Optimal Placement of Accelerometers for the Detection of Everyday Activities

Abstract

This article describes an investigation to determine the optimal placement of accelerometers for the purpose of detecting a range of everyday activities. The paper investigates the effect of combining data from accelerometers placed at various bodily locations on the accuracy of activity detection. Eight healthy males participated within the study. Data were collected from six wireless tri-axial accelerometers placed at the chest, wrist, lower back, hip, thigh and foot. Activities included walking, running on a motorized treadmill, sitting, lying, standing and walking up and down stairs. The Support Vector Machine provided the most accurate detection of activities of all the machine learning algorithms investigated. Although data from all locations provided similar levels of accuracy, the hip was the best single location to record data for activity detection using a Support Vector Machine, providing small but significantly better accuracy than the other investigated locations. Increasing the number of sensing locations from one to two or more statistically increased the accuracy of classification. There was no significant difference in accuracy when using two or more sensors. It was noted, however, that the difference in activity detection using single or multiple accelerometers may be more pronounced when OPEN ACCESSSensors 2013, 13 9184 trying to detect finer grain activities. Future work shall therefore investigate the effects of accelerometer placement on a larger range of these activities.

Validity of wrist worn accelerometers and comparability between hip and wrist placement sites in estimating physical activity behaviour in preschool children

“…Although it is agreed that accelerometer placement has an effect on the measurement of bodily acceleration, there is still some debate over the ideal location of the sensor for particular applications (Cleland et al 2013). A strength this study, is that it is one of the few studies to explore the relationship between output from hip and wrist worn Actigraph accelerometers and to date the first exploring this in the preschool population.…”

“…The rationale being that this attachment places the accelerometers close to the centre of body mass and aligns the sensitive axis with the vertical plane (Puyau et al 2002). However, a recognised limitation of hip worn accelerometers is that they are unable to take into account the increased energy expenditure which occurs with upper limb movements (Janz et al 2006) and as a consequence can underestimate activity (Cleland et al 2013). Following a review of the evidence, Cliff et al (2009) recommended the hip position for the placement of accelerometers during data collection.…”

Accuracy of identification of low or high risk lifting during standardised lifting situations

“…Two accelerometers (ActiGraph GT9X Link, ActiGraph, Pensacola, USA) were placed on the upper back at the level of T1-T2 ) and on the lower back at the level of L5 (Cleland et al 2013), and they were fixed using stretch tape (Fixomull stretch). The accelerometers were initiated to sample at a sampling rate of 100 Hz using the manufacturer's software (ActiLife version 6.13.1).…”

SisFall: A Fall and Movement Dataset

“…The device was fixed to the waist of the participants (Figure 1). This location provides high distinction among activities for a single accelerometer system [24,25]. Only acceleration data acquired with the ADXL345 sensor was used in this work, as it is energy efficient and provides the larger span.…”