Nigel H. Lovell

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 individuals 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

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.

Conducting polymers for neural interfaces: challenges in developing an effective long-term implant

Metal electrode materials used in active implantable devices are often associated with poor long-term stimulation and recording performance. Modification of these materials with conducting polymer coatings has been suggested as an approach for improving the neural tissue-electrode interface and increasing the effective lifetime of these implants. Neural interfaces ideally have intimate contact between the excitable tissue and the electrode to maintain signal quality and activation of neural cells. The outcomes of current research into conducting polymers as coatings has potential to enhance this tissue-material contact by increasing the electrode surface area and roughness as well as allowing delivery of bioactive signals to neural cells. However, challenges facing conducting polymers include poor electroactive stability and mechanical properties as well as control of the mobility, concentration and presentation of bioactive molecules. The impact of biological inclusions on polymer properties and their ongoing performance in neural prosthetics requires a greater understanding with future research aimed at controlling and optimising film characteristics for long-term performance. Optimising the electrode interface will require a trade-off between desired electrical, mechanical, chemical and biological properties.

Classification of basic daily movements using a triaxial accelerometer.

A generic framework for the automated classification of human movements using an accelerometry monitoring system is introduced. The framework was structured around a binary decision tree in which movements were divided into classes and subclasses at different hierarchical levels. General distinctions between movements were applied in the top levels, and successively more detailed subclassifications were made in the lower levels of the tree. The structure was modular and flexible: parts of the tree could be reordered, pruned or extended, without the remainder of the tree being affected. This framework was used to develop a classifier to identify basic movements from the signals obtained from a single, waist-mounted triaxial accelerometer. The movements were first divided into activity and rest. The activities were classified as falls, walking, transition between postural orientations, or other movement. The postural orientations during rest were classified as sitting, standing or lying. In controlled laboratory studies in which 26 normal, healthy subjects carried out a set of basic movements, the sensitivity of every classification exceeded 87%, and the specificity exceeded 94%; the overall accuracy of the system, measured as the number of correct classifications across all levels of the hierarchy, was a sensitivity of 97.7% and a specificity of 98.7% over a data set of 1309 movements.

Barometric Pressure and Triaxial Accelerometry-Based Falls Event Detection

Falls and fall related injuries are a significant cause of morbidity, disability, and health care utilization, particularly among the age group of 65 years and over. The ability to detect falls events in an unsupervised manner would lead to improved prognoses for falls victims. Several wearable accelerometry and gyroscope-based falls detection devices have been described in the literature; however, they all suffer from unacceptable false positive rates. This paper investigates the augmentation of such systems with a barometric pressure sensor, as a surrogate measure of altitude, to assist in discriminating real fall events from normal activities of daily living. The acceleration and air pressure data are recorded using a wearable device attached to the subjects waist and analyzed offline. The study incorporates several protocols including simulated falls onto a mattress and simulated activities of daily living, in a cohort of 20 young healthy volunteers (12 male and 8 female; age: 23.7 ±3.0 years). A heuristically trained decision tree classifier is used to label suspected falls. The proposed system demonstrated considerable improvements in comparison to an existing accelerometry-based technique; showing an accuracy, sensitivity and specificity of 96.9%, 97.5%, and 96.5%, respectively, in the indoor environment, with no false positives generated during extended testing during activities of daily living. This is compared to 85.3%, 75%, and 91.5% for the same measures, respectively, when using accelerometry alone. The increased specificity of this system may enhance the usage of falls detectors among the elderly population.

Detection of daily physical activities using a triaxial accelerometer

Triaxial accelerometers have been employed to monitor human movements in a variety of circumstances. The study considered the use of data from a single waist-mounted triaxial accelerometer to distinguish between activity states and rest. A method using acceleration magnitude was applied to data collected from 26 normal subjects performing sit-to-stand and stand-to-sittransitions and walking. The effects of three parameters were investigated: the length n of a smoothing median filter, the width w of the averaging window used to process the signal and the value of the acceleration magnitude threshold th. These were found to be inter-related, and sets of parameters that resulted in accurate discrimination were determined by the relationship between th and the product of w and n, and by the relationship between n and w. The subjects were randomly divided into control (N=13) and test (N=13) groups. Optimum parameter sets were determined using the control group. Eleven sets of parameters yielded the same optimum results of a sensitivity of 1.0 and a specificity of 0.96 in the control group. Upon application to the test group, using these parameters, the system successfully distinguished between activity and rest, giving sensitivities greater than 0.98 and specificities between 0.88 and 0.94.

