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An adaptive gyroscope-based algorithm for temporal gait analysis

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Body-worn kinematic sensors have been widely proposed as the optimal solution for portable, low cost, ambulatory monitoring of gait. This study aims to evaluate an adaptive gyroscope-based algorithm for automated temporal gait analysis using body-worn wireless gyroscopes. Gyroscope data from nine healthy adult subjects performing four walks at four different speeds were then compared against data acquired simultaneously using two force plates and an optical motion capture system. Data from a poliomyelitis patient, exhibiting pathological gait walking with and without the aid of a crutch, were also compared to the force plate. Results show that the mean true error between the adaptive gyroscope algorithm and force plate was −4.5 ± 14.4 ms and 43.4 ± 6.0 ms for IC and TC points, respectively, in healthy subjects. Similarly, the mean true error when data from the polio patient were compared against the force plate was −75.61 ± 27.53 ms and 99.20 ± 46.00 ms for IC and TC points, respectively. A comparison of the present algorithm against temporal gait parameters derived from an optical motion analysis system showed good agreement for nine healthy subjects at four speeds. These results show that the algorithm reported here could constitute the basis of a robust, portable, low-cost system for ambulatory monitoring of gait.

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This research was completed as part of a wider programme of research within the TRIL Centre (Technology Research for Independent Living). The TRIL Centre is a multi-disciplinary research centre, bringing together researchers from UCD, TCD, NUIG, Intel, and GE Healthcare, funded by Intel, GE Healthcare and IDA Ireland ( The authors would like to thank Dr. Emer Doheny for providing useful feedback on the manuscript as well as Mr. Ben Dromey for his help with graphically detailing the experimental layout.

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Correspondence to Barry R. Greene.

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Greene, B.R., McGrath, D., O’Neill, R. et al. An adaptive gyroscope-based algorithm for temporal gait analysis. Med Biol Eng Comput 48, 1251–1260 (2010).

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