for MSN.

Human-machine systems (HMS) need trustful cooperation between humans and machines for achieving a goal. Establishing such trust demands the machines’ adaptivity to the cognitive workload (CWL) of the humans. The CWL is conventionally measured as self-reported scores from a NASA-TLX questionnaire, susceptible to individual subjectivity. In contrast, logged brainwaves are useful for measuring the CWL objectively. However, there is a literature gap of mapping the brainwaves to a CWL factor - i.e., physical demand. As a feasibility, we thus proposed an activity ratio (AR) to measure the physical demand from the brainwaves. Statistical analyses indicated significant correlations between the AR and self-reported scores of the physical demand, compared to a well-known engagement ratio. This finding implied the feasibility of the AR to measure the physical demand.