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The paper aims to develop a novel method for the classification of different physical activities of a human being, using fabric sensors. This method focuses mainly on classifying the physical activity between normal action and violent attack on a victim and verifies its validity.

The system is realized as a protective jacket that can be worn by the subject. Stretch sensors, pressure sensors and a 9 degree of freedom accelerometer are strategically woven on the jacket. The jacket has an internal bus system made of conductive fabric that connects the sensors to the Flora chip, which acts as the data acquisition unit for the data generated. Different activities such as still, standing up, walking, twist-jump-turn, dancing and violent action are performed. The jacket in this study is worn by a healthy subject. The main phases which describe the activity recognition method undertaken in this study are the placement of sensors, pre-processing of data and deploying machine learning models for classification.

The effectiveness of the method was validated in a controlled environment. Certain challenges are also faced in building the experimental setup for the collection of data from the hardware. The most tedious challenge is to collect the data without noise and error, created by voltage fluctuations when stretched. The results show that the support vector machine classifier can classify different activities and is able to differentiate normal action and violent attacks with an accuracy of 98.8%, which is superior to other methods and algorithms.

This study leads to an understanding of human physical movement under violent activity. The results show that data compared with normal physical motion, which includes even a form of dance is quite different from the data collected during violent physical motion. This jacket construction with woven sensors can capture every dimension of the physical motion adding features to the data on which the machine learning model will be built.

Unlike other studies, where sensors are placed on isolated parts of the body, in this study, the fabric sensors are woven into the fabric itself to collect the data and to achieve maximum accuracy instead of using isolated wearable sensors. This method, together with a fabric pressure and stretch sensors, can provide key data and accurate feedback information when the victim is being attacked or is in a normal state of action.

The oil and gas construction projects are affected negatively by the drop in oil price in recent years. Thus, most engineering, procurement and construction (EPC) companies are opting to optimize the project mainly to mitigate the source of risks in construction to achieve the project expectation. Risk factors cause a threat to the project objectives regarding time, cost and quality. It is additionally a vital component in deviating from the client's expectation of productivity, safety and standards. This research aims to investigate the causes of risk in the oil and gas construction projects in Yemen.

A comprehensive literature review from various sources including books, conference proceedings, the Internet project management journals and oil and gas industry journals was conducted to achieve the objectives of this study. This initial work was predicated strictly on a literature review and the judgments of experts to develop the risk factor framework for the oil and gas construction projects in Yemen.

The authors found a few studies related to risk factors in oil and gas construction projects and shared a similar view about general construction projects. However, only a fraction of the factors accepted have included the variances of other studies on a regional basis or specific countries, such as the Yemen situation, due to the differences between the general construction industry and oil and gas industry. Moreover, the factors of these attributes were still accepted due to their applicability to the oil and gas industry, and no significant variances existed between countries. Research has indicated that 51 critical factors cause risks in the oil and gas construction projects in Yemen. Such risk factors can be divided into two major groups: (1) internal risk factors, including seven critical sources of risks, namely client, contractor, consultant, feasibility study and design, tendering and contract, resources and material supply and project management; and (2) external risk factors, including six sources of critical risk factors, namely national economic, political risk, local people, environment and safety, security risk and force-majeure-related risk factors. A risk factor framework was developed to identify the critical risk factors in the oil and gas construction projects in Yemen.

This research was limited to the oil and gas construction projects.

Practically, this study highlights the risk factors that cause a negative effect on the success of oil and gas construction projects in Yemen. The identification of these factors is the first step in the risk management process to develop strategic responses for risks and enhance the chances of project success.

The identification of risks factors that cause the failure of construction projects helps develop response strategies for these risks, thereby increasing the chances of project success reflected in the oil and gas sector, which is a main tributary of the national economy in developing countries.

This research is the pioneer for future investigations into this vital economic sector. Given the lack of resources and studies in the field of construction projects for the Yemeni oil and gas sector, the Yemeni government, oil companies and researchers in this field are expected to benefit from the results of this study. The critical risk factors specific to the oil and gas construction projects in Yemen should be further investigated with focus only on Yemen and its oil and gas industry players.