Lakhasly

Alternatively to pressure sensors, accelerometer-based systems were later developed [39].De Gauna et. al [45] proposed three methods, with different algorithmic complexities: an improved linear filtering, that computes compression depth signal from acceleration signal by using a designed band-pass filter that performs double-integration; the detection of the zero-crossing instants in the velocity signal, which computes velocity signal from acceleration signal by performing single-integration and calculates compression depth and rate from the analysis of zero crossing instants; and the spectral analysis of the acceleration signal, which computes compression depth and rate directly from the acceleration signal by using the fast Fourier transform.Among the discussed alternatives, the algorithm based on the spectral analysis of the acceleration signal provided the most accurate results, since it computes compression depth and rate directly from the acceleration signal, avoiding integration errors, although it has the highest algorithmic complexity.However, since all accelerometers are sensible to movement-associated noise, double-integrating the acceleration signal is prone to cumulative errors which cause a significant drift in displacement signal, impeding an accurate estimation of compression depth [39].Other errors are introduced in compression depth calculation by the use of accelerometers, with origin on the compliant surface effect [39], and on sensor tilting during compressions, since gravitational acceleration component is dependent of sensor orientation [39].Some developments have been made to surpass the integration cumulative errors by using signal processing techniques over the extracted acceleration signal.

Alternatively to pressure sensors, accelerometer-based systems were later developed [39]. These
devices measure acceleration of the manikin’s chest during chest compressions, and use in-built
processors to calculate compressions depth in real-time, leading to simple and cheap devices [39], as
Zoll PocketCPR [23], a commercially available accelerometer-based stand-alone feedback device.
Thus, it is not possible to obtain direct feedback on compression parameters from raw acceleration
signal, meaning that signal processing is always required. The most straightforward approach to obtain
chest displacement from acceleration is by applying double integration. However, since all
accelerometers are sensible to movement-associated noise, double-integrating the acceleration signal is
prone to cumulative errors which cause a significant drift in displacement signal, impeding an accurate
estimation of compression depth [39]. Considering the small distance intended to be measured, these
cumulative errors have a very relevant impact in compression depth calculations. Other errors are
introduced in compression depth calculation by the use of accelerometers, with origin on the compliant
surface effect [39], and on sensor tilting during compressions, since gravitational acceleration
component is dependent of sensor orientation [39]. Some developments have been made to surpass the
integration cumulative errors by using signal processing techniques over the extracted acceleration
signal. Resetting initial conditions of velocity and displacement after each compression is the most
common and simple approach to overcome this limitation, with the cost of chest recoil information
being lost, meaning that the actual compression depth value is unknown [45]. De Gauna et. al [45]
proposed three methods, with different algorithmic complexities: an improved linear filtering, that
computes compression depth signal from acceleration signal by using a designed band-pass filter that
performs double-integration; the detection of the zero-crossing instants in the velocity signal, which
computes velocity signal from acceleration signal by performing single-integration and calculates
compression depth and rate from the analysis of zero crossing instants; and the spectral analysis of the
acceleration signal, which computes compression depth and rate directly from the acceleration signal
by using the fast Fourier transform. Among the discussed alternatives, the algorithm based on the
spectral analysis of the acceleration signal provided the most accurate results, since it computes
compression depth and rate directly from the acceleration signal, avoiding integration errors, although
it has the highest algorithmic complexity.