Ming-Chun Huang

Rapid electrochemical detection on a mobile phone

We present a compact mobile phone platform for rapid, quantitative biomolecular detection. This system consists of an embedded circuit for signal processing and data analysis, and disposable microfluidic chips for fluidic handling and biosensing. Capillary flow is employed for sample loading, processing, and pumping to enhance operational portability and simplicity. Graphical step-by-step instructions displayed on the phone assists the operator through the detection process. After the completion of each measurement, the results are displayed on the screen for immediate assessment and the data is automatically saved to the phones memory for future analysis and transmission. Validation of this device was carried out by detecting Plasmodium falciparum histidine-rich protein 2 (PfHRP2), an important biomarker for malaria, with a lower limit of detection of 16 ng mL(-1) in human serum. The simple detection process can be carried out with two loading steps and takes 15 min to complete each measurement. Due to its compact size and high performance, this device offers immense potential as a widely accessible, point-of-care diagnostic platform, especially in remote and rural areas. In addition to its impact on global healthcare, this technology is relevant to other important applications including food safety, environmental monitoring and biosecurity.

Designing a Robust Activity Recognition Framework for Health and Exergaming Using Wearable Sensors

Detecting human activity independent of intensity is essential in many applications, primarily in calculating metabolic equivalent rates and extracting human context awareness. Many classifiers that train on an activity at a subset of intensity levels fail to recognize the same activity at other intensity levels. This demonstrates weakness in the underlying classification method. Training a classifier for an activity at every intensity level is also not practical. In this paper, we tackle a novel intensity-independent activity recognition problem where the class labels exhibit large variability, the data are of high dimensionality, and clustering algorithms are necessary. We propose a new robust stochastic approximation framework for enhanced classification of such data. Experiments are reported using two clustering techniques, K-Means and Gaussian Mixture Models. The stochastic approximation algorithm consistently outperforms other well-known classification schemes which validate the use of our proposed clustered data representation. We verify the motivation of our framework in two applications that benefit from intensity-independent activity recognition. The first application shows how our framework can be used to enhance energy expenditure calculations. The second application is a novel exergaming environment aimed at using games to reward physical activity performed throughout the day, to encourage a healthy lifestyle.

Smart insole: a wearable system for gait analysis

Gait analysis is an important medical diagnostic process and has many applications in rehabilitation, therapy and exercise training. However, standard human gait analysis has to be performed in a specific gait lab and operated by a medical professional. This traditional method increases the examination cost and decreases the accuracy of the natural gait model. In this paper, we present a novel portable system, called Smart Insole, to address the current issues. Smart Insole integrates low cost sensors and computes important gait features. In this way, patients or users can wear Smart Insole for gait analysis in daily life instead of participating in gait lab experiments for hours. With our proposed portable sensing system and effective feature extraction algorithm, the Smart Insole system enables precise gait analysis. Furthermore, taking advantage of the affordability and mobility of Smart Insole, pervasive gait analysis can be extended to many potential applications such as fall prevention, life behavior analysis and networked wireless health systems.

A dense pressure sensitive bedsheet design for unobtrusive sleep posture monitoring

Sleep plays a pivotal role in the quality of life, and sleep posture is related to many medical conditions such as sleep apnea. In this paper, we design a dense pressure-sensitive bedsheet for sleep posture monitoring. In contrast to existing techniques, our bedsheet system offers a completely unobtrusive method using comfortable textile sensors. Based on high-resolution pressure distributions from the bedsheet, we develop a novel framework for pressure image analysis to monitor sleep postures, including a set of geometrical features for sleep posture characterization and three sparse classifiers for posture recognition. We run a pilot study and evaluate the performance of our methods with 14 subjects to analyze 6 common postures. The experimental results show that our proposed method enables reliable sleep posture recognition and offers better overall performance than state-of-the-art methods, achieving up to 83.0% precision and 83.2% recall on average.

eCushion: A Textile Pressure Sensor Array Design and Calibration for Sitting Posture Analysis

Sitting posture analysis is widely applied in many daily applications in biomedical, education, and health care domains. It is interesting to monitor sitting postures in an economic and comfortable manner. Accordingly, we present a textile-based sensing system, called Smart Cushion, which analyzes the sitting posture of human being accurately and non-invasively. First, we introduce the electrical textile sensor and its electrical characteristics, such as offset, scaling, crosstalk, and rotation. Second, we present the design and implementation of the Smart Cushion system. Several effective techniques have been proposed to improve the recognition rate of sitting postures, including sensor calibration, data representation, and dynamic time warping-based classification. Last, our experimental results show that the recognition rate of our Smart Cushion system is in excess of 85.9%.

