ABSTRACT
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.
Subject(s)
Monitoring, Ambulatory/instrumentation , Adult , Algorithms , Equipment Design , Humans , Principal Component Analysis , Reproducibility of Results , Respiratory Rate , Sleep/physiology , Walking/physiologyABSTRACT
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.
Subject(s)
Biofeedback, Psychology/methods , Equipment Design , Monitoring, Ambulatory/instrumentation , Heart Rate/physiology , Humans , Respiratory Rate/physiologyABSTRACT
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.
Subject(s)
Biofeedback, Psychology , Heart Rate , Monitoring, Physiologic/instrumentation , Respiration , Adult , Computer Communication Networks , Computers , Electrocardiography/methods , Equipment Design/instrumentation , Humans , Reproducibility of Results , Signal Processing, Computer-Assisted/instrumentation , Sleep , Textiles , Time FactorsABSTRACT
In this paper we present a wearable device for continuous monitoring of respiration signal and the associated algorithm for signal evaluations. The device took advantages of a proven respiratory inductive plethysmograph (RIP) technology and a wireless body sensor networks (BSN) development platform. The textile RIP sensor was integrated into a suit that could be comfortably worn around thorax or abdomen for monitoring respiration during sleep. A smart signal processing algorithm was implemented for extracting the dynamic respiration rate. The results of in-situ experiments from ten healthy subjects suggested that our system worked as intended. Due to the high reliability and low cost of our system it is believed to meet the future demands on home-based monitoring and diagnosis of sleep disorder-related diseases.
Subject(s)
Equipment Design/methods , Monitoring, Physiologic/instrumentation , Monitoring, Physiologic/methods , Plethysmography/methods , Respiration , Algorithms , Humans , Sleep Apnea Syndromes/diagnosis , SoftwareABSTRACT
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.
