- Email: [email protected]
Influence of kinematics, anthropometry and kinetics data errors on inverse dynamics solutions during running
ESMAC Abstracts 2015 / Gait & Posture 42S (2015) S1–S101
footwear telemetry antenna system, respectively. For running gait, three different running speeds on a treadmill were tested in a systematic order; 3.5 m/s, 4.5 m/s and 5.4 m/s. Descriptive statistics for spatiotemporal parameters were calculated using 10 walking gait cycles and 6 running gait cycles. Gait and running parameter changes were analysed using linear regression. Results: During walking gait, there were statistically significant increases in foot off occurrence (p = 0.026) and opposite foot off occurrence (p = 0.031), and a statistically significant decrease in opposite foot contact occurrence (p = 0.011) when walking at a comfortable speed with the prototype system was compared to walking with no attached device. During running gait at 3.5 m/s, there was a statistically significant increase in stride frequency (p = 0.025) and a statistically significant decrease in relative stride length (p = 0.036) found when running with the prototype system was compared to running with no attached device. However, the magnitude of these statistically significant differences was found to be very small, and not clinically meaningful. Discussion: Wearing the prototype footwear telemetry antenna system does not significantly impact gait or running characteristics and therefore, results obtained using this device would be an accurate representation of natural gait and running in this population. However, further validation tests, comparing real time baropodometric data with that of a force plate platform or in-shoe plantar pressure measurement system during gait and running are necessary prior to clinical and research use. Funding acknowledgement: This research was funded by Science Foundation Ireland, Grant Number 09/IN.1/I2652 1512. Reference [1] Kong PW, De Heer H. Gait Posture 2009;29(1):143–5.
http://dx.doi.org/10.1016/j.gaitpost.2015.06.138
Session PS15 Methods and Models Comparison of an inertial sensor based motion measurement system with a 3D-reflex marker based motion capture system D.H. Seidel 1,∗ , S.F. D’Souza 2 , W.W. Alt 3 , M. Wachowsky 2 1
Dept. of Sports Science, Justus Liebig University Gießen, Gießen, Germany 2 Gait Lab, Orthopaedic Clinic, Olgahospital, Klinikum Stuttgart, Stuttgart, Germany 3 Dept. of Biomechanics & Sports Biology, University of Stuttgart, Stuttgart, Germany Research question: The aim of this study was to investigate the MyoMotion (MM) inertial sensor system in a clinical setting by quantifying the differences/similarities in the kinematic data against a reference marker-based 3D motion capture system viz. Qualisys (Q). Introduction: Marker based systems are location oriented, the capture volume restricted to several steps only, and very cost-intensive in terms of data acquisition, attendance and initial operation [1]. In contrast, inertial motion systems are an acceptable and convenient alternative in the daily clinical routine [2]. Materials and methods: Five trials of lower body barefoot gait at self-selected speed in 12 healthy adults (7 females, 5 males), aged 22–45 years, were tracked at 100 Hz using a MM system and a 13-camera Q system. The Q setup utilized the CAST model [3] comprising 22 markers and 4 clusters while the MM setup included 7 sensors. The synchronized and normalized flexion/extension of
S75
Fig. 1. Averaged, normalised joint waveforms of 12 participants (5 GC per subject) in light and grand mean curves in bold. Qualisys in solid-black and MyoMotion in dashed-red.
ankle (AF), knee (KF), hip (HF) and hip adduction/abduction (HA) of 1 gait cycle (GC) per trial for each subject were extracted (see Fig. 1). Range of Motion, Root Mean Square (RMS) and bootstrapmethod were utilized for comparison over the entire GC. Standard deviation (STD) at heel strike (HS), toe-off (TO) and extrema of each curve were examined. The t-test was utilized to check for significant variation at these events and visualized in Bland–Altman plots. Results: In general, Q data has higher absolute values. Differences in the angles between Q and MM ranged from −4.2◦ to 7.6◦ at TO, −2.9◦ to 10.8◦ at extrema and 4.1◦ to 6.1◦ at HS. Both systems were highly correlated (0.73–0.98). Mean RMS for AF, KF, HF and HA are 8.2 ± 1.8◦ , 5.2 ± 1.9◦ , 8.4 ± 4.2◦ and 5.5 ± 1.4◦ respectively. By using the bootstrap-method, significant differences in the GC between both systems have been detected at 0–38% and 55–100% AF, 0–22%, 49–84% and 90–98% KF, 0–13% and 27–81% HF and 8–23% and 38–94% HA. Discussion: Results of both systems have low STD and are highly correlated, however, the differences of parameters vary with plane of investigation and joint of interest. In general, HA has higher variance than HF. Further investigations of accuracy, precision and repeatability are necessary before MM can be included in our clinical setting. References [1] Khandelwal S, Wickström N. Biosignals 2014:197–204. [2] Ferrari A, et al. Med Biol Eng Comput 2010;48(1):1–15. [3] Cappozzo A, et al. Clin Biomech 1995;10(4):171–8.
