A wavelet-based energetic approach for the detection of contact events during movement

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Human movement has been the subject of investigation since the fifth century when early scientists and researchers attempted to model the human musculoskeletal system. The anatomical complexities of the human body have made it a constant source of research to this day with many anatomical, physiological, mechanical, environmental, sociological and psychological studies undertaken to define its key elements. These studies have utilised modern day techniques to assess human movement in many illnesses. One such modern technique has been direct measurement by accelerometry, which was first suggested in the 1970s but has only been refined and perfected during the last 10-15 years. Direct measurement by accelerometry has seen the introduction of the successful implementation of low power, low cost electronic sensors that have been employed in clinical and home environments for the constant monitoring of patients (and their controls). The qualitative and quantitative data provided by these sensors make it possible for engineers, clinicians and physicians to work together to be able to help their patients in overcoming their physical disability. This paper presents the underlying biomechanical elements necessary to understand and study human movement. It also reflects on the sociological elements of human movement and why it is important in patient life and well being. Finally the concept of direct measurement by accelerometry is presented with past studies and modern techniques used for data analysis.
The estimation of gait temporal parameters with inertial measurement units (IMU) is a research topic of interest in clinical gait analysis. Several methods, based on the use of a single IMU mounted at waist level, have been proposed for the estimate of these parameters showing satisfactory performance when applied to the gait of healthy subjects. However, the above mentioned methods were developed and validated on healthy subjects and their applicability in pathological gait conditions was not systematically explored. We tested the three best performing methods found in a previous comparative study on data acquired from 10 older adults, 10 hemiparetic, 10 Parkinson's disease and 10 Huntington's disease subjects. An instrumented gait mat was used as gold standard. When pathological populations were analyzed, missed or extra events were found for all methods and a global decrease of their performance was observed to different extents depending on the specific group analyzed. The results revealed that none of the tested methods outperformed the others in terms of accuracy of the gait parameters determination for all the populations except the Parkinson's disease subjects group for which one of the methods performed better than others. The hemiparetic subjects group was the most critical group to analyze (stride duration errors between 4-5 % and step duration errors between 8-13 % of the actual values across methods). Only one method provides estimates of the stance and swing durations which however should be interpreted with caution in pathological populations (stance duration errors between 6-14 %, swing duration errors between 10-32 % of the actual values across populations). Copyright © 2015 Elsevier B.V. All rights reserved.
Wavelet transform has emerged over recent years as a favoured tool for the investigation of biomedical signals, which are highly non-stationary by their nature. A relevant wavelet-based approach in the analysis of biomedical signals exploits the capability of wavelet transform to separate the signal energy among different frequency bands (i.e., different scales), realizing a good compromise between temporal and frequency resolution. The rationale of this paper is twofold: (i) to present a mathematical formalization of energy calculation from wavelet coefficients, in order to obtain uniformly time distributed atoms of energy across all the scales; (ii) to show two different applications of the wavelet-based energetic approach to biomedical signals. One application concerns the study of epileptic brain electrical activity, with the aim of identifying typical patterns of energy redistribution during the seizure. Results obtained from this method provide interesting indications on the complex spatio-temporal dynamics of the seizure. The other application concerns the electro-oculographic tracings, with the purpose of realizing an automatic detector of a particular type of eye movements (slow eye movements), important to identify sleep phases. The algorithm is able to identify this eye movement pattern efficiently, characterizing it in rigorous energetic terms. The energetic approach built within the framework of the multiresolution decomposition appears as a powerful and versatile tool for the investigation and characterization of transient events in biomedical signals.
The purpose of this study was to identify consistent features in the signals supplied by a single inertial measurement unit (IMU), or thereof derived, for the identification of foot-strike and foot-off instants of time and for the estimation of stance and stride duration during the maintenance phase of sprint running. Maximal sprint runs were performed on tartan tracks by five amateur and six elite athletes, and durations derived from the IMU data were validated using force platforms and a high-speed video camera, respectively, for the two groups. The IMU was positioned on the lower back trunk (L1 level) of each athlete. The magnitudes of the acceleration and angular velocity vectors measured by the IMU, as well as their wavelet-mediated first and second derivatives were computed, and features related to foot-strike and foot-off events sought. No consistent features were found on the acceleration signal or on its first and second derivatives. Conversely, the foot-strike and foot-off events could be identified from features exhibited by the second derivative of the angular velocity magnitude. An average absolute difference of 0.005 s was found between IMU and reference estimates, for both stance and stride duration and for both amateur and elite athletes. The 95% limits of agreement of this difference were less than 0.025 s. The results proved that a single, trunk-mounted IMU is suitable to estimate stance and stride duration during sprint running, providing the opportunity to collect information in the field, without constraining or limiting athletes' and coaches' activities.
  • Godfrey
Godfrey. Med. Eng. Phys 2008;30(December (10)):1364-86, 2015.
  • Trojaniello
Trojaniello, et al. Gait Posture 2014;40(September (4)):487-92.
  • Trojaniello
Trojaniello, et al. Gait Posture 2015;42(September (3)):310-6.
  • Bergamini
Bergamini, et al. J. Biomech 2012;45(April (6)):1123-6.
  • Lee
Lee, et al. J. Sci. Med. Sport 2010;13(March (2)):270-3.
  • Magosso
Magosso, et al. Appl. Math. Comput 2009;207:42-62.
O12 Frequency evaluation of gait trunk acceleration signal: A longitudinal study O12 Frequency evaluation of gait trunk acceleration signal: A longitudinal study
  • M Mancini
Mancini M, et al. J. Bioeng. Biomed. Sci 2011;
  • D Trojaniello
Trojaniello D, et al. J. Neuroeng. Rehabil 2014;11(1):152. References