AutoFi: Towards Automatic WiFi Human Sensing via Geometric Self-Supervised Learning

WiFi sensing technology has shown superiority in smart homes among various sensors for its cost-effective and privacy-preserving merits. It is empowered by Channel State Information (CSI) extracted from WiFi signals and advanced machine learning models to analyze motion patterns in CSI. Many learning-based models have been proposed for kinds of applications, but they severely suffer from environmental dependency. Though domain adaptation methods have been proposed to tackle this issue, it is not practical to collect high-quality, well-segmented and balanced CSI samples in a new environment for adaptation algorithms, but randomly-captured CSI samples can be easily collected. {\color{black}In this paper, we firstly explore how to learn a robust model from these low-quality CSI samples, and propose AutoFi, an annotation-efficient WiFi sensing model based on a novel geometric self-supervised learning algorithm.} The AutoFi fully utilizes unlabeled low-quality CSI samples that are captured randomly, and then transfers the knowledge to specific tasks defined by users, which is the first work to achieve cross-task transfer in WiFi sensing. The AutoFi is implemented on a pair of Atheros WiFi APs for evaluation. The AutoFi transfers knowledge from randomly collected CSI samples into human gait recognition and achieves state-of-the-art performance. Furthermore, we simulate cross-task transfer using public datasets to further demonstrate its capacity for cross-task learning. For the UT-HAR and Widar datasets, the AutoFi achieves satisfactory results on activity recognition and gesture recognition without any prior training. We believe that the AutoFi takes a huge step toward automatic WiFi sensing without any developer engagement.Comment: The paper has been accepted by IEEE Internet of Things Journa

SenseFi: A library and benchmark on deep-learning-empowered WiFi human sensing

Over the recent years, WiFi sensing has been rapidly developed for privacy-preserving, ubiquitous human-sensing applications, enabled by signal processing and deep-learning methods. However, a comprehensive public benchmark for deep learning in WiFi sensing, similar to that available for visual recognition, does not yet exist. In this article, we review recent progress in topics ranging from WiFi hardware platforms to sensing algorithms and propose a new library with a comprehensive benchmark, SenseFi. On this basis, we evaluate various deep-learning models in terms of distinct sensing tasks, WiFi platforms, recognition accuracy, model size, computational complexity, and feature transferability. Extensive experiments are performed whose results provide valuable insights into model design, learning strategy, and training techniques for real-world applications. In summary, SenseFi is a comprehensive benchmark with an open-source library for deep learning in WiFi sensing research that offers researchers a convenient tool to validate learning-based WiFi-sensing methods on multiple datasets and platforms.Nanyang Technological UniversityPublished versionThis research is supported by NTU Presidential Postdoctoral Fellowship, ‘‘Adaptive Multi-modal Learning for Robust Sensing and Recognition in Smart Cities’’ project fund (020977-00001), at the Nanyang Technological University, Singapore

Panax Quinquefolium Saponins Attenuate Myocardial Dysfunction Induced by Chronic Ischemia

Background/Aims: Previous studies in rat models of myocardial ischemia showed that Panax quinquefolium saponins (PQS) could attenuate ischemic/reperfusion injury, increase vessel density and improve cardiac function. In the current study, we examined whether PQS could attenuate myocardial dysfunction in a swine model of chronic myocardial ischemia (CMI). Methods: CMI was established in Bama mini-pigs by placing amroid constrictor on the left anterior descending artery (LAD). Starting from 2 months after the surgery, pigs randomly received PQS (30 mg/kg/day), atorvastatin (1.5 mg/kg/day), or no drug for one month (n=6). A group of pigs receiving sham surgery was included as an additional control. Glucose utilization was assessed with positron emission tomography-computer tomography (PET-CT). Cardiac function was assessed with echocardiography. Myocyte size, nuclear density, and arteriolar density were examined in tissue section obtained from the ischemia area. Potential molecular targets of PQS were identified using proteomic analysis with isobaric tags for relative and absolute quantitation (iTARQ) and network pharmacology. Results: In comparison to the sham controls, pigs implanted with ameroid constrictor had decreased ventricular wall motion, left ventricular ejection fraction (LVEF), and glucose utilization. PQS significantly increased cardiac function and glucose utilization. Arteriole density and myocyte nuclear density were increased. Myocyte diameter was decreased. PQS also attenuated the CMI-induced change of protein expression profile. The effects of atorvastatin were generally similar to that of PQS. However, PQS attenuated the reduction of left ventricular systolic WT induced by CMI more robustly than atorvastatin. Conclusion: The results from the current study supports the use of PQS in patients with coronary artery disease

Development of WiFi based human behavior detection in indoor environment

With the aging of population, fall is becoming a critical risk to this society, as it endangers elder people greatly. As the current technologies are no longer able to meet people’s increasing demand for convenience, privacy, accuracy and safety. A new solution of human behavior detection system need to be developed. This report is about the new solution: WiFi based human behavior detection for fall detection. It introduces about the theories about these technologies, the literature review of research and study done by previous researcher in this field, how the project of development of WiFi based human behavior detection is carried. This report gives detail about the objective and scope of this project and different components that constitutes this system. The first stage of this project is report reading, data collection and data analysis. The second stage is the development of the algorithm, which involves machine learning which could learn the pattern of different waveform, thus being able to decide whether there is any falling event. The third stage is development of the corresponding android application, which involves UDP connection protocol. The last stage is the translation of the algorithm into common used programming language. Finally, the objective of project is fulfilled with the system successfully designed, which is able do learn the waveform pattern and make decision from the structure.Bachelor of Engineerin

Robust CSI based smart human sensing

This thesis investigates the current challenges of CSI-based smart human sensing. The author has proposed several new techniques to improve the robustness and training efficiency. Finally, the future research directions in this field are also discussed.Doctor of Philosoph

Efficacy and Safety of Zhenyuan Capsule for Coronary Heart Disease with Abnormal Glucose and Lipid Metabolism: Study Protocol for a Randomized, Double-Blind, Parallel-Controlled, Multicenter Clinical Trial

Background. Coronary heart disease (CHD) and abnormal glucose and lipid metabolism are closely associated and generally coexist. The Qi and Yin deficiency syndrome is a common disease pattern encountered in traditional Chinese medicine. We designed a protocol to determine the effectiveness and safety of Zhenyuan capsules for CHD with abnormal glucose and lipid metabolism. Methods. This multicenter, randomized, double-blind, parallel-controlled trial was designed in accordance with the CONSORT. We will recruit 200 eligible male patients aged 45–75 years from three participating centers and randomly assign them to treatment and control groups (1 : 1). The primary indicators are glycosylated hemoglobin, fasting blood glucose, 2-hour postprandial blood glucose, and triglyceride levels. The secondary indicators are the Seattle Angina Questionnaire, TCM symptom indicators, ultrasonic cardiography finding, coagulation indicator, and P-selectin level. Measurements will be performed at baseline (T0), the end of the run-in period (T1), and weeks 4 (T2), 8 (T3), and 12 (T4) of the treatment period. Adverse events will be monitored during the trial. Discussion. This study aims to evaluate the efficacy and safety of Zhenyuan capsules in patients with CHD and abnormal glucose and lipid metabolism. The results will provide critical evidence of the usefulness of the Chinese herbal medicine for CHD with abnormal glucose and lipid metabolism. Trial Registration. This trial is registered with the Chinese Clinical Trials Registry, with identifier number ChiCTR-TRC-14004639, May 4, 2014