Validity and reliability of inertial sensors for elbow and wrist range of motion assessment

Background Elbow and wrist chronic conditions are very common among musculoskeletal problems. These painful conditions affect muscle function, which ultimately leads to a decrease in the joint’s Range Of Motion (ROM). Due to their portability and ease of use, goniometers are still the most widespread tool for measuring ROM. Inertial sensors are emerging as a digital, low-cost and accurate alternative. However, whereas inertial sensors are commonly used in research studies, due to the lack of information about their validity and reliability, they are not widely used in the clinical practice. The goal of this study is to assess the validity and intra-inter-rater reliability of inertial sensors for measuring active ROM of the elbow and wrist. Materials and Methods Measures were taken simultaneously with inertial sensors (Werium™ system) and a universal goniometer. The process involved two physiotherapists (“rater A” and “rater B”) and an engineer responsible for the technical issues. Twenty-nine asymptomatic subjects were assessed individually in two sessions separated by 48 h. The procedure was repeated by rater A followed by rater B with random order. Three repetitions of each active movement (elbow flexion, pronation, and supination; and wrist flexion, extension, radial deviation and ulnar deviation) were executed starting from the neutral position until the ROM end-feel; that is, until ROM reached its maximum due to be stopped by the anatomy. The coefficient of determination (r2) and the Intraclass Correlation Coefficient (ICC) were calculated to assess the intra-rater and inter-rater reliability. The Standard Error of the Measurement and the Minimum Detectable Change and a Bland–Altman plots were also calculated. Results Similar ROM values when measured with both instruments were obtained for the elbow (maximum difference of 3° for all the movements) and wrist (maximum difference of 1° for all the movements). These values were within the normal range when compared to literature studies. The concurrent validity analysis for all the movements yielded ICC values ≥0.78 for the elbow and ≥0.95 for the wrist. Concerning reliability, the ICC values denoted a high reliability of inertial sensors for all the different movements. In the case of the elbow, intra-rater and inter-rater reliability ICC values range from 0.83 to 0.96 and from 0.94 to 0.97, respectively. Intra-rater analysis of the wrist yielded ICC values between 0.81 and 0.93, while the ICC values for the inter-rater analysis range from 0.93 to 0.99. Conclusions Inertial sensors are a valid and reliable tool for measuring elbow and wrist active ROM. Particularly noteworthy is their high inter-rater reliability, often questioned in measurement tools. The lowest reliability is observed in elbow prono-supination, probably due to skin artifacts. Based on these results and their advantages, inertial sensors can be considered a valid assessment tool for wrist and elbow ROM.

[1]  Mauro Callejas-Cuervo,et al.  Joint amplitude MEMS based measurement platform for low cost and high accessibility telerehabilitation: Elbow case study. , 2017, Journal of bodywork and movement therapies.

[2]  Thomas Seel,et al.  Alignment-free, self-calibrating elbow angles measurement using inertial sensors , 2017, 2016 IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI).

[3]  C Sakarovitch,et al.  Inertial sensors as measurement tools of elbow range of motion in gerontology , 2015, Clinical interventions in aging.

[4]  Fiaz Ahmad,et al.  A power comparison of various normality tests , 2017 .

[5]  Brett Bowman,et al.  Reliability, Concurrent Validity, and Minimal Detectable Change for iPhone Goniometer App in Assessing Knee Range of Motion , 2017, The journal of knee surgery.

[6]  Richard W. Bohannon,et al.  Clinical measurement of range of motion. Review of goniometry emphasizing reliability and validity. , 1987, Physical therapy.

[7]  Leslie G. Portney Dpt PhD Fapta,et al.  Foundations of Clinical Research: Applications to Practice , 2015 .

[8]  Jun G San Juan,et al.  Concurrent validity of digital inclinometer and universal goniometer in assessing passive hip mobility in healthy subjects. , 2013, International journal of sports physical therapy.

[9]  Barry R. Greene,et al.  Assessment and Classification of Early-Stage Multiple Sclerosis With Inertial Sensors: Comparison Against Clinical Measures of Disease State , 2015, IEEE Journal of Biomedical and Health Informatics.

[10]  M. Kolber,et al.  The reliability and concurrent validity of scapular plane shoulder elevation measurements using a digital inclinometer and goniometer , 2012, Physiotherapy theory and practice.

[11]  Lawrence Wai-Choong Wong,et al.  Ubiquitous Human Upper-Limb Motion Estimation using Wearable Sensors , 2011, IEEE Transactions on Information Technology in Biomedicine.

[12]  Jennifer McGinley,et al.  Test-retest reliability and inter-tester reliability of kinematic data from a three-dimensional gait analysis system. , 2003, Journal of the Japanese Physical Therapy Association = Rigaku ryoho.

[13]  José Luis Pons Rovira,et al.  A Robust Kalman Algorithm to Facilitate Human-Computer Interaction for People with Cerebral Palsy, Using a New Interface Based on Inertial Sensors , 2012, Sensors.

[14]  Brian Caulfield,et al.  Rehabilitation exercise assessment using inertial sensors: a cross-sectional analytical study , 2014, Journal of NeuroEngineering and Rehabilitation.

