Technology in Rehabilitation: Evaluating the Single Leg Squat Exercise with Wearable Inertial Measurement Units

 

 Buy Article Permissions and Reprints

Summary

Background: The single leg squat (SLS) is a common lower limb rehabilitation exercise. It is also frequently used as an evaluative exercise to screen for an increased risk of lower limb injury. To date athlete/patient SLS technique has been assessed using expensive laboratory equipment or subjective clinical judgement; both of which are not without shortcomings. Inertial measurement units (IMUs) may offer a low cost solution for the objective evaluation of athlete/patient SLS technique.

Objectives: The aims of this study were to determine if in combination or in isolation IMUs positioned on the lumbar spine, thigh and shank are capable of: (a) distinguishing between acceptable and aberrant SLS technique; (b) identifying specific deviations from acceptable SLS technique.

Methods: Eighty-three healthy volunteers participated (60 males, 23 females, age: 24.68 +/− 4.91 years, height: 1.75 +/− 0.09 m, body mass: 76.01 +/− 13.29 kg). All participants performed 10 SLSs on their left leg. IMUs were positioned on participants’ lumbar spine, left shank and left thigh. These were utilized to record tri-axial accelerometer, gyroscope and magnetometer data during all repetitions of the SLS. SLS technique was labelled by a Chartered Physiotherapist using an evaluation framework. Features were extracted from the labelled sensor data. These features were used to train and evaluate a variety of random-forests classifiers that assessed SLS technique.

Results: A three IMU system was moderately successful in detecting the overall quality of SLS performance (77% accuracy, 77% sensitivity and 78% specificity). A single IMU worn on the shank can complete the same analysis with 76% accuracy, 75% sensitivity and 76% specificity. Single sensors also produce competitive classification scores relative to multi-sensor systems in identifying specific deviations from acceptable SLS technique.

Conclusions: A single IMU positioned on the shank can differentiate between acceptable and aberrant SLS technique with moderate levels of accuracy. It can also capably identify specific deviations from optimal SLS performance. IMUs may offer a low cost solution for the objective evaluation of SLS performance. Additionally, the classifiers described may provide useful input to an exercise biofeed-back application.

