A gait signal acquisition and parameter characterization method based on foot pressure detection combined with Azure Kinect system_Journal of Biomedical Engineering

Authors：

XU Guofeng , CHEN Kai ,  YANG Ying

School of Mechanical Engineering, Hangzhou Dianzi University, Hangzhou 310018, P. R. China;

Corresponding?author：

YANG Ying, Email: yingyang@hdu.edu.cn

Keywords：

Gait acquisition; Wearable; Azure Kinect system; Gait parameters; Consistency analysis

DOI：

10.7507/1001-5515.202210026

Video：

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The gait acquisition system can be used for gait analysis. The traditional wearable gait acquisition system will lead to large errors in gait parameters due to different wearing positions of sensors. The gait acquisition system based on marker method is expensive and needs to be used by combining with the force measurement system under the guidance of rehabilitation doctors. Due to the complex operation, it is inconvenient for clinical application. In this paper, a gait signal acquisition system that combines foot pressure detection and Azure Kinect system is designed. Fifteen subjects are organized to participate in gait test, and relevant data are collected. The calculation method of gait spatiotemporal parameters and joint angle parameters is proposed, and the consistency analysis and error analysis of the gait parameters of proposed system and camera marking method are carried out. The results show that the parameters obtained by the two systems have good consistency (Pearson correlation coefficient r ≥ 0.9, P < 0.05) and have small error (root mean square error of gait parameters is less than 0.1, root mean square error of joint angle parameters is less than 6). In conclusion, the gait acquisition system and its parameter extraction method proposed in this paper can provide reliable data acquisition results as a theoretical basis for gait feature analysis in clinical medicine.

Citation： XU Guofeng, CHEN Kai, YANG Ying. A gait signal acquisition and parameter characterization method based on foot pressure detection combined with Azure Kinect system. Journal of Biomedical Engineering, 2023, 40(2): 350-357, 364. doi: 10.7507/1001-5515.202210026 Copy

