IMU Sensor-based Electronic Goniometric Glove for clinical finger movement analysis

James Connolly, Joan Condell, Brendan O'Flynn, Javier Torres Sanchez, Philip Gardiner

Research output: Contribution to journalArticle

10 Citations (Scopus)

Abstract

Arthritis remains a disabling and painful disease, and involvement of finger joints is a major cause of disability and loss of employment. Traditional arthritis measurements require labour intensive examination by clinical staff. These manual measurements are inaccurate and open to observer variation.This paper presents the development and testing of a next generation wireless smart glove to facilitate the accurate measurement of finger movement through the integration of multiple IMU sensors, with bespoke controlling algorithms. Our main objective was to measure finger and thumb joint movement. These dynamic measurements will provide clinicians with a new and accurate way to measure loss of movement in patients with Rheumatoid Arthritis. Commercially available gaming gloves are not fitted with sufficient sensors for this particular application, and require calibration for each glove wearer. Unlike these state-of-the-art data gloves, the Inertial Measurement Unit (IMU) glove uses a combination of novel stretchable substrate material and 9 degree of freedom (DOF) inertial sensors in conjunction with complex data analytics to detect joint movement. Our novel iSEG-Glove requires minimal calibration and is therefore particularly suited to the healthcare environment. Inaccuracies may arise for wearers who have varying degrees of movement in their finger joints, variance in hand size or deformities. The developed glove is fitted with sensors to overcome these issues. This glove will help quantify joint stiffness and monitor patient progression during the arthritis rehabilitation process.
LanguageEnglish
Pages1273-1281
JournalIEEE Sensors
Volume18
Issue number3
Early online date22 Nov 2017
DOIs
Publication statusPublished - 1 Feb 2018

Fingerprint

Units of measurement
Sensors
Calibration
Patient rehabilitation
Stiffness
Personnel
Testing
Substrates

Keywords

  • Data glove
  • wireless sensor networks
  • Inertial Measurement Unit
  • Rheumatoid Arthritis
  • sensor calibration

