Publications [#371252] of Hau-Tieng Wu

Papers Published

1. Wu, H-T; Harezlak, J, Application of de-shape synchrosqueezing to estimate gait cadence from a single-sensor accelerometer placed in different body locations., Physiological measurement, vol. 44 no. 5 (May, 2023) [doi]
   (last updated on 2024/08/30)

   Abstract:
   Objective.Commercial and research-grade wearable devices have become increasingly popular over the past decade. Information extracted from devices using accelerometers is frequently summarized as 'number of steps' (commercial devices) or 'activity counts' (research-grade devices). Raw accelerometry data that can be easily extracted from accelerometers used in research, for instance ActiGraph GT3X+, are frequently discarded.Approach.Our primary goal is proposing an innovative use of thede-shape synchrosqueezing transformto analyze the raw accelerometry data recorded from a single sensor installed in different body locations, particularly the wrist, to extractgait cadencewhen a subject is walking. The proposed methodology is tested on data collected in a semi-controlled experiment with 32 participants walking on a one-kilometer predefined course. Walking was executed on a flat surface as well as on the stairs (up and down).Main results.The cadences of walking on a flat surface, ascending stairs, and descending stairs, determined from the wrist sensor, are 1.98 ± 0.15 Hz, 1.99 ± 0.26 Hz, and 2.03 ± 0.26 Hz respectively. The cadences are 1.98 ± 0.14 Hz, 1.97 ± 0.25 Hz, and 2.02 ± 0.23 Hz, respectively if determined from the hip sensor, 1.98 ± 0.14 Hz, 1.93 ± 0.22 Hz and 2.06 ± 0.24 Hz, respectively if determined from the left ankle sensor, and 1.98 ± 0.14 Hz, 1.97 ± 0.22 Hz, and 2.04 ± 0.24 Hz, respectively if determined from the right ankle sensor. The difference is statistically significant indicating that the cadence is fastest while descending stairs and slowest when ascending stairs. Also, the standard deviation when the sensor is on the wrist is larger. These findings are in line with our expectations.Conclusion.We show that our proposed algorithm can extract the cadence with high accuracy, even when the sensor is placed on the wrist.