Validity and reliability of the Kinect within functional assessment activities: Comparison with standard stereophotogrammetry

Gait & Posture 39 (2014) 593–598

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Validity and reliability of the Kinect within functional assessment activities: Comparison with standard stereophotogrammetry B. Bonneche`re a,*, B. Jansen b,c, P. Salvia a, H. Bouzahouene a, L. Omelina b, F. Moiseev a, V. Sholukha a, J. Cornelis b, M. Rooze a, S. Van Sint Jan a a b c

Laboratory of Anatomy, Biomechanics and Organogenesis (LABO), Universite´ Libre de Bruxelles, CP 610, Lennik Street 808, 1070 Brussels, Belgium Department of Electronics and Informatics – ETRO, Vrije Universiteit Brussel, Pleinlaan 2, B-1050 Brussels, Belgium iMinds, Dept. of Future Media and Imaging (FMI), Gaston Crommenlaan 8 (Box 102), B-9050 Ghent, Belgium

A R T I C L E I N F O

A B S T R A C T

Article history: Received 31 January 2013 Received in revised form 27 June 2013 Accepted 25 September 2013

The recent availability of the KinectTM sensor, a cost-effective markerless motion capture system (MLS), offers interesting possibilities in clinical functional analysis and rehabilitation. However, neither validity nor reproducibility of this device is known yet. These two parameters were evaluated in this study. Forty-eight volunteers performed shoulder abduction, elbow flexion, hip abduction and knee flexion motions; the same protocol was repeated one week later to evaluate reproducibility. Movements were simultaneously recorded by the Kinect (with Microsoft Kinect SDK v.1.5) MLS and a traditional markerbased stereophotogrammetry system (MBS). Considering the MBS as reference, discrepancies between MLS and MBS were evaluated by comparing the range of motion (ROM) between both systems. MLS reproducibility was found to be statistically similar to MBS results for the four exercises. Measured ROMs however were found different between the systems. ß 2013 Elsevier B.V. All rights reserved.

Keywords: Motion analysis Markerless motion capture Biomechanics New technology

1. Introduction Human motion tracking is widely used for movement analysis within a variety of purposes including rehabilitation and biomechanical representation of human motions [1]. Functional assessment has proven to be useful in clinics during rehabilitation [2]. Currently, most clinical motion analysis centers use a markerbased system (MBS) [1]. Although the MBS validity is high with respect to the positions of the markers in 3D space [3], some problems occur with MBS in daily practice: accuracy and mainly reproducibility of such a system is still controversial for the estimation of joint centers and relative segment orientations [4]. This can be explained by the fact that small errors in marker placement and soft tissue artifacts are causing larger errors in the estimation of the joint centers [5] and the relative segment orientations [6–9]. As such, marker setting is time-consuming and therefore not always suitable for young children and for patients not able to stand for a long period of time. Due to the high price and poor transportability of MBS, analysis must be performed within specialized departments. Such MBS analysis is therefore difficult to perform at the patient’s home by a therapist. Note that despite

* Corresponding author. Tel.: +32 2 555 6262. E-mail address: [email protected] (B. Bonneche`re). 0966-6362/$ – see front matter ß 2013 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.gaitpost.2013.09.018

the above-recognized error sources, MBS has been used in this study as reference system because it is largely adopted in the motion field. Markerless systems (MLS) are being developed for nearly 20 years [10]. They seem promising and open perspectives for complementary usages with respect to MBS analysis. Moreover, MLS shows an interesting perspective for functional assessment because the drawbacks due to marker placement are not present. MLS are usually less cumbersome than MBS and less expensive. To the authors’ knowledge MLS systems are unfortunately not widely used for functional analysis yet, probably because of the lack of dedicated hardware, related software and validation studies [10– 15]. The recent availability of the KinectTM sensor (based on PrimeSense Technology, Tel Aviv, Israel) [16–18] opens up interesting perspectives for functional analysis of patient assessment. This cost-effective and portable device is combining a regular color camera with a depth camera (consisting of an infrared laser projector and an infrared camera) and built-in software to detect a simple skeleton based on advanced pattern recognition methods [19] (note that due to the commercial nature of this hardware, no information on the underlying recognition can be found in the scientific literature). Before using this new hardware as MLS for functional assessment, scientific validation is required. No extensive

