The Requirements elicitation involves knowledge intensive and collaborative activities. Requirements engineering research has proposed a range of knowledge elicitation and requirements gathering techniques, few of which apply specific strategies for eliciting tacit knowledge from stakeholders. The difficulty of taking the deliberate advantage of important tacit knowledge of stakeholders is one of the eminent problems in requirements elicitation. This paper proposes a model to define and elicit the tacit knowledge that has been generated during the requirements elicitation process. The model is based on adopting and extending the rationale model for requirements rationale knowledge elicitation in the requirements elicitation process context. Moreover, this paper presents a representation code to express the tacit knowledge in this context. Finally, to evaluate the feasibility of the model, a survey instrument was applied on domain experts to gather their opinion regarding the ability of the proposed model in facilitating tacit knowledge elicitation. In addition, controlled experiment used to evaluate the proposed model. To explore the model, a post-questionnaire was used to identify participants' feedback. The findings of the evaluation methodologies that were used showed that the proposed model can facilitate tacit knowledge elicitation in the requirements elicitation context. Also, the proposed model has a significant effect on improving the requirements elicitation. INDEX TERMS Requirements engineering, requirements elicitation process, rationale-based model, tacit knowledge.
<abstract> <p>Rehabilitation engineering is playing a more vital role in the field of healthcare for humanity. It is providing many assistive devices to diplegia patients (The patients whose conditions are weak in terms of muscle mobility on both sides of the body and their paralyzing effects are high either in the arms or in the legs). Therefore, in order to rehabilitate such types of patients, an intelligent healthcare system is proposed in this research. The electric sticks and chairs are also a type of this system which was used previously to facilitate the diplegia patients. It is worth noting that a voice recognition system along with wireless control feature has been integrated intelligently in the proposed healthcare system in order to replace the common and conventional assistive tools for diplegia patients. These features will make the proposed system more user friendly, convenient and comfortable. The voice recognition system has been used for movements of system in any desired direction along with the ultrasonic sensor and light detecting technology. These sensors detect the obstacles and low light environment intelligently during the movement of the wheelchair and then take the necessary actions accordingly.</p> </abstract>
<abstract> <p>By upgrading medical facilities with internet of things (IoT), early researchers have produced positive results. Isolated COVID-19 patients in remote areas, where patients are not able to approach a doctor for the detection of routine parameters, are now getting feasible. The doctors and families will be able to track the patient's health outside of the hospital utilizing sensors, cloud storage, data transmission, and IoT mobile applications. The main purpose of the proposed research-based project is to develop a remote health surveillance system utilizing local sensors. The proposed system also provides GSM messages, live location, and send email to the doctor during emergency conditions. Based on artificial intelligence (AI), a feedback action is taken in case of the absence of a doctor, where an automatic injection system injects the dose into the patient's body during an emergency. The significant parameters catering to our project are limited to ECG monitoring, SpO2 level detection, body temperature, and pulse rate measurement. Some parameters will be remotely shown to the doctor via the Blynk application in case of any abrupt change in the parameters. If the doctor is not available, the IoT system will send the location to the emergency team and relatives. In severe conditions, an AI-based system will analyze the parameters and injects the dose.</p> </abstract>
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