Lightweight Deep Neural Network Embedded with Stochastic Variational Inference Loss Function for Fast Detection of Human Postures.

Fusing object detection techniques and stochastic variational inference, we proposed a new scheme for lightweight neural network models, which could simultaneously reduce model sizes and raise the inference speed. This technique was then applied in fast human posture identification. The integer-arithmetic-only algorithm and the feature pyramid network were adopted to reduce the computational complexity in training and to capture features of small objects, respectively. Features of sequential human motion frames (i.e., the centroid coordinates of bounding boxes) were extracted by the self-attention mechanism. With the techniques of Bayesian neural network and stochastic variational inference, human postures could be promptly classified by fast resolving of the Gaussian mixture model for human posture classification. The model took instant centroid features as inputs and indicated possible human postures in the probabilistic maps. Our model had better overall performance than the baseline model ResNet in mean average precision (32.5 vs. 34.6), inference speed (27 vs. 48 milliseconds), and model size (46.2 vs. 227.8 MB). The model could also alert a suspected human falling event about 0.66 s in advance.

Smart Fall Detection Framework Using Hybridized Video and Ultrasonic Sensors.

Fall accidents can cause severe impacts on the physical health and the quality of life of those who suffer limb diseases or injuries, the elderly, and their caregivers. Moreover, the later the accident is discovered, the lower the chance of recovery of the injured one. In order to detect accidents earlier, we propose a data-driven human fall detection framework. By combining the sensing mechanism of a commercialized webcam and an ultrasonic sensor array, we develop a probability model for automatic human fall monitoring. The webcam and ultrasonic array respectively collect the transverse and longitudinal time-series signals from a moving subject, and then these signals are assembled as a three-dimensional (3D) movement trajectory map. We also use two different detection-tracking algorithms for recognizing the tracked subjects. The mean height of the subjects is 164.2 ± 12 cm. Based on the data density functional theory (DDFT), we use the 3D motion data to estimate the cluster numbers and their cluster boundaries. We also employ the Gaussian mixture model as the DDFT kernel. Then, we utilize those features to build a probabilistic model of human falling. The model visually exhibits three possible states of human motions: normal motion, transition, and falling. The acceptable detection accuracy and the small model size reveals the feasibility of the proposed hybridized platform. The time from starting the alarm to an actual fall is on average about 0.7 s in our platform. The proposed sensing mechanisms offer 90% accuracy, 90% sensitivity, and 95% precision in the data validation. Then these vital results validate that the proposed framework has comparable performance to the contemporary methods.