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validation related to functional evaluation and motion assessment using the KinectTM sensor was found in the literature. A crude validation of the position of cubic objects within a workspace has been recently performed [20]. Another study demonstrated the KinectTM sensor validity to assess postural control [21]. In a coaching context of elderly people, the joint center positions obtained from Kinect were found to have a variability of 10 cm [22], but joint angles were not evaluated. The present paper is reporting a validation study of the KinectTM sensor in terms of angular measurement comparison with a standard MBS and reproducibility during functional assessment. Differences between the two systems were statistically processed. The main goals of this study were to determine the effectiveness of using the KinectTM sensor as a MLS tool for patient functional assessment, and to determine the limitations of such use. 2. Methods 2.1. Participants Forty-eight healthy adults (26  8 years old, 173  8 cm height, 70  11 kg weight, 23  3 kg/m2 BMI, 18 women) were recruited to participate to this study. This study was approved by the Ethical Committee of the Erasme Hospital (CCB: B406201111989) and written informed consent was obtained from all subjects prior to their participation. Prior to motion analysis, subjects were equipped with 31 reflective markers [23]. 2.2. Materials and data The MLS (KinectTM sensor, frequency: 30 Hz) was used as a motion analysis device. A skeleton model (i.e., a stick figure) was directly obtained from the Microsoft Kinect SDK v1.5 software. This model was used to further estimate relative segment orientation as explained in the next section. Prior to data collection, the MLS camera was placed on a tripod at 1.5 m above the floor. Subjects stood at 2 m from the camera, as recommended by the manufacturer [24]. Subjects were in underwear to allow reliable placement of the markers of the MBS; prior to the exercises, the subjects were asked to stand still in anatomical position facing the camera. The MLS camera does not require any calibration: whenever a subject is in the camera field-of-view, the related stick figure is automatically computed by the associated software. The stick figure includes 20 points which are estimates of the subject’s joint centers (JCs). Spatio-temporal locations of these JCs were stored on the hard disk for further processing. MBS data were simultaneously collected from a state-of-the-art stereophotogrammetric system (Vicon, 8 MXT40s cameras, Vicon Nexus software, frequency: 60 Hz) that tracks the spatial trajectories of the reflective markers on the subjects. Marker trajectories were stored on the hard disk for further processing. The marker trajectories allowed for the estimation of joint centers and the definition of 19 segments [23]. 2.3. Data collection Two similar motion data collection sessions (sessions 1 and 2) were organized as follows. Subjects were asked to perform 4 ‘‘primary’’ movements (i.e., limited to one anatomical plane) with the right arm and right leg: shoulder abduction (subjects were asked to raise the arm in the frontal plane), elbow flexion (subjects were asked to flex the elbow keeping the upper arm in anatomical position), hip abduction (along the frontal plane) and knee flexion (during a squat motion) (see Fig. 1). For each movement ten repetitions were performed. No particular instruction was given about speed or amplitude to reach. One week after the recording of

Fig. 1. Illustrations of the four motions performed in this study. (A) Shoulder abduction, (B) elbow flexion, (C) hip abduction, (D) knee flexion.

the data (session 1), the same subjects repeated the same experiment (session 2). Markers were positioned by the same observer in both sessions for all subjects. 2.4. Data processing and statistical analysis From the MBS and MLS data, segment angular values were obtained for each movement performed by the subjects. Because the stick figure model obtained from the MLS does not have sufficient markers to define (all) anatomical frames and associated angles in 3D according to the ISB guidelines [25,26], the following vector conventions were used (based on the marker names as defined in Fig. 2A): the elbow angle was defined by the estimated centers of the shoulder, elbow and wrist (nc d e in Fig. 2A). The shoulder angle is defined by the following points: right elbow (d), right shoulder (c) and a constructed point t. The latter t landmark was part of the line drawn between the pelvic girdle center (h) and the shoulder girdle center (b). The c–t line ran perpendicular to h–b at rest. The knee angle was defined by: right hip (j), knee (k) and ankle (l). The hip angle was defined by the left hip (i), right hip (j) and right knee (k). For the MBS, the shoulder angle was defined by the following three points (Fig. 2B): 0.25 * (RIAS + LIAS + RIPS + LIPS), 0.25 * (C7 + T10 + CLAV + STRN), 0.5 * (RHME + RHLE). Similarly, the elbow angle was defined by 0.25 * (C7 + T10 + CLAV + STRN), 0.5 * (RHME + RHLE), 0.5 * (RRSP + RUSP), the hip angle was