Cite this

Connolly, James ; Condell, Joan ; O'Flynn, Brendan ; Torres Sanchez, Javier ; Gardiner, Philip. / IMU Sensor-based Electronic Goniometric Glove for clinical finger movement analysis. 2018 ; Vol. 18, No. 3. pp. 1273-1281.
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title = "IMU Sensor-based Electronic Goniometric Glove for clinical finger movement analysis",
abstract = "Arthritis remains a disabling and painful disease, and involvement of finger joints is a major cause of disability and loss of employment. Traditional arthritis measurements require labour intensive examination by clinical staff. These manual measurements are inaccurate and open to observer variation.This paper presents the development and testing of a next generation wireless smart glove to facilitate the accurate measurement of finger movement through the integration of multiple IMU sensors, with bespoke controlling algorithms. Our main objective was to measure finger and thumb joint movement. These dynamic measurements will provide clinicians with a new and accurate way to measure loss of movement in patients with Rheumatoid Arthritis. Commercially available gaming gloves are not fitted with sufficient sensors for this particular application, and require calibration for each glove wearer. Unlike these state-of-the-art data gloves, the Inertial Measurement Unit (IMU) glove uses a combination of novel stretchable substrate material and 9 degree of freedom (DOF) inertial sensors in conjunction with complex data analytics to detect joint movement. Our novel iSEG-Glove requires minimal calibration and is therefore particularly suited to the healthcare environment. Inaccuracies may arise for wearers who have varying degrees of movement in their finger joints, variance in hand size or deformities. The developed glove is fitted with sensors to overcome these issues. This glove will help quantify joint stiffness and monitor patient progression during the arthritis rehabilitation process.",
keywords = "Data glove, wireless sensor networks, Inertial Measurement Unit, Rheumatoid Arthritis, sensor calibration",
author = "James Connolly and Joan Condell and Brendan O'Flynn and {Torres Sanchez}, Javier and Philip Gardiner",
note = "Reference text: [1] D. M. van der Heijde, van ’t H. M, P. L. van Riel, and L. B. van de Putte, “Development of a disease activity score based on judgment in clinical practice by rheumatologists,” J. Rheumatol., vol. 20, no. 3, pp. 579–81, 1993. [2] J. F. Fries, P. Spitz, R. G. Kraines, and H. R. Holman, “Measurement of patient outcome in arthritis,” Arthritis Rheum., vol. 23, no. 2, pp. 137–145, 1980. [3] J. F. Fries, P. W. Spitz, and D. Y. Young, “The dimensions of health outcomes: the health assessment questionnaire, disability and pain scales,” J. Rheumatol., vol. 9, no. 5, p. 789—793, 1982. [4] P. Emery, F. C. Breedveld, M. Dougados, J. R. Kalden, M. H. Schiff, and J. S. Smolen, “Early referral recommendation for newly diagnosed rheumatoid arthritis: evidence based development of a clinical guide.,” Ann. Rheum. Dis., vol. 61, no. 4, pp. 290–7, Apr. 2002. [5] F. C. Arnett, S. M. Edworthy, D. A. Bloch, D. J. McShane, J. F. Fries, N. S. Cooper, L. A. Healey, S. R. Kaplan, M. H. Liang, and H. S. Luthra, “The American Rheumatism Association 1987 revised criteria for the classification of rheumatoid arthritis.,” Arthritis and rheumatism, vol. 31, no. 3. pp. 315–24, Mar-1988. [6] E. Dionysian, J. M. Kabo, F. J. Dorey, and R. a Meals, “Proximal interphalangeal joint stiffness: measurement and analysis.,” J. Hand Surg. Am., vol. 30, no. 3, pp. 573–9, May 2005. [7] A. Howe, D. Thompson, and V. Wright, “Reference values for metacarpophalangeal joint stiffness in normals.,” Ann. Rheum. Dis., vol. 44, no. 7, pp. 469–76, Jul. 1985. [8] M. L. Ingpen and P. H. Kendall, “A simple apparatus for assessment of stiffness,” Ann. Phys. Med., vol. 9, no. 5, pp. 203–5, Feb. 1968. [9] J. T. Scott, “Morning stiffness in rheumatoid arthritis.,” Ann. Rheum. Dis., vol. 19, pp. 361–8, Dec. 1960. [10] a Unsworth, P. Yung, and I. Haslock, “Measurement of stiffness in the metacarpophalangeal joint: the arthrograph.,” Clin. Phys. Physiol. Meas., vol. 3, no. 4, pp. 273–81, Nov. 1982. [11] V. Wright and R. J. Johns, “Quantitative and qualitative analysis of joint stiffness in normal subjects and in patients with connective tissue diseases.,” Ann. Rheum. Dis., vol. 20, pp. 36–46, Mar. 1961. [12] L. Dipietro, A. M. Sabatini, and P. Dario, “Evaluation of an instrumented glove for hand-movement acquisition.,” J. Rehabil. Res. Dev., vol. 40, no. 2, pp. 179–89, 2003. [13] K. Li, I.