智能矫姿服装设计

doi:10.13475/j.fzxb.20240701801

摘要/Abstract

摘要：

针对久坐智能矫姿可穿戴设备普遍存在的监测精度低、姿态判断标准不明确、缺少坐姿动态监测识别等问题,以青年女性为对象,开展动态坐姿评价方法研究,并设计开发智能矫姿服装。选取髋部角度、上半身倾斜角、后背上角、后背下角4个坐姿特征角度,利用摄影法采集动态坐姿影像,分析动态坐姿变化规律并提取坐姿特征角度值,共得到649组实验数据,综合人体坐姿动态变化规律与静态坐姿判别标准,提出了动态坐姿监测识别方法。基于此方法设计以MPU6050加速度传感器为核心元件的智能矫姿服装,经功能测试与舒适性评价,该智能服装的识别精确率为97.33%,正确率为95%。本文为久坐动态坐姿识别与评估方法研究提供了理论参考,同时为智能矫姿服装和可穿戴设备的产品化开发与生产提供参考。

关键词: 坐姿识别, 加速度传感器, 智能服装, 坐姿特征角, 模块化设计

Abstract:

Objective Prolonged sitting and poor posture are common for people at modern times, causing shoulder and back pain, spinal deformities, muscle strain, and other health issues. Accelerometers are widely used in posture correction wearables due to their portability and accuracy. However, current devices have limitations, including narrow monitoring range, low scientific rigor in evaluation methods, and poor comfort. These issues reduce their effectiveness in dynamic posture correction. Developing better evaluation methods and more efficient smart posture correction clothing is important.

Methods This study was focused on improving posture recognition methods. Young women were selected as the research subjects. Dynamic posture variation patterns were analyzed. A recognition and correction method was proposed leading to the development of smart posture correction garment. Upper body posture data were collected using photography. Trunk inclination, hip joint angle, upper back angle, and lower back angle were chosen as variables. Static data analysis divided the samples into normal and slouched posture groups. Dynamic posture changes were observed and analyzed. A method to identify dynamic postures was developed. Postures were considered normal when the upper back angle was in the range from 12.6° to 20.8° and the lower back angle in the range from 7.6° to 14.6°. Abnormal postures lasting less than 8 s were excluded from slouched postures. The smart garment used MPU6050 accelerometers for dual-point monitoring. Core components included accelerometers, a STM32 board, and buzzers.

Results In the experiment designed to collect dynamically seated posture data, human posture data were collected using photography, with a 1 h continuous measurement cycle. A total of 18 monitoring cycles were collected, resulting in 164 self-adjustment cycles and 649 valid sample data sets. After judgment, 378 sets of normal posture feature angle data and 271 sets of slouched posture data were obtained. The experimental data were analyzed, and combined with previous research, a dynamic posture judgment method suitable for sedentary individuals was derived. The upper back angle threshold for normal posture was [12.6°, 20.8°], and the lower back angle threshold was [7.6°, 14.6°]. An accelerometer was used to monitor the dynamic angles of the posture. An intelligent posture correction garment was designed and manufactured, and testing showed that the posture recognition accuracy of the smart garment was 97.33%, with a correctness rate of 95%, indicating good recognition performance. Wearability evaluations indicated that the participants were generally satisfied, suggesting that the smart posture correction garment has practical value.

Conclusion This study takes young women as the research object. Through the study of dynamic sitting characteristics and the change rule of sitting posture, and the use of acceleration sensors to monitor and analyse the angle of the sitting characteristics, a more accurate range of activity of the dynamic sitting characteristics angle of young women was obtained, and a scientific method of sitting posture recognition was proposed. Combined with this recognition method, an efficient intelligent posture correction garment was developed, and the design and evaluation of the garment was completed. The above research provides a theoretical basis for the posture recognition and evaluation method, and provides an important reference for the commercialisation and promotion of smart posture correction garments and wearable devices. This research can help improve individuals' poor postural behaviour and increase the value of wearable technology in health management. In addition, the study of human posture characteristics is important for solving the back health problems of sedentary people, so the smart posture corrective clothing has a broad market prospect. Modularly designed smart posture corrective clothing must integrate accurate detection, wearing comfort, invisibility and flexibility to adapt to a wider range of users and practical application scenarios.

Key words: sitting posture recognition, accelerometer, smart garment, postural feature angle, modular design

中图分类号:

TS941

王军, 殷晓玉, 周晓琪, 王思远. 智能矫姿服装设计[J]. 纺织学报, 2025, 46(04): 179-186.

WANG Jun, YIN Xiaoyu, ZHOU Xiaoqi, WANG Siyuan. Design of smart garment for posture correction[J]. Journal of Textile Research, 2025, 46(04): 179-186.

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链接本文: http://www.fzxb.org.cn/CN/10.13475/j.fzxb.20240701801

http://www.fzxb.org.cn/CN/Y2025/V46/I04/179

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参考文献 21

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研究变量	坐姿状态	统计量	最小值/ (°)	最大值/ (°)	平均值/ (°)	标准差
后背上角	正常	378	10.6	23.8	17.8	2.7
	驼背	271	12.8	38.9	24.2	5.1
后背下角	正常	378	6.5	19.9	10.8	2.4
	驼背	271	-29.3	8.7	-10.4	9.8

电子元件名称	功能
STM32F103C8T6单片机	系统编译运算、数据存储
MPU6050加速度传感器	测量人体坐姿特征角度
蜂鸣器、扬声器	信号反馈与提醒
OLED显示屏	识别结果显示

测试编号	实际坐姿	特征角/(°)		是否提醒	识别情况
测试编号	实际坐姿	后背上角	后背下角	是否提醒	识别情况
1	正常	17.1	10.9	否	正确
	驼背	29.1	-14.7	是	正确
	正常	19.2	11.0	否	正确
2	正常	17.1	8.6	否	正确
	驼背	30.2	-15.5	是	正确
	正常	18.0	13.4	否	正确
3	正常	16.1	8.7	否	正确
	驼背	20.4	8.3	否	错误
	正常	16.4	13.2	否	正确
4	正常	16.3	9.1	否	正确
	驼背	27.5	-17.3	是	正确
	正常	14.7	13.7	否	正确
5	正常	20.6	11.3	否	正确
	驼背	31.2	-15.4	是	正确
	正常	16.8	11.2	否	正确
6	正常	18.7	8.2	否	正确
	驼背	26.5	-17.9	是	正确
	正常	14.9	9.6	否	正确
7	正常	20.6	10.6	否	正确
	驼背	22.3	-11.9	是	正确
	正常	20.1	11.1	否	正确
8	正常	17.4	14.4	否	正确
	驼背	23.7	-17.7	是	正确
	正常	28.8	16.3	否	正确
9	正常	29.1	19.4	否	正确
	驼背	34.5	-16.4	是	正确
	正常	14.9	13.2	否	正确
10	正常	20.6	14.6	否	正确
	驼背	28.8	-16.3	是	正确
	正常	21.7	7.4	是	错误

试穿者编号	舒适度	合体性	透气性	噪声感	技术接受度	实用性
1	5	4	5	4	5	4
2	5	5	4	5	4	5
3	4	5	5	4	5	5
4	4	4	5	5	5	4
5	4	5	5	4	4	5
6	4	5	4	4	5	5
7	4	4	4	5	5	5
8	5	5	4	4	4	5
9	4	4	5	4	5	4
10	4	5	5	5	5	5
平均分	4.3	4.6	4.8	4.4	4.7	4.7