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

• Published in: Medical engineering & physics Vol. 30; no. 7; pp. 937 - 946
• Main Authors: Bourke, A.K., O’Donovan, K.J., ÓLaighin, G.
• Format: Journal Article
• Language: English
• Published: England Elsevier Ltd 01.09.2008
• Subjects: Accelerometer; Accidental Falls - prevention & control; Activities of Daily Living; ADL; Algorithms; Biomechanical Phenomena; Calibration; Computational Biology; Computer Simulation; Diagnosis, Differential; Equipment Design; Falling; Gyroscope; Humans; Image Interpretation, Computer-Assisted - methods; Monitoring, Ambulatory - methods; Movement - physiology; Pre-impact fall-detection; Radiology; Reproducibility of Results; Software; Vertical velocity profiles; Pre-impact fall-detection; Accelerometer; Falling; ADL; Gyroscope; Vertical velocity profiles
• ISSN: 1350-4533; 1873-4030
• DOI: 10.1016/j.medengphy.2007.12.003

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.