http://dx.doi.org/10.1016/j.gaitpost.2015.06.139
Session PS15 Methods and Models Influence of kinematics, anthropometry and kinetics data errors on inverse dynamics solutions during running H. Kim Nanzan University, Seto, Japan Research question: The purpose of this study was to clarify the influence of alterations in kinematic, anthropometric, and kinetic variables on ankle joint moments and power during running. Introduction: Inverse dynamics analysis using rigid segment models is widely used to estimate joint moments and power during various types of human movement. Kim et al. [1] clarified the influence of measurement errors in the center of pressure (CoP) location, on a force platform, on estimates of three-dimensional
S76
ESMAC Abstracts 2015 / Gait & Posture 42S (2015) S1–S101
(3D) lower limb joint moment during walking. Other sources of error may be derived from the kinematic and anthropometric data used in the model, such as estimates of joint center location (JCL) [2] and body segment parameters (BSP) [3]. However, it is not yet clear how biomechanical errors, such as errors in JCL, BSP, and CoP data, affect estimates of joint kinetics. Materials and methods: Biomechanical data while running were obtained for one healthy male subject. Using inverse dynamics procedures, 2D ankle joint kinetics were computed for five successful trials. Thereafter, recalculations of joint moments and power were carried out under the following conditions: (1) altered ankle JCL in the anterior-posterior direction at distances of ±5% and ±10% of the length from the ankle joint center to the midpoint of the first and fifth metatarsal, (2) five different BSP models (i.e., Vaughan, Ae (japanese), Chandler, Zatsiorsky, and DeLeva), and (3) the CoP shifted in the anterior–posterior direction by ±5% and ±10% of the range of CoP locations. Results: Alterations to the ankle JCL and CoP in the anterior–posterior direction had a large effect on the moment and power of the ankle joint. Changes in the magnitude of the moment and power resulting from differences in BSP were small. The moment and power estimates for cases with CoP errors were much larger than those for BSP and JCL errors (Figure). Discussion: The results of this study clearly showed that alterations in biomechanical parameters, which simulate both kinematic and kinetic measurement errors, such as errors in JCL and CoP, had a significant influence on the 2D ankle joint moment and power obtained through inverse dynamics procedures. Furthermore, the selection of BSP values reported by several researchers has little effect on the magnitude and time-series patterns of moments and power computed by this method. The results suggest that we pay close attention to the level of experimental errors to ensure meaningful results when using inverse dynamics procedures. References [1] Kim HY, et al. Int J Sport Health Sci 2007;5:71–82. [2] Stagni R, et al. J Biomech 2000;33:1479–87. [3] Ganley KJ, Powers CM. Clin Biomech 2004;19:50–6.
http://dx.doi.org/10.1016/j.gaitpost.2015.06.140
Session PS15 Methods and Models Markerless motion capture: Validity of Microsoft Kinect cameras and iPisoft J. Arulampalam 1,∗ , J. Pierrepont 2 , L. Kark 1 1 University of New South Wales, Grad School of Biomed Eng, UNSW Sydney, Australia 2 Optimized Ortho, Sydney, Australia
Research question: To investigate the validity of iPisoft, LLC (Moscow, Russia) & Microsoft Kinect cameras (Redmond, WA, USA) for markerless motion capture. Introduction: Post-operative dislocation is a major concern to surgeons as it is one of the most common reasons for total hip arthroplasty revision [1,2]. Identifying patients prone to dislocation can be assisted through thorough understanding of patient kinematics. Currently, this is often achieved through marker-based motion capture, but cost and invasiveness make it unfeasible for routine clinical use. Markerless motion capture systems provide a promising alternative contingent on precision, accuracy and ease of implementation.
