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Concurrent validation of magnetic and inertial measurement units in estimating upper body posture during gait
Measurement 82 (2016) 240–245
Contents lists available at ScienceDirect
Measurement journal homepage: www.elsevier.com/locate/measurement
Concurrent validation of magnetic and inertial measurement units in estimating upper body posture during gait Gu Eon Kang ⇑, M. Melissa Gross Department of Movement Science, School of Kinesiology, University of Michigan, 401 Washtenaw Avenue, Ann Arbor, MI 48109-2214, USA
a r t i c l e
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Article history: Received 16 September 2015 Received in revised form 28 December 2015 Accepted 5 January 2016 Available online 8 January 2016 Keywords: Magnetic and inertial measurement Gait Head flexion Thorax flexion Shoulder girdle elevation
a b s t r a c t We assessed the concurrent validity of commercially available magnetic and inertial measurement units (MIMUs) for estimating mean postural angles for head flexion, thorax flexion and shoulder girdle elevation during gait in seven healthy individuals. Postural angles estimated with the MIMUs were compared with angles calculated using marker data from a gold standard motion capture system. Coefficients of determination of mean postural angles between measurement systems were 0.82 for head flexion, 0.58 for thorax flexion, and 0.77 for shoulder girdle elevation. Bland–Altman analysis showed good agreement between measurement systems. Intraclass correlation coefficients were 0.9 for head flexion, 0.73 for thorax flexion, and 0.87 for shoulder girdle elevation. Root mean square errors were less than 3° between measurement systems for all body segments. The present findings suggest that the MIMUs tested in this study are valid for estimating head flexion, thorax flexion and shoulder girdle elevation during gait. Ó 2016 Elsevier Ltd. All rights reserved.
1. Introduction Upper body posture is an important characteristic of gait that can be affected by aging, mood state, footwear, and load carriage. Elderly fallers show greater head flexion, trunk flexion and shoulder elevation during gait compared to healthy elderly [1]. A study reported that patients with major depressive disorders walk with more slumped head posture compared to never-depressed individuals [2]. While experiencing sadness, healthy individuals walk with shrinking, bowed and contracting torso [3], and increased head flexion [4,5], thoracic spine flexion and shoulder girdle elevation [5]. Also, increased head extension and thoracic spine extension occur in healthy individuals while experiencing anger or joy [5]. Shoe type is another important cause of postural changes during gait, and trunk
⇑ Corresponding author. Tel.: +1 734 763 0013; fax: +1 734 936 1925. E-mail address: [email protected] (G.E. Kang). http://dx.doi.org/10.1016/j.measurement.2016.01.007 0263-2241/Ó 2016 Elsevier Ltd. All rights reserved.
flexion decreases when healthy women wear high-heeled shoes compared to low-heeled shoes [6]. Changes in upper body posture are an important characteristic of gait with load carriage. In school children, carriage of backpacks increases head flexion, trunk flexion and shoulder elevation during gait [7–13]. In young adults, load carriage on the back and front significantly increases thorax flexion [14] and head flexion [15], respectively, during gait. In soldiers, carriage of heavy loads on the back during gait results in increased head flexion and trunk flexion [16–18]. Since load carriage is related to back pain [19– 21], quantifying upper body posture during gait with heavy load in these populations can provide useful knowledge for clinical, practical and scientific purposes. Typically, upper body posture has been quantified using optoelectronic motion capture systems in the laboratory [2–5,9,12,14,15,17,18]. Retroreflective markers attached to the body are tracked by cameras, and position data from the retroreflective markers are used for biomechanical analysis. Due to their accuracy and reliability, optoelectronic
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motion capture systems are considered as the gold standard for estimating postural angles in biomechanical studies. However, optoelectronic motion capture systems are expensive and require specialized laboratory settings so that they are less useful for collecting data in the field under natural circumstances. Video recording has also been used for quantifying body posture during gait [1,6–8,10,11, 13,16], but this technology also limits the choice of environment for data collection. Wearable magnetic and inertial measurement units (MIMUs), with gyroscopes, accelerometers and magnetometers, have become popular for human movement analysis and are possible devices for monitoring upper body posture outside of the laboratory. MIMUs have been used successfully to estimate angular orientation of the lower limb during level walking and stair climbing [22], of the thorax [23] and shoulder girdle [24] during range of motion tasks, and of the trunk during lifting tasks [25,26] and sprinting [27]. The feasibility of using MIMUs to estimate head, thorax and shoulder girdle postural angles during a movement task in which the amplitude of joint motion is expected to be relatively small has not been established. The purpose of this study was therefore to assess the concurrent validity of MIMUs for estimating upper body postural angles during gait in healthy individuals. 2. Materials and methods 2.1. Participants Seven healthy individuals (4 males and 3 females) recruited from the university community participated in the study. The participants ranged in age from 18 to 21 years (mean 19.6 years, SD 1.1 years) and ranged in height from 1.52 to 1.90 m (mean 1.71 m, SD 0.13 m). All participants gave written informed consent approved by the University of Michigan Institutional Review Board before completing the study. 2.2. Instrumentation Three commercially available TSS-WL MIMUs (YEI Technology, Portsmouth, OH, USA; 28 g; 35 60 15 mm) were used (Fig. 1). Each MIMU is comprised of a 3-axis accelerometer (±12 g), gyroscope (±500 °/s) and magnetic sensor (±1.3 G) to estimate real-time orientation (pitch/yaw/roll) using an extended Kalman filter. The MIMUs were placed on the forehead, sternum, and shoulder just medial to the right acromion using a head strap, cover-stretch tape and double-sided tape so that the pitch axis of the MIMUs was parallel to anatomical axes for head and thorax flexion/extension, and shoulder girdle elevation/depression (Fig. 1). The MIMU data were sampled at 60 Hz. An optoelectronic motion capture system with eight cameras (Motion Analysis, Santa Rosa, CA, USA) was used as the gold standard reference system. To build markerbased axes (XYZ) for each MIMU, four retroreflective markers were attached to the MIMUs using plastic rigid plates
