- Email: [email protected]
Walking speed in older adults under different walking challenges
ESMAC Abstracts 2015 / Gait & Posture 42S (2015) S1–S101
Comparison of the feedback gains between the young and the elderly. The first four bars represent the feedback gains that control the ankle joint; the last four bars represent the feedback gains that control the hip joint. Discussion: This result may indicate a deficient integration of somato-sensory information as feedback signal for postural control in the elderly. Therefore, we suggest that real-time feedback of the center of mass location could of added value for fall-prevention training protocols. A larger set of test subjects (13 young and 13 elderly) will be used to test these hypotheses. References [1] Winter. Gait Posture 1995. [2] Park. Exp Brain Res 2004.
S71
The Fast Fourier Transform (FFT) of the signals was used to determine: the frequency corresponding to the maximum of the signal spectrum (fMAX ) and the frequency corresponding to the median value of the signal spectrum (fMEDIAN ). The potential spectrum density (psd) was obtained with the matlab built-in function pwelch. The signal power was calculated integrating the psd. The percentage power at 32, 16, 10, 8 Hz was calculated. Results: For the young population fMAX varies from 1.9 to 3 Hz and fMEDIAN from 28.9 to 32.4 Hz. Instead for the elderly population fMAX is between 1.0 and 2.6 Hz and fMEDIAN from 26.8 to 34.3 Hz. The power analysis showed for young subjects that the signal power is between 99.9% at 32 Hz and 72.9% at 8 Hz. For the elderly population, instead, it varies form 99.2% at 32 Hz to 44.9% al 8 Hz. Discussion: On the medio-lateral axis the frequency range of the elderly is lower than young subjects; this could be explained by the fact that medio-lateral frequencies are influenced by stride time. Instead, for antero-posterior and vertical directions frequency range of the elderly are shifted to the higher frequencies than those of young; this could be due to the muscularity stiffness present in elderly subjects. The percentage power, in all the directions, is lower in elderly than in young subjects; this could be due to the no smooth body control during gait that introduces components at the high frequencies. References [1] Angeloni, et al. IEEE Trans Rehab Eng 1994;2(1). [2] Antonsson, et al. J Biomech 1985;18(1).
http://dx.doi.org/10.1016/j.gaitpost.2015.06.132 http://dx.doi.org/10.1016/j.gaitpost.2015.06.131
Session PS15 Methods and Models Session PS15 Methods and Models Accelerometric analysis of gait in young and elderly subjects: Frequency analysis P. Tamburini ∗ , R. Stagni University of Bologna, Bologna, Italy Research question: Are there significant differences in gait acceleration signal spectrum between young and elderly subjects? And could these improve the understanding of motor control? Introduction: From signals theory the knowledge of spectrum features is the starting point for the signal processing. In particular, know features of gait acceleration signal spectrum of young and elderly subjects could improve the understanding of the multiple factors that contribute to achieve the specific motor task. Studies about the range of gait acceleration frequency are presented in literature; these start from: kinematic data [1], acquired with stereo photogrammetry, and from ground reaction force data [2], acquired with force platform. The first method is affected by the well-known drifts problem; instead the second measures the acceleration of the center of pressure (COP) and not those of the center of mass that is a follower of the COP. The study aims to assess the features of gait acceleration signal spectrum, directly obtained from inertial measurement unit (IMU), in young and elderly subjects. Materials and methods: The study was conducted on 10 healthy young people and 10 elderly subjects. The participants performed an instrumented over ground gait task wearing an IMU located on the trunk at the height of fifth lumbar vertebra. The acceleration signals in antero-posterior, medio-lateral and vertical directions were acquired with a sample frequency of 128 Hz.
Walking speed in older adults under different walking challenges K. Duffy 1,∗ , M. Taylor 1 , J. Jackson 2 1
University of Essex, School of Biological Science, Colchester, United Kingdom 2 University of Essex, School of Health and Human Sciences, Colchester, United Kingdom Research question: How does walking speed of older adults change under different challenges? Introduction: Walking around a changing environment underlies many tasks necessary for independence such as negotiating obstacles. The attention required to complete such tasks may be divided depending on task complexity. Normal walking (NW) requires little attention demand and is said to have automaticity [1]. More complex walking tasks are likely to result in a declining performance, i.e. reduced walking speed (WS). However, the effect of additional environmental demand on WS in older adults is unknown. This study examined the WS of older adults performing 4 walking tasks to establish if the decline in WS was greater with age and task. Materials and methods: 136 older adults were grouped into 3 age categories: 55–64 (N = 62), 65–74 (N = 61) and 75 (N = 13). Timing gates were placed 2.28 m apart in the middle of a 12 m walkway to calculate WS. Participants performed 4 tasks: NW, carrying a cup of water (dual-task, DT), obstacle clearance (0.10 m × 1.6 m × 0.4 m) (stepping onto/off (SON) and over (SOV)) at their preferred speed. Automaticity index (AI) was calculated using WS of each task as a percentage under NW, with 100% indicating no reduction in performance. A mixed ANOVA assessed the effects of age and task respectively on WS.
