Bang-Yu Huang

A Wearable Respiratory Biofeedback System Based on Generalized Body Sensor Network

Wearable medical devices have enabled unobtrusive monitoring of vital signs and emerging biofeedback services in a pervasive manner. This article describes a wearable respiratory biofeedback system based on a generalized body sensor network (BSN) platform. The compact BSN platform was tailored for the strong requirements of overall system optimizations. A waist-worn biofeedback device was designed using the BSN. Extensive bench tests have shown that the generalized BSN worked as intended. In-situ experiments with 22 subjects indicated that the biofeedback device was discreet, easy to wear, and capable of offering wearable respiratory trainings. Pilot studies on wearable training patterns and resultant heart rate variability suggested that paced respirations at abdominal level and with identical inhaling/exhaling ratio were more appropriate for decreasing sympathetic arousal and increasing parasympathetic activities.

A Body Sensor Networks Development Platform for Pervasive Healthcare

Technology advantages of wireless sensor networks have shown great deal of promises in various biomedical applications. In this paper we introduced a specially-designed body sensor networks (BSN) development platform for on-body physiological measurements and body-proximal wireless data communications. The BSN platform we have developed insofar include BSN node boards, a photoplethysmograph sensor electronics board, a respiratory inductive plethysmograph sensor electronics board, a battery board with a charger IC, a base-station board and several prototyping boards. We have designed the software architecture towards an efficient self-contained baseband protocol to network several BSN modules. Evaluation results from several applications indicated that the complete platform works as intended. Our BSN platform was designed in a low power and stackable manner so that in the future it could be easily extended with more functionality. We believe the BSN platform will greatly facilitate the research and development activities for pervasive healthcare, telemedicine, wearable medical devices and other emerging biomedical engineering fields.

A Low-Cost Body Inertial-Sensing Network for Practical Gait Discrimination of Hemiplegia Patients

Gait analysis is widely used in detecting human walking disorders. Current gait analysis methods like video- or optical-based systems are expensive and cause invasion of human privacy. This article presents a self-developed low-cost body inertial-sensing network, which contains a base station, three wearable inertial measurement nodes, and the affiliated wireless communication protocol, for practical gait discrimination between hemiplegia patients and asymptomatic subjects. Every sensing node contains one three-axis accelerometer, one three-axis magnetometer, and one three-axis gyroscope. Seven hemiplegia patients (all were abnormal on the right side) and 7 asymptomatic subjects were examined. The three measurement nodes were attached on the thigh, the shank, and the dorsum of the foot, respectively (all on the right side of the body). A new method, which does not need to obtain accurate positions of the sensors, was used to calculate angles of knee flexion/extension and foot in the gait cycle. The angle amplitudes of initial contact, toe off, and knee flexion/extension were extracted. The results showed that there were significant differences between the two groups in the three angle amplitudes examined (-0.52±0.98° versus 6.94±2.63°, 28.33±11.66° versus 47.34±7.90°, and 26.85±8.6° versus 50.91±6.60°, respectively). It was concluded that the body inertial-sensing network platform provided a practical approach for wearable biomotion acquisition and was effective for discriminating gait symptoms between hemiplegia and asymptomatic subjects.

Estimation of Respiration Rate from Three-Dimensional Acceleration Data Based on Body Sensor Network

Respiratory monitoring is widely used in clinical and healthcare practice to detect abnormal cardiopulmonary function during ordinary and routine activities. There are several approaches to estimate respiratory rate, including accelerometer(s) worn on the torso that are capable of sensing the inclination changes due to breathing. In this article, we present an adaptive band-pass filtering method combined with principal component analysis to derive the respiratory rate from three-dimensional acceleration data, using a body sensor network platform previously developed by us. In situ experiments with 12 subjects indicated that our method was capable of offering dynamic respiration rate estimation during various body activities such as sitting, walking, running, and sleeping. The experimental studies also suggested that our frequency spectrum-based method was more robust, resilient to motion artifact, and therefore outperformed those algorithms primarily based on spatial acceleration information.