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so that the pitch axis (Z) of the marker-based axes was parallel to anatomical axes for head and thorax flexion/extension, and shoulder girdle elevation/depression (Fig. 1). Additionally, markers were attached to participants’ shoes at four locations: anterior (distal end of the second toe), medial (head of the first metatarsal), lateral (head of the fifth metatarsal) and posterior (calcaneus). Gait cycles were identified based on forward movement of the posterior shoe markers. Marker data were sampled at 60 Hz and low-pass filtered at 6 Hz using a 4th order Butterworth filter. 2.3. Experimental procedures Each gait trial began with the participant standing upright (reference posture), and then performing a neck flexion task to synchronize MIMU and motion capture data [27,28]. Next, participants performed a gait trial on a 7-m walkway at comfortable speed. Each participant repeated these tasks three times. 2.4. Data analysis Since head posture and thorax posture in the sagittal plane, and shoulder girdle posture in the frontal plane are important characteristics of body posture, we compared mean postural angles for head flexion/extension angle, thorax flexion/extension angle, and shoulder girdle elevation/depression angle between MIMUs and an optoelectronic motion capture system during gait. Angular output from the MIMUs consisted of roll, pitch and yaw angles estimated with a sensor fusion algorithm. The roll, pitch and yaw angles obtained from the head and thorax MIMUs were considered as the anatomical angles for rotation, flexion/extension and lateral tilt, respectively, in the corresponding body segment (Fig. 1). In addition, the pitch and yaw angles that the shoulder MIMU estimated were considered as the anatomical angles for elevation/depression and protraction/retraction, respectively, in the shoulder girdle (Fig. 1). We followed previously reported methods for considering angles estimated with the MIMUs as postural angles for head flexion, thorax flexion and shoulder girdle elevation [23,25–27]. Specifically, while each participant was standing upright quietly at the onset of each trial, global reference frames for the MIMUs were defined such that the roll axis of the head and trunk MIMUs and the yaw axis of the shoulder MIMU were aligned with the gravity vector. Then, the pitch angle estimated with each MIMU, expressed in relation to the global reference frames during the reference posture, was considered as head flexion, thorax flexion and shoulder girdle elevation angles. To validate MIMU-based estimates, we built markerbased axes (XYZ) for the head, thorax and shoulder girdle MIMUs using marker data obtained by the reference system (Fig. 1). Then, head flexion, thorax flexion and shoulder girdle elevation angles from marker-based axes were calculated as pitch angles of the marker-based axes relative to the reference posture using Matlab (MathWorks, Natick, MA, USA). Specifically, the direction cosine matrix
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Yaw
Roll
Pitch X Z Z
Y
Y
X
X Z
Y
Fig. 1. MIMUs with marker clusters attached to the head, thorax and shoulder girdle. Marker-based axes (XYZ) were built for each MIMU using marker data. Roll axes on the MIMUs correspond to X axes in the marker-based axes. Yaw axes on the MIMUs correspond to Y axes in the marker-based axes. Pitch axes on the MIMUs correspond to Z axes in the marker-based axes.
(RReference using:
system)
RReference system
was derived from marker-based axes
2 3 ~ X 6 7 ¼ 4~ Y5 ~ Z
Current frame
2 3Transpose ~ X 6 7 4~ Y5 ~ Z
Reference posture
Then, we used the custom Matlab function (dcm2angle) to calculate postural angles for head flexion/extension, thorax flexion/extension and shoulder girdle elevation for the direction cosine matrices. The default Euler ZYX sequence for the custom Matlab function was used to calculate postural angles. In each gait trial, postural angles for head flexion, thorax flexion, and shoulder girdle elevation from each measurement system were first averaged during three gait cycles. Then, we averaged data across the gait trials for each participant and used the averaged data in the statistical analyses.
2.5. Statistical analysis To assess the strength of the relation between the reference system and the MIMUs, we calculated the coefficient of determination (R2). Then, to assess agreement between the reference system and the MIMUs, we used a Bland–Altman analysis [29]. Finally, we calculated a two-way, mixed-effects, single measure intraclass correlation coefficient (ICC) to assess reliability of the MIMUs. ICCs for the mean postural angles were evaluated according to Shrout
and Fleiss [30] (i.e., ICC > 0.75: excellent, 0.4 < ICC 6 0.75: fair-to-good). To evaluate curve similarity between the reference system and the MIMUs estimates, we computed the root mean square errors (RMSE) between curves for postural angles during three gait cycles in each gait trial. Finally, a mixed model analysis with random effects of participants was used to calculate within- and between-subject variability for RMSE. Variability was estimated as the square root of variance between- and within-subject.