Fig. 1. Step up for subject 1. Table 1 Summary of the confidence intervals (95%). Subject
Step up (deg)
Sit-to-stand (deg)
Stoop (deg)
1 2 3 4 5 6
(2.2, 11.5)* (−2.0, 7.3) (−1.7, 9.7) (12.3, 21.5)** (32.8, 42.1)** (6.1, 17.5)
(2.1, 13.1)* (−0.5, 10.4) (8.08, 19.1)** (11.2, 22.2)** (10.2, 21.2)** (8.5, 19.5)**
(17.6, 33.5)** (−3.6, 12.3) (3.74, 19.7)* (24.7, 40.7)** (5.7, 21.6)* (−3.5, 12.4)
Confidence intervals for difference of means. * Explainable error. ** Significant error.
Materials and methods: 6 healthy subjects (ethics HC15129) were recruited (mean age 21.7 yrs). Anthropometrics were recorded, and markers were applied according to the modified Helen Hayes marker set [3]. Subjects were familiarised with the 3 functional activities assessed: sit-to-stand (STS; stool of 50 cm), step-up (SU; step of 25 cm) and stoop [4]. Subjects completed each activity in the order above 3 times whilst being recorded by a 6camera Vicon Bonita (Oxford UK) system and a 2-camera Microsoft Kinect system articulated with iPiSoft. All data were processed using algorithms inbuilt to the respective softwares. Confidence Intervals (CI) of the peak differences were calculated to compare systems. Results: A representative graph of SU (subject 1) is presented in Fig. 1 (Table 1). Discussion: The trend line of iPiSoft follows that of Vicon, which occurred for all subjects. The CI’s showed that significant differences were seen in particular subjects (4 and 5) for all actions and could be due to non-optimal setup (e.g. camera set up and subject clothing). Despite drawbacks in accuracy of iPiSoft, the system has promise and warrants further study. Further studies should investigate larger sample sizes and different pathologies. References [1] [2] [3] [4]
Australian Orthopaedic Association National Joint Replacement Registry, 2014. Graves. Annual report of the AOANJMR; 2014. Kadaba, et al. JOR 1990;8(3):383–92. Nadzadi, et al. J Biomech 2003;36(4):577–91.
http://dx.doi.org/10.1016/j.gaitpost.2015.06.141
footwear telemetry antenna system, respectively. For running gait, three different running speeds on a treadmill were tested in a systematic order; 3.5 m/s, 4.5 m/s and 5.4 m/s. Descriptive statistics for spatiotemporal parameters were calculated using 10 walking gait cycles and 6 running gait cycles. Gait and running parameter changes were analysed using linear regression. Results: During walking gait, there were statistically significant increases in foot off occurrence (p = 0.026) and opposite foot off occurrence (p = 0.031), and a statistically significant decrease in opposite foot contact occurrence (p = 0.011) when walking at a comfortable speed with the prototype system was compared to walking with no attached device. During running gait at 3.5 m/s, there was a statistically significant increase in stride frequency (p = 0.025) and a statistically significant decrease in relative stride length (p = 0.036) found when running with the prototype system was compared to running with no attached device. However, the magnitude of these statistically significant differences was found to be very small, and not clinically meaningful. Discussion: Wearing the prototype footwear telemetry antenna system does not significantly impact gait or running characteristics and therefore, results obtained using this device would be an accurate representation of natural gait and running in this population. However, further validation tests, comparing real time baropodometric data with that of a force plate platform or in-shoe plantar pressure measurement system during gait and running are necessary prior to clinical and research use. Funding acknowledgement: This research was funded by Science Foundation Ireland, Grant Number 09/IN.1/I2652 1512. Reference [1] Kong PW, De Heer H. Gait Posture 2009;29(1):143–5.
