Simultaneous Indoor Tracking and Activity Recognition Using Pyroelectric Infrared Sensors.

Indoor human tracking and activity recognition are fundamental yet coherent problems for ambient assistive living. In this paper, we propose a method to address these two critical issues simultaneously. We construct a wireless sensor network (WSN), and the sensor nodes within WSN consist of pyroelectric infrared (PIR) sensor arrays. To capture the tempo-spatial information of the human target, the field of view (FOV) of each PIR sensor is modulated by masks. A modified partial filter algorithm is utilized to decode the location of the human target. To exploit the synergy between the location and activity, we design a two-layer random forest (RF) classifier. The initial activity recognition result of the first layer is refined by the second layer RF by incorporating various effective features. We conducted experiments in a mock apartment. The mean localization error of our system is about 0.85 m. For five kinds of daily activities, the mean accuracy for 10-fold cross-validation is above 92%. The encouraging results indicate the effectiveness of our system.

Abnormal Activity Detection Using Pyroelectric Infrared Sensors.

Healthy aging is one of the most important social issues. In this paper, we propose a method for abnormal activity detection without any manual labeling of the training samples. By leveraging the Field of View (FOV) modulation, the spatio-temporal characteristic of human activity is encoded into low-dimension data stream generated by the ceiling-mounted Pyroelectric Infrared (PIR) sensors. The similarity between normal training samples are measured based on Kullback-Leibler (KL) divergence of each pair of them. The natural clustering of normal activities is discovered through a self-tuning spectral clustering algorithm with unsupervised model selection on the eigenvectors of a modified similarity matrix. Hidden Markov Models (HMMs) are employed to model each cluster of normal activities and form feature vectors. One-Class Support Vector Machines (OSVMs) are used to profile the normal activities and detect abnormal activities. To validate the efficacy of our method, we conducted experiments in real indoor environments. The encouraging results show that our method is able to detect abnormal activities given only the normal training samples, which aims to avoid the laborious and inconsistent data labeling process.

Multicentric intelligent cardiotocography signal interpretation using deep semi-supervised domain adaptation via minimax entropy and domain invariance.

BACKGROUND AND OBJECTIVE: Obstetricians use Cardiotocography (CTG), which is the continuous recording of fetal heart rate and uterine contraction, to assess fetal health status. Deep learning models for intelligent fetal monitoring trained on extensively labeled and identically distributed CTG records have achieved excellent performance. However, creation of these training sets requires excessive time and specialist labor for the collection and annotation of CTG signals. Previous research has demonstrated that multicenter studies can improve model performance. However, models trained on cross-domain data may not generalize well to target domains due to variance in distribution among datasets. Hence, this paper conducted a multicenter study with Deep Semi-Supervised Domain Adaptation (DSSDA) for intelligent interpretation of antenatal CTG signals. This approach helps to align cross-domain distribution and transfer knowledge from a label-rich source domain to a label-scarce target domain. METHODS: We proposed a DSSDA framework that integrated Minimax Entropy and Domain Invariance (DSSDA-MMEDI) to reduce inter-domain gaps and thus achieve domain invariance. The networks were developed using GoogLeNet to extract features from CTG signals, with fully connected, softmax layers for classification. We designed a Dynamic Gradient-driven strategy based on Mutual Information (DGMI) to unify the losses from Minimax Entropy (MME), Domain Invariance (DI), and supervised cross-entropy during iterative learning. RESULTS: We validated our DSSDA model on two datasets collected from collaborating healthcare institutions and mobile terminals as the source and target domains, which contained 16,355 and 3,351 CTG signals, respectively. Compared to the results achieved with deep learning networks without DSSDA, DSSDA-MMEDI significantly improved sensitivity and F1-score by over 6%. DSSDA-MMEDI also outperformed other state-of-the-art DSSDA approaches for CTG signal interpretation. Ablation studies were performed to determine the unique contribution of each component in our DSSDA mechanism. CONCLUSIONS: The proposed DSSDA-MMEDI is feasible and effective for alignment of cross-domain data and automated interpretation of multicentric antenatal CTG signals with minimal annotation cost.

The effect of chinese herbal medicine Banxia Baizhu Tianma Decoction for the treatment of vertebrobasilar insufficiency vertigo: A systematic review and meta-analysis of randomized controlled trials.

OBJECTIVES: Banxia Baizhu Tianma Decoction (BBTD) is widely used to treat vertebrobasilar insufficiency vertigo (VBIV) in China, but its efficacy remains largely unexplored. We systemically summarized relevant evidence from randomized controlled trials (RCTs) to assess the therapeutic effect of BBTD. METHODS: Seven electronic databases were searched for relevant electronic studies published before July 2016. We evaluated RCTs that compared BBTD, anti-vertigo drugs and a combination of BBTD and anti-vertigo drugs. We performed a meta-analysis in accordance with the Cochrane Collaboration criteria. The outcomes were clinical efficacy (CE), blood flow velocity of the vertebrobasilar artery by transcranial Doppler (TCD), and adverse effects. RESULTS: Twenty-seven studies with a total of 2796 patients were identified. Compared with anti-vertigo drugs, BBTD showed slight effects on CE (n=350; RR, 1.09; 95% CI, 1.01-1.18; p=0.03; I2=0%); however, BBTD plus anti-vertigo drugs (BPAD) significantly improved the clinical efficacy (n=2446; RR, 1.20; 95% CI, 1.16-1.24; p<0.00001; I2=0%) and accelerated the blood flow velocity of the left vertebral artery (LVA) (n=1444; WMD, 5.21cm/s; 95% CI, 3.72-6.70cm/s; p<0.00001; I2=91%), the blood flow velocity of the right vertebral artery (RVA) (n=1444; WMD, 5.45cm/s; 95% CI, 4.02-6.88cm/s; p<0.00001; I2=89%), and the blood flow velocity of the basilar artery (BA) (n=1872; WMD, 5.20cm/s; 95% CI, 3.86-6.54cm/s; p<0.00001; I2=90%). Adverse effects were mentioned in six studies. CONCLUSIONS: The current evidence indicates that BPAD is effective for the treatment of VBIV, but the efficacy and safety of BBTD is uncertain because of the limited number of trials and low methodological quality. Hence, high-quality and adequately powered RCTs are warranted.