Neural Networks For Human Recognition Research Articles

Using a convolutional neural network for human recognition in a staff dose management software for fluoroscopic interventional procedures.

Staff dose management is a continuing concern in fluoroscopically-guided interventional (FGI) procedures. Being unaware of radiation scatter levels can lead to unnecessarily high stochastic and deterministic risks due to the effects of absorbed dose by staff members. Our group has developed a scattered-radiation display system (SDS) capable of monitoring system parameters in real-time using a controller-area network (CAN) bus interface and displaying a color-coded mapping of the Compton-scatter distribution. This system additionally uses a time-of-flight depth sensing camera to track staff member positional information for dose rate updates. The current work capitalizes on our body tracking methodology to facilitate individualized dose recording via human recognition using 16-bit grayscale depth maps acquired using a Microsoft Kinect V2. Background features are removed from the images using a depth threshold technique and connected component analysis, which results in a body silhouette binary mask. The masks are then fed into a convolutional neural network (CNN) for identification of unique body shape features. The CNN was trained using 144 binary masks for each of four individuals (total of 576 images). Initial results indicate high-fidelity prediction (97.3% testing accuracy) from the CNN irrespective of obstructing objects (face masks and lead aprons). Body tracking is still maintained when protective attire is introduced, albeit with a slight increase in positional data error. Dose reports are then able to be produced which contain cumulative dose to each staff member at the eye lens level, waist level, and collar level. Individualized cumulative dose reporting through the use of a CNN in addition to real-time feedback in the clinic will lead to improved radiation dose management.

Comparison of particle swarm optimization variants with fuzzy dynamic parameter adaptation for modular granular neural networks for human recognition

Comparison of particle swarm optimization variants with fuzzy dynamic parameter adaptation for modular granular neural networks for human recognition

Attention-Based Convolutional Neural Network for Weakly Labeled Human Activities’ Recognition With Wearable Sensors

Unlike images or videos data which can be easily labeled by human being, sensor data annotation is a time-consuming process. However, traditional methods of human activity recognition require a large amount of such strictly labeled data for training classifiers. In this paper, we present an attention-based convolutional neural network for human recognition from weakly labeled data. The proposed attention model can focus on labeled activity among a long sequence of sensor data, and while filter out a large amount of background noise signals. In experiment on the weakly labeled dataset, we show that our attention model outperforms classical deep learning methods in accuracy. Besides, we determine the specific locations of the labeled activity in a long sequence of weakly labeled data by converting the compatibility score which is generated from attention model to compatibility density. Our method greatly facilitates the process of sensor data annotation, and makes data collection more easy.

A Grey Wolf Optimizer for Modular Granular Neural Networks for Human Recognition.

A grey wolf optimizer for modular neural network (MNN) with a granular approach is proposed. The proposed method performs optimal granulation of data and design of modular neural networks architectures to perform human recognition, and to prove its effectiveness benchmark databases of ear, iris, and face biometric measures are used to perform tests and comparisons against other works. The design of a modular granular neural network (MGNN) consists in finding optimal parameters of its architecture; these parameters are the number of subgranules, percentage of data for the training phase, learning algorithm, goal error, number of hidden layers, and their number of neurons. Nowadays, there is a great variety of approaches and new techniques within the evolutionary computing area, and these approaches and techniques have emerged to help find optimal solutions to problems or models and bioinspired algorithms are part of this area. In this work a grey wolf optimizer is proposed for the design of modular granular neural networks, and the results are compared against a genetic algorithm and a firefly algorithm in order to know which of these techniques provides better results when applied to human recognition.