3. Results Postural angles from 21 gait trials (7 participants 3 trials) were analyzed. Postural angles estimated with the MIMUs compared well with the reference system (Fig. 2). Coefficients of determination of mean postural angles for each gait trial were R2 = 0.82 (95% confidence interval (CI) 0.50–0.99) for mean head flexion angle, R2 = 0.58 (95% CI 0.001–0.96) for mean thorax flexion angle, and R2 = 0.77 (95% CI 0.37–0.98) for mean shoulder girdle elevation angle (Fig. 3). Differences in mean postural angles between measurement systems for each participant were between 2.1° and 3.7° for all body segments (Fig. 3). Bland–Altman plots showed no consistent bias between estimates for all body segments (Fig. 3), indicating a good agreement between two measurement systems, for all postural angles. ICCs with 95% CI were computed for mean postural angles for head flexion, thorax flexion, and shoulder girdle
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Fig. 2. Exemplar postural angles for the reference system (solid line) and the MIMUs (dashed line) during a representative gait trial.
Fig. 3. Correlations (top row) of mean postural angles (°) for all participants (N = 7) for the reference system and the MIMUs. Bland–Altman plots (bottom row) for average postural angles (°) for the reference system and the MIMUs, and differences between postural angles (MIMUs-reference) (°). In the Bland– Altman plots, 95% limits of agreement was calculated as mean +2sd (top solid line), mean (middle solid line), and mean-2sd (bottom solid line).
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motion of the head and thorax in the sagittal plane, and the shoulder girdle in the frontal plane were 11.2°, 8.1°, and 5.7°, respectively, when estimated with the reference system (Table 1). These motions occurred during the same time interval (i.e., the same three gait cycles) thus the angular velocities and accelerations were the greatest for the head and the least for the shoulder girdle. Since the orientation error in MIMUs increases with the angular velocity [31], the greatest angular velocities in the head MIMU would have caused the greatest errors among the MIMUs tested in this study. Consistent with our results, others have reported increases in errors between MIMUs and reference systems with increased velocity. When estimating knee angles with MIMUs, errors increased during running compared to walking [32]. Despite differences in ranges of motion between body segments, the magnitude of MIMUs error was relatively small (<2°) for these postural angles during gait. A limitation in this study is the relatively small sample size. Our sample size followed previous studies (sample size ranged between 1 and 10) that reported validity of MIMUs [22,24,25,27,31–36]. Nonetheless, the small sample size could limit the generalizability of the correlations and agreements between the reference system and the MIMUs tested in this study. Another limitation is that the pitch, roll and yaw axes of the MIMUs might not be perfectly aligned with anatomical axes for head, thorax and shoulder girdle. When attaching the MIMUs on each participant’s body, we visually checked the alignment between the MIMUs and anatomical axes. If a slight misalignment between the MIMUs axes (pitch/yaw/roll) and anatomical axes occurred, it may be that anatomical flexion in the head and thorax, and elevation in the shoulder girdle is a combination of pitch and a slight amount of roll/yaw of the MIMUs. In addition, the musculoskeletal anatomy of the shoulder girdle is not ideal for placing a MIMU to estimate shoulder girdle angles in the frontal plane. In a previous study, a MIMU was attached to the bony spine of the scapula [24], and scapulothoracic joint motion was used for estimating shoulder girdle elevation/depression. In this study, however, we placed the shoulder MIMU on the right acromion to align the pitch axis of the MIMU so that we could estimate elevation/ depression of the shoulder girdle in the frontal plane (Fig. 1). Because the bony surface of the scapula at the acromion is smaller than the size of the MIMU, the motion of the shoulder girdle MIMU might have been more affected by soft tissue movement than the head and thorax MIMUs. However, the magnitude of error was the least for the shoulder MIMU, thus we do not expect that this potential soft tissue effect was important. Finally, we attached
elevation. ICC values were 0.9 (95% CI 0.53–0.98) for mean head flexion angle, 0.73 (95% CI 0.06–0.95) for mean thorax flexion angle, and 0.87 (95% CI 0.44–0.98) for mean shoulder girdle elevation angle. Differences between measurement systems for mean angles across all participants were 0.6°, 0.1° and 0.0° for head flexion, thorax flexion and shoulder girdle elevation, respectively (Table 1). When averaged across participants, mean RMSE were 2.9°, 2.7° and 2.2° for head flexion, thorax flexion and shoulder girdle elevation, respectively (Table 1). Mean between-subject variability was 0.9°, 1.1° and 0.5° for head flexion, thorax flexion and shoulder girdle elevation, respectively (Table 1). Within-subject variability was less than 1.5° across body segments and participants (Table 1). 