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Advanced medical technology has reduced the mortality rate among traumatic brain injury (TBI) patients. This, however, has led to an increasing number of surviving patients with a major disability. As a consequence, these patients need attentive care which becomes an important issue for the society, particularly family members. Thus, this paper aims to review some of the salient roles, challenges and needs of the family caregivers in caring or nursing for their family members diagnosed with TBI.

An inclusive search of the literature was undertaken to identify the family roles, challenges and needs in supporting and nursing TBI patients.

Previous studies have shown that the family needs to address two important aspects of taking care of TBI patients, which involve emotional and physical affairs. Hence, it is essential for the family members to have adequate information on healing treatment, nursing and care methods, financial support, support groups, managing self-care and, more importantly, emotional and social support.

This paper is not currently under consideration, in press or published elsewhere. In Malaysian culture, nursing disabled patients have always been a family responsibility. The role of nursing the patients has been done domestically and is considered a private affair. In order to execute the role, some put the patient needs as their priority and leave aside their needs and matters.

Many popular simultaneous localization and mapping (SLAM) techniques have low accuracy, especially when localizing environments containing dynamically moving objects since their presence can potentially cause inaccurate data associations. To address this issue, the proposed FADM-SLAM system aims to improve the accuracy of SLAM techniques in environments containing dynamically moving objects. It uses a pipeline of feature-based approaches accompanied by sparse optical flow and multi-view geometry as constraints to achieve this goal.

FADM-SLAM, which works with monocular, stereo and RGB-D sensors, combines an instance segmentation network incorporating an intelligent motion detection strategy (iM) with an optical flow technique to improve location accuracy. The proposed AS-SLAM system comprises four principal modules, which are the optical flow mask and iM, the ego motion estimation, dynamic point detection and the feature-based extraction framework.

Experiment results using the publicly available RGBD-Bonn data set indicate that FADM-SLAM outperforms established visual SLAM systems in highly dynamic conditions.

In summary, the first module generates the indication of dynamic objects by using the optical flow and iM with geometric-wise segmentation, which is then used by the second module to compute the starting point of a posture. The third module, meanwhile, first searches for the dynamic feature points in the environment, and second, eliminates them from further processing. An algorithm based on epipolar constraints is implemented to do this. In this way, only the static feature points are retained, which are then fed to the fourth module for extracting important features.