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Fig. 2. (A) Stick figure model obtained from MLS. (a) Head, (b) shoulder girdle center (called ‘shoulder centre’ in the MLS SDK), (c) right shoulder, (d) right elbow, (e) right wrist, (f) right hand, (g) spine, (h) pelvic girdle center (called ‘hip center’ in the MLS SDK), (i) left hip, (j) right hip, (k) right knee, (l) right ankle. (B) Markers name and placement for MBS.

defined by LFTC, RFTC, 0.5 * (RFME + RFLE) and the knee angle was defined by RFTC, 0.5 * (RFME + RFLE), 0.5 * (RTAM + RFAL). Data processing was as follows. A fourth order zero lag low-pass Butterworth filter [27] with a cut-off frequency of 6 Hz was applied on all angle curves recorded by the MBS and the MLS. Data frequency for both devices was normalized to obtain the same timestamp (from 0 to 100) to facilitate comparison. Statistical analysis was then performed using SPSS 18. For each device and each session, range of motion was computed, averaged over the ten repetitions of each collected movement. MLS vs. MBS angular discrepancies were assessed using correlation coefficients (R2), limits of agreement analysis (LOA) with Bland Altman plots [28] and coefficients of variation of the method error (CVME). Testretest reliability was investigated using intra-class correlation coefficients (ICC). Furthermore, differences between the two devices (i.e., interdevice difference) and between the sessions (i.e., intra-device) were computed and root mean square (RMS) differences were calculated. Coefficients of multiple correlations (CMC) were applied to determine the inter-device difference. Kolmogorov– Smirnov tests were used to assess normality of the data. As data were normally distributed, parametric tests were used. Paired Student t-tests were used to compare the results as follows: interdevice difference between MLS and MBS within the same session, intra-device difference between sessions 1 and 2, and differences within session 1 and within session 2. The level of significance for all tests was set at p < 0.05. 3. Results 3.1. MLS-MBS discrepancies Results for the four movements analyzed in this study are presented separately. Results are summarized in Table 1. Bland Altman plots are presented in Fig. 3. For shoulder abduction, in session 1 a ROM (averaged over all subjects) of 111 (17)8 was measured by the MLS, a ROM of 111

(16)8 was measured by the MBS. Values between brackets are standard deviations. For session 2, average ROMs of 109 (18)8 and 109 (20)8 were respectively obtained. Inter-device ROM differences of 0.4 (2.5)8 and 0.9 (9.1)8 for sessions 1 and 2 respectively (p = 0.301 and 0.531) were non-significant, with limits of agreement of [ 5 5] and [ 17 19] respectively. Coefficients of variation of the method error for ROM in shoulder abduction were 1.6% and 5.9% for sessions 1 and 2 respectively. Pearson correlations between MLS and MBS for ROM in shoulder abduction were R2 = 0.98 and R2 = 0.80 for sessions 1 and 2 respectively. RMS inter-device difference was 38 and 48 for sessions 1 and 2, respectively. CMC were 0.997 (0.003) and 0.981 (0.095) during sessions 1 and 2 respectively. For elbow flexion, in session 1 a ROM (averaged over all subjects) of 127 (11)8 was measured by the MLS, a ROM of 119 (8)8 was measured by the MBS. For session 2, average ROMs of 127 (14)8 and 119 (8)8 respectively were obtained. Significant inter-device ROM differences of 8 (9)8 and 8 (10)8 for sessions 1 and 2 respectively (p < 0.001) were observed, with limits of agreement of [ 10 27] and [ 11 27] respectively. Coefficients of variation of the method error for ROM in elbow flexion were 5.3% and 5.5% for sessions 1 and 2 respectively. Pearson correlations between MLS and MBS for ROM in elbow flexion were R2 = 0.33 and R2 = 0.154 for sessions 1 and 2 respectively. RMS inter-device difference was 118 both for sessions 1 and 2. CMC were 0.981 (0.014) and 0.982 (0.011) during sessions 1 and 2 respectively. For hip abduction, in session 1 a ROM (averaged over all subjects) of 58 (11)8 was measured by the MLS, a ROM of 64 (10)8 was measured by the MBS. For session 2, average ROMs of 60 (11)8 and 64 (12)8 respectively were obtained. Inter-device ROM differences of 5 (14)8 and 4 (14)8 for sessions 1 and 2 respectively (p = 0.021 and 0.075) were observed, with limits of agreement of [ 34 23] and [ 33 25] respectively. Coefficients of variation of the method error for ROM in hip abduction were 16.5% and 16.6% for sessions 1 and 2 respectively. Pearson correlations between MLS and MBS for ROM in hip abduction were R2 = 0.00 and R2 = 0.04 for sessions 1 and 2 respectively. RMS inter-device