4. Discussion The goal of this study was to assess the concurrent validity of commercially available MIMUs for estimating mean postural angles for head flexion, thorax flexion and shoulder girdle elevation during gait in comparison with the gold standard optoelectronic motion capture system. We assessed the concurrent validity by calculating the agreement, reliability, and accuracy between postural angles estimated with MIMUs and measured by a motion capture system. If postural angles estimated with MIMUs are shown to be valid, MIMUs can be used in field studies to assess upper body posture during gait. We calculated RMSE values to estimate differences between postural angle curves estimated with each measurement system. Errors with the MIMUs tested in this study were less than 3°, that is, RMSE values were 2.9°, 2.7°, and 2.2° for head flexion, thorax flexion, and shoulder girdle elevation, respectively. These errors were comparable to, or less than, RMSE values reported in previous studies for angles estimated with MIMUs. When attached to the upper body, RMSE for MIMUs were approximately 4° for trunk inclination during lifting [25,26], 6° for the thorax during thorax range of motion [23], and 3.6° for shoulder girdle during shoulder joint range of motion [24]. Our results extend the set of movement tasks for which MIMU validity has been established for assessing thorax and shoulder girdle postures. In addition, this study establishes the validity of using MIMUs to assess head posture, to our knowledge for the first time, in any movement task. Although errors between measurement systems were small overall, we found that the largest errors occurred for the head and the smallest errors for the shoulder girdle. This is likely related to the range of motion of these body segments during walking. In this study, mean ranges of
Table 1 Mean postural angles (°) and mean range of motion (ROM) (°) across participants measured with the reference system and the MIMUs, mean RMSE (°) between the reference system and the MIMUs, and within- and between-subject variability (°) for RMSE. Values in parentheses are standard deviation. Posture
Head flexion Thorax flexion Shoulder girdle elevation
Mean angles
ROM
Reference
MIMUs
Reference
MIMUs
0.7 (2.7) 4.6 (2.7) 0.1 (1.1)
1.3 (3.2) 4.5 (2.1) 0.1 (1.1)
11.2 (4.6) 8.1 (1.0) 5.7 (2.2)
12.1 (3.4) 7.6 (1.4) 7.8 (2.3)
RMSE
Subject variability Within
Between
2.9 (1.2) 2.7 (1.2) 2.2 (0.6)
1.4 1.0 0.4
0.9 1.1 0.5
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the markers on the MIMUs instead of bony landmarks in each participant. This might have affected the alignment between the marker-based axes and anatomical axes, however the alignment was carefully checked, and was defined according to the existing literature [25,26]. 5. Conclusion We examined the concurrent validity of commercial MIMUs in estimating postural angles for head flexion, thorax flexion and shoulder girdle elevation during gait in healthy individuals. Overall, results from this study suggest that mean postural angles estimated with the MIMUs tested in this study are valid, and that the MIMUs used in this study can be used to estimate mean head flexion, thorax flexion and shoulder girdle elevation angles during gait. Before deploying MIMUs in the field for assessment of upper body posture in real-world contexts such as aging, mood state, footwear, and load carriage, future work is needed to address the MIMU’s test–retest reliability, long-term monitoring and sensitivity to different walking conditions. In addition, the relevance of the obtained errors with the MIMUs tested in this study must be evaluated in terms of impact on the parameters considered for the application of interest. Acknowledgements We thank Kelsey Galang, Emily Rozin and Matthew Mastenbrook for assistance with data collection and analysis, David Childers for statistical analyses, and Professor Noel Perkins, Dr. Stephen Cain and Dr. Ryan McGinnis for technical consultation. This study was supported by a graduate research grant from Horace H. Rackham School of Graduate Studies in the University of Michigan. References [1] L. Wolfson, R. Whipple, P. Amerman, J.N. Tobin, Gait assessment in elderly: a gait abnormality rating scale and its relation to falls, J. Gerontol. 45 (1990) N12–N19. [2] J. Michalak, F.T. Troje, J. Fischer, P. Vollmar, T. Hedenreich, D. Schulte, Embodiment of sadness and depression-gait patterns associated with dysphoric mood, Psychosom. Med. 71 (2009) 580–587. [3] E.A. Crane, M.M. Gross, Effort-shape characteristics of emotionrelated body movement, J. Nonverbal Behav. 37 (2013) 91–105. [4] C.L. Roether, L. Omlor, A. Christensen, M.A. Giese, Critical features for the perception of emotion from gait, J. Vis. 9 (2009) 1–32. [5] M.M. Gross, E.A. Crane, B.L. Fredrickson, Effort-shape and kinematic assessment of bodily expression of emotion during gait, Hum. Mov. Sci. 31 (2012) 202–221. [6] C.H. Lee, E.H. Jeong, A. Freivalds, Biomechanical effects of wearing high-heeled shoes, Int. J. Ind. Ergon. 28 (2001) 321–326. [7] D.D. Pascoe, D.E. Pascoe, Y.T. Wang, D.M. Shim, C.K. Kim, Influence of carrying book bags on gait cycle and posture of youths, Ergonomics 40 (1997) 631–641. [8] Y. Hong, G.P. Brueggemann, Changes in gait patterns in 10-year-old boys with increasing loads when walking on a treadmill, Gait Posture 11 (2000) 254–259. [9] S. Goodgold, K. Mohr, A. Samant, T. Parke, T. Burns, L. Gardner, Effects of backpack load and task demand on trunk forward lean: pilot findings on two boys, Work 18 (2002) 213–220. [10] Y. Hong, C.K. Cheung, Gait and posture response to backpack load during level walking in children, Gait Posture 17 (2003) 28–33. [11] J.X. Li, Y. Hong, P.D. Robinson, The effect of load carriage on movement kinematics and respiratory parameters in children during walking, Eur. J. Appl. Physiol. 90 (2003) 35–43.
