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A marker based kinematic method of identifying initial contact during gait for use in real-time visual feedback applications
S98
Abstracts / Gait & Posture 36 (2012) S1–S101
References [1] Lance. Spasticity: disordered motor control. Chicago; 1980, p. 485–5. [2] Crenna. Neurosci Biobehav Rev 1998;22:571–8.
doi:10.1016/j.gaitpost.2011.10.346 P75 Evaluating gait adaptability in adolescents with CP—A treadmill approach M. Hoesl 1,∗ , L. Bruinink 2 , J. Harlaar 3 , H. Houdijk 1 1
Research Institute Move, Faculty of Human Movement Sciences, VU University Amsterdam, Amsterdam, Netherlands 2 Research & Development, Heliomare Rehabilitation, Wijk aan Zee, Netherlands 3 Department of Rehabilitation Medicine, VU University Medical Center, Amsterdam, Netherlands
Introduction: Next to efficiency, adolescents with CP are concerned about safe mobility [1]. To keep up with peers and navigate in complex environments of daily life, their gait needs to be adaptable. Muscle weakness, spasticity and a lack of selective motor control may affect their ability to execute gait adaptations, e.g. when stepping across obstacles. We investigated whether time constrained gait adaptability can be measured objectively using an instrumented treadmill with visual obstacles and which impairments interfere with gait adaptability. Patients/materials and methods: 12 adolescents with CP (15.6 ± 1.7 years, 6 GMFCS I, 6 GMFCS II) and 12 age-matched healthy controls participated. They walked at comfortable speed on a treadmill (Forcelink, NL) while avoiding visual obstacles projected on the belt as bars of lights. Obstacles were presented randomly, appearing either 4 (anticipatory) or 2 steps (reactive) in advance of possible collision. The obstacle’s location was timed according to the subjects’ walking pattern such that it would coincide with foot placement when subjects did not react. Physical examination included manual spasticity assessment (adductor, hamstrings, soleus, gastrocnemius) [2], evaluation of lower limb selectivity [3] and measurement of isometric muscle strength (plantar flexors, knee extensors, and hip abductors) by means of hand held dynamometry. Success of obstacle avoidance was scored by inspection of video recordings. Results: In the reactive condition CP GMFCS I and II both had a significantly lower success rate than controls (p = .005 and p = .018), with no significant (p = .394) difference in between GMFCS levels (Fig. 1). In the anticipatory condition CP GMFCS II scored sig-
Fig. 1.
nificantly lower than controls (p < .001) and GMFCS I (p = .019). Differences between CP GMFCS I and controls were significant (p = .041) but less pronounced. Pearson’s r revealed that the success rate in the anticipatory condition significantly correlated with the overall level of spasticity (r = −.66, p = .02) and selectivity (r = .62, p = .03) in the lower limbs. No significant relations with physical impairments could be found during the reactive task. Strength measures did not correlate in either of both tasks. Discussion and conclusion: We showed that gait adaptability in adolescents with CP is reduced in comparison to healthy peers. In general, GMFCS II faced greater problems than GMFCS I. The significant correlations of selectivity and spasticity with the success rate in the anticipatory conditions indicate that decreased motor control limits adaptability, while strength seems to be of minor importance. Adolescents with CP were also more likely to fail in the reactive condition, but physical impairments were not significantly correlated. Slowed processing of visual information and attentional deficits [4] might have affected their performance. Whereas Ref. [5] showed that adolescents with CP are able to successfully clear an obstacle during overground walking, provided that they have sufficient time, our time constrained treadmill set-up accentuated coordinative problems and revealed reduced gait adaptability in CP. Disclosure: No significant relationships. References [1] [2] [3] [4] [5]
Palisano RJ, et al. Phys Occup Ther Pediatr 2009;29:135–55. van den Noort JC, et al. Arch Phys Med Rehabil 2010;91:615–23. Smits DW, et al. Dev Neurorehabil 2010;13:258–65. Shank LK, et al. Rehabil Psychol 2010;55:188–93. Law LS, Webb V. Dev Med Child Neurol 2005;47:321–8.