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Table 1 Mean results (SD) expressed in degrees. Difference = MLS–MBS *p < 0.05, **p < 0.01, ***p < 0.001 (paired-sample T-test). Root mean square error (RMSE) is expressed in degrees (st. dev.) and in percentage (values in between brackets), Coefficient of multiple correlation (CMC), LOA = limits of agreements (Bland and Altman), R2 = coefficient of determination, CVME = coefficient of variation of the method error, ROM = range of motion. LOA

R2

CVME

0.301 3 (1) [3%] 0.997 (0.003)

[ 5 5]

0.98

1.6

< 0.001 11 (5) [9%] 0.981 (0.014)

[ 10 27]

0.33

5.3

5 (14)

0.021 10 (5) [19%] 0.908 (0.077)

[ 34 23]

0.44

16.5

111 (17)

7 (18)

0.019 13 (7) [12%] 0.956 (0.052)

[ 43 29]

0.32

11.8

109 (18)

109 (20)

0.9 (9.1)

0.531 4 (3) [5%] 0.981 (0.095)

[ 17 19]

0.80

5.9

ROM RMSE CMC

126 (14)

119 (8)

8 (10)

<0.001 11 (4) [9%] 0.982 (0.011)

[ 11 27]

0.54

5.5

Hip

ROM RMSE CMC

60 (11)

64 (12)

4 (15)

0.075 9 (5) [17%] 0.924 (0.070)

[ 33 25]

0.04

16.6

Knee

ROM RMSE CMC

97 (24)

108 (21)

11 (21)

0.003 14 (10) [14%] 0.936

[ 53 32]

0.32

14.6

MLS

MBS

MLS-MBS

ROM RMSE CMC

111 (17)

110 (16)

0.4 (2.5)

Elbow

ROM RMSE CMC

127 (11)

119 (8)

8 (9)

Hip

ROM RMSE CMC

58 (11)

64 (10)

Knee

ROM RMSE CMC

104 (21)

ROM RMSE CMC

Elbow

Session 1 Shoulder

Session 2 Shoulder

p

Fig. 3. Bland and Altman plots. X axes are the means of the two systems in degrees; Y axes are the mean of the differences in degrees. Red line (middle one) is used to indicate a perfect agreement between devices (no difference). Blue lines (extremities) indicate upper and lower agreements. Black line is a regression line. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

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Table 2 Test-retest reliability analysis for both the MLS and MBS system. Mean (std) values are expressed in degrees. p values are given for the paired t-test between both sessions’ means difference. ICC = intra-class correlation coefficient. MBS

MLS

Shoulder Elbow Hip Knee

ROM ROM ROM ROM

Session 1

Session 2

p

ICC

Session 1

Session 2

p

ICC

111 127 58 104

109 126 60 97

0.57 0.69 0.283 0.078

0.73 0.70 0.84 0.66

110 119 64 111

109 119 64 108

0.53 0.79 0.878 0.115

0.61 0.77 0.83 0.89

(17) (11) (11) (21)