http://dx.doi.org/10.1016/j.gaitpost.2015.06.138
Session PS15 Methods and Models Comparison of an inertial sensor based motion measurement system with a 3D-reflex marker based motion capture system D.H. Seidel 1,∗ , S.F. D’Souza 2 , W.W. Alt 3 , M. Wachowsky 2 1
Dept. of Sports Science, Justus Liebig University Gießen, Gießen, Germany 2 Gait Lab, Orthopaedic Clinic, Olgahospital, Klinikum Stuttgart, Stuttgart, Germany 3 Dept. of Biomechanics & Sports Biology, University of Stuttgart, Stuttgart, Germany Research question: The aim of this study was to investigate the MyoMotion (MM) inertial sensor system in a clinical setting by quantifying the differences/similarities in the kinematic data against a reference marker-based 3D motion capture system viz. Qualisys (Q). Introduction: Marker based systems are location oriented, the capture volume restricted to several steps only, and very cost-intensive in terms of data acquisition, attendance and initial operation [1]. In contrast, inertial motion systems are an acceptable and convenient alternative in the daily clinical routine [2]. Materials and methods: Five trials of lower body barefoot gait at self-selected speed in 12 healthy adults (7 females, 5 males), aged 22–45 years, were tracked at 100 Hz using a MM system and a 13-camera Q system. The Q setup utilized the CAST model [3] comprising 22 markers and 4 clusters while the MM setup included 7 sensors. The synchronized and normalized flexion/extension of
S75
Fig. 1. Averaged, normalised joint waveforms of 12 participants (5 GC per subject) in light and grand mean curves in bold. Qualisys in solid-black and MyoMotion in dashed-red.
ankle (AF), knee (KF), hip (HF) and hip adduction/abduction (HA) of 1 gait cycle (GC) per trial for each subject were extracted (see Fig. 1). Range of Motion, Root Mean Square (RMS) and bootstrapmethod were utilized for comparison over the entire GC. Standard deviation (STD) at heel strike (HS), toe-off (TO) and extrema of each curve were examined. The t-test was utilized to check for significant variation at these events and visualized in Bland–Altman plots. Results: In general, Q data has higher absolute values. Differences in the angles between Q and MM ranged from −4.2◦ to 7.6◦ at TO, −2.9◦ to 10.8◦ at extrema and 4.1◦ to 6.1◦ at HS. Both systems were highly correlated (0.73–0.98). Mean RMS for AF, KF, HF and HA are 8.2 ± 1.8◦ , 5.2 ± 1.9◦ , 8.4 ± 4.2◦ and 5.5 ± 1.4◦ respectively. By using the bootstrap-method, significant differences in the GC between both systems have been detected at 0–38% and 55–100% AF, 0–22%, 49–84% and 90–98% KF, 0–13% and 27–81% HF and 8–23% and 38–94% HA. Discussion: Results of both systems have low STD and are highly correlated, however, the differences of parameters vary with plane of investigation and joint of interest. In general, HA has higher variance than HF. Further investigations of accuracy, precision and repeatability are necessary before MM can be included in our clinical setting. References [1] Khandelwal S, Wickström N. Biosignals 2014:197–204. [2] Ferrari A, et al. Med Biol Eng Comput 2010;48(1):1–15. [3] Cappozzo A, et al. Clin Biomech 1995;10(4):171–8.
http://dx.doi.org/10.1016/j.gaitpost.2015.06.139
Session PS15 Methods and Models Influence of kinematics, anthropometry and kinetics data errors on inverse dynamics solutions during running H. Kim Nanzan University, Seto, Japan Research question: The purpose of this study was to clarify the influence of alterations in kinematic, anthropometric, and kinetic variables on ankle joint moments and power during running. Introduction: Inverse dynamics analysis using rigid segment models is widely used to estimate joint moments and power during various types of human movement. Kim et al. [1] clarified the influence of measurement errors in the center of pressure (CoP) location, on a force platform, on estimates of three-dimensional
S76
ESMAC Abstracts 2015 / Gait & Posture 42S (2015) S1–S101
(3D) lower limb joint moment during walking. Other sources of error may be derived from the kinematic and anthropometric data used in the model, such as estimates of joint center location (JCL) [2] and body segment parameters (BSP) [3]. However, it is not yet clear how biomechanical errors, such as errors in JCL, BSP, and CoP data, affect estimates of joint kinetics. Materials and methods: Biomechanical data while running were obtained for one healthy male subject. Using inverse dynamics procedures, 2D ankle joint kinetics were computed for five successful trials. Thereafter, recalculations of joint moments and power were carried out under the following conditions: (1) altered ankle JCL in the anterior-posterior direction at distances of ±5% and ±10% of the length from the ankle joint center to the midpoint of the first and fifth metatarsal, (2) five different BSP models (i.e., Vaughan, Ae (japanese), Chandler, Zatsiorsky, and DeLeva), and (3) the CoP shifted in the anterior–posterior direction by ±5% and ±10% of the range of CoP locations. Results: Alterations to the ankle JCL and CoP in the anterior–posterior direction had a large effect on the moment and power of the ankle joint. Changes in the magnitude of the moment and power resulting from differences in BSP were small. The moment and power estimates for cases with CoP errors were much larger than those for BSP and JCL errors (Figure). Discussion: The results of this study clearly showed that alterations in biomechanical parameters, which simulate both kinematic and kinetic measurement errors, such as errors in JCL and CoP, had a significant influence on the 2D ankle joint moment and power obtained through inverse dynamics procedures. Furthermore, the selection of BSP values reported by several researchers has little effect on the magnitude and time-series patterns of moments and power computed by this method. The results suggest that we pay close attention to the level of experimental errors to ensure meaningful results when using inverse dynamics procedures. References [1] Kim HY, et al. Int J Sport Health Sci 2007;5:71–82. [2] Stagni R, et al. J Biomech 2000;33:1479–87. [3] Ganley KJ, Powers CM. Clin Biomech 2004;19:50–6.