doi:10.1016/j.gaitpost.2011.10.347 P76 A marker based kinematic method of identifying initial contact during gait for use in real-time visual feedback applications A.R. De Asha 1 , M.A. Robinson 2 , G.J. Barton 2,∗ 1
School of Engineering, Design & Technology, University of Bradford, Bradford, United Kingdom 2 Research Institute for Sport and Exercise Sciences, Liverpool John Moores University, Liverpool, United Kingdom
Introduction: Ideally initial contact (IC) is identified using kinetic data from force platforms. When these data are not available then other methods of event definition using kinematic data are required. Accurate methods of identifying IC have been described based on marker accelerations but these require further processing of marker trajectories which is not appropriate for real-time applications [1,2]. The aim of this study was to identify a simple marker based method for determining gait events which could be used in real-time applications such as providing visual feedback in a virtual rehabilitation environment. Patients/material and methods: Ten (5 male, 5 female) unimpaired subjects (mean ± SD age, height, mass, 25.1 ± 6.6 years, 1.76 ± 0.11 m, 71 ± 7.7 kg) were recorded completing a minimum of 25 overground gait cycles while walking at a self-selected speed along a 10 m walkway. A pelvis and lower limb model was used, captured by a Vicon system operating at 100 Hz. IC was defined at an ascending threshold of the vertical component of the ground reaction force (20 N). Contralateral peak hip extension was used to define IC kinematically i.e. right IC occurred at left hip peak extension (minimum flexion). In all a total of 566 kinetically and kinematically defined IC events were compared to determine the accuracy of match. A limits of agreement analysis (LOA) was con-
Abstracts / Gait & Posture 36 (2012) S1–S101
ducted [3] and the 95% confidence intervals established in order to quantify agreement or otherwise between kinematic and kinetic events. Results: The mean difference between the kinematically and kinetically defined events was +0.003 ± 0.009 s which is less than 1 frame when recorded at 100 Hz. The range was between 0.02 s before and 0.05 s after the force defined IC event and the 95% LOA was ±0.018 s. There was no statistically significant difference caused by sex or walking speed on the accuracy of this kinematic event with respect to the kinetic IC. Discussion and conclusion: The results of this study identified a new algorithm based upon the contralateral hip flexion–extension angle to identify IC. This method provides simple to implement and relatively accurate gait events for use when kinetic data are not available in both a clinical and research setting or possibly when kinetically defined gait events are inappropriate to use due to foot scuffing or dragging at the beginning and end of stance phases. It also lends itself to use within real-time biofeedback applications due to the small amount of processing time required. Disclosure: No significant relationships.
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Results: Each body posture could easily be recognized based on the acceleration of the markers placed at the pelvis and at least one placed on the thigh. For the different activities, the markers placed at the pelvis and those placed at the both thighs were needed. The biomechanical analysis of the different activities enabled the detection of typical pattern of coordination of thigh acceleration, whatever the subjects as illustrated on the figure hereafter. Discussion and conclusion: The results of this study confirmed that the biomechanical approach enables the recognition of different physical activities. This approach has to be now implemented on ambulatory systems with inertial sensors. Disclosure: No significant relationships.
References [1] Oshima, et al. Classifying household and locomotive activities using a triaxial accelerometer. Gait Posture 2010;31:370–4. [2] Bonomi AG, Goris AH, Yin B, Westerterp KR. Detection of type, duration, and intensity of physical activity using an accelerometer. Med Sci Sports Exerc 2009;41(9):1770–7.
doi:10.1016/j.gaitpost.2011.10.349 References
P78
[1] Hreljac A, Marshall RN. Algorithms to determine event timing during normal walking using kinematic data. J Biomech 2000;33:783–6. [2] O’Connor CM, Thorpe SK, O’Malley MJ, Vaughan CL. Automatic detection of gait events using kinematic data. Gait Posture 2007;25:469–74. [3] Bland JM, Altman DG. Measuring agreement in method comparison studies. Stat Methods Med Res 1999;8:135–60.
The estimation of muscle fatigue during running at different intensities
doi:10.1016/j.gaitpost.2011.10.348 P77 Recognition of physical activity adapted to rehabilitation L. Fradet 1,∗ , F. Marin 2 1
Umr-6600 Bmbi, Université de Technologie de Compiègne, Compiègne, France 2 Umr-6600, Université de Technologie de Compiègne, Compiègne, France Introduction: New systems based on inertial sensors for ambulatory movement analysis are currently under development. This technique promising for rehabilitation purpose requires the development of algorithms for the recognition of the physical activities recommended by the clinical staff. The algorithms currently proposed are mainly consisting in decisional trees and neural networks having as inputs diverse acceleration frequency parameters defined by analyzing data of specific group of subjects [1,2]. The aim of the present study is to propose a method using biomechanical analysis of the activities to classify to perform activity recognition, whatever the subject characteristics are. Patients/materials and methods: For the European project Physical Activity Monitoring of Elderly People (www.pamap.org), the postures sitting/standing/lying are to be classified as well as the endurance activities walking/running/cycling. 16 subjects of different age and physical condition took part in this study. They had to take the different postures during period of 6 × 5 s, to walk/run/cycle 6 times through a 10m-pathway at 3 different paces (slow/normal/fast) and they had to perform 2 daily activities also at 3 different paces. The motion capture was performed using an optoelectronical system (Vicon System). Markers were placed to track not only the body segments but also potential inertial sensor locations. The signals analysed to determine the “universal” characteristics of each activity were the accelerations deduced from the marker trajectories.
