## 3 Novel Human Machine Team Design
### VBSA Design  
A voice-based SA uses voice interaction as the primary communication mode, and communication with it occurs under the human-based task. The VBSA has been developed to follow major medically approved emergency protocols such as massive hemorrhage, airway blockage, respiration, circulation, hypothermia and eye injury (MARCHE) and airway, breathing, circulation (ABC) [43; 44]. This is done to train emergency care providers (ECPs). The developed assistant tracks the training progress of the ECP in real-time and helps the ECP to correct the errors committed, with predefined steps in addition to a timely warning. The SA's secondary role is to provide an ECP with the job details required and to maintain a record of the training results, including the total number of errors and frequent errors as well as the execution time. After reviewing 29 existing autonomous systems, the architecture of the VBSA has been developed with a combination of a natural language processor (NLP), a speech synthesizer (SS) and primary operation blocks (data pre-processor, scenario calculator, process monitor, result synthesizer and data post-processor). The developed SA technology is integrated into Amazon Web Services (AWS) with respect to language features and micro-services available. Ref. [8] states that Alexa Voice Services (AVS) uses both NLP and SS and can be accurately trained for different accents. The database was developed and stored in Amazon DynamoDB, and the primary operation blocks (scenario calculator, process monitor, result synthesizer) were developed in AWS Lambda. Users can access the VBSA using either the Echo smart speaker or the Amazon Alexa mobile application. In our process, the user's performance and the SA were tracked using a dashboard projected on a screen. The errors made during the communication, along with step numbers, were displayed on the dashboard for correction later. A detailed discussion of the tracking process has been discussed in our previous paper [8]. The detailed version of the VBSA architecture and development process has been inspired by our previous research on HMT [8; 45; 46; 47]. Figure 4 presents modes of operation of the VBSA, while Figure 5 presents the Unified Modeling Language (UML) diagram of the VBSA architecture depicting the AWS components that were used to develop the cloud-based IA. VBSA was developed as a cloud-based architecture using AWS because, according to [48], cloud architectures are easy to develop, scale, and deploy. However, this is at a tradeoff of significant delays in the system response based on network and communication protocols, which in turn may affect the performance of the VBSA. This VBSA is capable of eight modes of operations, as shown in Figure 4. In the user training phase, the VBSA briefs the trainee regarding the task procedure and proceeds to real-time monitoring of the training session with a recording option if necessary. In conjunction, the VBSA is also capable of providing the user with task updates and runs a scenario upon instruction that includes forecasting statistics. Indeed the VBSA can execute all the modes of operations highlighted in Figure 4 collectively with an option to disable either of them if needed. On the other hand, an observer uses a web-based dashboard [8], as shown in Figure 4, to monitor the training progress of all the trainees, whereas the administrative staff uses a web-based form as an interface to build new training scenarios. This HTML web-based interface plays a key role in collecting interface, error, and performance data from experiments, and it acts as repository for all participants data if observers need to return and see any data from past participants. Limitations of VBSA rendered by scripted vocabulary had an observable effect on an ECP's interaction with the SA in training that needs further investigation.