[15]  Sheroz Khan,et al.  Motion capture sensing techniques used in human upper limb motion: a review , 2019, Sensor Review.

[16]  Francisco J. Badesa,et al.  Estimation of Human Arm Joints Using Two Wireless Sensors in Robotic Rehabilitation Tasks , 2015, Sensors.

[17]  Norbert Schmitz,et al.  Survey of Motion Tracking Methods Based on Inertial Sensors: A Focus on Upper Limb Human Motion , 2017, Sensors.

[18]  Huosheng Hu,et al.  Use of multiple wearable inertial sensors in upper limb motion tracking. , 2008, Medical engineering & physics.

[19]  Ramón Ceres Ruíz,et al.  Assistive Robots for Physical and Cognitive Rehabilitation in Cerebral Palsy , 2015, Intelligent Assistive Robots.

[20]  Jacqueline Alderson,et al.  Elbow joint kinematics during cricket bowling using magneto-inertial sensors: A feasibility study , 2018, Journal of sports sciences.

[21]  Behnam Behnoush,et al.  Smartphone and Universal Goniometer for Measurement of Elbow Joint Motions: A Comparative Study , 2016, Asian journal of sports medicine.

[22]  Raja Ariffin Raja Ghazilla,et al.  Reviews on Various Inertial Measurement Unit (IMU) Sensor Applications , 2013, SiPS 2013.

[23]  J. L. Garrido-Castro,et al.  Concurrent Validity and Reliability of an Inertial Measurement Unit for the Assessment of Craniocervical Range of Motion in Subjects with Cerebral Palsy , 2020, Diagnostics.

[24]  Bruce Elliott,et al.  A CALIBRATION PROCEDURE FOR MIMU SENSORS ALLOWING FOR THE CALCULATION OF ELBOW ANGLES , 2015 .

[25]  Yvan Petit,et al.  Validity of Goniometric Elbow Measurements: Comparative Study with a Radiographic Method , 2011, Clinical orthopaedics and related research.

[26]  M. Muhlenhaupt Measurement of Joint Motion: A Guide to Goniometry , 1986 .

[27]  Nurettin Özgür Doğan,et al.  Bland-Altman analysis: A paradigm to understand correlation and agreement , 2018, Turkish journal of emergency medicine.

[28]  Yong Yan,et al.  Quantitative Assessment of Upper Limb Motion in Neurorehabilitation Utilizing Inertial Sensors , 2015, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[29]  Fredrik Öhberg,et al.  A new way of assessing arm function in activity using kinematic Exposure Variation Analysis and portable inertial sensors--A validity study. , 2016, Manual therapy.

[30]  S L Wolf,et al.  Upper extremity joint movement: comparison of two measurement devices. , 1989, Archives of physical medicine and rehabilitation.

[31]  T. Vos,et al.  Global, regional, and national incidence and prevalence, and years lived with disability for 328 diseases and injuries in 195 countries, 1990-2016: a systematic analysis for the Global Burden of Disease Study 2016 , 2017 .

[32]  Tetsuya Hirotomi,et al.  Using Motion Sensors to Support Seating and Positioning Assessments of Individuals with Neurological Disorders , 2015, DSAI.

[33]  Abraham Otero,et al.  An Inexpensive and Easy to Use Cervical Range of Motion Measurement Solution Using Inertial Sensors , 2018, Sensors.

[34]  Cristina Roldán-Jiménez,et al.  Age-related changes analyzing shoulder kinematics by means of inertial sensors. , 2016, Clinical biomechanics.

[35]  Haniye Sadat Sajadi,et al.  Global, regional, and national incidence, prevalence, and years lived with disability for 354 diseases and injuries for 195 countries and territories, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017 , 2018, The Lancet.

[36]  Valentina Camomilla,et al.  Trends Supporting the In-Field Use of Wearable Inertial Sensors for Sport Performance Evaluation: A Systematic Review , 2018, Sensors.

[37]  Thomas Seel,et al.  An Inertial Sensor-based Trigger Algorithm for Functional Electrical Stimulation-Assisted Swimming in Paraplegics , 2019 .

[38]  Edward J. Harvey,et al.  The smartphone inclinometer: A new tool to determine elbow range of motion? , 2018, European Journal of Orthopaedic Surgery & Traumatology.

[39]  Kun-Hui Chen,et al.  Wearable Sensor-Based Rehabilitation Exercise Assessment for Knee Osteoarthritis , 2015, Sensors.

[40]  Chen Feng,et al.  Upper limb motion tracking with the integration of IMU and Kinect , 2015, Neurocomputing.

[41]  Christian Larue,et al.  Validation of inertial measurement units with an optoelectronic system for whole-body motion analysis , 2017, Medical & Biological Engineering & Computing.

[42]  Leon Straker,et al.  Inter-tester reliability of scapular position in junior elite swimmers , 2004 .

[43]  Kyle J Boddy,et al.  Exploring wearable sensors as an alternative to marker-based motion capture in the pitching delivery , 2019, PeerJ.