A. Mastalerz Physical Education and Sport, University of Physical Education, Biala Podlaska, Poland Introduction: Surface electromyography (sEMG) is one of methods which have been used to investigate mechanisms of neuromuscular fatigue [1]. The muscle fatigue is specific to contraction type, intensity and duration of activity therefore relationships seen e.g. in the isometric muscle contraction are not the same in the dynamic exercise. Therefore, based on previous experience in that evaluation the effectiveness of the estimation of fatigue for individual lower extremities muscles during the run at various intensity was the aim. Patients/materials and methods: Four athletes took part in this research. EMG measurements were recorded during the run on tartan athletic track. The athlete had to run 400 m distance with a different intensity. The first distance of 400 m took 90 s, the second one 70 s, the third one 60 s and the last one was performed with maximal intensity. Bipolar surface EMG recordings were obtained from the rectus femoris (RF) and biceps femoris – long head (BF) of right and left thigh were obtained. The raw SEMG signal was recorded at the sampling rate of 1000 Hz using a device ME3000P4. Power spectral analysis were performed to calculate MPF on 1024-point (Hamming window processing) by fast Fourier transformation (FFT) technique. All participants signed written consent form and proper consent was obtained from Warsaw UPE Ethical Committee. Results: Fatigue comparison for individual muscles depending on the intensity of run was described by slopes of regression lines estimated by method of least squares. The values of slope coefficients are presented in Table 1. Significant differences between the slopes for muscles of left and right limbs were noticed. For both muscles slopes increased with increasing intensity of the race. That increase was, however, stronger for the left limb. It is worth noting that the differences between left and right limbs are more strongly marked for the RF muscle. Table 1 – average values and standard deviation (SD) of the regression line slopes computed on the value of MPF for the run at different intensities: 1 – the 90 s run, 2 – 70 s, 3 – 60 s, 4 – run with maximal intensity.
Abstracts / Gait & Posture 36 (2012) S1–S101
References [1] Lance. Spasticity: disordered motor control. Chicago; 1980, p. 485–5. [2] Crenna. Neurosci Biobehav Rev 1998;22:571–8.
doi:10.1016/j.gaitpost.2011.10.346 P75 Evaluating gait adaptability in adolescents with CP—A treadmill approach M. Hoesl 1,∗ , L. Bruinink 2 , J. Harlaar 3 , H. Houdijk 1 1
Research Institute Move, Faculty of Human Movement Sciences, VU University Amsterdam, Amsterdam, Netherlands 2 Research & Development, Heliomare Rehabilitation, Wijk aan Zee, Netherlands 3 Department of Rehabilitation Medicine, VU University Medical Center, Amsterdam, Netherlands
Introduction: Next to efficiency, adolescents with CP are concerned about safe mobility [1]. To keep up with peers and navigate in complex environments of daily life, their gait needs to be adaptable. Muscle weakness, spasticity and a lack of selective motor control may affect their ability to execute gait adaptations, e.g. when stepping across obstacles. We investigated whether time constrained gait adaptability can be measured objectively using an instrumented treadmill with visual obstacles and which impairments interfere with gait adaptability. Patients/materials and methods: 12 adolescents with CP (15.6 ± 1.7 years, 6 GMFCS I, 6 GMFCS II) and 12 age-matched healthy controls participated. They walked at comfortable speed on a treadmill (Forcelink, NL) while avoiding visual obstacles projected on the belt as bars of lights. Obstacles were presented randomly, appearing either 4 (anticipatory) or 2 steps (reactive) in advance of possible collision. The obstacle’s location was timed according to the subjects’ walking pattern such that it would coincide with foot placement when subjects did not react. Physical examination included manual spasticity assessment (adductor, hamstrings, soleus, gastrocnemius) [2], evaluation of lower limb selectivity [3] and measurement of isometric muscle strength (plantar flexors, knee extensors, and hip abductors) by means of hand held dynamometry. Success of obstacle avoidance was scored by inspection of video recordings. Results: In the reactive condition CP GMFCS I and II both had a significantly lower success rate than controls (p = .005 and p = .018), with no significant (p = .394) difference in between GMFCS levels (Fig. 1). In the anticipatory condition CP GMFCS II scored sig-
Fig. 1.