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.

Unobtrusive Sleep Stage Identification Using a Pressure-Sensitive Bed Sheet

Sleep constitutes a big portion of our lives and is a major part of health and well-being. Monitoring the quality of sleep can aid in the medical diagnosis of a variety of sleep and psychiatric disorders and can serve as an indication of several chronic diseases. Sleep stage analysis plays a pivotal role in the evaluation of the quality of sleep and is a proven biometric in diagnosing cardiovascular disease, diabetes, and obesity [32]. We describe an unobtrusive framework for sleep stage identification based on a high-resolution pressure-sensitive e-textile bed sheet. We extract a set of sleep-related biophysical and geometric features from the bed sheet and use a two-phase classification procedure for Wake-Non Rapid Eye Movement-Rapid Eye Movement stage identification. A total of seven all-night polysomnography recordings from healthy subjects were used to validate the proposed bed sheet system and the ability to extract sleep stage information from it. When compared with the gold standard, the described system achieved 70.3% precision and 71.1% recall on average. These results suggest that unobtrusive sleep macrostructure analysis could be a viable option in clinical and home settings in the near future. Compared with existing techniques for sleep stage identification, the described system is unobtrusive, fits seamlessly into the users familiar sleep environment, and has additional advantages of comfort, low cost, and simplicity.

A Self-Calibrating Radar Sensor System for Measuring Vital Signs

Vital signs (i.e., heartbeat and respiration) are crucial physiological signals that are useful in numerous medical applications. The process of measuring these signals should be simple, reliable, and comfortable for patients. In this paper, a noncontact self-calibrating vital signs monitoring system based on the Doppler radar is presented. The system hardware and software were designed with a four-tiered layer structure. To enable accurate vital signs measurement, baseband signals in the radar sensor were modeled and a framework for signal demodulation was proposed. Specifically, a signal model identification method was formulated into a quadratically constrained l1 minimization problem and solved using the upper bound and linear matrix inequality (LMI) relaxations. The performance of the proposed system was comprehensively evaluated using three experimental sets, and the results indicated that this system can be used to effectively measure human vital signs.

Robust human intensity-varying activity recognition using Stochastic Approximation in wearable sensors

Detecting human activity independent of intensity is essential in many applications, primarily in calculating metabolic equivalent rates (MET) and extracting human context awareness from on-body inertial sensors. Many classifiers that train on an activity at a subset of intensity levels fail to classify the same activity at other intensity levels. This demonstrates weakness in the underlying activity model. Training a classifier for an activity at every intensity level is also not practical. In this paper we tackle a novel intensity-independent activity recognition application where the class labels exhibit large variability, the data is of high dimensionality, and clustering algorithms are necessary. We propose a new robust Stochastic Approximation framework for enhanced classification of such data. Experiments are reported for each dataset using two clustering techniques, K-Means and Gaussian Mixture Models. The Stochastic Approximation algorithm consistently outperforms other well-known classification schemes which validates the use of our proposed clustered data representation.

Sleep posture analysis using a dense pressure sensitive bedsheet

Sleep posture affects the quality of our sleep and is especially important for such medical conditions as sleep apnea and pressure ulcers. In this paper, we propose a design for a dense pressure-sensitive bedsheet along with an algorithmic framework to recognize and monitor sleeping posture. The bedsheet system uses comfortable textile sensors that produces high-resolution pressure maps. We develop a novel framework for pressure image analysis to monitor sleep postures, including a set of geometrical features for sleep posture characterization and three sparse classifiers for posture recognition. In demonstrating this system, we run 2 pilot studies: one evaluates the performance of our methods with 14 subjects to analyze 6 common postures; the other is a series of overnight studies to verify continuous performance. The experimental results show that our proposed method enables reliable sleep posture recognition and offers better overall performance than traditional methods, achieving up to 83.0% precision and 83.2% recall on average.