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).
Fall detection represents an important issue when dealing with Ambient Assisted Living for the elder. The vast majority of fall detection approaches have been developed for healthy and relatively young people. Moreover, plenty of these approaches make use of sensors placed on the hip. Considering the focused population of elderly people, there are clear differences and constraints. On the one hand, the patterns and times in the normal activities-and also the falls-are different from younger people: elders move slowly. On the second hand, solutions using uncomfortable sensory systems would be rejected by many candidates. In this research, one of the proposed solutions in the literature has been adapted to use a smartwatch on a wrist, solving some problems and modifying part of the algorithm. The experimentation includes a publicly available dataset. Results point to several enhancements in order to be adapted to the focused population.
This study focuses on the performance of a fall detection method using data coming from real falls performed by relatively young people and the application of this technique in the case of an elder person. Although the vast majority of studies concerning fall detection place the sensory on the waist, in this research the wearable device must be placed on the wrist because it's usability. A first pre-processing stage is carried out as stated in [1,17]; this stage detects the most relevant points to label. This study analyzes the suitability of different models in solving this classification problem: a feed-forward Neural Network and a rule based system generated with the C5.0 algorithm. A discussion about the results and the deployment issues is included
This paper focuses on a novel Electroencephalography (EEG) based one dimensional convolution neural network (CNN) to classify emotional states. Differential entropy (DE) is considered as a feature extraction method after pre-processing phase. Besides, feature smoothing-linear dynamic system (LDS) and min-max normalization are used on the DE features before feeding into deep model. We design a one dimensional CNN model with six convolutions and fully connected blocks which gives outstanding performance in six combinations of SEED dataset. The model presented average accuracy of 98.55% and 95.91% in binary and single sessions respectively by using 10 fold cross validation. The proposed results fully demonstrate that our method achieves out of the best performance compare with other EEG based emotion recognition systems. Therefore, this model can be applied to other emotional datasets as a classifier and health care decision support system (DSS) as well.
Fall detection for elder people is a very challenging task that has not been solved yet. In this population, fall detection devices must not introduce extra constraints, like carrying with a belt fixed device or a mobile phone. This paper describes one fall detection approach accomplishing with these constraints. Afterwards, a discussion on some model decisions concerning the computational restrictions according to where the data processing and classification are performed is also included.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.