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Contents lists available at ScienceDirect
Measurement journal homepage: www.elsevier.com/locate/measurement
Concurrent validation of magnetic and inertial measurement units in estimating upper body posture during gait Gu Eon Kang ⇑, M. Melissa Gross Department of Movement Science, School of Kinesiology, University of Michigan, 401 Washtenaw Avenue, Ann Arbor, MI 48109-2214, USA
a r t i c l e
i n f o
Article history: Received 16 September 2015 Received in revised form 28 December 2015 Accepted 5 January 2016 Available online 8 January 2016 Keywords: Magnetic and inertial measurement Gait Head flexion Thorax flexion Shoulder girdle elevation
a b s t r a c t We assessed the concurrent validity of commercially available magnetic and inertial measurement units (MIMUs) for estimating mean postural angles for head flexion, thorax flexion and shoulder girdle elevation during gait in seven healthy individuals. Postural angles estimated with the MIMUs were compared with angles calculated using marker data from a gold standard motion capture system. Coefficients of determination of mean postural angles between measurement systems were 0.82 for head flexion, 0.58 for thorax flexion, and 0.77 for shoulder girdle elevation. Bland–Altman analysis showed good agreement between measurement systems. Intraclass correlation coefficients were 0.9 for head flexion, 0.73 for thorax flexion, and 0.87 for shoulder girdle elevation. Root mean square errors were less than 3° between measurement systems for all body segments. The present findings suggest that the MIMUs tested in this study are valid for estimating head flexion, thorax flexion and shoulder girdle elevation during gait. Ó 2016 Elsevier Ltd. All rights reserved.
1. Introduction Upper body posture is an important characteristic of gait that can be affected by aging, mood state, footwear, and load carriage. Elderly fallers show greater head flexion, trunk flexion and shoulder elevation during gait compared to healthy elderly [1]. A study reported that patients with major depressive disorders walk with more slumped head posture compared to never-depressed individuals [2]. While experiencing sadness, healthy individuals walk with shrinking, bowed and contracting torso [3], and increased head flexion [4,5], thoracic spine flexion and shoulder girdle elevation [5]. Also, increased head extension and thoracic spine extension occur in healthy individuals while experiencing anger or joy [5]. Shoe type is another important cause of postural changes during gait, and trunk
⇑ Corresponding author. Tel.: +1 734 763 0013; fax: +1 734 936 1925. E-mail address: [email protected] (G.E. Kang). http://dx.doi.org/10.1016/j.measurement.2016.01.007 0263-2241/Ó 2016 Elsevier Ltd. All rights reserved.
flexion decreases when healthy women wear high-heeled shoes compared to low-heeled shoes [6]. Changes in upper body posture are an important characteristic of gait with load carriage. In school children, carriage of backpacks increases head flexion, trunk flexion and shoulder elevation during gait [7–13]. In young adults, load carriage on the back and front significantly increases thorax flexion [14] and head flexion [15], respectively, during gait. In soldiers, carriage of heavy loads on the back during gait results in increased head flexion and trunk flexion [16–18]. Since load carriage is related to back pain [19– 21], quantifying upper body posture during gait with heavy load in these populations can provide useful knowledge for clinical, practical and scientific purposes. Typically, upper body posture has been quantified using optoelectronic motion capture systems in the laboratory [2–5,9,12,14,15,17,18]. Retroreflective markers attached to the body are tracked by cameras, and position data from the retroreflective markers are used for biomechanical analysis. Due to their accuracy and reliability, optoelectronic
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motion capture systems are considered as the gold standard for estimating postural angles in biomechanical studies. However, optoelectronic motion capture systems are expensive and require specialized laboratory settings so that they are less useful for collecting data in the field under natural circumstances. Video recording has also been used for quantifying body posture during gait [1,6–8,10,11, 13,16], but this technology also limits the choice of environment for data collection. Wearable magnetic and inertial measurement units (MIMUs), with gyroscopes, accelerometers and magnetometers, have become popular for human movement analysis and are possible devices for monitoring upper body posture outside of the laboratory. MIMUs have been used successfully to estimate angular orientation of the lower limb during level walking and stair climbing [22], of the thorax [23] and shoulder girdle [24] during range of motion tasks, and of the trunk during lifting tasks [25,26] and sprinting [27]. The feasibility of using MIMUs to estimate head, thorax and shoulder girdle postural angles during a movement task in which the amplitude of joint motion is expected to be relatively small has not been established. The purpose of this study was therefore to assess the concurrent validity of MIMUs for estimating upper body postural angles during gait in healthy individuals. 2. Materials and methods 2.1. Participants Seven healthy individuals (4 males and 3 females) recruited from the university community participated in the study. The participants ranged in age from 18 to 21 years (mean 19.6 years, SD 1.1 years) and ranged in height from 1.52 to 1.90 m (mean 1.71 m, SD 0.13 m). All participants gave written informed consent approved by the University of Michigan Institutional Review Board before completing the study. 2.2. Instrumentation Three commercially available TSS-WL MIMUs (YEI Technology, Portsmouth, OH, USA; 28 g; 35 60 15 mm) were used (Fig. 1). Each MIMU is comprised of a 3-axis accelerometer (±12 g), gyroscope (±500 °/s) and magnetic sensor (±1.3 G) to estimate real-time orientation (pitch/yaw/roll) using an extended Kalman filter. The MIMUs were placed on the forehead, sternum, and shoulder just medial to the right acromion using a head strap, cover-stretch tape and double-sided tape so that the pitch axis of the MIMUs was parallel to anatomical axes for head and thorax flexion/extension, and shoulder girdle elevation/depression (Fig. 1). The MIMU data were sampled at 60 Hz. An optoelectronic motion capture system with eight cameras (Motion Analysis, Santa Rosa, CA, USA) was used as the gold standard reference system. To build markerbased axes (XYZ) for each MIMU, four retroreflective markers were attached to the MIMUs using plastic rigid plates
