Long-term mobility monitoring of older adults using accelerometers in a clinical environment

Direct measurement of human movement by accelerometry

Human movement has been the subject of investigation since the fifth century when early scientists and researchers attempted to model the human musculoskeletal system. The anatomical complexities of the human body have made it a constant source of research to this day with many anatomical, physiological, mechanical, environmental, sociological and psychological studies undertaken to define its key elements. These studies have utilised modern day techniques to assess human movement in many illnesses. One such modern technique has been direct measurement by accelerometry, which was first suggested in the 1970s but has only been refined and perfected during the last 10-15 years. Direct measurement by accelerometry has seen the introduction of the successful implementation of low power, low cost electronic sensors that have been employed in clinical and home environments for the constant monitoring of patients (and their controls). The qualitative and quantitative data provided by these sensors make it possible for engineers, clinicians and physicians to work together to be able to help their patients in overcoming their physical disability. This paper presents the underlying biomechanical elements necessary to understand and study human movement. It also reflects on the sociological elements of human movement and why it is important in patient life and well being. Finally the concept of direct measurement by accelerometry is presented with past studies and modern techniques used for data analysis.

Comparison of low-complexity fall detection algorithms for body attached accelerometers

The elderly population is growing rapidly. Fall related injuries are a central problem for this population. Elderly people desire to live at home, and thus, new technologies, such as automated fall detectors, are needed to support their independence and security. The aim of this study was to evaluate different low-complexity fall detection algorithms, using triaxial accelerometers attached at the waist, wrist, and head. The fall data were obtained from standardized types of intentional falls (forward, backward, and lateral) in three middle-aged subjects. Data from activities of daily living were used as reference. Three different detection algorithms with increasing complexity were investigated using two or more of the following phases of a fall event: beginning of the fall, falling velocity, fall impact, and posture after the fall. The results indicated that fall detection using a triaxial accelerometer worn at the waist or head is efficient, even with quite simple threshold-based algorithms, with a sensitivity of 97-98% and specificity of 100%. The most sensitive acceleration parameters in these algorithms appeared to be the resultant signal with no high-pass filtering, and the calculated vertical acceleration. In this study, the wrist did not appear to be an applicable site for fall detection. Since a head worn device includes limitations concerning usability and acceptance, a waist worn accelerometer, using an algorithm that recognizes the impact and the posture after the fall, might be optimal for fall detection.

Determination of simple thresholds for accelerometry-based parameters for fall detection

The increasing population of elderly people is mainly living in a home-dwelling environment and needs applications to support their independency and safety. Falls are one of the major health risks that affect the quality of life among older adults. Body attached accelerometers have been used to detect falls. The placement of the accelerometric sensor as well as the fall detection algorithms are still under investigation. The aim of the present pilot study was to determine acceleration thresholds for fall detection, using triaxial accelerometric measurements at the waist, wrist, and head. Intentional falls (forward, backward, and lateral) and activities of daily living (ADL) were performed by two voluntary subjects. The results showed that measurements from the waist and head have potential to distinguish between falls and ADL. Especially, when the simple threshold-based detection was combined with posture detection after the fall, the sensitivity and specificity of fall detection were up to 100 %. On the contrary, the wrist did not appear to be an optimal site for fall detection.

Comparison of the performance of the activPAL™ Professional physical activity logger to a discrete accelerometer-based activity monitor

The aim of this study was to assess the accuracy of the 'activPAL Professional' physical activity logger by comparing its output to that of a proven discrete accelerometer-based activity monitor during extended measurements on healthy subjects while performing activities of daily living (ADL). Ten healthy adults, with unrestricted mobility, wore both the activPAL and the discrete dual accelerometer (Analog Devices ADXL202)-based activity monitor that recorded in synchronization with each other. The accelerometer derived data were then compared to that generated by the activPAL and a complete statistical and error analysis was performed using a Matlab program. This program determined trunk and thigh inclination angles to distinguish between sitting/lying, standing and stepping for the discrete accelerometer device and amount of time spent on each activity. Analysis was performed on a second-by-second basis and then categorized at 15s intervals in direct comparison with the activPAL generated data. Of the total time monitored (approximately 60 h) the detection accuracies for static and dynamic activities were approximately 98%. In a population of healthy adults, the data obtained from the activPAL Professional physical activity logger for both static and dynamic activities showed a close match to a proven discrete accelerometer data with an offset of approximately 2% between the two systems.

Evaluation of a threshold-based tri-axial accelerometer fall detection algorithm

Using simulated falls performed under supervised conditions and activities of daily living (ADL) performed by elderly subjects, the ability to discriminate between falls and ADL was investigated using tri-axial accelerometer sensors, mounted on the trunk and thigh. Data analysis was performed using MATLAB to determine the peak accelerations recorded during eight different types of falls. These included; forward falls, backward falls and lateral falls left and right, performed with legs straight and flexed. Falls detection algorithms were devised using thresholding techniques. Falls could be distinguished from ADL for a total data set from 480 movements. This was accomplished using a single threshold determined by the fall-event data-set, applied to the resultant-magnitude acceleration signal from a tri-axial accelerometer located at the trunk.

A review of approaches to mobility telemonitoring of the elderly in their living environment

Rapid technological advances have prompted the development of a wide range of telemonitoring systems to enable the prevention, early diagnosis and management, of chronic conditions. Remote monitoring can reduce the amount of recurring admissions to hospital, facilitate more efficient clinical visits with objective results, and may reduce the length of a hospital stay for individuals who are living at home. Telemonitoring can also be applied on a long-term basis to elderly persons to detect gradual deterioration in their health status, which may imply a reduction in their ability to live independently. Mobility is a good indicator of health status and thus by monitoring mobility, clinicians may assess the health status of elderly persons. This article reviews the architecture of health smart home, wearable, and combination systems for the remote monitoring of the mobility of elderly persons as a mechanism of assessing the health status of elderly persons while in their own living environment.

Accelerometers in rehabilitation medicine for older adults

Recent technological developments have led to the production of inexpensive, miniature accelerometer sensors with potential for use in a clinical setting. These sensors can provide reliable information on mobility and objective measurement of gait. They are currently used mainly in a research setting; however, with recent advances, incorporation into clinical practice is possible. For illustrative purposes this paper describes some current applications of accelerometers in gait and balance evaluation, falls risk assessment and mobility monitoring. Accelerometers provide quantitative measures of gait, they are capable of identifying specific gait changes in older adults and in fallers and can be used to objectively quantify ambulatory activity levels. Accelerometers have many potential uses in monitoring of patients in rehabilitation. They provide an added objective and quantitative dimension to gait analysis when combined with clinical assessment. They have the potential in the future to stratify falls risk facilitating early initiation of appropriate therapeutic intervention, thus reducing further falls. The challenge facing clinicians and biomedical engineers is to further harness this technology making it part of everyday clinical practice.