Biomedical subjects

Branko G Celler

Publications and source records attributed to Branko G Celler.

7 recordsLinked to original sources

PubMed2006

Classification of a known sequence of motions and postures from accelerometry data using adapted Gaussian mixture models.

Accelerometry shows promise in providing an inexpensive but effective means of long-term ambulatory monitoring of elderly patients. The accurate classification of everyday movements should allow such a monitoring system to exhibit greater 'intelligence', improving its ability to detect and predict falls by forming a more specific picture of the activities of a person and thereby allowing more accurate tracking of the health parameters associated with those activities. With this in mind, this study aims to develop more robust and effective methods for the classification of postures and motions from data obtained using a single, waist-mounted, triaxial accelerometer; in particular, aiming to improve the flexibility and generality of the monitoring system, making it better able to detect and identify short-duration movements and more adaptable to a specific person or device. Two movement classification methods were investigated: a rule-based Heuristic system and a Gaussian mixture model (GMM)-based system. A novel time-domain feature extraction method is proposed for the GMM system to allow better detection of short-duration movements. A method for adapting the GMMs to compensate for the problem of limited user-specific training data is also proposed and investigated. Classification performance was considered in relation to data gathered in an unsupervised, directed routine conducted in a three-month field trial involving six elderly subjects. The GMM system was found to achieve a mean accuracy of 91.3%, distinguishing between three postures (sitting, standing and lying) and five movements (sit-to-stand, stand-to-sit, lie-to-stand, stand-to-lie and walking), compared to 71.1% achieved by the Heuristic system. The adaptation method was found to offer a mean accuracy of 92.2%; a relative improvement of 20.2% over tests without subject-specific data and 4.5% over tests using only a limited amount of subject-specific data. While limited to a restricted subset of possible motions and postures, these results provide a significant step in the search for a more robust and accurate ambulatory classification system.

Acceleration↗

PubMed2006

Implementation of a real-time human movement classifier using a triaxial accelerometer for ambulatory monitoring.

The real-time monitoring of human movement can provide valuable information regarding an individual's degree of functional ability and general level of activity. This paper presents the implementation of a real-time classification system for the types of human movement associated with the data acquired from a single, waist-mounted triaxial accelerometer unit. The major advance proposed by the system is to perform the vast majority of signal processing onboard the wearable unit using embedded intelligence. In this way, the system distinguishes between periods of activity and rest, recognizes the postural orientation of the wearer, detects events such as walking and falls, and provides an estimation of metabolic energy expenditure. A laboratory-based trial involving six subjects was undertaken, with results indicating an overall accuracy of 90.8% across a series of 12 tasks (283 tests) involving a variety of movements related to normal daily activities. Distinction between activity and rest was performed without error; recognition of postural orientation was carried out with 94.1% accuracy, classification of walking was achieved with less certainty (83.3% accuracy), and detection of possible falls was made with 95.6% accuracy. Results demonstrate the feasibility of implementing an accelerometry-based, real-time movement classifier using embedded intelligence.

Acceleration↗

PubMed2005

Designing home telecare: a case study in monitoring cystic fibrosis.

The objective of this study was to investigate the feasibility of using home telecare for monitoring cystic fibrosis (CF). Five adolescents were asked to use a home telecare system during a routine hospital visit over one week. Frequency of use was measured from computer logs. Unacceptable measurements were identified by visual inspection. User impressions of home telecare and appropriateness of the system for managing CF was determined from observations of user interaction, survey and qualitative analysis. Patients used the system to record lung function measurements without any supervision and indicated that the system was easy to learn and use. The role of home telecare in supporting collaborative self-management appeared to be well understood. Home telecare was seen as a supplement to standard care that would provide a link to the hospital between clinic visits. Participants indicated that feedback provided by the system and ongoing clinical support would determine long-term use and compliance with the monitoring protocol. Clinicians reported the usefulness of home telecare in maintaining a longitudinal record of their patient's health that would supplement verbal description of symptoms and reduce time to treatment by increasing patient self-awareness of health status. Home telecare may be a feasible intervention for monitoring CF. Feedback provided by the system must be presented in a format that is familiar and easily understood by users. Further system refinement and evaluation is required to determine patient compliance with their customized monitoring protocol prior to assessing impact on clinical outcomes.

