Falling is among the most damaging event elderly people may experience. With the ever-growing aging population, there is an urgent need for the development of fall detection systems. Thanks to the rapid development of sensor networks and the Internet of Things (IoT), human-computer interaction using sensor fusion has been regarded as an effective method to address the problem of fall detection. In this paper, we provide a literature survey of work conducted on elderly fall detection using sensor networks and IoT. Although there are various existing studies which focus on the fall detection with individual sensors, such as wearable ones and depth cameras, the performance of these systems are still not satisfying as they suffer mostly from high false alarms. Literature shows that fusing the signals of different sensors could result in higher accuracy and lower false alarms, while improving the robustness of such systems. We approach this survey from different perspectives, including data collection, data transmission, sensor fusion, data analysis, security, and privacy. We also review the benchmark data sets available that have been used to quantify the performance of the proposed methods. The survey is meant to provide researchers in the field of elderly fall detection using sensor networks with a summary of progress achieved up to date and to identify areas where further effort would be beneficial.

Falls have been widely recognized as one of the most dangerous incidents for the elderly and other people with mobility limitations. This problem has attracted wide scientific interest, which has led to several investigations based on non-vision wearable sensors and static cameras. We investigate the challenge of fall detection and recognition using egocentric wearable cameras, which besides portability and affordability, capture visual information that can be further leveraged for a broad set of lifelogging applications. In this work, five volunteers were equipped with two cameras each, one attached to the neck and the other to the waist. They were asked to simulate four kinds of falls and nine types of non-falls. The newly collected dataset consists of 5858 short video clips, which we make available online. The proposed approach is a late fusion methodology that combines spatial and motion descriptors along with deep features extracted by a pre-trained convolutional neural network. For the spatial and deep features, we consider the similarity of such features between frames in regular intervals of a given time window. In this way, it is the transition between such frames that are encoded in our approach, while the actual scene content does not play a role. We design several experiments to investigate the best camera location and performance for indoor and outdoor activities and employ leave-one-subject-out cross validation to test the generalization ability of our approach. For the fall detection (i.e. two-class) problem, our approach achieves 91.8% accuracy, 93.6% sensitivity and 89.2% specificity.

Pairwise structure alignment commonly uses root mean square deviation (RMSD) to measure the structural similarity, and methods for optimizing RMSD are well established. We extend RMSD to weighted RMSD for multiple structures. By using multiplicative weights, we show that weighted RMSD for all pairs is the same as weighted RMSD to an average of the structures. Thus, using RMSD or weighted RMSD implies that the average is a consensus structure. Although we show that in general, the two tasks of finding the optimal translations and rotations for minimizing weighted RMSD cannot be separated for multiple structures like they can for pairs, an inherent difficulty and a fact ignored by previous work, we develop a near-linear iterative algorithm to converge weighted RMSD to a local minimum. 10,000 experiments of gapped alignment done on each of 23 protein families from HOMSTRAD (where each structure starts with a random translation and rotation) converge rapidly to the same minimum. Finally we propose a heuristic method to iteratively remove the effect of outliers and find well-aligned positions that determine the structural conserved region by modeling B-factors and deviations from the average positions as weights and iteratively assigning higher weights to better aligned atoms.