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so that the pitch axis (Z) of the marker-based axes was parallel to anatomical axes for head and thorax flexion/extension, and shoulder girdle elevation/depression (Fig. 1). Additionally, markers were attached to participants’ shoes at four locations: anterior (distal end of the second toe), medial (head of the first metatarsal), lateral (head of the fifth metatarsal) and posterior (calcaneus). Gait cycles were identified based on forward movement of the posterior shoe markers. Marker data were sampled at 60 Hz and low-pass filtered at 6 Hz using a 4th order Butterworth filter. 2.3. Experimental procedures Each gait trial began with the participant standing upright (reference posture), and then performing a neck flexion task to synchronize MIMU and motion capture data [27,28]. Next, participants performed a gait trial on a 7-m walkway at comfortable speed. Each participant repeated these tasks three times. 2.4. Data analysis Since head posture and thorax posture in the sagittal plane, and shoulder girdle posture in the frontal plane are important characteristics of body posture, we compared mean postural angles for head flexion/extension angle, thorax flexion/extension angle, and shoulder girdle elevation/depression angle between MIMUs and an optoelectronic motion capture system during gait. Angular output from the MIMUs consisted of roll, pitch and yaw angles estimated with a sensor fusion algorithm. The roll, pitch and yaw angles obtained from the head and thorax MIMUs were considered as the anatomical angles for rotation, flexion/extension and lateral tilt, respectively, in the corresponding body segment (Fig. 1). In addition, the pitch and yaw angles that the shoulder MIMU estimated were considered as the anatomical angles for elevation/depression and protraction/retraction, respectively, in the shoulder girdle (Fig. 1). We followed previously reported methods for considering angles estimated with the MIMUs as postural angles for head flexion, thorax flexion and shoulder girdle elevation [23,25–27]. Specifically, while each participant was standing upright quietly at the onset of each trial, global reference frames for the MIMUs were defined such that the roll axis of the head and trunk MIMUs and the yaw axis of the shoulder MIMU were aligned with the gravity vector. Then, the pitch angle estimated with each MIMU, expressed in relation to the global reference frames during the reference posture, was considered as head flexion, thorax flexion and shoulder girdle elevation angles. To validate MIMU-based estimates, we built markerbased axes (XYZ) for the head, thorax and shoulder girdle MIMUs using marker data obtained by the reference system (Fig. 1). Then, head flexion, thorax flexion and shoulder girdle elevation angles from marker-based axes were calculated as pitch angles of the marker-based axes relative to the reference posture using Matlab (MathWorks, Natick, MA, USA). Specifically, the direction cosine matrix
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Yaw
Roll
Pitch X Z Z
Y
Y
X
X Z
Y
Fig. 1. MIMUs with marker clusters attached to the head, thorax and shoulder girdle. Marker-based axes (XYZ) were built for each MIMU using marker data. Roll axes on the MIMUs correspond to X axes in the marker-based axes. Yaw axes on the MIMUs correspond to Y axes in the marker-based axes. Pitch axes on the MIMUs correspond to Z axes in the marker-based axes.
(RReference using:
system)
RReference system
was derived from marker-based axes
2 3 ~ X 6 7 ¼ 4~ Y5 ~ Z
Current frame
2 3Transpose ~ X 6 7 4~ Y5 ~ Z
Reference posture
Then, we used the custom Matlab function (dcm2angle) to calculate postural angles for head flexion/extension, thorax flexion/extension and shoulder girdle elevation for the direction cosine matrices. The default Euler ZYX sequence for the custom Matlab function was used to calculate postural angles. In each gait trial, postural angles for head flexion, thorax flexion, and shoulder girdle elevation from each measurement system were first averaged during three gait cycles. Then, we averaged data across the gait trials for each participant and used the averaged data in the statistical analyses.
2.5. Statistical analysis To assess the strength of the relation between the reference system and the MIMUs, we calculated the coefficient of determination (R2). Then, to assess agreement between the reference system and the MIMUs, we used a Bland–Altman analysis [29]. Finally, we calculated a two-way, mixed-effects, single measure intraclass correlation coefficient (ICC) to assess reliability of the MIMUs. ICCs for the mean postural angles were evaluated according to Shrout
and Fleiss [30] (i.e., ICC > 0.75: excellent, 0.4 < ICC 6 0.75: fair-to-good). To evaluate curve similarity between the reference system and the MIMUs estimates, we computed the root mean square errors (RMSE) between curves for postural angles during three gait cycles in each gait trial. Finally, a mixed model analysis with random effects of participants was used to calculate within- and between-subject variability for RMSE. Variability was estimated as the square root of variance between- and within-subject.