S72
ESMAC Abstracts 2015 / Gait & Posture 42S (2015) S1–S101
Fig. 1. Mean and SD plot of walking speed for all tasks for each age group. Black line: 55–64, grey line: 65–74 and dashed line is 75 years. a – sign. diff NW (p < .001), b – sign. diff DT (p < .002), c – sign. diff SOV (p = .004), d – sign. diff 55–64 (p < .001), e – sign. diff 65–74 (p < .01).
Results: AI was lower for SON compared to DT and SOV for all groups. The over 75 group had lower AI for all tasks. There was a significant effect and interaction between age and task, with WS for SON and SOV significantly slower than NW and DT for all ages. The 75 group was significantly slower for all tasks compared to other age groups (Fig. 1). Discussion: As the tasks became more demanding (SON and SOV) WS and AI reduced for all age groups, with a significantly greater reduction for 75. Compensatory strategies such as reduced WS occurs when walking stability is challenged [2]. Obstacle tasks are more likely to place higher demands on balance which necessitates much higher conscious control in older adults compared to NW [3]. Obstacle clearance challenged automaticity more so than DT, causing a reduced WS for all age groups. Therefore, increasing task demand during walking has an impact upon WS irrespective of age. Future research is required to determine the effect of ageing upon these walking tasks using a longitudinal design. References [1] Canning, et al. Disabil Rehab 2006;28:97–102. [2] Hollman, et al. Gait Posture 2007;26:113–9. [3] Deshpande. Age Ageing 2009;38:509–14.
http://dx.doi.org/10.1016/j.gaitpost.2015.06.133
Session PS15 Methods and Models Responses of knee kinematics to controlled lateral perturbations J. van den Noort 1,∗ , L. Sloot 1 , S. Bruijn 2 , J. Harlaar 1 1
VU Medical Center, Dept. Rehabilitation Medicine, Amsterdam, Netherlands 2 VU University, Faculty of Human Movement Science, Amsterdam, Netherlands Research question: How does the knee respond to lateral perturbations? Introduction: Patients with knee osteoarthritis (KOA) often report knee instability [1]. It is unclear what an unstable knee implies during dynamic tasks [1–3]. To evaluate stability of the knee, kinematic responses of the knee to controlled perturbations may be a meaningful measure. This can be parameterized by a gait sensitivity norm (GSN) [4], expressing the variability of gait indicators (i.e. response (GSN-numerator)) as results of a perturbation
Fig. 1.
(GSN-denominator). While the GSN has been used in bipedal walking robots [4] it has not been applied in human gait. Therefore, the aim of the current study was to evaluate stability of the knee by means of the GSN concept. Materials and methods: Nine healthy participants (20–30 years) walked on a treadmill, while 3D knee kinematics were captured. Position controlled lateral perturbations of the treadmill were randomly applied at 5 different intensities (1–5 cm, 15 repetitions each) during 20–50% of the right leg gait cycle. GSNnumerators of 1–5 steps after perturbation were calculated. Gait indicators (g) were knee flexion, abduction and rotation angles. A generalized estimating equation analysis was performed to test significance of intensity and of number of steps after perturbation on the GNS-numerators during initial contact (IC), opposite toe-off (OTO), mid-stance (MST), opposite initial contact (OIC), toe-off (TO) and mid-swing (MSW). Results: The highest perturbation intensity resulted in a significant increase in GSN-numerators with respect to all other intensities. Lower perturbations showed smaller or no significant increase with respect to each other. Right leg knee flexion was affected during TO-OTO, whereas left knee flexion and abduction were affected during MSW and IC. Knee flexion deviated until the 5th step (Fig. 1) after perturbation. Knee abduction deviated until the 3rd step. No changes were seen in knee rotation. Discussion: Increased knee kinematic responses were observed with increased perturbation intensity. However, healthy subjects showed minimal/no response to lower perturbation intensities. These findings could serve as norm-data and are promising for successful application of the GSN in KOA patients, in which higher responses are expected at lower intensities over a larger number of steps. References [1] [2] [3] [4]
Felson, et al. Ann Intern Med 2007. van der Esch, et al. Osteoarthr Cartil 2008. Knoop, et al. Arthritis Care Res (Hoboken) 2012. Hobbelen, Wisse. IEEE Trans Robotics 2007.
http://dx.doi.org/10.1016/j.gaitpost.2015.06.134
Comparison of the feedback gains between the young and the elderly. The first four bars represent the feedback gains that control the ankle joint; the last four bars represent the feedback gains that control the hip joint. Discussion: This result may indicate a deficient integration of somato-sensory information as feedback signal for postural control in the elderly. Therefore, we suggest that real-time feedback of the center of mass location could of added value for fall-prevention training protocols. A larger set of test subjects (13 young and 13 elderly) will be used to test these hypotheses. References [1] Winter. Gait Posture 1995. [2] Park. Exp Brain Res 2004.