nificantly lower than controls (p < .001) and GMFCS I (p = .019). Differences between CP GMFCS I and controls were significant (p = .041) but less pronounced. Pearson’s r revealed that the success rate in the anticipatory condition significantly correlated with the overall level of spasticity (r = −.66, p = .02) and selectivity (r = .62, p = .03) in the lower limbs. No significant relations with physical impairments could be found during the reactive task. Strength measures did not correlate in either of both tasks. Discussion and conclusion: We showed that gait adaptability in adolescents with CP is reduced in comparison to healthy peers. In general, GMFCS II faced greater problems than GMFCS I. The significant correlations of selectivity and spasticity with the success rate in the anticipatory conditions indicate that decreased motor control limits adaptability, while strength seems to be of minor importance. Adolescents with CP were also more likely to fail in the reactive condition, but physical impairments were not significantly correlated. Slowed processing of visual information and attentional deficits [4] might have affected their performance. Whereas Ref. [5] showed that adolescents with CP are able to successfully clear an obstacle during overground walking, provided that they have sufficient time, our time constrained treadmill set-up accentuated coordinative problems and revealed reduced gait adaptability in CP. Disclosure: No significant relationships. References [1] [2] [3] [4] [5]
Palisano RJ, et al. Phys Occup Ther Pediatr 2009;29:135–55. van den Noort JC, et al. Arch Phys Med Rehabil 2010;91:615–23. Smits DW, et al. Dev Neurorehabil 2010;13:258–65. Shank LK, et al. Rehabil Psychol 2010;55:188–93. Law LS, Webb V. Dev Med Child Neurol 2005;47:321–8.
doi:10.1016/j.gaitpost.2011.10.347 P76 A marker based kinematic method of identifying initial contact during gait for use in real-time visual feedback applications A.R. De Asha 1 , M.A. Robinson 2 , G.J. Barton 2,∗ 1
School of Engineering, Design & Technology, University of Bradford, Bradford, United Kingdom 2 Research Institute for Sport and Exercise Sciences, Liverpool John Moores University, Liverpool, United Kingdom
Introduction: Ideally initial contact (IC) is identified using kinetic data from force platforms. When these data are not available then other methods of event definition using kinematic data are required. Accurate methods of identifying IC have been described based on marker accelerations but these require further processing of marker trajectories which is not appropriate for real-time applications [1,2]. The aim of this study was to identify a simple marker based method for determining gait events which could be used in real-time applications such as providing visual feedback in a virtual rehabilitation environment. Patients/material and methods: Ten (5 male, 5 female) unimpaired subjects (mean ± SD age, height, mass, 25.1 ± 6.6 years, 1.76 ± 0.11 m, 71 ± 7.7 kg) were recorded completing a minimum of 25 overground gait cycles while walking at a self-selected speed along a 10 m walkway. A pelvis and lower limb model was used, captured by a Vicon system operating at 100 Hz. IC was defined at an ascending threshold of the vertical component of the ground reaction force (20 N). Contralateral peak hip extension was used to define IC kinematically i.e. right IC occurred at left hip peak extension (minimum flexion). In all a total of 566 kinetically and kinematically defined IC events were compared to determine the accuracy of match. A limits of agreement analysis (LOA) was con-
Abstracts / Gait & Posture 36 (2012) S1–S101
ducted [3] and the 95% confidence intervals established in order to quantify agreement or otherwise between kinematic and kinetic events. Results: The mean difference between the kinematically and kinetically defined events was +0.003 ± 0.009 s which is less than 1 frame when recorded at 100 Hz. The range was between 0.02 s before and 0.05 s after the force defined IC event and the 95% LOA was ±0.018 s. There was no statistically significant difference caused by sex or walking speed on the accuracy of this kinematic event with respect to the kinetic IC. Discussion and conclusion: The results of this study identified a new algorithm based upon the contralateral hip flexion–extension angle to identify IC. This method provides simple to implement and relatively accurate gait events for use when kinetic data are not available in both a clinical and research setting or possibly when kinetically defined gait events are inappropriate to use due to foot scuffing or dragging at the beginning and end of stance phases. It also lends itself to use within real-time biofeedback applications due to the small amount of processing time required. Disclosure: No significant relationships.