Analysis of filtering methods for 3D acceleration signals in body sensor network

Development of denoising algorithm for 3D acceleration signals is essential to facilitate accurate assessment of human movement in body sensor networks (BSN). In this study, firstly 3D acceleration signals were captured by self-developed nine-axis wireless BSN platform during 12 subjects performing regular walking. Then, acceleration noise was filtered using four common filters respectively: median filter, Butterworth low-pass filter, discrete wavelet package shrinkage and Kalman filter. Finally, signal-to-noise ratio (SNR) and correlation coefficient(R) between filtered signal and reference signal were determined. We found that (1) Kalman filter showed the largest SNR and R values, followed by median filter, discrete wavelet package shrinkage and finally Butterworth low-pass filter; whereas, after correcting waveform delay for Butterworth low-pass filter, its performance was a little better than that of Kalman filter; (2) Real-time performance of median filter related to its window length; Decomposition level influenced real-time performance of discrete wavelet package shrinkage; Butterworth low-pass filter could bring large waveform delay if filter order and cut-off frequency were not properly selected. The algorithms of these filters would be further investigated to achieve best noise reduction of 3D acceleration signals in future.

A low-complexity medium access control framework for body sensor networks

This paper proposed a low-complexity medium access control (MAC) protocol tailored for body sensor networks (BSN) applications. The MAC protocol was designated to handle collision avoidance by reducing the numbers of the overhead packets for handshake control within the BSN. We also suggested a novel message recovery mechanism for getting back the lost physiological information. The adaptive synchronization scheme we have implemented exploited the features of multiple data-rate and adjustable precision design to support differentiated healthcare applications. The MAC protocol was fully implemented using our BSN development platform. The experimental results suggested the improved MAC design was compact and energy-efficient.

A Multiple-Hop Synchronization Protocol with Packet Reconstitution

This paper proposes a multi-hop synchronization protocol for multiple physiological information transmission over the body sensor network (BSN). A packet reconstitution mechanism was designed to achieve synchronous transmission. The experimental results validated the efficiency of the protocol on continuous real-time monitoring of multiple physiological parameters over multi-hop BSNs.

A Pilot Study on BSN-Based Ubiquitous Energy Expenditure Monitoring

This paper presented a wearable body sensor network (BSN) that could be potentially employed for dynamic body energy expenditure monitoring. Three compact BSN nodes were deployed at wrist, abdomen and ankle, respectively. Acceleration signals from the multiple body sites were used to calculate a whole body weighted acceleration value. Preliminary results indicated that the standard deviation of the whole body value was smaller than that from any individual body site. There was a strong linear correlation between the whole body weighted acceleration value and the speed, but this correlation was highly subject-dependant. The pilot study presented the first several steps towards a pervasive approach for body energy expenditure monitoring.

An acceleration-based control framework for interactive gaming

In this paper we presented a 3-D acceleration-based interactive framework using Body Sensor Networks (BSN) for real-time game controls. The framework consists of three modules: a wireless signal acquisition module that senses the accelerations of body movements, a signal processing module that uses the Kalman filter to rectify the contaminated acceleration data, and a control module that makes interactive gaming strategies. Our framework enables a wearable gaming control solution that differs from the conventional methods using joysticks, key boards or mice. The framework was implemented on a racing-type game. The results suggested that our framework was fully functioning. It was capable of combining moderate physical exercises with the computer game, at the meantime brought in more funs and motivations to exercises.

A wearable respiratory biofeedback system based on body sensor networks

Technology advantages of body sensor networks (BSN) have shown great deal of promises in medical applications. In this paper we introduced a wearable device for biofeedback application based on the BSN platform we had developed. The biofeedback device we have developed includes the heart rate monitoring belt with conductive fabric and the biofeedback device with respiration belt. A wearable respiratory biofeedback system was preliminarily explored based on the BSN platform. In-situ experiments showed that the BSN platform and the biofeedback device worked as intended.