S71
The Fast Fourier Transform (FFT) of the signals was used to determine: the frequency corresponding to the maximum of the signal spectrum (fMAX ) and the frequency corresponding to the median value of the signal spectrum (fMEDIAN ). The potential spectrum density (psd) was obtained with the matlab built-in function pwelch. The signal power was calculated integrating the psd. The percentage power at 32, 16, 10, 8 Hz was calculated. Results: For the young population fMAX varies from 1.9 to 3 Hz and fMEDIAN from 28.9 to 32.4 Hz. Instead for the elderly population fMAX is between 1.0 and 2.6 Hz and fMEDIAN from 26.8 to 34.3 Hz. The power analysis showed for young subjects that the signal power is between 99.9% at 32 Hz and 72.9% at 8 Hz. For the elderly population, instead, it varies form 99.2% at 32 Hz to 44.9% al 8 Hz. Discussion: On the medio-lateral axis the frequency range of the elderly is lower than young subjects; this could be explained by the fact that medio-lateral frequencies are influenced by stride time. Instead, for antero-posterior and vertical directions frequency range of the elderly are shifted to the higher frequencies than those of young; this could be due to the muscularity stiffness present in elderly subjects. The percentage power, in all the directions, is lower in elderly than in young subjects; this could be due to the no smooth body control during gait that introduces components at the high frequencies. References [1] Angeloni, et al. IEEE Trans Rehab Eng 1994;2(1). [2] Antonsson, et al. J Biomech 1985;18(1).
http://dx.doi.org/10.1016/j.gaitpost.2015.06.132 http://dx.doi.org/10.1016/j.gaitpost.2015.06.131
Session PS15 Methods and Models Session PS15 Methods and Models Accelerometric analysis of gait in young and elderly subjects: Frequency analysis P. Tamburini ∗ , R. Stagni University of Bologna, Bologna, Italy Research question: Are there significant differences in gait acceleration signal spectrum between young and elderly subjects? And could these improve the understanding of motor control? Introduction: From signals theory the knowledge of spectrum features is the starting point for the signal processing. In particular, know features of gait acceleration signal spectrum of young and elderly subjects could improve the understanding of the multiple factors that contribute to achieve the specific motor task. Studies about the range of gait acceleration frequency are presented in literature; these start from: kinematic data [1], acquired with stereo photogrammetry, and from ground reaction force data [2], acquired with force platform. The first method is affected by the well-known drifts problem; instead the second measures the acceleration of the center of pressure (COP) and not those of the center of mass that is a follower of the COP. The study aims to assess the features of gait acceleration signal spectrum, directly obtained from inertial measurement unit (IMU), in young and elderly subjects. Materials and methods: The study was conducted on 10 healthy young people and 10 elderly subjects. The participants performed an instrumented over ground gait task wearing an IMU located on the trunk at the height of fifth lumbar vertebra. The acceleration signals in antero-posterior, medio-lateral and vertical directions were acquired with a sample frequency of 128 Hz.
Walking speed in older adults under different walking challenges K. Duffy 1,∗ , M. Taylor 1 , J. Jackson 2 1
University of Essex, School of Biological Science, Colchester, United Kingdom 2 University of Essex, School of Health and Human Sciences, Colchester, United Kingdom Research question: How does walking speed of older adults change under different challenges? Introduction: Walking around a changing environment underlies many tasks necessary for independence such as negotiating obstacles. The attention required to complete such tasks may be divided depending on task complexity. Normal walking (NW) requires little attention demand and is said to have automaticity [1]. More complex walking tasks are likely to result in a declining performance, i.e. reduced walking speed (WS). However, the effect of additional environmental demand on WS in older adults is unknown. This study examined the WS of older adults performing 4 walking tasks to establish if the decline in WS was greater with age and task. Materials and methods: 136 older adults were grouped into 3 age categories: 55–64 (N = 62), 65–74 (N = 61) and 75 (N = 13). Timing gates were placed 2.28 m apart in the middle of a 12 m walkway to calculate WS. Participants performed 4 tasks: NW, carrying a cup of water (dual-task, DT), obstacle clearance (0.10 m × 1.6 m × 0.4 m) (stepping onto/off (SON) and over (SOV)) at their preferred speed. Automaticity index (AI) was calculated using WS of each task as a percentage under NW, with 100% indicating no reduction in performance. A mixed ANOVA assessed the effects of age and task respectively on WS.