3. Results Postural angles from 21 gait trials (7 participants 3 trials) were analyzed. Postural angles estimated with the MIMUs compared well with the reference system (Fig. 2). Coefficients of determination of mean postural angles for each gait trial were R2 = 0.82 (95% confidence interval (CI) 0.50–0.99) for mean head flexion angle, R2 = 0.58 (95% CI 0.001–0.96) for mean thorax flexion angle, and R2 = 0.77 (95% CI 0.37–0.98) for mean shoulder girdle elevation angle (Fig. 3). Differences in mean postural angles between measurement systems for each participant were between 2.1° and 3.7° for all body segments (Fig. 3). Bland–Altman plots showed no consistent bias between estimates for all body segments (Fig. 3), indicating a good agreement between two measurement systems, for all postural angles. ICCs with 95% CI were computed for mean postural angles for head flexion, thorax flexion, and shoulder girdle
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Fig. 2. Exemplar postural angles for the reference system (solid line) and the MIMUs (dashed line) during a representative gait trial.
Fig. 3. Correlations (top row) of mean postural angles (°) for all participants (N = 7) for the reference system and the MIMUs. Bland–Altman plots (bottom row) for average postural angles (°) for the reference system and the MIMUs, and differences between postural angles (MIMUs-reference) (°). In the Bland– Altman plots, 95% limits of agreement was calculated as mean +2sd (top solid line), mean (middle solid line), and mean-2sd (bottom solid line).
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motion of the head and thorax in the sagittal plane, and the shoulder girdle in the frontal plane were 11.2°, 8.1°, and 5.7°, respectively, when estimated with the reference system (Table 1). These motions occurred during the same time interval (i.e., the same three gait cycles) thus the angular velocities and accelerations were the greatest for the head and the least for the shoulder girdle. Since the orientation error in MIMUs increases with the angular velocity [31], the greatest angular velocities in the head MIMU would have caused the greatest errors among the MIMUs tested in this study. Consistent with our results, others have reported increases in errors between MIMUs and reference systems with increased velocity. When estimating knee angles with MIMUs, errors increased during running compared to walking [32]. Despite differences in ranges of motion between body segments, the magnitude of MIMUs error was relatively small (<2°) for these postural angles during gait. A limitation in this study is the relatively small sample size. Our sample size followed previous studies (sample size ranged between 1 and 10) that reported validity of MIMUs [22,24,25,27,31–36]. Nonetheless, the small sample size could limit the generalizability of the correlations and agreements between the reference system and the MIMUs tested in this study. Another limitation is that the pitch, roll and yaw axes of the MIMUs might not be perfectly aligned with anatomical axes for head, thorax and shoulder girdle. When attaching the MIMUs on each participant’s body, we visually checked the alignment between the MIMUs and anatomical axes. If a slight misalignment between the MIMUs axes (pitch/yaw/roll) and anatomical axes occurred, it may be that anatomical flexion in the head and thorax, and elevation in the shoulder girdle is a combination of pitch and a slight amount of roll/yaw of the MIMUs. In addition, the musculoskeletal anatomy of the shoulder girdle is not ideal for placing a MIMU to estimate shoulder girdle angles in the frontal plane. In a previous study, a MIMU was attached to the bony spine of the scapula [24], and scapulothoracic joint motion was used for estimating shoulder girdle elevation/depression. In this study, however, we placed the shoulder MIMU on the right acromion to align the pitch axis of the MIMU so that we could estimate elevation/ depression of the shoulder girdle in the frontal plane (Fig. 1). Because the bony surface of the scapula at the acromion is smaller than the size of the MIMU, the motion of the shoulder girdle MIMU might have been more affected by soft tissue movement than the head and thorax MIMUs. However, the magnitude of error was the least for the shoulder MIMU, thus we do not expect that this potential soft tissue effect was important. Finally, we attached
elevation. ICC values were 0.9 (95% CI 0.53–0.98) for mean head flexion angle, 0.73 (95% CI 0.06–0.95) for mean thorax flexion angle, and 0.87 (95% CI 0.44–0.98) for mean shoulder girdle elevation angle. Differences between measurement systems for mean angles across all participants were 0.6°, 0.1° and 0.0° for head flexion, thorax flexion and shoulder girdle elevation, respectively (Table 1). When averaged across participants, mean RMSE were 2.9°, 2.7° and 2.2° for head flexion, thorax flexion and shoulder girdle elevation, respectively (Table 1). Mean between-subject variability was 0.9°, 1.1° and 0.5° for head flexion, thorax flexion and shoulder girdle elevation, respectively (Table 1). Within-subject variability was less than 1.5° across body segments and participants (Table 1). 