Abstract-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.

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.

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.

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-sit transitions 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.

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.

Abstract-Accelerometers can be used to monitor physical activity in the home over prolonged periods. We describe a novel system for objectively and continuously monitoring movement, suitable for patients with chronic diseases including congestive heart failure and chronic obstructive pulmonary disease. The key design criteria were ease of use by, and comfort for, the patient together with the provision of clinically relevant information. The patient's posture, energy expenditure and movement are clinically important parameters that can be measured by accelerometry. A data processing schema in which these parameters are extracted is described.

Objective: To find out if patients with chronic illnesses living in the community are at risk of unplanned hospital admissions through emergency departments; what types of chronic illnesses may be putative risk factors; and if an increase in the number of chronic illnesses may be associated with increased risk. Methods: The survey included the completion of a standardized questionnaire for medical illnesses in a random sample of older people dwelling in the community and analysis of admission records to our hospital. The principal diagnoses for admissions were recorded. The risk factors for admissions were analysed. Results: Five hundred and twenty‐six (239 men and 287 women) people aged 55 years and over were interviewed. Musculoskeletal disorders, hypertension, gastrointestinal disorders and ischaemic heart disease were the most frequently reported of the chronic illnesses surveyed. A total number of 70 people from the survey group with a total of 115 admissions through emergency departments were recorded. Using logistic regression model, hypertension, ischaemic heart disease and age were found to be risk factors for emergency admissions amongst this group of community‐dwelling residents. The ratios were 2.03 (95% confidence interval (CI): 1.2–3.44), 2.02 (95% CI: 1.16–3.49) and 1.05 (95% CI: 1.02–1.09), respectively. Furthermore, multiple (three or more) chronic illnesses were found to be a strong predictor of hospital admission via emergency department (chi‐square = 16.647, DF = 1, P‐value < 0.001). Conclusion: We conclude that there was significant association between multiple chronic diseases and emergency admissions for older people. Of these, hypertension and ischaemic heart disease were found to be significant predictors. Age per se was found to be of borderline significance.

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