Evaluating Feasibility of Pose Detection with Image Rotation for Monitoring Elderly People at Home †
Abstract
1. Introduction
2. Preliminaries
2.1. Technical Challenges
- Differences in Posture: For standing humans, the positions of the joints are identifiable, resulting in higher accuracy of pose detection. In contrast, for lying humans, the positions of the joints may be obscured, making pose detection more challenging.
- Differences in Viewpoint: Standing humans are typically captured from the front, allowing for clear identification of the joint positions. Lying humans, however, are often captured from the side, making it more difficult to discern joint positions.
- Image Resolution: Standing humans appear larger within the image, leading to higher resolution. Lying humans appear smaller, resulting in lower resolution. Lower resolution decreases the accuracy of pose detection.
- Effect of Occlusion: Standing humans are less likely to be affected by occlusion, whereas lying humans may have parts of their body concealed by obstructions, thereby reducing the accuracy of pose detection.
2.2. Image Rotation with OpenCV
Listing 1. A sample code for rotating an image using Python. |
import cv2 import numpy as np img = cv2 . imread ( ‘ image . j p g ’ ) rows , c o l s = img . shape [ : 2 ] c e n t e r = ( c o l s / 2 , rows / 2 ) a n g l e = 45 s c a l e = 1 . 0 r o t a t i o n m a t r i x = cv2 . g e t R o t a t i o n M a t r i x 2 D ( c e n t e r , angle , s c a l e ) r o t a t e d i m g = cv2 . w a r p A f f i n e ( img , r o t a t i o n m a t r i x , ( c o l s , rows ) ) cv2 . imshow ( ‘ R o t a t e d Image ’ , r o t a t e d i m g ) cv2 . wait Key ( 0 ) cv2 . destroy All Windows ( ) |
3. Proposed Method
3.1. Goal and Key Idea
3.2. Overall Architecture
3.3. Method Description of Key Steps
Listing 2. Three ways to calculate slope using Python. |
# Method 1 from scipy.stats import linregress x_ = [keypoints_xlist[5], keypoints_xlist[6]] y_ = [keypoints_ylist[5], keypoints_ylist[6]] slope, intercept, r_value, p_value, std_err = linregress(x_, y_) print(slope) # Method 2 x1_ = keypoints_xlist[5] y1_ = keypoints_ylist[5] x2_ = keypoints_xlist[6] y2_ = keypoints_ylist[6] def slopee(x1, y1, x2, y2): x = (y1 - y2) / (x1 - x2) return x print(slopee(x1_, y1_, x2_, y2_)) # Method 3 import numpy as np slope_, intercept_ = np.polyfit(x_, y_, 1) print(slope_) |
Listing 3. One way to convert ratio to angle using Python. |
import math angle = math.degrees(math.atan(slope)) print(angle) |
Listing 4. One way to rotate an image using Python. |
from PIL import Image im = Image.open(file_path) im_rotate = im.rotate(angle, expand=True) plt.figure(figsize=(16, 12), dpi=20) plt.imshow(im_rotate) plt.gca().set_axis_off() plt.subplots_adjust(top = 1, bottom = 0, right = 1, left = 0, hspace = 0, wspace = 0) plt.margins(0,0) plt.savefig(file_path + "_rotate.jpg") |
4. Case Study and Discussion
- Reconsideration of Preprocessing Methods: Since image rotation is effective for some joints but not for others, it is necessary to explore preprocessing methods beyond rotation, such as interpolation using augmented reality technology or image completion from other viewpoints.
- Model Improvement: Current pose detection models, including the MoveNet model, may be optimized for specific postures. To maintain high accuracy for lying positions as well, it is necessary to improve the model and introduce additional training data.
- Utilization of Multi-view Data: Integrating data from multiple viewpoints, rather than relying solely on images from a single viewpoint, has the potential to improve overall detection accuracy. Especially for lying postures, combining information from different angles can mitigate the effects of occlusion.
- Challenges Toward Practical Application: To realize a monitoring system for the elderly, a flexible detection system that can accommodate various postures and environments is required. Based on the current results, it is necessary to conduct further validation in a wider range of environments and postures to clarify the challenges toward practical application.
5. Conclusions
- Validation with Diverse Datasets: This study was conducted under specific conditions, so it is necessary to examine the generalizability of the preprocessing methods using more diverse datasets.
- Real-Time Processing: It is crucial to evaluate whether the implementation of preprocessing methods is suitable for real-time processing and to ensure practical applicability.
- Optimization of Preprocessing Methods: There is a need to optimize combinations of different preprocessing methods to develop a more accurate preprocessing pipeline.
- Comparison with Other Pose Detection Models: It is important to compare the proposed method with other pose detection models besides MoveNet to verify its generality and effectiveness. Addressing these challenges is expected to lead to the development of more accurate pose detection technologies and represent a significant step toward practical implementation.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
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Chen, S.; Nakamura, M. Evaluating Feasibility of Pose Detection with Image Rotation for Monitoring Elderly People at Home. Eng. Proc. 2025, 89, 28. https://doi.org/10.3390/engproc2025089028
Chen S, Nakamura M. Evaluating Feasibility of Pose Detection with Image Rotation for Monitoring Elderly People at Home. Engineering Proceedings. 2025; 89(1):28. https://doi.org/10.3390/engproc2025089028
Chicago/Turabian StyleChen, Sinan, and Masahide Nakamura. 2025. "Evaluating Feasibility of Pose Detection with Image Rotation for Monitoring Elderly People at Home" Engineering Proceedings 89, no. 1: 28. https://doi.org/10.3390/engproc2025089028
APA StyleChen, S., & Nakamura, M. (2025). Evaluating Feasibility of Pose Detection with Image Rotation for Monitoring Elderly People at Home. Engineering Proceedings, 89(1), 28. https://doi.org/10.3390/engproc2025089028