Palsson T.S.1, Christensen S.W.1,2, Thomsen M.H.1, Hirata R.P.1
1Aalborg University, Department of Health Science and Technology (SMI), Aalborg, Denmark, 2University College North Denmark, Department of Physical Therapy, Aalborg, Denmark
Background: Assessing human performance in a clinical setting can be challenging as this requires the clinician to detect subtle variations in movement and range of motion with the naked eye. Research findings consistently demonstrate differences between clinical groups and healthy volunteers during standardized clinical tests but this is often done with the help of advanced research equipment which is regarded as ´gold standard´. Such assessment methods are however, both expensive, occupy space and require expertise which is why they are not feasible to use in a clinical setting. Modern smartphone technology has accelerometers and gyroscopes embedded, making it possible to measure range of motion (ROM). What is unclear is whether such technology can be used to assess ROM as consistently as gold standard research equipment.
Purpose: To measure ROM using a smartphone-based application and comparing it with the outcome of 3D-motion capture analysis.
Methods: The study included 30 healthy individuals (11 female) and was conducted in one session. Assessments of full active movement in transverse (rotation left and right) and sagittal (flexion and extension) planes were performed. A helmet was mounted on the subjects head which was securely fastened with a strap under the chin to minimize movement. Markers for motion capture analysis (Optotrak (NDI), Ontario, Canada) where placed on the side of the helmet and a smartphone (iPhone 6, Apple Inc.) was securely fastened on the top of the helmet on a flat surface. The smartphone recorded data using a beta version of Balancy (MEDEI, Aalborg, Denmark). Subjects were asked to slowly move the head into full range of left and right neck rotation as well as end of range flexion and extension. During head movements, recordings were made simultaneously with the camera system and the smartphone. A matlab-based algorithm was used to extract data from both modalities. A correlation coefficient was calculated to compare the outcomes from the different applications.
Results: A statistically significant difference (approx. 5 degrees on average) was found when comparing the outcome from the two modalities into all directions (P 0.05). A moderate to strong correlation was found between the measurements in both smartphone-based measurements and 3D-motion capture in all directions (ICC: 0.67 0.93, P 0.05).
Conclusion(s): This study showed that even though small but significant differences were found between a smartphone-based application and a ´gold standard´, there was a strong correlation between measurements in both applications. This indicates the utility of an affordable smartphone-based technology to assess active range of motion in a simple manner in clinical practice. An assessment of similar parameters in a clinical setting is warranted.
Implications: These preliminary results indicate the utility of using a smartphone-based application to assess joint movement in humans. The findings show that clinicians can make an accurate assessment of joint range of motion and thereby accurately quantify progression in relation to treatment.
Funding acknowledgements: Department of Health Science and Technology (SMI), Aalborg University supported the study. Authors have no conflicts of interest to report.
Topic: Human movement analysis
Ethics approval: This study was reported to the Danish Registry for Data Security. The study design did not require ethical approval.
All authors, affiliations and abstracts have been published as submitted.
