Classification of gait quality for biofeedback to improve heel-to-toe gait

A feature of healthy gait is a clearly defined heel strike upon initial contact of the foot with the ground. However, a common consequence of ageing is deterioration of the heel first nature of gait towards a shuffling gait (flat foot at contact). Physiotherapy can be effective in correcting this but is costly and labour intensive. Gait rehabilitation could be accelerated with home exercise, guided by a biofeedback device that distinguishes between heel first and shuffling gait. This paper describes an algorithm that distinguishes between heel-to-toe gait and shuffling gait on the basis of angular velocity of the foot, using an inertial measurement unit. Measurements were made of normal and abnormal gait and used to develop an algorithm that distinguishes between good and bad steps. Results demonstrate very good algorithm performance, with a classification accuracy at the accuracy-optimal threshold of 92.7% when compared with physiotherapist labels. The sensitivity and specificity at this threshold are 84.4% and 97.5% respectively. These performance metrics suggest that this algorithm is usable in a biofeedback device.

[1]  L. Rubenstein Falls in older people: epidemiology, risk factors and strategies for prevention. , 2006, Age and ageing.

[2]  Richard A. Brand,et al.  The biomechanics and motor control of human gait: Normal, elderly, and pathological , 1992 .

[3]  R. Cumming,et al.  Prospective study of the impact of fear of falling on activities of daily living, SF-36 scores, and nursing home admission. , 2000, The journals of gerontology. Series A, Biological sciences and medical sciences.

[4]  U. Croce,et al.  A kinematic and kinetic comparison of overground and treadmill walking in healthy subjects. , 2007, Gait & posture.

[5]  O. Edholm,et al.  Studies of gait and mobility in the elderly. , 1981, Age and ageing.

[6]  R. Lipton,et al.  Epidemiology of Gait Disorders in Community‐Residing Older Adults , 2006, Journal of the American Geriatrics Society.

[7]  Yosuke Kurihara,et al.  Accelerometry-Based Gait Analysis and Its Application to Parkinson's Disease Assessment— Part 1: Detection of Stride Event , 2014, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[8]  D. Winter Biomechanics and motor control of human gait: normal, elderly and pathological - 2nd edition , 1991 .

[9]  Ruud W. Selles,et al.  Automated Detection of Instantaneous Gait Events Using Time Frequency Analysis and Manifold Embedding , 2013, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[10]  Amy Loutfi,et al.  Evaluation of the android-based fall detection system with physiological data monitoring , 2013, 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[11]  G. Deuschl,et al.  Falls in frequent neurological diseases , 2004, Journal of Neurology.

[12]  Jeffrey M. Hausdorff Gait dynamics in Parkinson's disease: common and distinct behavior among stride length, gait variability, and fractal-like scaling. , 2009, Chaos.

[13]  W. Marsden I and J , 2012 .

[14]  B. Bloem,et al.  Neurological gait disorders in elderly people: clinical approach and classification , 2007, The Lancet Neurology.

[15]  R. Baloh,et al.  The effect of aging on visual-vestibuloocular responses , 2004, Experimental Brain Research.