Alan K. Bourke

Evaluation of waist-mounted tri-axial accelerometer based fall-detection algorithms during scripted and continuous unscripted activities.

It is estimated that by 2050 more than one in five people will be aged 65 or over. In this age group, falls are one of the most serious life-threatening events that can occur. Their automatic detection would help reduce the time of arrival of medical attention, thus reducing the mortality rate and in turn promoting independent living. This study evaluated a variety of existing and novel fall-detection algorithms for a waist-mounted accelerometer based system. In total, 21 algorithms of varying degrees of complexity were tested against a comprehensive data-set recorded from 10 young healthy volunteers performing 240 falls and 120 activities of daily living (ADL) and 10 elderly healthy volunteers performing 240 scripted ADL and 52.4 waking hours of continuous unscripted normal ADL. Results show that using an algorithm that employs thresholds in velocity, impact and posture (velocity+impact+posture) achieves 100% specificity and sensitivity with a false-positive rate of less than 1 false-positive (0.6 false-positives) per day of waking hours. This algorithm is the most suitable method of fall-detection, when tested using continuous unscripted activities performed by elderly healthy volunteers, which is the target environment for a fall-detection device.

The identification of vertical velocity profiles using an inertial sensor to investigate pre-impact detection of falls

This study investigates distinguishing falls from normal Activities of Daily Living (ADL) by thresholding of the vertical velocity of the trunk. Also presented is the design and evaluation of a wearable inertial sensor, capable of accurately measuring these vertical velocity profiles, thus providing an alternative to optical motion capture systems. Five young healthy subjects performed a number of simulated falls and normal ADL and their trunk vertical velocities were measured by both the optical motion capture system and the inertial sensor. Through vertical velocity thresholding (VVT) of the trunk, obtained from the optical motion capture system, at -1.3 m/s, falls can be distinguished from normal ADL, with 100% accuracy and with an average of 323 ms prior to trunk impact and 140 ms prior to knee impact, in this subject group. The vertical velocity profiles obtained using the inertial sensor, were then compared to those obtained using the optical motion capture system. The signals from the inertial sensor were combined to produce vertical velocity profiles using rotational mathematics and integration. Results show high mean correlation (0.941: Coefficient of Multiple Correlations) and low mean percentage error (6.74%) between the signals generated from the inertial sensor to those from the optical motion capture system. The proposed system enables vertical velocity profiles to be measured from elderly subjects in a home environment where as this has previously been impractical.

Activity classification using a single chest mounted tri-axial accelerometer

Accelerometer-based activity monitoring sensors have become the most suitable means for objective assessment of mobility trends within patient study groups. The use of minimal, low power, IC (integrated circuit) components within these sensors enable continuous (long-term) monitoring which provides more accurate mobility trends (over days or weeks), reduced cost, longer battery life, reduced size and weight of sensor. Using scripted activities of daily living (ADL) such as sitting, standing, walking, and numerous postural transitions performed under supervised conditions by young and elderly subjects, the ability to discriminate these ADL were investigated using a single tri-axial accelerometer, mounted on the trunk. Data analysis was performed using Matlab® to determine the accelerations performed during eight different ADL. Transitions and transition types were detected using the scalar (dot) product technique and vertical velocity estimates on a single tri-axial accelerometer was compared to a proven discrete wavelet transform method that incorporated accelerometers and gyroscopes. Activities and postural transitions were accurately detected by this simplified low-power kinematic sensor and activity detection algorithm with a sensitivity and specificity of 86-92% for young healthy subjects in a controlled setting and 83-89% for elderly healthy subjects in a home environment.

Testing of a long-term fall detection system incorporated into a custom vest for the elderly

A fall detection system and algorithm, incorporated into a custom designed garment has been developed. The developed fall detection system uses a tri-axial accelerometer to detect impacts and monitor posture. This sensor is attached to a custom designed vest, designed to be worn by the elderly person under clothing. The fall detection algorithm was developed and incorporates both impact and posture detection capability. The vest and fall algorithm was tested by two teams of 5 elderly subjects who wore the sensor system in turn for 2 week each and were monitored for 8 hours a day.

