Reliability and concurrent validity of a Smartphone, bubble inclinometer and motion analysis system for measurement of hip joint range of motion

BackgroundAnalysis of lower limb exercises is traditionally completed with four distinct methods (i) 3D motion capture; (ii) depth-camera based systems (iii) visual analysis from a qualified exercise professional; (iv) self-assessment. Each method is associated with a number of limitations. ObjectiveThe aim of this systematic review is to synthesize and evaluate studies which have investigated the capacity for inertial measurement unit (IMU) technologies to assess movement quality in lower limb exercises. Data SourcesA systematic review of PubMed, ScienceDirect and Scopus was conducted. Study Eligibility CriteriaArticles written in English and published in the last 10 years which contained an IMU system for the analysis of repetition-based targeted lower limb exercises were included. Study Appraisal and Synthesis MethodsThe quality of included studies was measured using an adapted version of the STROBE assessment criteria for cross-sectional studies. The studies were categorised in to three groupings: exercise detection, movement classification or measurement validation. Each study was then qualitatively summarised. ResultsFrom the 2452 articles that were identified with the search strategies, 47 papers are included in this review. ConclusionsWearable inertial sensor systems for analysing lower limb exercises are a rapidly growing technology. Research over the past ten years has predominantly focused on validating measurements that the systems produce and classifying users' exercise quality. There have been very few user evaluation studies and no clinical trials in this field to date. Key PointsInertial measurement unit (IMU) systems have been extensively validated to successfully measure joint angle and temporal features during lower limb exercises.It is less understood if IMU systems can validly compute kinetic measures pertaining to lower limb exercises.IMU systems, which incorporate machine learning in to their data analysis pathways, have also been found to be effective in automated exercise detection and in classifying movement quality across a range of lower limb exercises.3

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“…2015 [50]. The results show that the IMU measures are able to capture the trend of patient improvement over the course of rehabilitation.…”

Section: Inclusion Criteriamentioning

confidence: 89%

“…goniometer for joint angle measurement) or gold standard biomechanical measurement tools (e.g. optoelectronic motion capture systems and force plates) [7,20,21,24,[38][39][40][41][42][43][44][45][46][47][48][49][50]. These studies are summarised in Table 7.…”

Section: Concurrent Validitymentioning

confidence: 99%

Wearable Inertial Sensor Systems for Lower Limb Exercise Detection and Evaluation: A Systematic Review

et al. 2018

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“…One example is a musculoskeletal system assessment. The authors of a similar study claim that a smartphone can be used to make reliable measurements of the hip joint ranges of motion consistent with examinations performed using a bubble inclinometer [10].…”

Section: Discussionmentioning

confidence: 80%

“…Jednym z przykładów ich zastosowania może być również ocena stanu narządu ruchu. Autorzy podobnych badań podają, że telefon komórkowy można wykorzystać do wiarygodnych, zgodnych ze specjalną poziomicą, pomiarów zakresu ruchów w stawie biodrowym [10].…”

Section: Discussionunclassified

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Using smartphones in the evaluation of spinal curvatures in a sagittal plane

Waś¹,

Sitarski²,

Ewertowska³

et al. 2016

Advances in Rehabilitation

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Introduction: In physiotherapeutic diagnostic processes, various tools and methods may be used. However, price and availability may limit their daily use in clinical practice. Therefore, the suggestion that smartphones with specific applications may be useful as diagnostic tools can be found in the literature. However, before using them in clinical practice, it is important to verify their reliability. The aim of the study was to evaluate the consistency of measurements of the curvatures in the sagittal plane performed with the Saunders digital inclinometer and a smartphone application. Materials and methods: The study included 40 subjects aged 22-39 years (23.0 ± 3.7). All subjects had sagittal spinal curvatures (sacral slope - SS, lumbar lordosis - L, thoracic kyphosis - K, upper thoracic kyphosis - K1, lower thoracic kyphosis - K2) measured in both standing and sitting positions with the Saunders digital inclinometer and a smartphone application. Results: In measurements performed with the use of the Saunders inclinometer and the smartphone, no significant differences were found between the size of the curvatures of the spine in a standing position (SS 19° ±8.2 vs. 17° ±8.4 p=0.3; L 32° ±11.1 vs. 29° ±10.3 p=0.2; K 43° ±8.4 vs. 41° ±8.1 p=0.2; K1 31° ±7.2 vs. 29.0° ±7.3; K2 11o ±7.4 vs. 11.0° ±6.7) and a sitting position (SS 3.75° ±8.9° p=0.8; L 5.8° ±9.06 vs. 5.2° ±8.5 p=0.75; K 40.0° ±8.1 vs. 36.7° ±7.9 p=0.6; K1 25.8° ±7.1 vs. 24.9° ±7.1; K2 14.5° ±9.9 vs. 11.5° ±9.4). Conclusions: The examination of spinal curvatures in the sagittal plane using Saunders digital inclinometer and a smartphone application allows researchers to obtain reliable results. Therefore, smartphones can be used for an objective evaluation of the musculoskeletal system in daily clinical practice.

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