-M. Chen, S. H. Yeo, and C. K. Lim, “Development of finger-motion capturing device based on optical linear encoder,” J. Rehabil. Res. Dev., vol. 48, no. 1, p. 69, 2011. [14] G. Saggio, S. Bocchetti, C. A. Pinto, G. Orengo, and F. Giannini, “A novel application method for wearable bend sensors,” in 2009 2nd International Symposium on Applied Sciences in Biomedical and Communication Technologies, 2009, pp. 1–3. [15] L. K. Simone and D. G. Kamper, “Design considerations for a wearable monitor to measure finger posture.,” J. Neuroeng. Rehabil., vol. 2, no. 1, p. 5, Mar. 2005. [16] L. K. Simone, N. Sundarrajan, X. Luo, Y. Jia, and D. G. Kamper, “A low cost instrumented glove for extended monitoring and functional hand assessment.,” J. Neurosci. Methods, vol. 160, no. 2, pp. 335–48, Mar. 2007. [17] S. Wise, W. Gardner, E. Sabelman, E. Valainis, Y. Wong, K. Glass, J. Drace, and J. M. Rosen, “Evaluation of a fiber optic glove for semi-automated goniometric measurements,” J. Rehabil. Res. Dev., vol. 27, no. 4, p. 411, 1990. [18] 5DT, “5DT Data Glove 14 Ultra,” 2011. [Online]. Available: http://www.5dt.com/downloads/dataglove/ultra/5DT Data Glove Ultra Manual v1.3.pdf. [Accessed: 10-Jan-2012]. [19] Vicon Motion Systems, “Vicon,” 2013. [Online]. Available: http://www.vicon.com/. [20] J. Connolly, K. Curran, J. Condell, and P. Gardiner, “Wearable Rehab Technology for Automatic Measurement of Patients with Arthritis,” Sensors (Peterborough, NH), pp. 508–509, 2011. [21] B. O’Flynn, J. Sanchez, P. Angrove, J. Connolly, J. Condell, K. Curran, and P. Gardiner, “Novel Smart Sensor Glove for Arthritis Rehabilitation,” in The Smart Systems Integration Conference, 2013. [22] B. O’Flynn, J. Sanchez, T. S, B. Downes, J. Connolly, J. Condell, and K. Curran, “Novel Smart Glove Technology as a Biomechanical Monitoring Tool,” Sensors & Transducers, vol. 193, no. 10, pp. 23–32, 2015. [23] QPI, “Stretch-rigid PCB,” Stretchable PCB technology, 2014. [Online]. Available: http://www.qpigroup.com/en/products-services/pcb-technology/stretchable-pcb-technology. [24] Atmel, “32-bit AVR UC3 Microcontrollers,” The World’s Most-Efficient 32-Bit Microcontroller, 2014. [Online]. Available: http://www.atmel.com/products/microcontrollers/avr/32-bitavruc3.aspx. [Accessed: 20-Sep-2014]. [25] Redpine Signals, “RS9110-N-11-22: 802.11BGN wireless device server,” RS9110-N-11-22 Product brief, 2008. [Online]. Available: http://www.redpinesignals.com/pdfs/RS9110-N-11-22 Wlan Module.pdf. [26] InvenSense, “MPU-9150 Product Specification Revision 4.0,” vol. 1, no. 408. InvenSense, California, pp. 1–52, 2012. [27] C. Fisher, “Using an accelerometer for inclination sensing,” 2010. [28] R. Gentner and J. Classen, “Development and evaluation of a low-cost sensor glove for assessment of human finger movements in neurophysiological settings.,” J. Neurosci. Methods, vol. 178, no. 1, pp. 138–47, Mar. 2009. [29] G. Kessler, N. Walker, and L. Hodges, “Evaluation of the CyberGlove (TM) as a whole hand input device,” 1995. [30] M. Mentzel, F. Hofmann, T. Ebinger, B. Jatzold, L. Kinzl, and N. J. Wachter, “Reproducibility of measuring the finger joint angle with a sensory glove,” Handchir Mikrochir Plast Chir, vol. 33, no. 1, pp. 9–64, 2001. [31] N. W. Williams, J. M. T. Penrose, C. M. Caddy, E. Barnes, D. R. Hose, and P. Harley, “A goniometric glove for clinical hand assessment,” vol. 25, no. 2, pp. 200–207, 2000. [32] A. Hellebrandt, E. Duvall, and M. Moore, “The measurement of joint motion. Part III : Reliability of goniometry,” Phys Ther Rev, vol. 29, no. 6, pp. 302–307, 1949. [33] E. Lewis, L. Fors, and W. J. Tharion, “Interrater and intrarater reliability of finger goniometrie measurements,” Am. J. Occup. Ther., vol. 64, pp. 555–561, 2010. [34] N. B. Reese and W. D. Bandy, “Measurement of Range of motion and muscle length: background, history , and basic principles,” in Joint Range of Motion and Muscle Length testing, 2010, pp. 3–29.",
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IMU Sensor-based Electronic Goniometric Glove for clinical finger movement analysis. / Connolly, James; Condell, Joan; O'Flynn, Brendan; Torres Sanchez, Javier; Gardiner, Philip.