http://dx.doi.org/10.1016/j.gaitpost.2015.06.140
Session PS15 Methods and Models Markerless motion capture: Validity of Microsoft Kinect cameras and iPisoft J. Arulampalam 1,∗ , J. Pierrepont 2 , L. Kark 1 1 University of New South Wales, Grad School of Biomed Eng, UNSW Sydney, Australia 2 Optimized Ortho, Sydney, Australia
Research question: To investigate the validity of iPisoft, LLC (Moscow, Russia) & Microsoft Kinect cameras (Redmond, WA, USA) for markerless motion capture. Introduction: Post-operative dislocation is a major concern to surgeons as it is one of the most common reasons for total hip arthroplasty revision [1,2]. Identifying patients prone to dislocation can be assisted through thorough understanding of patient kinematics. Currently, this is often achieved through marker-based motion capture, but cost and invasiveness make it unfeasible for routine clinical use. Markerless motion capture systems provide a promising alternative contingent on precision, accuracy and ease of implementation.
Fig. 1. Step up for subject 1. Table 1 Summary of the confidence intervals (95%). Subject
Step up (deg)
Sit-to-stand (deg)
Stoop (deg)
1 2 3 4 5 6
(2.2, 11.5)* (−2.0, 7.3) (−1.7, 9.7) (12.3, 21.5)** (32.8, 42.1)** (6.1, 17.5)
(2.1, 13.1)* (−0.5, 10.4) (8.08, 19.1)** (11.2, 22.2)** (10.2, 21.2)** (8.5, 19.5)**
(17.6, 33.5)** (−3.6, 12.3) (3.74, 19.7)* (24.7, 40.7)** (5.7, 21.6)* (−3.5, 12.4)
Confidence intervals for difference of means. * Explainable error. ** Significant error.
Materials and methods: 6 healthy subjects (ethics HC15129) were recruited (mean age 21.7 yrs). Anthropometrics were recorded, and markers were applied according to the modified Helen Hayes marker set [3]. Subjects were familiarised with the 3 functional activities assessed: sit-to-stand (STS; stool of 50 cm), step-up (SU; step of 25 cm) and stoop [4]. Subjects completed each activity in the order above 3 times whilst being recorded by a 6camera Vicon Bonita (Oxford UK) system and a 2-camera Microsoft Kinect system articulated with iPiSoft. All data were processed using algorithms inbuilt to the respective softwares. Confidence Intervals (CI) of the peak differences were calculated to compare systems. Results: A representative graph of SU (subject 1) is presented in Fig. 1 (Table 1). Discussion: The trend line of iPiSoft follows that of Vicon, which occurred for all subjects. The CI’s showed that significant differences were seen in particular subjects (4 and 5) for all actions and could be due to non-optimal setup (e.g. camera set up and subject clothing). Despite drawbacks in accuracy of iPiSoft, the system has promise and warrants further study. Further studies should investigate larger sample sizes and different pathologies. References [1] [2] [3] [4]
Australian Orthopaedic Association National Joint Replacement Registry, 2014. Graves. Annual report of the AOANJMR; 2014. Kadaba, et al. JOR 1990;8(3):383–92. Nadzadi, et al. J Biomech 2003;36(4):577–91.
http://dx.doi.org/10.1016/j.gaitpost.2015.06.141