1.	Jochymczyk-Wo?niak K, Nowakowska-Lipiec K, Zadon H, et al. Gait kinematics index, global symmetry index and gait deviations profile: concept of a new comprehensive method of gait pathology evaluation. Acta of Bioengineering and Biomechanics, 2020, 22(4): 61-73.
2.	Stork M, Weissar P, Kosturik K, et al. Use of accelerometer for walk-run or shot analysis for sport and rehabilitation purposes//International Conference on Applied Electronics (AE), 2016, 6: 261-264.
3.	Lopez-Meyer P, Fulk G D, Sazonov E S, et al. Automatic detection of temporal gait parameters in poststroke individuals. IEEE Transactions on Information Technology in Biomedicine, 2011, 15(4): 594-601.
4.	朱琳, 劉洋, 劉元旻, 等. 腦卒中患者常用下肢輔助設備干預下步態分析的對比研究. 中國康復醫學雜志, 2022, 37(7): 901-906.
5.	劉展豪, 馮重睿, 魯發華, 等. 三維步態指導下的骨盆控制訓練對偏癱患者的影響. 國際醫藥衛生導報, 2022, 28(9): 1292-1296.
6.	丁航, 沈林勇, 吳曦, 等. 用于改善帕金森病凍結步態的可穿戴技術. 傳感技術學報, 2017, 30(6): 807-813.
7.	Schmitt A C, Daniels J N, Baudendistel S T, et al. The primary gait screen in Parkinson's disease: comparison to standardized measures. Gait & Posture, 2019, 73: 71-73.
8.	Shin J H, Yu R, Kang M K, et al. High preoperative gait variability is a prognostic predictor of gait and balance in Parkinson disease patients with deep brain stimulation. Parkinsonism & Related Disorders, 2022, 100: 1-5.
9.	Behboodi A, Zahradka N, Wright H, et al. Real-time detection of seven phases of gait in children with cerebral palsy using two gyroscopes. Sensors, 2019, 19(11): 2517.
10.	Tsitlakidis S, Schwarze M, Westhauser F, et al. Gait indices for characterization of patients with unilateral cerebral palsy. Journal of Clinical Medicine, 2020, 9(12): 3888.
11.	Vij N, Laber C, Schmidt K. Current applications of gait analysis after total knee arthroplasty: a scoping review. Journal of Clinical Orthopaedics and Trauma, 2022, 33: 102014.
12.	Booij M J, van Royen B J, Nolte P A, et al. Total knee arthroplasty improves gait adaptability in osteoarthritis patients; a pilot study. Journal of Orthopaedics, 2022, 34: 304-309.
13.	Aqueveque P, Germany E, Osorio R, et al. Simple gait segmentation method using a novel plantar pressure measurement system with custom-made capacitive sensors: preliminary results//IEEE Global Humanitarian Technology Conference (GHTC), 2019, 17: 1-4.
14.	孟青云, 談士力, 喻洪流, 等. 基于青年人足底壓力測試的步態實驗研究. 生物醫學工程學雜志, 2014, 31(5): 984-988, 1000.
15.	方正, 張興亮, 王超, 等. 基于青年人足底壓力測試的步態實驗研究. 生物醫學工程學雜志, 2014, 31(6): 1278-1282, 1293.
16.	Wang C, Wang X, Long Z, et al. Estimation of spatial-temporal gait parameters based on the fusion of inertial and film-pressure signals//IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2018, 3: 1232-1239.
17.	李春華, 郇戰, 陳學杰, 等. 基于加速度變化的步態識別方法. 傳感技術學報, 2020, 33(5): 693-698.
18.	汪濤, 汪泓章, 夏懿, 等. 基于卷積神經網絡與注意力模型的人體步態識別. 傳感技術學報, 2019, 32(7): 1027-1033.
19.	Natarajan P, Fonseka R D, Sy L W, et al. Analysing gait patterns in degenerative lumbar spine disease using inertial wearable sensors: an observational study. World Neurosurgery, 2022, 163: e501-e515.
20.	Chen Y W, Liao W W, Chen C L, et al. Kinematic descriptions of upper limb function using simulated tasks in activities of daily living after stroke. Human Movement Science, 2021, 79: 102834.
21.	Yalamanchili S, Abboud R, Wang W. A model to calculate the joint movements and forces in the foot//9th International Conference on Electronic Measurement & Instruments, Beijing: IEEE, 2009. DOI: 10.1109/ICEMI.2009.5274026.
22.	Antico M, Balletti N, Laudato G, et al. Postural control assessment via Microsoft Azure Kinect DK: an evaluation study. Computer Methods and Programs in Biomedicine, 2021, 209: 106324.
23.	Schlagenhauf F, Sreeram S, Singhose W. Comparison of Kinect and Vicon Motion Capture of upper-body joint angle tracking//IEEE 14th International Conference on Control and Automation (ICCA), 2018: 674-679.
24.	Summa S, Tartarisco G, Favetta M, et al. Spatio-temporal parameters of ataxia gait dataset obtained with the Kinect. Data in Brief, 2020, 32: 106307.
25.	Pashley G L, Kahn M B, Williams G, et al. Assessment of upper limb abnormalities using the Kinect: reliability, validity and detection accuracy in people living with acquired brain injury. Journal of Biomechanics, 2021, 129: 110825.
26.	Jamali Z, Behzadipour S. Quantitative evaluation of parameters affecting the accuracy of Microsoft Kinect in gait analysis//23rd Iranian Conference on Biomedical Engineering and 1st International Iranian Conference on Biomedical Engineering (ICBME), 2016, 24: 306-311.
27.	Ashwini K, Amutha R, Nagarajan K K, et al. Kinect based upper limb performance assessment in daily life activities//International Conference on Wireless Communications Signal Processing and Networking (WISPNET), 2019: 201-205.
28.	Cimolin V, Galli M. Summary measures for clinical gait analysis: a literature review. Gait & Posture, 2014, 39(4): 1005-1010.
29.	Morel E, Armand S, Assal F, et al. Normal pressure hydrocephalus and CSF tap test response: the gait phenotype matters. Journal of Neural Transmission (Vienna), 2021, 128(1): 121-125.
30.	Hessert M J, Vyas M, Leach J, et al. Foot pressure distribution during walking in young and old adults. BMC Geriatrics, 2005, 5: 8.
31.	張笑宇,陳凱,楊穎. 基于Kinect系統的步態參數提取方法. 數據采集與處理, 2022, 37(4): 872-882.