(18) (14) (11) (25)

difference was 108 and 98 for sessions 1 and 2, respectively. CMC were 0.908 (0.077) and 0.924 (0.070) during sessions 1 and 2 respectively. For knee flexion, in session 1 a ROM (averaged over all subjects) of 104 (21)8 was measured by the MLS, a ROM of 111 (17)8 was measured by the MBS. For session 2, average ROMs of 98 (24)8 and 108 (21)8 respectively were obtained. Significant inter device ROM differences of 7 (18)8 and 11 (21)8 for sessions 1 and 2 respectively were observed (p = 0.019 and 0.003), with limits of agreement of [ 43 29] and [ 53 32] respectively. Coefficients of variation of the method error for ROM in knee flexion were 11.8% and 14.6% for sessions 1 and 2 respectively. Pearson correlations between MLs and MBS for ROM in knee flexion were R2 = 0.32 and R2 = 0.03 for sessions 1 and 2 respectively. RMS inter-device difference was 138 and 148 for sessions 1 and 2, respectively. CMC were 0.956 (0.052) and 0.936 (0.092) during sessions 1 and 2 respectively. 3.2. Test–retest reliability Results for test–retest reliability analysis are presented in Table 2. Results show that no significant MLS or MBS intra-device differences were found for average ROM between sessions 1 and 2 for any of the studied motions. All ICC’s for test–retest of MLS and MBS were moderate to high for MLS (>0.66) and MLS (>0.61). 4. Discussion From a functional assessment point-of-view, ROM is certainly one of the most interesting parameters to study. With respect to test-retest reliability, MLS and MBS results were similar, and showed no differences at group level between sessions 1 and 2. For both MLS and MBS, the intra-class correlation coefficients were ranging from moderate to good (values between 0.61 and 0.89). ICC’s for MBS and MLS were similar (Table 2). These results confirm that both devices show reasonable to good repeatability for the measurements performed within this study. In terms of measurement discrepancies between both systems, results were different according to the studied exercises. For shoulder abduction, excellent agreement was found between MLS and MBS based on the small discrepancies that were observed. However, for hip abduction and for knee flexion, poor to no agreement between both systems was observed. For elbow flexion, the results were less clear: In session 1 we observed a mean ROM difference of 88, R2 of 0.33 and CVME of 5.3%, while in session 2 we observed a slightly better R2 of 0.54. It is unclear what caused these differences between sessions 1 and 2. Altogether, differences between MLS and MBS were clearly smaller in upper body analysis compared to lower body analysis. In summary, these results indicate important discrepancies between the angles obtained from both systems. Some supplementary analysis (focusing on elbow flexion and knee flexion, both in session 1) was performed to investigate what MLS factors could contribute to the differences, assuming the MBS as reference. It has been observed that knee and elbow JC locations were estimated

(16) (8) (10) (17)

(20) (8) (12) (21)