Vol. 18, No. 3, 01.02.2018, p. 1273-1281.

Research output: Contribution to journalArticle

TY - JOUR

T1 - IMU Sensor-based Electronic Goniometric Glove for clinical finger movement analysis

AU - Connolly, James

AU - Condell, Joan

AU - O'Flynn, Brendan

AU - Torres Sanchez, Javier

AU - Gardiner, Philip

N1 - Reference text: [1] D. M. van der Heijde, van ’t H. M, P. L. van Riel, and L. B. van de Putte, “Development of a disease activity score based on judgment in clinical practice by rheumatologists,” J. Rheumatol., vol. 20, no. 3, pp. 579–81, 1993. [2] J. F. Fries, P. Spitz, R. G. Kraines, and H. R. Holman, “Measurement of patient outcome in arthritis,” Arthritis Rheum., vol. 23, no. 2, pp. 137–145, 1980. [3] J. F. Fries, P. W. Spitz, and D. Y. Young, “The dimensions of health outcomes: the health assessment questionnaire, disability and pain scales,” J. Rheumatol., vol. 9, no. 5, p. 789—793, 1982. [4] P. Emery, F. C. Breedveld, M. Dougados, J. R. Kalden, M. H. Schiff, and J. S. Smolen, “Early referral recommendation for newly diagnosed rheumatoid arthritis: evidence based development of a clinical guide.,” Ann. Rheum. Dis., vol. 61, no. 4, pp. 290–7, Apr. 2002. [5] F. C. Arnett, S. M. Edworthy, D. A. Bloch, D. J. McShane, J. F. Fries, N. S. Cooper, L. A. Healey, S. R. Kaplan, M. H. Liang, and H. S. Luthra, “The American Rheumatism Association 1987 revised criteria for the classification of rheumatoid arthritis.,” Arthritis and rheumatism, vol. 31, no. 3. pp. 315–24, Mar-1988. [6] E. Dionysian, J. M. Kabo, F. J. Dorey, and R. a Meals, “Proximal interphalangeal joint stiffness: measurement and analysis.,” J. Hand Surg. Am., vol. 30, no. 3, pp. 573–9, May 2005. [7] A. Howe, D. Thompson, and V. Wright, “Reference values for metacarpophalangeal joint stiffness in normals.,” Ann. Rheum. Dis., vol. 44, no. 7, pp. 469–76, Jul. 1985. [8] M. L. Ingpen and P. H. Kendall, “A simple apparatus for assessment of stiffness,” Ann. Phys. Med., vol. 9, no. 5, pp. 203–5, Feb. 1968. [9] J. T. Scott, “Morning stiffness in rheumatoid arthritis.,” Ann. Rheum. Dis., vol. 19, pp. 361–8, Dec. 1960. [10] a Unsworth, P. Yung, and I. Haslock, “Measurement of stiffness in the metacarpophalangeal joint: the arthrograph.,” Clin. Phys. Physiol. Meas., vol. 3, no. 4, pp. 273–81, Nov. 1982. [11] V. Wright and R. J. Johns, “Quantitative and qualitative analysis of joint stiffness in normal subjects and in patients with connective tissue diseases.,” Ann. Rheum. Dis., vol. 20, pp. 36–46, Mar. 1961. [12] L. Dipietro, A. M. Sabatini, and P. Dario, “Evaluation of an instrumented glove for hand-movement acquisition.,” J. Rehabil. Res. Dev., vol. 40, no. 2, pp. 179–89, 2003. [13] K. Li, I.-M. Chen, S. H. Yeo, and C. K. Lim, “Development of finger-motion capturing device based on optical linear encoder,” J. Rehabil. Res. Dev., vol. 48, no. 1, p. 69, 2011. [14] G. Saggio, S. Bocchetti, C. A. Pinto, G. Orengo, and F. Giannini, “A novel application method for wearable bend sensors,” in 2009 2nd International Symposium on Applied Sciences in Biomedical and Communication Technologies, 2009, pp. 1–3. [15] L. K. Simone and D. G. Kamper, “Design considerations for a wearable monitor to measure finger posture.,” J. Neuroeng. Rehabil., vol. 2, no. 1, p. 5, Mar. 2005. [16] L. K. Simone, N. Sundarrajan, X. Luo, Y. Jia, and