This article concerns the issue of how to combine the multidomainradar information, including range–Doppler, time–Doppler, and time–range, for human activity recognition. Specifically, to fully make use of radar information, instead of using a single-domain spectrum as inputs, a novel hybrid neural network model is developed for exploring multidomain fusion of radar information. In doing this, three kinds of 2-D domain spectra are used in a fashion of supplementing each other with a hybrid framework that combines three models: 1-D convolution neural network, recurrent neural network, and 2-D convolution network. It is advantageous to use such a hybrid model to capture much rich features through multidomain feature fusion, so as to improve the accuracy of human activity recognition effectively. Experimental results validate the proposed method.

Objective : Non-invasive foetal electrocardiography (NI-FECG) has the potential to provide more additional clinical information for detecting and diagnosing fetal diseases. We propose and demonstrate a deep learning approach for fetal QRS complex detection from raw NI-FECG signals by using a convolutional neural network (CNN) model. The main objective is to investigate whether reliable fetal QRS complex detection performance can still be obtained from features of single-channel NI-FECG signals, without canceling maternal ECG (MECG) signals. Approach : A deep learning method is proposed for recognizing fetal QRS complexes. Firstly, we collect data from set-a of the PhysioNet/computing in Cardiology Challenge database. The sample entropy method is used for signal quality assessment. Part of the bad quality signals is excluded in the further analysis. Secondly, in the proposed method, the features of raw NI-FECG signals are normalized before they are fed to a CNN classifier to perform fetal QRS complex detection. We use precision, recall, F-measure and accuracy as the evaluation metrics to assess the performance of fetal QRS complex detection. Main results : The proposed deep learning method can achieve relatively high precision (75.33%), recall (80.54%), and F-measure scores (77.85%) compared with three other well-known pattern classification methods, namely KNN, naive Bayes and SVM. Significance : the proposed deep learning method can attain reliable fetal QRS complex detection performance from the raw NI-FECG signals without canceling MECG signals. In addition, the influence of different activation functions and signal quality assessment on classification performance are evaluated, and results show that Relu outperforms the Sigmoid and Tanh on this particular task, and better classification performance is obtained with the signal quality assessment step in this study.

This article proposes a two-branch learning model, namely, the joint global–local network, for human pose estimation (HPE) using millimeter wave radar. The aim of this work is to remediate the ill-posed problems in HPE arising from using the destructive observations with superimposed reflection signals. In the developed two-branch learning model, the global branch takes use of the superimposed signals from the whole human body to reconstruct the coarse pose estimation from a global perspective, and the local branch is responsible for fining the pose estimations with the decomposed signals from individual body parts in a complementary way. In doing this, two branch learning processes will be coordinated with the followed attention-based fusion module in terms of the local and global consistency. It is remarkable that the learning driven by the decomposed signals is motivated by exploiting the spatial-temporal evolution patterns of individual body parts for inferring the corresponding movements, which plays a crucial yet complementary role in the collaboration with the learning driven by the superimposed signals. With the two-branch learning architecture, the proposed method is advantageous in incorporating the local motion constraints from individual body parts into the coarse global estimation from the whole human body, which contributes to reconstructing plausible yet accurate pose estimations with the local and global kinematic consistency. Extensive experiments are presented to demonstrate the effectiveness of the proposed method.

Pervasive healthcare is one of the most important applications of the Internet of Things (IoT). As part of the IoT, the wireless sensor networks (WSNs) are responsible for sensing the abnormal behavior of the elderly or patients. In this article, we design and implement a fall detection system called SensFall. With the resource restricted sensor nodes, it is vital to find an efficient feature to describe the scene. Based on the optical flow analysis, it can be observed that the thermal energy variation of each sub-region of the monitored region is a salient spatio-temporal feature that characterizes the fall. The main contribution of this study is to develop a feature-specific sensing system to capture this feature so as to detect the occurrence of a fall. In our system, the three-dimensional (3D) object space is segmented into some distinct discrete sampling cells, and pyroelectric infrared (PIR) sensors are employed to detect the variance of the thermal flux within these cells. The hierarchical classifier (two-layer HMMs) is proposed to model the time-varying PIR signal and classify different human activities. We use self-developed PIR sensor nodes mounted on the ceiling and construct a WSN based on ZigBee (802.15.4) protocol. We conduct experiments in a real office environment. The volunteers simulate several kinds of activities including falling, sitting down, standing up from a chair, walking, and jogging. Encouraging experimental results confirm the efficacy of our system.