^* These authors contributed equally to this work

• References

• 1 Zwerver J, Bredeweg SW, Hof AL. Biomechanical analysis of the single-leg decline squat. Br J Sports Med. 2007; 41 (04) 264-268.
  CrossrefPubMedGoogle Scholar
• 2 Willson JD, Ireland ML, Davis I. Core strength and lower extremity alignment during single leg squats. Med Sci Sports Exerc. 2006; 38 (05) 945-952.
  CrossrefPubMedGoogle Scholar
• 3 Whatman C, Hing W, Hume P. Physiotherapist agreement when visually rating movement quality during lower extremity functional screening tests. Phys Ther Sport. 2012; 13 (02) 87-96.
  CrossrefPubMedGoogle Scholar
• 4 Ahmadi A, Mitchell E, Destelle F, Gowing M, O’Connor NE, Richter C. et al. Automatic Activity Classification and Movement Assessment During a Sports Training Session Using Wearable Inertial Sensors. Proceedings of the 11th International Conference on Wearable and Implantable Body Sensor Networks (BSN). 2014. IEEE.;
  Google Scholar
• 5 Chmielewski TL, Hodges MJ, Horodyski M, Bishop MD, Conrad BP, Tillman SM. Investigation of clinician agreement in evaluating movement quality during unilateral lower extremity functional tasks: a comparison of 2 rating methods. J Orthop Sports Phys Ther. 2007; 37 (03) 122-129.
  CrossrefPubMedGoogle Scholar
• 6 McGrath D, Greene BR, O’Donovan KJ, Caulfield B. Gyroscope-based assessment of temporal gait parameters during treadmill walking and running. Sports Eng. 2012; 15 (04) 207-213.
  CrossrefPubMedGoogle Scholar
• 7 Chang K-H, Chen MY, Canny J, Chang KH, Chen MY, Canny J. Tracking free-weight exercises. Krumm J, Abowd GD, Seneviratne A, Strang T. Ubicomp 2007: Ubiquitous Computing. Austria: Springer; 2007: 19-37.
  Google Scholar
• 8 Fitzgerald D, Foody J, Kelly D, Ward T, Markham C, McDonald J. et al. Development of a wearable motion capture suit and virtual reality biofeedback system for the instruction and analysis of sports rehabilitation exercises. Conf Proc IEEE Eng Med Biol Soc. 2007; 2007: 4870-4874.
  PubMedGoogle Scholar
• 9 Seeger C, Buchmann A, Van Laerhoven K. my-HealthAssistant: a phone-based body sensor network that captures the wearer’s exercises throughout the day. Proceedings of the 6th International Conference on Body Area. Networks: ICST; 2011: 1-7.
  Google Scholar
• 10 Morris D, Saponas TS, Guillory A, Kelner I. RecoFit: using a wearable sensor to find, recognize, and count repetitive exercises. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. 2014. ACM.;
  Google Scholar
• 11 Giggins O, Sweeney KT, Caulfield B. The use of inertial sensors for the classification of rehabilitation exercises. Conf Proc IEEE Eng Med Biol Soc. 2014; 2014: 2965-2968.
  PubMedGoogle Scholar
• 12 Muehlbauer M, Bahle G, Lukowicz P. What can an arm holster worn smart phone do for activity recognition?. Proceedings of the 15th Annual International Symposium on Wearable Computers (ISWC). San Francisco, CA, USA: 12–15 June 2011; 79-82.
  Google Scholar
• 13 Pernek I, Hummel KA, Kokol P. Exercise repetition detection for resistance training based on smartphones. Pers Ubiquitous Comput. 2013; 17 (04) 771-782.
  CrossrefPubMedGoogle Scholar
• 14 Taylor PE, Almeida GJ, Hodgins JK, Kanade T. Multi-label classification for the analysis of human motion quality. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). 2012. IEEE.;
  Google Scholar
• 15 Velloso E, Bulling A, Gellersen H, Ugulino W, Fuks H. Qualitative activity recognition of weight lifting exercises. Proceedings of the 4th Augmented Human International Conference. 2013. ACM.;
  Google Scholar
• 16 Giggins OM, Sweeney KT, Caulfield B. Rehabilitation exercise assessment using inertial sensors: a cross-sectional analytical study. J Neuroeng Rehabil. 2014; 11 (01) 158-168.
  CrossrefPubMedGoogle Scholar
• 17 Jerri AJ. The Shannon sampling theorem – Its various extensions and applications: A tutorial review. Proc. IEEE. 1977; 65 (11) 1565-1596.
  CrossrefPubMedGoogle Scholar
• 18 Shimmer 9DOF calibration. Available from: http://www.shimmersensing.com/shop/shimmer9dof-calibration.
  PubMed
• 19 Madgwick SOH, Harrison AJL, Vaidyanathan R. Estimation of IMU and MARG orientation using a gradient descent algorithm. Proceedings of the IEEE International Conference on Rehabilitation Robotics (ICORR). 2011. IEEE.;
  Google Scholar
• 20 Breiman L. Random forests. Mach Learn. 2001; 45 (01) 5-32.
  CrossrefPubMedGoogle Scholar
• 21 Mitchell E, Ahmadi A, O’Connor NE, Richter C, Farrell E, Kavanagh J. et al. Automatically detecting asymmetric running using time and frequency domain features. Proceedings of the 12th International Conference on Wearable and Implantable Body Sensor Networks (BSN). 2015. IEEE.;
  Google Scholar
• 22 Ugalde V, Brockman C, Bailowitz Z, Pollard CD. Single Leg Squat Test and Its Relationship to Dynamic Knee Valgus and Injury Risk Screening. PM&R. 2014; 7 (03) 229-235.
  PubMedGoogle Scholar
• 23 Cook G, Burton L, Hoogenboom B. Pre-participation screening: the use of fundamental movements as an assessment of function – part 1. N Am J Sports Phys Ther. 2006; 1 (02) 62-72.
  PubMedGoogle Scholar
• 24 Cook G, Burton L, Hoogenboom B. Pre-participation screening: the use of fundamental movements as an assessment of function – part 2. N Am J Sports Phys Ther. 2006; 1 (03) 132-139.
  PubMedGoogle Scholar
• 25 Myer GD, Ford KR, Hewett TE. Tuck jump assessment for reducing anterior cruciate ligament injury risk. Athl Ther Today. 2008; 13 (05) 39-44.
  PubMedGoogle Scholar
• 26 Giggins O, Kelly D, Caulfield B. Evaluating rehabilitation exercise performance using a single inertial measurement unit. Proceedings of the 7th International Conference on Pervasive Computing Technologies for Healthcare. 2013. ICST.;
  Google Scholar
• 27 Avci A, Bosch S, Marin-Perianu M, Marin-Perianu R, Havinga P. Activity recognition using inertial sensing for healthcare, wellbeing and sports applications: a survey. Proceedings of the 23rd International Conference on Architecture of computing systems (ARCS):. VDE; 2010: 1-10.
  Google Scholar