D. G. Kamper, “A low cost instrumented glove for extended monitoring and functional hand assessment.,” J. Neurosci. Methods, vol. 160, no. 2, pp. 335–48, Mar. 2007. [17] S. Wise, W. Gardner, E. Sabelman, E. Valainis, Y. Wong, K. Glass, J. Drace, and J. M. Rosen, “Evaluation of a fiber optic glove for semi-automated goniometric measurements,” J. Rehabil. Res. Dev., vol. 27, no. 4, p. 411, 1990. [18] 5DT, “5DT Data Glove 14 Ultra,” 2011. [Online]. Available: http://www.5dt.com/downloads/dataglove/ultra/5DT Data Glove Ultra Manual v1.3.pdf. [Accessed: 10-Jan-2012]. [19] Vicon Motion Systems, “Vicon,” 2013. [Online]. Available: http://www.vicon.com/. [20] J. Connolly, K. Curran, J. Condell, and P. Gardiner, “Wearable Rehab Technology for Automatic Measurement of Patients with Arthritis,” Sensors (Peterborough, NH), pp. 508–509, 2011. [21] B. O’Flynn, J. Sanchez, P. Angrove, J. Connolly, J. Condell, K. Curran, and P. Gardiner, “Novel Smart Sensor Glove for Arthritis Rehabilitation,” in The Smart Systems Integration Conference, 2013. [22] B. O’Flynn, J. Sanchez, T. S, B. Downes, J. Connolly, J. Condell, and K. Curran, “Novel Smart Glove Technology as a Biomechanical Monitoring Tool,” Sensors & Transducers, vol. 193, no. 10, pp. 23–32, 2015. [23] QPI, “Stretch-rigid PCB,” Stretchable PCB technology, 2014. [Online]. Available: http://www.qpigroup.com/en/products-services/pcb-technology/stretchable-pcb-technology. [24] Atmel, “32-bit AVR UC3 Microcontrollers,” The World’s Most-Efficient 32-Bit Microcontroller, 2014. [Online]. Available: http://www.atmel.com/products/microcontrollers/avr/32-bitavruc3.aspx. [Accessed: 20-Sep-2014]. [25] Redpine Signals, “RS9110-N-11-22: 802.11BGN wireless device server,” RS9110-N-11-22 Product brief, 2008. [Online]. Available: http://www.redpinesignals.com/pdfs/RS9110-N-11-22 Wlan Module.pdf. [26] InvenSense, “MPU-9150 Product Specification Revision 4.0,” vol. 1, no. 408. InvenSense, California, pp. 1–52, 2012. [27] C. Fisher, “Using an accelerometer for inclination sensing,” 2010. [28] R. Gentner and J. Classen, “Development and evaluation of a low-cost sensor glove for assessment of human finger movements in neurophysiological settings.,” J. Neurosci. Methods, vol. 178, no. 1, pp. 138–47, Mar. 2009. [29] G. Kessler, N. Walker, and L. Hodges, “Evaluation of the CyberGlove (TM) as a whole hand input device,” 1995. [30] M. Mentzel, F. Hofmann, T. Ebinger, B. Jatzold, L. Kinzl, and N. J. Wachter, “Reproducibility of measuring the finger joint angle with a sensory glove,” Handchir Mikrochir Plast Chir, vol. 33, no. 1, pp. 9–64, 2001. [31] N. W. Williams, J. M. T. Penrose, C. M. Caddy, E. Barnes, D. R. Hose, and P. Harley, “A goniometric glove for clinical hand assessment,” vol. 25, no. 2, pp. 200–207, 2000. [32] A. Hellebrandt, E. Duvall, and M. Moore, “The measurement of joint motion. Part III : Reliability of goniometry,” Phys Ther Rev, vol. 29, no. 6, pp. 302–307, 1949. [33] E. Lewis, L. Fors, and W. J. Tharion, “Interrater and intrarater reliability of finger goniometrie measurements,” Am. J. Occup. Ther., vol. 64, pp. 555–561, 2010. [34] N. B. Reese and W. D. Bandy, “Measurement of Range of motion and muscle length: background, history , and basic principles,” in Joint Range of Motion and Muscle Length testing, 2010, pp. 3–29.