S72
ESMAC Abstracts 2015 / Gait & Posture 42S (2015) S1–S101
Fig. 1. Mean and SD plot of walking speed for all tasks for each age group. Black line: 55–64, grey line: 65–74 and dashed line is 75 years. a – sign. diff NW (p < .001), b – sign. diff DT (p < .002), c – sign. diff SOV (p = .004), d – sign. diff 55–64 (p < .001), e – sign. diff 65–74 (p < .01).
Results: AI was lower for SON compared to DT and SOV for all groups. The over 75 group had lower AI for all tasks. There was a significant effect and interaction between age and task, with WS for SON and SOV significantly slower than NW and DT for all ages. The 75 group was significantly slower for all tasks compared to other age groups (Fig. 1). Discussion: As the tasks became more demanding (SON and SOV) WS and AI reduced for all age groups, with a significantly greater reduction for 75. Compensatory strategies such as reduced WS occurs when walking stability is challenged [2]. Obstacle tasks are more likely to place higher demands on balance which necessitates much higher conscious control in older adults compared to NW [3]. Obstacle clearance challenged automaticity more so than DT, causing a reduced WS for all age groups. Therefore, increasing task demand during walking has an impact upon WS irrespective of age. Future research is required to determine the effect of ageing upon these walking tasks using a longitudinal design. References [1] Canning, et al. Disabil Rehab 2006;28:97–102. [2] Hollman, et al. Gait Posture 2007;26:113–9. [3] Deshpande. Age Ageing 2009;38:509–14.
http://dx.doi.org/10.1016/j.gaitpost.2015.06.133
Session PS15 Methods and Models Responses of knee kinematics to controlled lateral perturbations J. van den Noort 1,∗ , L. Sloot 1 , S. Bruijn 2 , J. Harlaar 1 1
VU Medical Center, Dept. Rehabilitation Medicine, Amsterdam, Netherlands 2 VU University, Faculty of Human Movement Science, Amsterdam, Netherlands Research question: How does the knee respond to lateral perturbations? Introduction: Patients with knee osteoarthritis (KOA) often report knee instability [1]. It is unclear what an unstable knee implies during dynamic tasks [1–3]. To evaluate stability of the knee, kinematic responses of the knee to controlled perturbations may be a meaningful measure. This can be parameterized by a gait sensitivity norm (GSN) [4], expressing the variability of gait indicators (i.e. response (GSN-numerator)) as results of a perturbation
Fig. 1.
(GSN-denominator). While the GSN has been used in bipedal walking robots [4] it has not been applied in human gait. Therefore, the aim of the current study was to evaluate stability of the knee by means of the GSN concept. Materials and methods: Nine healthy participants (20–30 years) walked on a treadmill, while 3D knee kinematics were captured. Position controlled lateral perturbations of the treadmill were randomly applied at 5 different intensities (1–5 cm, 15 repetitions each) during 20–50% of the right leg gait cycle. GSNnumerators of 1–5 steps after perturbation were calculated. Gait indicators (g) were knee flexion, abduction and rotation angles. A generalized estimating equation analysis was performed to test significance of intensity and of number of steps after perturbation on the GNS-numerators during initial contact (IC), opposite toe-off (OTO), mid-stance (MST), opposite initial contact (OIC), toe-off (TO) and mid-swing (MSW). Results: The highest perturbation intensity resulted in a significant increase in GSN-numerators with respect to all other intensities. Lower perturbations showed smaller or no significant increase with respect to each other. Right leg knee flexion was affected during TO-OTO, whereas left knee flexion and abduction were affected during MSW and IC. Knee flexion deviated until the 5th step (Fig. 1) after perturbation. Knee abduction deviated until the 3rd step. No changes were seen in knee rotation. Discussion: Increased knee kinematic responses were observed with increased perturbation intensity. However, healthy subjects showed minimal/no response to lower perturbation intensities. These findings could serve as norm-data and are promising for successful application of the GSN in KOA patients, in which higher responses are expected at lower intensities over a larger number of steps. References [1] [2] [3] [4]
Felson, et al. Ann Intern Med 2007. van der Esch, et al. Osteoarthr Cartil 2008. Knoop, et al. Arthritis Care Res (Hoboken) 2012. Hobbelen, Wisse. IEEE Trans Robotics 2007.
http://dx.doi.org/10.1016/j.gaitpost.2015.06.134