Adolescent↗

PubMed2004

Accelerometry: providing an integrated, practical method for long-term, ambulatory monitoring of human movement.

Accelerometry offers a practical and low cost method of objectively monitoring human movements, and has particular applicability to the monitoring of free-living subjects. Accelerometers have been used to monitor a range of different movements, including gait, sit-to-stand transfers, postural sway and falls. They have also been used to measure physical activity levels and to identify and classify movements performed by subjects. This paper reviews the use of accelerometer-based systems in each of these areas. The scope and applicability of such systems in unsupervised monitoring of human movement are considered. The different systems and monitoring techniques can be integrated to provide a more comprehensive system that is suitable for measuring a range of different parameters in an unsupervised monitoring context with free-living subjects. An integrated approach is described in which a single, waist-mounted accelerometry system is used to monitor a range of different parameters of human movement in an unsupervised setting.

Gait↗

PubMed2004

A pilot study of long-term monitoring of human movements in the home using accelerometry.

We assessed the feasibility of using a waist-mounted, wireless triaxial accelerometer (TA) to monitor human movements in an unsupervised home setting to detect changes in functional status. A pilot study was carried out with six healthy subjects aged 80-86 years. The subjects wore a TA unit every day for two to three months. Each morning they carried out a short routine of directed movements that included standing, sitting, lying and walking. Important movement variables were measured. During the rest of the day, subjects were monitored for falls, and variables such as metabolic energy expenditure were measured. All subjects remained healthy; there was no overall change in functional status and there were only slight fluctuations in health status. No longitudinal changes were detected in any of the variables measured during the directed routine. There was a moderate correlation between weekly self-reported health status and energy expenditure: subjects reported a lower health status for weeks in which they expended less energy. The TA system was found to be practical for long-term, unsupervised home monitoring. All subjects found the system simple to use and the TA unit unobtrusive and comfortable to wear. High compliance rates were achieved: the TA units were worn on 88% of the days in the study, for an average of 11.2 hours per day.

Acceleration↗

PubMed2003

Using information technology to improve the management of chronic disease.

Information and communications technology (ICT) is increasingly being used in management of chronic illness to facilitate shared services (virtual health networks and electronic health records), knowledge management (care rules and protocols, scheduling, information directories), as well as consumer-based health education and evidence-based clinical protocols. Common applications of ICT include home monitoring of vital signs for patients with chronic disease, as well as replacing home visits by nurses in person with telemedicine videophone consultations. A patient-managed Home Telecare System with integrated clinical signs monitoring, automated scheduling and medication reminders, as well as access to health education and daily logs, is presented as an example of ICT use for chronic disease self-management. A clinical case study demonstrates how early identification of adverse trends in clinical signs recorded in the home can either avoid hospital readmission or reduce the length of hospital stay.

Australia↗

PubMedDate not supplied

A gradient model of cardiac pacemaker myocytes.

We have formulated a spatial-gradient model of action potential heterogeneity within the rabbit sinoatrial node (SAN), based on cell-specific ionic models of electrical activity from its central and peripheral regions. The ionic models are derived from a generic cell model, incorporating five background and exchange currents, and seven time-dependent currents based on three- or four-state Markov schemes. State transition rates are given by non-linear sigmoid functions of membrane potential. By appropriate selection of parameters, the generic model is able to accurately reproduce a wide range of action potential waveforms observed experimentally. Specifically, the model can fit recordings from central and peripheral regions of the SAN with RMS errors of 0.3987 and 0.7628 m V, respectively. Using a custom least squares parameter optimisation routine, we have constructed a spatially-varying gradient model that exhibits a smooth transition in action potential characteristics from the central to the peripheral region, whilst ensuring individual membrane currents remain physiologically accurate. Smooth transition action potential characteristics include maximum diastolic potential, overshoot potential, upstroke velocity, action potential duration and cycle length. The gradient model is suitable for developing higher dimensional models of the right atrium, in which action potential heterogeneity within nodal tissue may be readily incorporated.

Action Potentials↗