PY - 2018/2/1

Y1 - 2018/2/1

N2 - Arthritis remains a disabling and painful disease, and involvement of finger joints is a major cause of disability and loss of employment. Traditional arthritis measurements require labour intensive examination by clinical staff. These manual measurements are inaccurate and open to observer variation.This paper presents the development and testing of a next generation wireless smart glove to facilitate the accurate measurement of finger movement through the integration of multiple IMU sensors, with bespoke controlling algorithms. Our main objective was to measure finger and thumb joint movement. These dynamic measurements will provide clinicians with a new and accurate way to measure loss of movement in patients with Rheumatoid Arthritis. Commercially available gaming gloves are not fitted with sufficient sensors for this particular application, and require calibration for each glove wearer. Unlike these state-of-the-art data gloves, the Inertial Measurement Unit (IMU) glove uses a combination of novel stretchable substrate material and 9 degree of freedom (DOF) inertial sensors in conjunction with complex data analytics to detect joint movement. Our novel iSEG-Glove requires minimal calibration and is therefore particularly suited to the healthcare environment. Inaccuracies may arise for wearers who have varying degrees of movement in their finger joints, variance in hand size or deformities. The developed glove is fitted with sensors to overcome these issues. This glove will help quantify joint stiffness and monitor patient progression during the arthritis rehabilitation process.

AB - Arthritis remains a disabling and painful disease, and involvement of finger joints is a major cause of disability and loss of employment. Traditional arthritis measurements require labour intensive examination by clinical staff. These manual measurements are inaccurate and open to observer variation.This paper presents the development and testing of a next generation wireless smart glove to facilitate the accurate measurement of finger movement through the integration of multiple IMU sensors, with bespoke controlling algorithms. Our main objective was to measure finger and thumb joint movement. These dynamic measurements will provide clinicians with a new and accurate way to measure loss of movement in patients with Rheumatoid Arthritis. Commercially available gaming gloves are not fitted with sufficient sensors for this particular application, and require calibration for each glove wearer. Unlike these state-of-the-art data gloves, the Inertial Measurement Unit (IMU) glove uses a combination of novel stretchable substrate material and 9 degree of freedom (DOF) inertial sensors in conjunction with complex data analytics to detect joint movement. Our novel iSEG-Glove requires minimal calibration and is therefore particularly suited to the healthcare environment. Inaccuracies may arise for wearers who have varying degrees of movement in their finger joints, variance in hand size or deformities. The developed glove is fitted with sensors to overcome these issues. This glove will help quantify joint stiffness and monitor patient progression during the arthritis rehabilitation process.

KW - Data glove

KW - wireless sensor networks

KW - Inertial Measurement Unit

KW - Rheumatoid Arthritis

KW - sensor calibration

U2 - 10.1109/JSEN.2017.2776262

DO - 10.1109/JSEN.2017.2776262

M3 - Article

VL - 18

SP - 1273

EP - 1281

IS - 3

ER -