Cell attachment functionality of bioactive conducting polymers for neural interfaces

Bioactive coatings for neural electrodes that are tailored for cell interactions have the potential to produce superior implants with improved charge transfer capabilities. In this study synthetically produced anionically modified laminin peptides DEDEDYFQRYLI and DCDPGYIGSR were used to dope poly(3,4-ethylenedioxythiophene) (PEDOT) electrodeposited on platinum (Pt) electrodes. Performance of peptide doped films was compared to conventional polymer PEDOT/paratoluene sulfonate (pTS) films using SEM, XPS, cyclic voltammetry, impedance spectroscopy, mechanical hardness and adherence. Bioactivity of incorporated peptides and their affect on cell growth was assessed using a PC12 neurite outgrowth assay. It was demonstrated that large peptide dopants produced softer PEDOT films with a minimal decrease in electrochemical stability, compared to the conventional dopant, pTS. Cell studies revealed that the YFQRYLI ligand retained neurite outgrowth bioactivity when DEDEDYFQRYLI was used as a dopant, but the effect was strongly dependant on initial cell attachment. Alternate peptide dopant, DCDPGYIGSR was found to impart superior cell attachment properties when compared to DEDEDYFQRYLI, but attachment on both peptide doped polymers could be enhanced by coating with whole native laminin.

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.

CMOS neurostimulation ASIC with 100 channels, scaleable output, and bidirectional radio-frequency telemetry

100-channel neurostimulation circuit comprising a complementary metal oxide semiconductor (CMOS), application-specific integrated circuit (ASIC) has been designed, constructed and tested. The ASIC forms a significant milestone and an integral component of a 100-electrode neurostimulation system being developed by the authors. The system comprises an externally worn transmitter and a body implantable stimulator. The purpose of the system is to communicate both data and power across tissue via radio-frequency (RF) telemetry such that externally programmable, constant current, charge balanced, biphasic stimuli may be delivered to neural tissue at 100 unique sites. An intrinsic reverse telemetry feature of the ASIC has been designed such that information pertaining to the device function, reconstruction of the stimulation voltage waveform, and the measurement of impedance may be obtained through noninvasive means. To compensate for the paucity of data pertaining to the stimulation thresholds necessary in evoking a physiological response, the ASIC has been designed with scaleable current output. The ASIC has been designed primarily as a treatment of degenerative disorders of the retina whereby the 100 channels are to be utilized in the delivery of a pattern of stimuli of varying intensity and or duty cycle to the surviving neural tissue of the retina. However, it is conceivable that other fields of neurostimulation such as cochlear prosthetics and functional electronic stimulation may benefit from the employment of the system.

Sensors-Based Wearable Systems for Monitoring of Human Movement and Falls

The rapid aging of the worlds population, along with an increase in the prevalence of chronic illnesses and obesity, requires adaption and modification of current healthcare models. One such approach involves telehealth applications, many of which are based on sensor technologies for unobtrusive monitoring. Recent technological advances, in particular, involving microelectromechnical systems, have resulted in miniaturized wearable devices that can be used for a range of applications. One of the leading areas for utilization of body-fixed sensors is the monitoring of human movement. An overview of common ambulatory sensors is presented, followed by a summary of the developments in this field, with an emphasis on the clinical applications of falls detection, falls risk assessment, and energy expenditure. The importance of these applications is considerable in light of the global demographic trends and the resultant rise in the occurrence of injurious falls and the decrease of physical activity. The potential of using such monitors in an unsupervised manner for community-dwelling individuals is immense, but entails an array of challenges with regards to design c onsiderations, implementation protocols, and signal analysis processes. Some limitations of the research to date and suggestions for future research are also discussed.