differently between both systems, although the MLS JCs appeared to be well-located within the volume of the segments-of-interest. The MLS appeared to have difficulties in estimating the JC location even if the segments themselves were detected correctly. We estimated the maximum difference in angle estimation by taking the absolute difference between internal (shoulder–internal elbow–external wrist) and external elbow epicondyle (shoulder–external elbow–internal wrist) angle as obtained from MBS. This difference was 128 (58). For the knee (difference between medial and lateral condyle), the difference was found to be 58 (38). On the other hand, for the shoulder, the joint center was estimated by the MLS more accurately. This seems to be one of the reasons for the better results for shoulder angle estimation compared to elbow and knee angles. Secondly, JC location variations by the MLS were caused by important segment length variations of the extremities over time. During elbow flexion, these variations were 86 (14) mm, 35 (22) mm, 20 (65) mm and 140 (70) for the length of the arm, the forearm, the thigh and the shank, respectively. Although the MLS can suffer from occlusions, the available software does provide an estimate of the joint centers, even if they are not visible in the image. Only in extreme cases, the skeleton is not detected (e.g., if the subject is not fully visible). Under the reported experimental conditions, this was never the case. Results of this study are difficult to compare with other studies because results are largely depending of joints and motions performed. The level of precision of the device is depending of the targeted goal. One recent study shows that the KinectTM can be used to provide lateral trunk feedback [29]. In this study, after calibration authors found a difference of 18 for motion of 108, the overall error is thus of approximately 10%. In our study errors are less than 1% for shoulder abduction, 6% for elbow flexion, 7% for hip abduction and 9% for knee flexion. In view of these results and the good reproducibility the KinectTM could be used to provide lean feedback during different tasks. This study was limited to motion performed along the anatomical planes, as a first step to evaluate the validity and reliability of the MLS system. The results reported do not per se extrapolate to more complex motions (not limited to one anatomical plane). However, the origins of the differences reported between MLs and MBS are inaccuracies in the estimation of the joint centers. For the MLS, these errors are mostly independent of the exact motion, as the estimation of the joint center locations is done on a frame-by-frame basis. A second limitation of this study is that the results are obtained using a specific version of the available commercial software, which is improving with every new release. Future work will evaluate more complex tasks (e.g., taking objects, circumduction, etc.) and should objectively investigate the impact of the newer software versions on the validity and reliability. A running work is focusing on processing the MLS data using optimization methods in order to improve the result accuracy [30]. In conclusion, the user must bear in mind that comparison with standard MBS was not fully satisfactory for most studied motions. However, the reliability of the MBS joint center estimation is

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recognized in the literature to be low [5], so it is difficult to conclude which system is the less accurate. Further research must be performed to gain better insight on this topic. Results from this study show on the other hand that the KinectTM sensor used as MLS device is as reproducible as standard MBS when analyzing planar motions. Such reproducibility, associated with the system’s costeffectiveness and portability should allow functional assessment of patients and rehabilitation following-up in conditions where MBS are difficult to use (e.g., patient’s home). Current studies are running in order to assess the use of MLS devices on large normal and patient populations. Acknowledgement This study is a part of the ICT4Rehab project (www.ict4rehab.org). This project is funded by Innoviris (Brussels Capital Region). Conflict of interest None declared. References [1] Zhou H, Hu H. Human motion tracking for rehabilitation – a survey. Biomed Signal Process Control 2008;3:1–18. [2] Guiraud T, Granger R, Gremeaux V, Bousquet M, Richard L, Soukarie L, Babin T, Labrunee M, Bosquet L, Pathak A. Accelerometer as a tool to assess sedentarity and adherence to physical activity recommendations after cardiac rehabilitation program. Ann Phys Rehabil Med 2012;55(5):312–21. [3] Windolf M, Go¨tzen N, Morlock M. Systematic accuracy and precision analysis of video motion capturing systems – exemplified on the Vicon-460 system. J Biochem 2008;41(12):2776–80. [4] Della Croce U, Leardini A, Chiaria L, Cappozzo A. Human movement analysis using stereophotogrammetry. Part 4: assessment of anatomical landmark misplacement and its effects on joint kinematics. Gait Posture 2005;21(2):226–37. [5] Taylor WR, Ehrig RM, Duda GN, Schell H, Seebeck P, Heller MO. On the influence of soft tissue coverage in the determination of bone kinematics using skin markers. J Orthop Res 2005;23(4):726–34. [6] Lu TW, O’Connor JJ. Bone position estimation from skin marker co-ordinates using global optimization with joint constraints. J Biochem 1999;32(2):129– 34. [7] Stagni R, Fantozzi S, Cappello A. Double calibration vs. global optimization: performance and effectiveness for clinical application. Gait Posture 2009;29(1):119–22. [8] Duprey S, Cheze L, Dumas R. Influence of joint constraints on lower limb kinematics estimation from skin markers using global optimization. J Biochem 2010;43(14):2858–62. [9] Andersen MS, Benoit DL, Damsgaard M, Ramsey DK, Rasmussen J. Do kinematic models reduce the effects of soft tissue artefacts in skin marker-based motion analysis? An in vivo study of knee kinematics. J Biochem 2010;43(2):268–73.

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