4. Discussion The goal of this study was to assess the concurrent validity of commercially available MIMUs for estimating mean postural angles for head flexion, thorax flexion and shoulder girdle elevation during gait in comparison with the gold standard optoelectronic motion capture system. We assessed the concurrent validity by calculating the agreement, reliability, and accuracy between postural angles estimated with MIMUs and measured by a motion capture system. If postural angles estimated with MIMUs are shown to be valid, MIMUs can be used in field studies to assess upper body posture during gait. We calculated RMSE values to estimate differences between postural angle curves estimated with each measurement system. Errors with the MIMUs tested in this study were less than 3°, that is, RMSE values were 2.9°, 2.7°, and 2.2° for head flexion, thorax flexion, and shoulder girdle elevation, respectively. These errors were comparable to, or less than, RMSE values reported in previous studies for angles estimated with MIMUs. When attached to the upper body, RMSE for MIMUs were approximately 4° for trunk inclination during lifting [25,26], 6° for the thorax during thorax range of motion [23], and 3.6° for shoulder girdle during shoulder joint range of motion [24]. Our results extend the set of movement tasks for which MIMU validity has been established for assessing thorax and shoulder girdle postures. In addition, this study establishes the validity of using MIMUs to assess head posture, to our knowledge for the first time, in any movement task. Although errors between measurement systems were small overall, we found that the largest errors occurred for the head and the smallest errors for the shoulder girdle. This is likely related to the range of motion of these body segments during walking. In this study, mean ranges of
Table 1 Mean postural angles (°) and mean range of motion (ROM) (°) across participants measured with the reference system and the MIMUs, mean RMSE (°) between the reference system and the MIMUs, and within- and between-subject variability (°) for RMSE. Values in parentheses are standard deviation. Posture
Head flexion Thorax flexion Shoulder girdle elevation
Mean angles
ROM
Reference
MIMUs
Reference
MIMUs
0.7 (2.7) 4.6 (2.7) 0.1 (1.1)
1.3 (3.2) 4.5 (2.1) 0.1 (1.1)
11.2 (4.6) 8.1 (1.0) 5.7 (2.2)
12.1 (3.4) 7.6 (1.4) 7.8 (2.3)
RMSE
Subject variability Within
Between
2.9 (1.2) 2.7 (1.2) 2.2 (0.6)
1.4 1.0 0.4
0.9 1.1 0.5
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the markers on the MIMUs instead of bony landmarks in each participant. This might have affected the alignment between the marker-based axes and anatomical axes, however the alignment was carefully checked, and was defined according to the existing literature [25,26]. 5. Conclusion We examined the concurrent validity of commercial MIMUs in estimating postural angles for head flexion, thorax flexion and shoulder girdle elevation during gait in healthy individuals. Overall, results from this study suggest that mean postural angles estimated with the MIMUs tested in this study are valid, and that the MIMUs used in this study can be used to estimate mean head flexion, thorax flexion and shoulder girdle elevation angles during gait. Before deploying MIMUs in the field for assessment of upper body posture in real-world contexts such as aging, mood state, footwear, and load carriage, future work is needed to address the MIMU’s test–retest reliability, long-term monitoring and sensitivity to different walking conditions. In addition, the relevance of the obtained errors with the MIMUs tested in this study must be evaluated in terms of impact on the parameters considered for the application of interest. Acknowledgements We thank Kelsey Galang, Emily Rozin and Matthew Mastenbrook for assistance with data collection and analysis, David Childers for statistical analyses, and Professor Noel Perkins, Dr. Stephen Cain and Dr. Ryan McGinnis for technical consultation. This study was supported by a graduate research grant from Horace H. Rackham School of Graduate Studies in the University of Michigan. References [1] L. Wolfson, R. Whipple, P. Amerman, J.N. Tobin, Gait assessment in elderly: a gait abnormality rating scale and its relation to falls, J. Gerontol. 45 (1990) N12–N19. [2] J. Michalak, F.T. Troje, J. Fischer, P. Vollmar, T. Hedenreich, D. Schulte, Embodiment of sadness and depression-gait patterns associated with dysphoric mood, Psychosom. Med. 71 (2009) 580–587. [3] E.A. Crane, M.M. Gross, Effort-shape characteristics of emotionrelated body movement, J. Nonverbal Behav. 37 (2013) 91–105. [4] C.L. Roether, L. Omlor, A. Christensen, M.A. Giese, Critical features for the perception of emotion from gait, J. Vis. 9 (2009) 1–32. [5] M.M. Gross, E.A. Crane, B.L. Fredrickson, Effort-shape and kinematic assessment of bodily expression of emotion during gait, Hum. Mov. Sci. 31 (2012) 202–221. [6] C.H. Lee, E.H. Jeong, A. Freivalds, Biomechanical effects of wearing high-heeled shoes, Int. J. Ind. Ergon. 28 (2001) 321–326. [7] D.D. Pascoe, D.E. Pascoe, Y.T. Wang, D.M. Shim, C.K. Kim, Influence of carrying book bags on gait cycle and posture of youths, Ergonomics 40 (1997) 631–641. [8] Y. Hong, G.P. Brueggemann, Changes in gait patterns in 10-year-old boys with increasing loads when walking on a treadmill, Gait Posture 11 (2000) 254–259. [9] S. Goodgold, K. Mohr, A. Samant, T. Parke, T. Burns, L. Gardner, Effects of backpack load and task demand on trunk forward lean: pilot findings on two boys, Work 18 (2002) 213–220. [10] Y. Hong, C.K. Cheung, Gait and posture response to backpack load during level walking in children, Gait Posture 17 (2003) 28–33. [11] J.X. Li, Y. Hong, P.D. Robinson, The effect of load carriage on movement kinematics and respiratory parameters in children during walking, Eur. J. Appl. Physiol. 90 (2003) 35–43.
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