Fall detection is a very important challenge that affects both elderly people and the carers. Improvements in fall detection would reduce the aid response time. This research focuses on a method for fall detection with a sensor placed on the wrist. Falls are detected using a published threshold-based solution, although a study on threshold tuning has been carried out. The feature extraction is extended in order to balance the dataset for the minority class. Alternative models have been analyzed to reduce the computational constraints so the solution can be embedded in smart-phones or smart wristbands. Several published datasets have been used in the Materials and Methods section. Although these datasets do not include data from real falls of elderly people, a complete comparison study of fall-related datasets shows statistical differences between the simulated falls and real falls from participants suffering from impairment diseases. Given the obtained results, the rule-based systems represent a promising research line as they perform similarly to neural networks, but with a reduced computational cost. Furthermore, support vector machines performed with a high specificity. However, further research to validate the proposal in real on-line scenarios is needed. Furthermore, a slight improvement should be made to reduce the number of false alarms.

We propose a stochastic programming model as a solution for optimizing the problem of locating and allocating medical supplies used in disaster management. To prepare for natural disasters, we developed a stochastic optimization approach to select the storage location of medical supplies and determine their inventory levels and to allocate each type of medical supply. Our model also captures disaster elaborations and possible effects of disasters by using a new classification for major earthquake scenarios. We present a case study for our model for the preparedness phase. As a case study, we applied our model to earthquake planning in Adana, Turkey. The experimental evaluations showed that the model is robust and effective. (Disaster Med Public Health Preparedness. 2017;11:747–755)

Purpose: Minimally-invasive surgery (MIS) reduces the trauma compared to open surgery but is challenging for endophytic lesion localisation. Augmented reality (AR) is a promising assistance, which superimposes a preoperative 3D lesion model onto the MIS images. It requires solving the difficult problem of 3D model to MIS image registration. We propose MIS-NeRF, a neural radiance field (NeRF) which provides high-fidelity intraoperative 3D reconstruction, used to bootstrap iterative closest point (ICP) registration. Methods: Existing NeRF methods break down in MIS because of the moving light source and specular highlights. The proposed MIS-NeRF is adapted to these conditions. First, it incorporates the camera centre as an additional input to the radiance function, which allows MIS-NeRF to handle the moving light source. Second, it uses a modified volume rendering which handles specular highlights. Third, it uses a regularised compound loss to enhance surface reconstruction. Results: MIS-NeRF was tested on three synthetic datasets and retrospectively on four laparoscopic surgeries. It successfully reconstructed high-fidelity liver and uterus surfaces, reducing common artefacts including high-frequency noise and bumps caused by specular highlights. ICP registration achieved faithful alignment between the preoperative and intraoperative 3D models, with an average error of 3.25 mm, outperforming the second-best method by a $$15\%$$ 15 % margin. Conclusion: MIS-NeRF improves AR-based lesion localisation by facilitating accurate 3D model registration to multiple MIS images.