Cross-Sectional analysis of levels and patterns of objectively measured sedentary time in adolescent females

BackgroundAdolescent females have been highlighted as a particularly sedentary population and the possible negative effects of a sedentary lifestyle are being uncovered. However, much of the past sedentary research is based on self-report or uses indirect methods to quantity sedentary time. Total time spent sedentary and the possible intricate sedentary patterns of adolescent females have not been described using objective and direct measure of body inclination. The objectives of this article are to examine the sedentary levels and patterns of a group of adolescent females using the ActivPAL™ and to highlight possible differences in sedentary levels and patterns across the week and within the school day. A full methodological description of how the data was analyzed is also presented.MethodsOne hundred and eleven adolescent females, age 15-18 yrs, were recruited from urban and rural areas in the Republic of Ireland. Participants wore an ActivPAL physical activity monitor for a 7.5 day period. The ActivPAL directly reports total time spent sitting/lying every 15 seconds and accumulation (frequency and duration) of sedentary activity was examined using a customized MATLAB® computer software programme.ResultsWhile no significant difference was found in the total time spent sitting/lying over the full 24 hour day between weekday and weekend day (18.8 vs. 18.9 hours; p = .911), significantly more sedentary bouts of 1 to 5 minutes and 21 to 40 minutes in duration were accumulated on weekdays compared to weekend days (p < .001). The mean length of each sedentary bout was also longer (9.8 vs. 8.8 minutes; p < .001). When school hours (9 am-3 pm) and after school hours (4 pm-10 pm) were compared, there was no difference in total time spent sedentary (3.9 hours; p = .796) but the pattern of accumulation of the sedentary time differed. There were a greater number of bouts of > 20 minutes duration during school hours than after school hours (4.7 vs. 3.5 bouts; p < .001) while after school time consisted of shorter bouts < 20 minutes.ConclusionsSchool is highlighted as a particularly sedentary setting for adolescent females. Interventions to decrease sedentary time at school and the use of wearable devices which distinguish posture should be encouraged when examining sedentary patterns and behaviors in this population.

Assessment of waist-worn tri-axial accelerometer based fall-detection algorithms using continuous unsupervised activities

This study aims to evaluate a variety of existing and novel fall detection algorithms, for a waist mounted accelerometer based system. Algorithms were tested against a comprehensive data-set recorded from 10 young healthy subjects performing 240 falls and 120 activities of daily living and 10 elderly healthy subjects performing 240 scripted and 52.4 hours of continuous unscripted normal activities.

Fall-detection through vertical velocity thresholding using a tri-axial accelerometer characterized using an optical motion-capture system

Falls in the elderly population are a major problem for todays society. The immediate automatic detection of such events would help reduce the associated consequences of falls. This paper describes the development of an accurate, accelerometer-based fall detection system to distinguish between Activities of Daily Living (ADL) and falls. It has previously been shown that falls can be distinguished from normal ADL through vertical velocity thresholding using an optical motion capture system. In this study however accurate vertical velocity profiles of the trunk were generated by simple signal processing of the signals from a tri-axial accelerometer (TA).

The measurement of sedentary patterns and behaviors using the activPAL™ Professional physical activity monitor

Epidemiological studies have associated the negative effects of sedentary time and sedentary patterns on health indices. However, these studies have used methodologies that do not directly measure the sedentary state. Recent technological developments in the area of motion sensors have incorporated inclinometers, which can measure the inclination of the body directly, without relying on self-report or count thresholds. This paper aims to provide a detailed description of methodologies used to examine a range of relevant variables, including sedentary levels and patterns from an inclinometer-based motion sensor. The activPAL Professional physical activity logger provides an output which can be interpreted and used without the need for further processing and additional variables were derived using a custom designed MATLAB® computer program. The methodologies described have been implemented on a sample of 44 adolescent females, and the results of a range of daily physical activity and sedentary variables are described and presented. The results provide a range of objectively measured and objectively processed variables, including total time spent sitting/lying, standing and stepping, number and duration of daily sedentary bouts and both bed hours and non-bed hours, which may be of interest when making association between physical activity, sedentary behaviors and health indices.

The design and development of a long-term fall detection system incorporated into a custom vest for the elderly.

A fall detection system and algorithm, incorporated into a custom designed garment has been developed. The developed fall detection system uses a tri-axial accelerometer, microcontroller, battery and Bluetooth module. This sensor is attached to a custom designed vest, designed to be worn by the elderly person under clothing. The fall detection algorithm was developed and incorporates both impact and posture detection capability.

Embedded fall and activity monitoring for a wearable ambient assisted living solution for older adults

With the rapidly increasing over 60 and over 80 age groups in society, greater emphasis will be put on technology to detect emergency situations, such as falls, in order to promote independent living. This paper describes the development and deployment of fall-detection, activity classification and energy expenditure algorithms, deployed in a tele-monitoring system. These algorithms were successfully tested in an end-user trial involving 9 elderly volunteers using the system for 28 days.