Michael R. Narayanan

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

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.

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.

Longitudinal Falls-Risk Estimation Using Triaxial Accelerometry

Falls among the elderly population are a major cause of morbidity and injury-particularly among the over 65 years age group. Validated clinical tests and associated models, built upon assessment of functional ability, have been devised to estimate an individuals risk of falling in the near future. Those identified as at-risk of falling may be targeted for interventative treatment. The migration of these clinical models estimating falls risk to a surrogate technique, for use in the unsupervised environment, might broaden the reach of falls-risk screening beyond the clinical arena. This study details an approach that characterizes the movements of 68 elderly subjects performing a directed routine of unsupervised physical tasks. The movement characterization is achieved through the use of a triaxial accelerometer. A number of fall-related features, extracted from the accelerometry signals, combined with a linear least squares model, maps to a clinically validated measure of falls risk with a correlation of ¿ = 0.81(p < 0.001).

Falls Management: Detection and Prevention, using a Waist-mounted Triaxial Accelerometer

We describe a distributed falls management system capable of real-time falls detection in an unsupervised living context and remote longitudinal tracking of falls risk parameters using a waist-mounted triaxial accelerometer. A self-administrable falls risk assessment is used to facilitate falls prevention. A Web-interface allows clinicians to monitor the status of individuals and track their compliance with exercise interventions. Early identification of increased falls risk allows targeted interventions to be promptly administered. Real-time detection of falls allows immediate emergency response protocols to be deployed, reducing morbidity and increasing the independence of the community-dwelling elderly community.

A wearable triaxial accelerometry system for longitudinal assessment of falls risk

Falls-related injuries in the elderly population are a major cause of morbidity and represent one of the most significant contributors to hospitalizations and rising health care expense in developed countries. Many laboratory-based studies have described falls detection systems using wearable accelerometry. However, only a limited number of reports have tried to address the difficult issues of falls detection and falls prevention in unsupervised or free-living environments. We describe a waist-mounted triaxial accelerometry (Triax) system with a remote data collection capability to provide unsupervised monitoring of the elderly. The basis of the monitoring is a self-administered directed-routine (DR) comprising three separate tests measured by way of the Triax. We present an initial evaluation of the DR results in 36 patients to detect early changes in functional ability and facilitate falls risk stratification. Extracted features considered alone show a correlation with falls risk of approximately ρ=0.5. Estimation of falls risk using a linear least squares model provides a root-mean-squared error of 0.69 (ρ=0.58, p<0.0002).

Falls event detection using triaxial accelerometry and barometric pressure measurement

A falls detection system, employing a Bluetooth-based wearable device, containing a triaxial accelerometer and a barometric pressure sensor, is described. The aim of this study is to evaluate the use of barometric pressure measurement, as a surrogate measure of altitude, to augment previously reported accelerometry-based falls detection algorithms. The accelerometry and barometric pressure signals obtained from the waist-mounted device are analyzed by a signal processing and classification algorithm to discriminate falls from activities of daily living. This falls detection algorithm has been compared to two existing algorithms which utilize accelerometry signals alone. A set of laboratory-based simulated falls, along with other tasks associated with activities of daily living (16 tests) were performed by 15 healthy volunteers (9 male and 6 female; age: 23.7 ± 2.9 years; height: 1.74 ± 0.11 m). The algorithm incorporating pressure information detected falls with the highest sensitivity (97.8%) and the highest specificity (96.7%).

Spectral Analysis of Accelerometry Signals From a Directed-Routine for Falls-Risk Estimation

Injurious falls are a prevalent and serious problem faced by a growing elderly population. Accurate assessment and long-term monitoring of falls-risk could prove useful in the prevention of falls, by identifying those at risk of falling early so targeted intervention may be prescribed. Previous studies have demonstrated the feasibility of using triaxial accelerometry to estimate the risk of a person falling in the near future, by characterizing their movement as they execute a restricted sequence of predefined movements in an unsupervised environment, termed a directed routine. This study presents an improvement on this previously published system, which relied explicitly on time-domain features extracted from the accelerometry signals. The proposed improvement incorporates features derived from spectral analysis of the same accelerometry signals; in particular the harmonic ratios between signal harmonics and the fundamental frequency component are used. Employing these additional frequency-domain features, in combination with the previously reported time-domain features, an increase in the observed correlation with the clinical gold-standard risk of falling, from ρ = 0.81 to ρ = 0.96, was achieved when using manually annotated event segmentation markers; using an automated algorithm to segment the signals gave corresponding results of ρ = 0.73 and ρ = 0.99, before and after the inclusion of spectral features. The strong correlation with falls-risk observed in this preliminary study further supports the feasibility of using an unsupervised assessment of falls-risk in the home environment.

Design of an unobtrusive wireless sensor network for nighttime falls detection

A significant portion of government health care funding is spent treating falls-related injuries among older adults. This cost is set to rise due to population aging in developed societies. Wearable sensors systems, often comprised of triaxial accelerometers and/or gyroscopes, have proven useful for real-time falls detection. However, a large percentage of falls occur at home and many of those happen at nighttime, when the person is unlikely to be wearing such an ambulatory monitoring device. It is envisaged that systems utilizing unobtrusive wireless sensors can be employed to survey the living space and identify unusual activity patterns which may indicate that a fall has happened at nighttime. In this study, a nighttime falls detection system designed for a single individual living at home, based on the use of passive infrared and pressure mat sensors, is explored. This paper describes both the sensor and system design, and investigates the feasibility of performing nighttime falls detection through the use of scripted scenarios using a single healthy test volunteer. In addition to normal movement activity, falls with unconsciousness, falls with repeated failed attempts to recover, and falls with successful recovery, are considered. By analyzing the location of sensor activity, periods of sensor inactivity, and unusual sensor activation patterns in uncommon locations, a sensitivity and specificity of 88.89% and 100%, respectively, are obtained (excluding falls followed by complete recovery). This demonstrates a proof-of-principle that nighttime falls detection might be achieved using a low complexity and completely unobtrusive wireless sensor network in the home.

Automatic segmentation of triaxial accelerometry signals for falls risk estimation

Falls-related injuries in the elderly population represent one of the most significant contributors to rising health care expense in developed countries. In recent years, falls detection technologies have become more common. However, very few have adopted a preferable falls prevention strategy through unsupervised monitoring in the free-living environment. The basis of the monitoring described herein was a self-administered directed-routine (DR) comprising three separate tests measured by way of a waist-mounted triaxial accelerometer. Using features extracted from the manually segmented signals, a reasonable estimate of falls risk can be achieved. We describe here a series of algorithms for automatically segmenting these recordings, enabling the use of the DR assessment in the unsupervised and home environments. The accelerometry signals, from 68 subjects performing the DR, were manually annotated by an observer. Using the proposed signal segmentation routines, an good agreement was observed between the manually annotated markers and the automatically estimated values. However, a decrease in the correlation with falls risk to 0.73 was observed using the automatic segmentation, compared to 0.81 when using markers manually placed by an observer.