research-article

Using Inertial Sensors to Evaluate Exercise Correctness in Electromyography-based Home Rehabilitation Systems

Authors:

Duarte Folgado,

Francisco Nunes,

Inês SousaAuthors Info & Claims

2019 IEEE International Symposium on Medical Measurements and Applications (MeMeA)

Pages 1 - 6

https://doi.org/10.1109/MeMeA.2019.8802152

Published: 26 June 2019 Publication History

Abstract

Home-based rehabilitation systems can speed up recovery by enabling patients to exercise at home between rehabilitation sessions. However, home-based rehabilitation systems need to monitor and feedback exercises appropriately, as incorrect or imperfect exercises negatively impact the recovery of the patient. This paper describes a methodology for assessing the quality of rehabilitation exercises using inertial sensors, for a system that tracks exercises using surface electromyography sensors. This duality extends the information provided by the electromyography system since it provides a more comprehensive evaluation of posture and movement correctness. The methodology was evaluated with 17 physiotherapy patients, obtaining an average accuracy of 96% in detecting issues in the exercises monitored. The insights of this work are a first step to complement an electromyography-based home system to detect issues in movement and inform patients in real time about the correctness of their exercises.

References

[1]

Plux and Fraunhofer AICOS, “Physio@home: Extending physiotherapy programs to people’s home,” Plux and Fraunhofer AICOS, Tech. Rep., 2018. [Online]. Available: https://www.aicos.fraunhofer.pt/content/dam/portugal/en/documents/projectspecsheets/Ficha%20de%20projecto%20Physio@home_EN.pdf

[2]

A. Vasconcelos, F. Nunes, A. Carvalho, and C. Correia, “Mobile, exercise-agnostic, sensor-based serious games for physical rehabilitation at home,” in Proceedings of the Twelfth International Conference on Tangible, Embedded, and Embodied Interaction, ser. TEI ’18. New York, NY, USA: ACM, 2018, pp. 271–278.

[3]

World Health Organization, “World report on ageing and health,” World Health Organization, Tech. Rep., 2015.

[4]

S. F. Bassett, “The assessment of patient adherence to physiotherapy rehabilitation,” New Zealand journal of physiotherapy, vol. 31, no. 2, pp. 60–66, 2003.

[5]

M. Faber, M. H. Andersen, C. Sevel, K. Thorborg, T. Bandholm, and M. Rathleff, “The majority are not performing home-exercises correctly two weeks after their initial instructionan assessor-blinded study,” PeerJ, vol. 3, p. e1102, 2015.

[6]

J. Smith, J. Lewis, and D. Prichard, “Physiotherapy exercise programmes: Are instructional exercise sheets effective?” Physiotherapy theory and practice, vol. 21, no. 2, pp. 93–102, 2005.

[7]

L. Herrington, “EMG biofeedback: What can it actually show?” Physiotherapy, vol. 82, no. 10, pp. 581–583, 1996.

[8]

O. M. Giggins, U. M. Persson, and B. Caulfield, “Biofeedback in rehabilitation,” Journal of neuroengineering and rehabilitation, vol. 10, no. 1, p. 60, 2013.

[9]

C. J. De Luca, “The use of surface electromyography in biomechanics,” Journal of applied biomechanics, vol. 13, no. 2, pp. 135–163, 1997.

[10]

R. A. Ekstrom, R. A. Donatelli, and K. C. Carp, “Electromyographic analysis of core trunk, hip, and thigh muscles during 9 rehabilitation exercises,” Journal of orthopaedic & sports physical therapy, vol. 37, no. 12, pp. 754–762, 2007.

[11]

S. H. Roy, M. S. Cheng, S.-S. Chang, J. Moore, G. De Luca, S. H. Nawab, and C. J. De Luca, “A combined semg and accelerometer system for monitoring functional activity in stroke,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 17, no. 6, pp. 585–594, 2009.

[12]

Q. Qian, X. Hu, Q. Lai, S. C. Ng, Y. Zheng, and W. Poon, “Early stroke rehabilitation of the upper limb assisted with an electromyography-driven neuromuscular electrical stimulation-robotic arm,” Frontiers in neurology, vol. 8, p. 447, 2017.

[13]

A. B. Farjadian, M. L. Sivak, and C. Mavroidis, “Squid: Sensorized shirt with smartphone interface for exercise monitoring and home rehabilitation,” in 2013 IEEE 13th International Conference on Rehabilitation Robotics (ICORR). IEEE, 2013, pp. 1–6.

[14]

A. Holtermann, P. Mork, L. Andersen, H. B. Olsen, and K. Søgaard, “The use of emg biofeedback for learning of selective activation of intramuscular parts within the serratus anterior muscle: a novel approach for rehabilitation of scapular muscle imbalance,” Journal of Electromyog-raphy and Kinesiology, vol. 20, no. 2, pp. 359–365, 2010.

[15]

S. Nomm and K. Buhhalko, “Monitoring of the human motor functions rehabilitation by neural networks based system with kinect sensor,” IFAC Proceedings Volumes, vol. 46, no. 15, pp. 249–253, 2013.

[16]

F. Cary, O. Postolache, and P. S. Girao, “Kinect based system and artificial neural networks classifiers for physiotherapy assessment,” in Medical Measurements and Applications (MeMeA), 2014 IEEE International Symposium on. IEEE, 2014, pp. 1–6.

