A Comparison of Four Approaches to Evaluate the Sit-to-Stand Movement
Summary (2 min read)
Introduction
- In older people that can identify people at risk of falls.
- Other studies have used visual sensors to evaluate the STS movement.
- It fails in a cluttered environment when silhouette extraction becomes difficult.
- A wooden chair with a 47cm seat height was instrumented with four load cells, which were positioned in a cross with a distance of 31 cm between each adjacent pair of load cells.
B. Single Camera-based Posture Analysis
- Cameras are readily available in the form of android devices or installed surveillance cameras.
- One way of accomplishing this is by background subtraction and extraction of the human silhouette.
- Poses estimated using this library are accurate at assessing human movement [25].
- The Stacked Hourglass Network method defines local features such as the wrist, ankle, elbow and the orientation and arrangement of these features with respect to each other.
- Calibration of the camera was performed using the chair as a reference, with the back of the chair measuring 0.5m.
C. STS Parameter Calculation
- The total time taken for each 5STS was estimated for each of the four recording systems.
- An example of head position signals during the 5STS for the RGB and Kinect systems is shown in Fig. 4(a-b).
- Force data were also low pass filtered with a 4th order Butterworth filter with a 2Hz cut-off frequency.
- Accordingly, for the chair sit-to-stand phase, when vertical force decreased below 90% of peak force, subjects were considered to have started to stand up, while a subject was considered to be standing when their force decreased below 10% of peak.
- STS velocity was calculated for the two camera-based systems using the method proposed by Ejupi et al. [15] for the period between the end of the sitting phase and the standing phase of each STS movement.
D. Comparison of STS Parameters
- The performance of the four systems was compared using data collected from a sample of 21 healthy younger subjects and a sample of 16 older fallers.
- The ethics committee of the Asian Centre for Medical Education, Research & Innovation approved the study (ACMERI/18/001), with all subjects giving informed consent.
- Comparative performances of the four methods of obtaining STS time and STS velocity were undertaken using correlation analysis and limits of agreement, using Bland-Altman plots [26].
- All data processing was performed using custom-built software developed using LabVIEW (Version 2018, National Instruments Corporation, Austin, Texas, USA).
A. Total STS Time
- The performances of the four systems for young subjects for 5STS time against the expert time of 11.7 ± 2.1 s are shown in Table 1.
- The performance for 5STS time for the older fallers compared to the expert time of 18.0 ± 3.4 s is shown in Table 2.
- Bland Altman plots of the limits of agreement for the four methods for both groups of subjects combined when compared to the expert values are shown in Fig.
- When the ranking of each system was compared for the four measures of performance used against the expert, the chair had the best performance.
- When the younger subjects were considered, the chair was the best for both LOA measures, along with twosecond rankings for the correlation and error measures.
B. STS Velocity
- Comparisons for STS velocity are shown in Table 3 for younger participants and in Table 4 for the older fallers.
- When the younger and older faller results were compared, greater discrepancies for a given system were observed for the two camera-based systems than for the two force-based systems, with lower correlations and higher mean differences, especially for the fallers.
- A comparison of the STS velocity measures from the four devices was made with gait velocity for the group of older fallers.
- Both new methods had an excellent agreement with an expert estimation of STS in terms of the number of data points that fell within 2SD of the mean difference.
- The camera method underestimated the total STS time compared to the expert by around one second.
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...Consequently, many studies have attempted to gain insight into the STS movement through biomechanical analyses with various systems such as force plates, combined with or without optoelectronic systems [8,11–15], video analysis [16], goniometry [17,18], and more recently accelerometry [15,19–21]....
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...Ellipsoid tracking was then used, along with the Weka Machine Toolkit, to classify postures based on the position of the head, feet and torso [18], with an excellent correlation observed between...
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...Comparative performances of the four methods of obtaining STS time and STS velocity were undertaken using correlation analysis and limits of agreement, using Bland-Altman plots [26]....
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...With the advent of deep-learning techniques, many solutions to human pose estimation have been introduced, such as the recentlyintroduced Stacked Hourglass Network method [24]....
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Frequently Asked Questions (11)
Q2. What is the recent introduction of the Stacked Hourglass Network method?
With the advent of deep-learning techniques, many solutions to human pose estimation have been introduced, such as the recentlyintroduced Stacked Hourglass Network method [24].
Q3. Why was the use of a stopwatch chosen as the gold standard for STS time?
The use of an expert assessment of the video as the gold-standard for STS time was chosen rather than a stopwatch, as previous research has reported errors due to delays in starting the stopwatch after the command was given to start being included in the time, while errors also occur when stopping the timer [13].
Q4. What is the method used to estimate the power produced during the STS?
It would also be possible to estimate the power produced during the STS using the method proposed by Lindemann et al., in which the difference between seated height and standing height is combined with the rate of force development to estimate power [32].
Q5. What was the mean force of the STS for the two camera-based systems?
STS velocity was calculated for the two camera-based systems using the method proposed by Ejupi et al. [15] for the period between the end of the sitting phase and the standing phase of each STS movement.
Q6. What was the mean force of the sit-to-stand phase?
For the force plate, the start of each sit-to-TNSRE-2019-003524stand phase was taken to be 10% of the peak force obtained during the transition to a standing position, which corresponds to the same ratio as the 5cm value used for the two camerabased systems when compared to the mean standing height of 50 cm.
Q7. How long did the 5STS take to detect the error?
The error of the chair method was less than 10% of the minimal detectable change for the 5STS, which has been reported to be 2.5 seconds [29].
Q8. What is the way to estimate muscle mass?
Power during the STS is a strong predictor of overall muscle power and even cross-sectional area of the quadriceps [33, 34], which means the instrumented chair might be able to estimate muscle mass.
Q9. What is the way to capture the right description of human joints?
In order to capture the right description of human joints, the images are analyzed at different scales, with a low-level resolution for joints and a high-level resolution for orientation.
Q10. What is the highest correlation with gait velocity?
The highest correlation with gait velocity was obtained for chair STS velocity (r=0.76), followed by the force plate (r=0.49), RGB camera (r=0.12), and the Kinect (r=0.07).
Q11. What was the way to measure the STS?
Although the observed relationship between STS velocity and gait velocity was encouraging, it would have been useful to have measures of leg strength for the older subjects rather than using gait velocity as a proxy measure.