Real-time gait event detection for transfemoral amputees during ramp ascending and descending

Events and phases detection of the human gait are vital for controlling prosthesis, orthosis and functional electrical stimulation (FES) systems. Wearable sensors are inexpensive, portable and have fast processing capability. They are frequently used to assess spatio-temporal, kinematic and kinetic parameters of the human gait which in turn provide more details about the human voluntary control and ampute-eprosthesis interaction. This paper presents a reliable real-time gait event detection algorithm based on simple heuristics approach, applicable to signals from tri-axial gyroscope for lower limb amputees during ramp ascending and descending. Experimental validation is done by comparing the results of gyroscope signal with footswitches. For healthy subjects, the mean difference between events detected by gyroscope and footswitches is 14 ms and 10.5 ms for initial contact (IC) whereas for toe off (TO) it is -5 ms and -25 ms for ramp up and down respectively. For transfemoral amputee, the error is slightly higher either due to the placement of footswitches underneath the foot or the lack of proper knee flexion and ankle plantarflexion/dorsiflexion during ramp up and down. Finally, repeatability tests showed promising results.

[1]  Paola Catalfamo,et al.  Gait Event Detection on Level Ground and Incline Walking Using a Rate Gyroscope , 2010, Sensors.

[2]  Rafael C González,et al.  Real-time gait event detection for normal subjects from lower trunk accelerations. , 2010, Gait & posture.

[3]  W. Jeffcoate,et al.  Variation in the recorded incidence of amputation of the lower limb in England , 2012, Diabetologia.

[4]  M H Granat,et al.  Virtual artificial sensor technique for functional electrical stimulation. , 1998, Medical engineering & physics.

[5]  H.B.K. Boom,et al.  Automatic stance-swing phase detection from accelerometer data for peroneal nerve stimulation , 1990, IEEE Transactions on Biomedical Engineering.

[6]  Guido Pasquini,et al.  Online Phase Detection Using Wearable Sensors for Walking with a Robotic Prosthesis , 2014, Sensors.

[7]  M. Hanlon,et al.  Real-time gait event detection using wearable sensors. , 2006, Gait & posture.

[8]  W. Ondo,et al.  Ambulatory monitoring of freezing of gait in Parkinson's disease , 2008, Journal of Neuroscience Methods.

[9]  A L Hof,et al.  Uphill and downhill walking in unilateral lower limb amputees. , 2008, Gait & posture.

[10]  J Y Goulermas,et al.  Predicting lower limb joint kinematics using wearable motion sensors. , 2008, Gait & posture.

[11]  Kamiar Aminian,et al.  Spatio-temporal parameters of gait measured by an ambulatory system using miniature gyroscopes. , 2002, Journal of biomechanics.

[12]  M H Granat,et al.  A practical gait analysis system using gyroscopes. , 1999, Medical engineering & physics.

[13]  Salim Ghoussayni,et al.  Application of angular rate gyroscopes as sensors in electrical orthoses for foot drop correction , 2004 .

[14]  Kathryn Ziegler-Graham,et al.  Estimating the prevalence of limb loss in the United States: 2005 to 2050. , 2008, Archives of physical medicine and rehabilitation.

[15]  R. J. Jefferson,et al.  Performance of three walking orthoses for the paralysed: a case study using gait analysis , 1990, Prosthetics and orthotics international.

[16]  J. Allum,et al.  Gait event detection using linear accelerometers or angular velocity transducers in able-bodied and spinal-cord injured individuals. , 2006, Gait & posture.

[17]  Jung-Keun Lee,et al.  Quasi real-time gait event detection using shank-attached gyroscopes , 2011, Medical & Biological Engineering & Computing.

[18]  C. M. Lessells,et al.  Unrepeatable repeatabilities: a common mistake , 1987 .

[19]  Peter J Beek,et al.  Online gait event detection using a large force platform embedded in a treadmill. , 2008, Journal of biomechanics.

[20]  Malte Bellmann,et al.  Comparative biomechanical analysis of current microprocessor-controlled prosthetic knee joints. , 2010, Archives of physical medicine and rehabilitation.

[21]  J. S. Rietman,et al.  Gait analysis in prosthetics: Opinions, ideas and conclusions , 2002, Prosthetics and orthotics international.

[22]  Qiang He,et al.  Individual recognition from periodic activity using hidden Markov models , 2000, Proceedings Workshop on Human Motion.