S99
Results: Each body posture could easily be recognized based on the acceleration of the markers placed at the pelvis and at least one placed on the thigh. For the different activities, the markers placed at the pelvis and those placed at the both thighs were needed. The biomechanical analysis of the different activities enabled the detection of typical pattern of coordination of thigh acceleration, whatever the subjects as illustrated on the figure hereafter. Discussion and conclusion: The results of this study confirmed that the biomechanical approach enables the recognition of different physical activities. This approach has to be now implemented on ambulatory systems with inertial sensors. Disclosure: No significant relationships.
References [1] Oshima, et al. Classifying household and locomotive activities using a triaxial accelerometer. Gait Posture 2010;31:370–4. [2] Bonomi AG, Goris AH, Yin B, Westerterp KR. Detection of type, duration, and intensity of physical activity using an accelerometer. Med Sci Sports Exerc 2009;41(9):1770–7.
doi:10.1016/j.gaitpost.2011.10.349 References
P78
[1] Hreljac A, Marshall RN. Algorithms to determine event timing during normal walking using kinematic data. J Biomech 2000;33:783–6. [2] O’Connor CM, Thorpe SK, O’Malley MJ, Vaughan CL. Automatic detection of gait events using kinematic data. Gait Posture 2007;25:469–74. [3] Bland JM, Altman DG. Measuring agreement in method comparison studies. Stat Methods Med Res 1999;8:135–60.
The estimation of muscle fatigue during running at different intensities
doi:10.1016/j.gaitpost.2011.10.348 P77 Recognition of physical activity adapted to rehabilitation L. Fradet 1,∗ , F. Marin 2 1
Umr-6600 Bmbi, Université de Technologie de Compiègne, Compiègne, France 2 Umr-6600, Université de Technologie de Compiègne, Compiègne, France Introduction: New systems based on inertial sensors for ambulatory movement analysis are currently under development. This technique promising for rehabilitation purpose requires the development of algorithms for the recognition of the physical activities recommended by the clinical staff. The algorithms currently proposed are mainly consisting in decisional trees and neural networks having as inputs diverse acceleration frequency parameters defined by analyzing data of specific group of subjects [1,2]. The aim of the present study is to propose a method using biomechanical analysis of the activities to classify to perform activity recognition, whatever the subject characteristics are. Patients/materials and methods: For the European project Physical Activity Monitoring of Elderly People (www.pamap.org), the postures sitting/standing/lying are to be classified as well as the endurance activities walking/running/cycling. 16 subjects of different age and physical condition took part in this study. They had to take the different postures during period of 6 × 5 s, to walk/run/cycle 6 times through a 10m-pathway at 3 different paces (slow/normal/fast) and they had to perform 2 daily activities also at 3 different paces. The motion capture was performed using an optoelectronical system (Vicon System). Markers were placed to track not only the body segments but also potential inertial sensor locations. The signals analysed to determine the “universal” characteristics of each activity were the accelerations deduced from the marker trajectories.
A. Mastalerz Physical Education and Sport, University of Physical Education, Biala Podlaska, Poland Introduction: Surface electromyography (sEMG) is one of methods which have been used to investigate mechanisms of neuromuscular fatigue [1]. The muscle fatigue is specific to contraction type, intensity and duration of activity therefore relationships seen e.g. in the isometric muscle contraction are not the same in the dynamic exercise. Therefore, based on previous experience in that evaluation the effectiveness of the estimation of fatigue for individual lower extremities muscles during the run at various intensity was the aim. Patients/materials and methods: Four athletes took part in this research. EMG measurements were recorded during the run on tartan athletic track. The athlete had to run 400 m distance with a different intensity. The first distance of 400 m took 90 s, the second one 70 s, the third one 60 s and the last one was performed with maximal intensity. Bipolar surface EMG recordings were obtained from the rectus femoris (RF) and biceps femoris – long head (BF) of right and left thigh were obtained. The raw SEMG signal was recorded at the sampling rate of 1000 Hz using a device ME3000P4. Power spectral analysis were performed to calculate MPF on 1024-point (Hamming window processing) by fast Fourier transformation (FFT) technique. All participants signed written consent form and proper consent was obtained from Warsaw UPE Ethical Committee. Results: Fatigue comparison for individual muscles depending on the intensity of run was described by slopes of regression lines estimated by method of least squares. The values of slope coefficients are presented in Table 1. Significant differences between the slopes for muscles of left and right limbs were noticed. For both muscles slopes increased with increasing intensity of the race. That increase was, however, stronger for the left limb. It is worth noting that the differences between left and right limbs are more strongly marked for the RF muscle. Table 1 – average values and standard deviation (SD) of the regression line slopes computed on the value of MPF for the run at different intensities: 1 – the 90 s run, 2 – 70 s, 3 – 60 s, 4 – run with maximal intensity.