1. Jochymczyk-Wo?niak K, Nowakowska-Lipiec K, Zadon H, et al. Gait kinematics index, global symmetry index and gait deviations profile: concept of a new comprehensive method of gait pathology evaluation. Acta of Bioengineering and Biomechanics, 2020, 22(4): 61-73.
2. Stork M, Weissar P, Kosturik K, et al. Use of accelerometer for walk-run or shot analysis for sport and rehabilitation purposes//International Conference on Applied Electronics (AE), 2016, 6: 261-264.
3. Lopez-Meyer P, Fulk G D, Sazonov E S, et al. Automatic detection of temporal gait parameters in poststroke individuals. IEEE Transactions on Information Technology in Biomedicine, 2011, 15(4): 594-601.
4. 朱琳, 劉洋, 劉元旻, 等. 腦卒中患者常用下肢輔助設備干預下步態分析的對比研究. 中國康復醫學雜志, 2022, 37(7): 901-906.
5. 劉展豪, 馮重睿, 魯發華, 等. 三維步態指導下的骨盆控制訓練對偏癱患者的影響. 國際醫藥衛生導報, 2022, 28(9): 1292-1296.
6. 丁航, 沈林勇, 吳曦, 等. 用于改善帕金森病凍結步態的可穿戴技術. 傳感技術學報, 2017, 30(6): 807-813.
7. Schmitt A C, Daniels J N, Baudendistel S T, et al. The primary gait screen in Parkinson's disease: comparison to standardized measures. Gait & Posture, 2019, 73: 71-73.
8. Shin J H, Yu R, Kang M K, et al. High preoperative gait variability is a prognostic predictor of gait and balance in Parkinson disease patients with deep brain stimulation. Parkinsonism & Related Disorders, 2022, 100: 1-5.
9. Behboodi A, Zahradka N, Wright H, et al. Real-time detection of seven phases of gait in children with cerebral palsy using two gyroscopes. Sensors, 2019, 19(11): 2517.
10. Tsitlakidis S, Schwarze M, Westhauser F, et al. Gait indices for characterization of patients with unilateral cerebral palsy. Journal of Clinical Medicine, 2020, 9(12): 3888.
11. Vij N, Laber C, Schmidt K. Current applications of gait analysis after total knee arthroplasty: a scoping review. Journal of Clinical Orthopaedics and Trauma, 2022, 33: 102014.
12. Booij M J, van Royen B J, Nolte P A, et al. Total knee arthroplasty improves gait adaptability in osteoarthritis patients; a pilot study. Journal of Orthopaedics, 2022, 34: 304-309.
13. Aqueveque P, Germany E, Osorio R, et al. Simple gait segmentation method using a novel plantar pressure measurement system with custom-made capacitive sensors: preliminary results//IEEE Global Humanitarian Technology Conference (GHTC), 2019, 17: 1-4.
14. 孟青云, 談士力, 喻洪流, 等. 基于青年人足底壓力測試的步態實驗研究. 生物醫學工程學雜志, 2014, 31(5): 984-988, 1000.
15. 方正, 張興亮, 王超, 等. 基于青年人足底壓力測試的步態實驗研究. 生物醫學工程學雜志, 2014, 31(6): 1278-1282, 1293.
16. Wang C, Wang X, Long Z, et al. Estimation of spatial-temporal gait parameters based on the fusion of inertial and film-pressure signals//IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2018, 3: 1232-1239.
17. 李春華, 郇戰, 陳學杰, 等. 基于加速度變化的步態識別方法. 傳感技術學報, 2020, 33(5): 693-698.
18. 汪濤, 汪泓章, 夏懿, 等. 基于卷積神經網絡與注意力模型的人體步態識別. 傳感技術學報, 2019, 32(7): 1027-1033.
19. Natarajan P, Fonseka R D, Sy L W, et al. Analysing gait patterns in degenerative lumbar spine disease using inertial wearable sensors: an observational study. World Neurosurgery, 2022, 163: e501-e515.
20. Chen Y W, Liao W W, Chen C L, et al. Kinematic descriptions of upper limb function using simulated tasks in activities of daily living after stroke. Human Movement Science, 2021, 79: 102834.
21. Yalamanchili S, Abboud R, Wang W. A model to calculate the joint movements and forces in the foot//9th International Conference on Electronic Measurement & Instruments, Beijing: IEEE, 2009. DOI: 10.1109/ICEMI.2009.5274026.
22. Antico M, Balletti N, Laudato G, et al. Postural control assessment via Microsoft Azure Kinect DK: an evaluation study. Computer Methods and Programs in Biomedicine, 2021, 209: 106324.
23. Schlagenhauf F, Sreeram S, Singhose W. Comparison of Kinect and Vicon Motion Capture of upper-body joint angle tracking//IEEE 14th International Conference on Control and Automation (ICCA), 2018: 674-679.
24. Summa S, Tartarisco G, Favetta M, et al. Spatio-temporal parameters of ataxia gait dataset obtained with the Kinect. Data in Brief, 2020, 32: 106307.
25. Pashley G L, Kahn M B, Williams G, et al. Assessment of upper limb abnormalities using the Kinect: reliability, validity and detection accuracy in people living with acquired brain injury. Journal of Biomechanics, 2021, 129: 110825.
26. Jamali Z, Behzadipour S. Quantitative evaluation of parameters affecting the accuracy of Microsoft Kinect in gait analysis//23rd Iranian Conference on Biomedical Engineering and 1st International Iranian Conference on Biomedical Engineering (ICBME), 2016, 24: 306-311.
27. Ashwini K, Amutha R, Nagarajan K K, et al. Kinect based upper limb performance assessment in daily life activities//International Conference on Wireless Communications Signal Processing and Networking (WISPNET), 2019: 201-205.
28. Cimolin V, Galli M. Summary measures for clinical gait analysis: a literature review. Gait & Posture, 2014, 39(4): 1005-1010.
29. Morel E, Armand S, Assal F, et al. Normal pressure hydrocephalus and CSF tap test response: the gait phenotype matters. Journal of Neural Transmission (Vienna), 2021, 128(1): 121-125.
30. Hessert M J, Vyas M, Leach J, et al. Foot pressure distribution during walking in young and old adults. BMC Geriatrics, 2005, 5: 8.
31. 張笑宇,陳凱,楊穎. 基于Kinect系統的步態參數提取方法. 數據采集與處理, 2022, 37(4): 872-882.

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