Application of the Teagar-Kaiser energy operator and wavelet transform for detection of finger tapping contact and release times using accelerometery

The Teager-Kaiser energy operator (TKEO), when applied to a signal gives an estimation of the instantaneous energy of that signal. It, therefore, accentuates both frequency and amplitude changes in a signal. To date, it has been primarily used in communications systems and most popularly in electromyographic signal analysis to detect bursts of muscle activity, however, it has the potential to be used in a number of applications including accelerometer and movement data.A new algorithm was developed which used the TKEO to detect contact times during a finger tapping task from accelerometer data recorded from the index finger. The accuracy of the algorithm was assessed in 7 healthy control subjects during continuous finger tapping across a range of frequencies from 0.5Hz to 2.5Hz. The algorithm proved to be sensitive, correctly identifying at least 99% of all contacts in each of the finger tapping conditions that were tested. The mean absolute error of the contact detection is 14.7 ± 6 ms, while the mean absolute error of the release detection is 36.5 ± 36.3 ms. The proposed algorithm provides a method for the automatic detection of the temporal occurrences of the events of the finger tapping task using only a tri-axial accelerometer. The approach presented provides a means for objective assessment of finger tapping tasks for evaluation of the fine dexterity of the upper limb.