Short communicationEvaluation of waist-mounted tri-axial accelerometer based fall-detection algorithms during scripted and continuous unscripted activities
Introduction
By 2050, the proportion of the world’s population aged 65 or older is set to double to more than 1 in 5, with those aged 80 or older set to almost treble (UN, 2009). Falls and related injuries are not only life threatening to older people (Noury et al., 2008b), but also herald an inability to live independently (Gurley et al., 1996). Conversely, the automatic detection of falls facilitates the provision of early medical attention, reduces the consequences of prolonged lying following a fall (Lord et al., 2001) and promotes an independent lifestyle (Brownsell et al., 2000).
In recent years the number of proposed fall-detection systems developed has increased dramatically (Noury et al., 2008b). The waist is a popular location for fall-detection systems (Noury et al., 2008a), as it provides reliable indications of full-body movement, in addition to its ease of acceptance by allowing attachment to an existing waist band (Mathie et al., 2004).
Recently, Kangas et al. (2009) evaluated a set of fall-detection algorithms (Kangas et al., 2008) on data recorded from 20 middle-aged volunteers (40–65 years old) performing 6 different falls (240 falls in total) and 4 scripted activities of daily living (ADL), these same ADL were also recorded from 21 adults (aged 58–98 years) from a residential care unit (164 ADL in total). They showed that thresholding of impact and posture can achieve 97.5% sensitivity and 100% specificity.
Chao et al. (2009) recorded 7 male subjects (25±1.5 years) performing 8 different fall types and 17 functional ADL (total of 56 falls and 119 ADL). Using a combination algorithm of acceleration cross-product and post-fall posture, a sensitivity of 100% and >98% specificity were obtained.
The aim of this study is to evaluate the performance of 21, novel and existing, fall-detection algorithms of varying complexity on a comprehensive data-set containing simulated falls, normal ADL and including; continuous unscripted ADL performed by both urban and rural based elderly volunteers over extended time periods, which has not been previously performed. It is envisaged, such an evaluation will uncover a more appropriate fall-detection algorithm for the detection of falls in the elderly, using a waist-worn device.
Section snippets
Materials and methods
Waist tri-axial accelerometer readings were recorded (right anterior iliac crest of the pelvis) during simulated falls by young volunteers and ADL performed by young and elderly volunteers, using a custom designed sensor (van de Ven et al., 2008), shown in Fig. 1.
The accelerometer signals were sampled at 200 Hz at 12 bit resolution; each signal was low-pass filtered using an onboard first-order low-pass Butterworth analogue filter at a cut-off frequency of 100 Hz. The University of Limerick
Results
By examining the recorded values for signals RSS, RSSD (defined below), Vve, tFE and tRE (defined in Fig. 2), thresholds that ensure 100% sensitivity were obtained from Fig. 4, Fig. 5 and are displayed in Table 1. Thus all falls are detected correctly.
The minimum peak values for all the falls performed were used to determine the UFT, LFT, UFTD and VT thresholds for the RSS, RSSD and the Vve profiles respectively. The dynamic Root-Sum-of-Squares, RSSD was calculated using the formula by Kangas
Discussion and conclusion
This study evaluated the performance of different combinations of novel and existing fall-detection algorithms of varying complexity on a comprehensive data-set of falls and ADL, including scripted and continuous unscripted activities from urban and rural dwelling elderly volunteers. Previous studies (Kangas et al., 2008, Kangas et al., 2009) found reliable fall-detection can be achieved using simple algorithms with IMPACT+POSTURE (z-axis), which agrees with our findings. However, the most
Conflict of interest
The authors have no commercial stake in the outcomes of this research and no patent has been filed in relation to this work.
Acknowledgments
We are grateful to all volunteers who gave their time to the CAALYX project 〈www.caalyx.eu〉 and the eCAALYX project 〈www.ecaalyx.org〉.
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