Zhinan Li

eCushion: An eTextile Device for Sitting Posture Monitoring

Sitting posture analysis is critical for daily applications in biomedical, education and healthcare fields. However, it remains unclear how to monitor sitting posture economically and comfortably. To this end, we presented an eTextile device, called eCushion, in this paper, which can analyze the sitting posture of human being accurately and non-invasively. First, we discussed the implementation of eCushion and design challenges of sensing data, such as scale, offset, rotation and crosstalk. Then, several effective techniques have been proposed to improve the recognition rate of sitting posture. Our experimental results show that the recognition rate of our eCushion system could achieve 92% for object-oriented cases and 79% for general cases.

System light-loading technology for mHealth: Manifold-learning-based medical data cleansing and clinical trials in WE-CARE Project.

Cardiovascular disease (CVD) is a major issue to public health. It contributes 41% to the Chinese death rate each year. This huge loss encouraged us to develop a Wearable Efficient teleCARdiology systEm (WE-CARE) for early warning and prevention of CVD risks in real time. WE-CARE is expected to work 24/7 online for mobile health (mHealth) applications. Unfortunately, this purpose is often disrupted in system experiments and clinical trials, even if related enabling technologies work properly. This phenomenon is rooted in the overload issue of complex Electrocardiogram (ECG) data in terms of system integration. In this study, our main objective is to get a system light-loading technology to enable mHealth with a benchmarked ECG anomaly recognition rate. To achieve this objective, we propose an approach to purify clinical features from ECG raw data based on manifold learning, called the Manifold-based ECG-feature Purification algorithm. Our clinical trials verify that our proposal can detect anomalies with a recognition rate of up to 94% which is highly valuable in daily public health-risk alert applications based on clinical criteria. Most importantly, the experiment results demonstrate that the WE-CARE system enabled by our proposal can enhance system reliability by at least two times and reduce false negative rates to 0.76%, and extend the battery life by 40.54%, in the system integration level.

Dimensionality Reduction for Anomaly Detection in Electrocardiography: A Manifold Approach

ECG analysis is universal and important in miscellaneous medical applications. However, high computation complexity is a problem which has been shown in several levels of conventional data mining algorithms for ECG analysis. In this paper, we presented a novel manifold approach to visualize and analyze the ECG signal. According to regularity of the data, our algorithm can discover the intrinsic structure and represent the streaming data with a 1-D manifold on a 2-D space. Furthermore, the proposed algorithm can reliably detect the anomaly in ECG streaming data. We evaluated the performance of the algorithm with two different anomalies in wearable applications: for the anomaly from heart disorders such as apnea, arrythmia, our algorithm could achieve up to 90% recognition rate, for the anomaly from the ECG device, our algorithm could detect the outlier with 100%.

A comparative study of smart insole on real-world step count

Daily step count is an important parameter in energy expenditure estimation, medical treatment, and rehabilitation. However, traditional step count methods are not user-friendly or require adhesive equipment. In this paper, we present our Smart Insole system design and evaluate its step count performance. Smart Insole is lightweight, thin, and convenient to use, providing an unobtrusive way to perform step counting. The Smart Insole step count method is based on the differential value threshold of the average plantar pressure obtained from the ambulatory gait assessment. We perform a set of real-world experiments considering different arm positions, walking styles, and daily life activities to evaluate the step count performance. The results show Smart Insole can achieve nearly 100% accuracy in step count under various circumstances, which outperforms other existing solutions.

Fall perception for elderly care: A fall detection algorithm in Smart Wristlet mHealth system

Mobile Health (mHealth) is expected to play a special role in today and the future healthcare delivery. Based on this trend, we design a Smart Wristlet mHealth system with mobile interface. The designed Smart Wristlet is dedicated to offer real-time alert for elderly fall, which is the most important when population ageing is becoming. In the Smart Wristlet mHealth system, fall detection is the “bottleneck” of the system operation. To remove this bottleneck away, we propose a fall perception solution for elderly care. In this proposal, we abstract and construct primitive-based features from raw data collected by the Smart Wristlet mHealth system, in which the most valuable features can be selected by using a TF-IDF (Term Frequency-Inverse Document Frequency) metric. In reality, these selected features are the most effective to perform fall detection. Our system tests and clinical trials demonstrate that this proposal is eligible to turn the Smart Wristlet mHealth system into a real solution for elderly care. Results show that the recognition precision and recall can reach 93% and 88%, respectively. Compared with existing solutions, the gain from our proposal is an efficient prevention method for elderly fall, and can save more than 800 million dollars per year at todays socio-economic level.

A novel multi-resolution SVM (MR-SVM) algorithm to detect ECG signal anomaly in WE-CARE project

Cardiovascular disease (CVD) has become the leading cause of human deaths today. In order to combat this disease, many professionals are using mobile electrocardiogram (ECG) remote monitoring system. While using mobile ECG systems, most of the cardiac anomalies can be observed, especially when serious myocardial ischemia, heart failure, and malignant arrhythmia occur. Thus, ECG anomaly detection and analysis have attracted more and more attention in the clinical and research communities. Currently, the existing solutions of ECG automatic detection and analysis technologies are challenged by an accuracy requirement. Based on this motivation, we propose a novel Multi-Resolution Support Vector Machine (MR-SVM) algorithm to detect ECG waveform anomaly. This proposal is tested in our WE-CARE (a Wearable Efficient telecardiology system) project. Clinical trials and experimental results show that the algorithm can successfully extract original QRS complex waves and T waves regardless of noise magnitude and distinguish the ST segment morphological anomalies. Compared with European standard ST-T database, our solution can achieve the average T wave recognition accuracy rate of 97.5% and ST anomaly detection accuracy rate of 93%.

A Wireless Healthcare System Based on Mobile Internet Platform

This design combines sensor network,embedded system,Internet information service and database to provide patients a safe,easy and integral wireless healthcare system.Safety is ensured by the advantage of applications based on Windows Mobile. Moreover,the GUI made it possible for elder patients to use the software easily,while Internet Health Service makes the healthcare be more integral.

Validation of a novel gait analysis system

Many researches have proved the significance of gait analysis since it strongly relates to several urgent health issues. As a response, we develop an unobtrusive sensor device, named SennoGait, which provides comprehensive gait information. In this paper, we verify the robustness and reliability of the devices hardware components, including a 16 pressure sensors array and a 9-axis inertial measurement unit with state-of-the-art tools.