[17]

M. Barandas, H. Gamboa, and J. Fonseca, “A real time biofeedback system using visual user interface for physical rehabilitation,” Procedia Manufacturing, vol. 3, pp. 823–828, 2015.

[18]

M. Huber, A. L. Seitz, M. Leeser, and D. Sternad, “Validity and reliability of kinect skeleton for measuring shoulder joint angles: a feasibility study,” Physiotherapy, vol. 101, no. 4, pp. 389–393, 2015.

[19]

A. Yurtman and B. Barshan, “Detection and evaluation of physical therapy exercises by dynamic time warping using wearable motion sensor units,” in Information Sciences and Systems 2013. Springer, 2013, pp. 305–314.

[20]

O. M. Giggins, K. T. Sweeney, and B. Caulfield, “Rehabilitation exercise assessment using inertial sensors: a cross-sectional analytical study,” Journal of neuroengineering and rehabilitation, vol. 11, no. 1, p. 158, 2014.

[21]

B. Huang, O. Giggins, T. Kechadi, and B. Caulfield, “The limb movement analysis of rehabilitation exercises using wearable inertial sensors,” in Engineering in Medicine and Biology Society (EMBC), 2016 IEEE 38th Annual International Conference of the. IEEE, 2016, pp. 4686–4689.

[22]

A. Bevilacqua, B. Huang, R. Argent, B. Caulfield, and T. Kechadi, “Automatic classification of knee rehabilitation exercises using a single inertial sensor: a case study,” in IEEE 15th International Conference on Wearable and Implantable Body Sensor Networks (BSN). IEEE, 2018.

[23]

D. Whelan, M. O’Reilly, T. Ward, E. Delahunt, and B. Caulfield, “Evaluating performance of the single leg squat exercise with a single inertial measurement unit,” in Proceedings of the 3rd 2015 Workshop on ICTs for improving Patients Rehabilitation Research Techniques. ACM, 2015, pp. 144–147.

[24]

A. Pereira, D. Folgado, R. Cotrim, and I. Sousa, “Physiotherapy exercises evaluation using a combined approach based on sEMG and wearable inertial sensors,” in BIOSIGNALS, in press.

[25]

C. Costa, D. Tacconi, R. Tomasi, F. Calva, and V. Terreri, “Riablo: a game system for supporting orthopedic rehabilitation,” in Proceedings of the Biannual Conference of the Italian Chapter of SIGCHI. ACM, 2013, p. 11.

[26]

G. Spina, G. Huang, A. Vaes, M. Spruit, and O. Amft, “Copdtrainer: a smartphone-based motion rehabilitation training system with real-time acoustic feedback,” in Proceedings of the 2013 ACM international joint conference on Pervasive and ubiquitous computing. ACM, 2013, pp. 597–606.

[27]

B. Gamecho, H. Silva, J. Guerreiro, L. Gardeazabal, and J. Abascal, “A context-aware application to increase elderly users compliance with physical rehabilitation exercises at home via animatronic biofeedback,” Journal of medical systems, vol. 39, no. 11, p. 135, 2015.

[28]

L. Liu, X. Chen, Z. Lu, S. Cao, D. Wu, and X. Zhang, “Development of an emg-acc-based upper limb rehabilitation training system,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 25, no. 3, pp. 244–253, 2017.

[29]

Fraunhofer AICOS, “A Day with Pandlets,” Fraunhofer Portugal AICOS, Tech. Rep., 2016. [Online]. Available: https://www.fraunhofer.pt/content/dam/portugal/en/documents/news/white paperA%20day%20with%20pandlets.pdf

[30]

J. Rodrigues, D. Folgado, D. Belo, and H. Gamboa, “SSTS: A syntactic tool for pattern search on time series,” Information Processing & Management, vol. 56, no. 1, pp. 61–76, 2019.

[31]

A. Pereira, V. Guimarães, and I. Sousa, “Joint angles tracking for rehabilitation at home using inertial sensors: a feasibility study,” in Proceedings of the 11th EAI International Conference on Pervasive Computing Technologies for Healthcare. ACM, 2017, pp. 146–154.

Cited By

Ahmetovic DPugliese AMascetti SBegnozzi VBoccalandro EGualtierotti RPeyvandi F(2021)Rehabilitation through Accessible Mobile Gaming and Wearable SensorsProceedings of the 23rd International ACM SIGACCESS Conference on Computers and Accessibility10.1145/3441852.3476544(1-4)Online publication date: 17-Oct-2021
https://dl.acm.org/doi/10.1145/3441852.3476544

Index Terms

Using Inertial Sensors to Evaluate Exercise Correctness in Electromyography-based Home Rehabilitation Systems
1. Applied computing
  1. Life and medical sciences
2. Human-centered computing

Index terms have been assigned to the content through auto-classification.

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2019 IEEE International Symposium on Medical Measurements and Applications (MeMeA)

Jun 2019

554 pages

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Published: 26 June 2019

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Cited By

Ahmetovic DPugliese AMascetti SBegnozzi VBoccalandro EGualtierotti RPeyvandi F(2021)Rehabilitation through Accessible Mobile Gaming and Wearable SensorsProceedings of the 23rd International ACM SIGACCESS Conference on Computers and Accessibility10.1145/3441852.3476544(1-4)Online publication date: 17-Oct-2021
https://dl.acm.org/doi/10.1145/3441852.3476544

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