Classification of lunge biomechanics with multiple and individual inertial measurement units

Abstract

Lunges are a common, compound lower limb resistance exercise. If completed with aberrant technique, the increased stress on the joints used may increase risk of injury. This study sought to first investigate the ability of inertial measurement units (IMUs), when used in isolation and combination, to (a) classify acceptable and aberrant lunge technique (b) classify exact deviations in lunge technique. We then sought to investigate the most important features and establish the minimum number of top-ranked features and decision trees that are needed to maintain maximal system classification efficacy. Eighty volunteers performed the lunge with acceptable form and 11 deviations. Five IMUs positioned on the lumbar spine, thighs, and shanks recorded these movements. Time and frequency domain features were extracted from the IMU data and used to train and test a variety of classifiers. A single-IMU system achieved 83% accuracy, 62% sensitivity, and 90% specificity in binary classification and a five-IMU system achieved 90% accuracy, 80% sensitivity, and 92% specificity. A five-IMU set-up can also detect specific deviations with 70% accuracy. System efficiency was improved and classification quality was maintained when using only 20% of the top-ranked features for training and testing classifiers.

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

“…Eleven studies investigated the utilisation of wearable IMU systems for quantifying exercise technique [3,19,23,64,[69][70][71][72][73][74][75]. Table 10 summarises the sensing set-ups, movement measure which was classified, methodology and performance metrics for each system identified in this area.…”

“…System efficacy can then be assessed when using data from multiple sensing units and each reduced combination of sensing units. This approach has been applied in movement classification and has shown promising results for single sensor systems in analysing early stage and late stage, lower limb rehabilitation exercises [3,70,71]. Using reduced sensing set-ups potentially reduces the total cost of systems and increases their practicality for end users.…”

“…Future work may also benefit from utilising deep learning techniques for classification such as the convolutional neural networks approach demonstrated by Veiga et al [68]. Such classification methodologies have recently been shown to have many benefits when compared to traditional machine learning classification techniques when analysing timeseries data, including reducing the risk of overfitting and improving system accuracy [77,78] (Table 10) [3,19,23,64,[70][71][72]74]. There is therefore potential to investigate movement classification with larger data sets and across a range of other exercises.…”

“…STROBE = STrengthening the Reporting of OBservational studies in Epidemiology. [69], [60] Single sensor units (n=19) [14], [52], [58], [24], [53], [7] [67], [70], [71], [72], [73], [74] Charlton et al…”

Smart exercise application to improve leg function and short-term memory through game-like lunge exercises

“…IMUs were also used combined with a vision depth sensor and increased the rate of classification of the lunge motion overall up to 100% (Chen, Jafari, & Kehtarnavaz, 2016;Gowing et al, 2014). In addition to sensor accuracy, one to five IMUs were used to discriminate between correct and incorrect performance of the lunges (O'Reilly, Whelan, Ward, Delahunt, & Caulfield, 2017;Whelan, O'Reilly, Ward, Delahunt, & Caulfield, 2016). All the above solutions can provide relatively accurate motion data.…”