Evaluating Performance of the Single Leg Squat Exercise with a Single Inertial Measurement Unit

The single leg squat (SLS) is an important component of lower limb rehabilitation and injury risk screening tools. This study sought to investigate whether a single lumbar-worn IMU is capable of discriminating between correct and incorrect performance of the SLS. Nineteen healthy volunteers (15 males, 4 females, age: 26.09±3.98 years, height: 1.75±0.14m, body mass: 75.2±14.2kg) were fitted with a single IMU on the lumbar spine and asked to perform 10 left leg SLS. These repetitions were recorded and labelled by a chartered physiotherapist. Features were extracted from the labelled sensor data. These features were used to train and evaluate a random-forests classifier. The system achieved an average of 92% accuracy, 78% sensitivity and 97% specificity. These results indicate that a single IMU has the potential to differentiate between a correctly and incorrectly completed SLS. This may allow such devices to be used by clinicians to help track rehabilitation of patients and screen for potential injury risks. Furthermore, the classifier described may be a useful input to an exercise biofeedback application

Evaluating Performance of the Single Leg Squat Exercise with a Single Inertial Measurement Unit

The single leg squat (SLS) is an important component of lower limb rehabilitation and injury risk screening tools. This study sought to investigate whether a single lumbar-worn IMU is capable of discriminating between correct and incorrect performance of the SLS. Nineteen healthy volunteers (15 males, 4 females, age: 26.09±3.98 years, height: 1.75±0.14m, body mass: 75.2±14.2kg) were fitted with a single IMU on the lumbar spine and asked to perform 10 left leg SLS. These repetitions were recorded and labelled by a chartered physiotherapist. Features were extracted from the labelled sensor data. These features were used to train and evaluate a random-forests classifier. The system achieved an average of 92% accuracy, 78% sensitivity and 97% specificity. These results indicate that a single IMU has the potential to differentiate between a correctly and incorrectly completed SLS. This may allow such devices to be used by clinicians to help track rehabilitation of patients and screen for potential injury risks. Furthermore, the classifier described may be a useful input to an exercise biofeedback application

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

The purpose of this study was to evaluate the functionality, accuracy, and usability of a novel smart exercise application (SEA). The functionality such as counting lunges, providing task-related auditory feedback, and testing short-term memory was examined while thirteen young adults (six men, age 25.4 ± 8.3 years) performed the lunge exercise with the SEA. The accuracy of logged motion data including angles and accelerations were also tested. Another twenty-five participants (11 men, age 23.2 ± 5.7 years) evaluated the usability of the SEA interest, motivation, convenience, and strength/cognitive benefit via a questionnaire. The SEA assessed the lunge motion correctly, provided auditory feedback, and tested users’ short-term memory as required. High correlations (r = 0.90 to 0.99) with low RMSE (4.85 ̊ for direction angle, 0.13 to 0.22 m/ s2 for acceleration) were observed between the sensor output and the reference output. Bland-Altman plot also showed a low discrepancy between each of the two measures. Most participants positively answered all questions about interest (60%), motivation (40%), convenience (80%), strength benefits (92%), and cognitive benefits (88%) of the SEA. The SEA demonstrated accurate kinematic assessment of accelerations and directions, assessed the lunge motion correctly, and created the appropriate auditory feedback on the short- term memory task. The high rate of positive responses suggested the potential of the application in future use

Inertial sensors-based lower-limb rehabilitation assessment: A comprehensive evaluation of gait, kinematic and statistical metrics

Analysis of biomechanics is frequently used in both clinical and sporting practice in order to assess human motion and their performance of defined tasks. Whilst camera-based motion capture systems have long been regarded as the ‘Gold-standard’ for quantitative movement-based analysis, their application is not without limitations as regards potential sources of variability in measurements, high cost, and practicality of use for larger patient/subject groups. Another more practical approach, which presents itself as a viable solution to biomechanical motion capture and monitoring in sporting and patient groups, is through the use of small-size low-cost wearable Micro-ElectroMechanical Systems (MEMs)-based inertial sensors. The clinical aim of the present work is to evaluate rehabilitation progress following knee injuries, identifying a number of metrics measured via a wireless inertial sensing system. Several metrics in the time-domain have been considered to be reliable for measuring and quantifying patient progress across multiple exercises in different activities. This system was developed at the Tyndall National Institute and is able to provide a complete and accurate biomechanics assessment without the constraints of a motion capture laboratory. The results show that inertial sensors can be used for a quantitative assessment of knee joint mobility, providing valuable information to clinical experts as regards the trend of patient progress over the course of rehabilitation

Wearable inertial sensors and range of motion metrics in physical therapy remote support

Abstract. The practice of physiotherapy diagnoses patient ailments which are often treated by the daily repetition of prescribed physiotherapeutic exercise. The effectiveness of the exercise regime is dependent on regular daily repetition of the regime and the correct execution of the prescribed exercises. Patients often have issues learning unfamiliar exercises and performing the exercise with good technique. This design science research study examines a back squat classifier design to appraise patient exercise regime away from the physiotherapy practice. The scope of the exercise appraisal is limited to one exercise, the back squat. Kinematic data captured with commercial inertial sensors is presented to a small group of physiotherapists to illustrate the potential of the technology to measure range of motion (ROM) for back squat appraisal. Opinions are considered from two fields of physiotherapy, general musculoskeletal and post-operative rehabilitation. While the exercise classifier is considered not suitable for post-operative rehabilitation, the opinions expressed for use in general musculoskeletal physiotherapy are positive. Kinematic data captured with gyroscope sensors in the sagittal plane is analysed with Matlab to develop a method for back squat exercise recognition and appraisal. The artefact, a back squat classifier with appraisal features is constructed from Matlab scripts which are proven to be effective with kinematic data from a novice athlete

Gait characterization using wearable inertial sensors in healthy and pathological populations

Gait analysis is emerging as an effective tool to detect an incipient neurodegenerative disease or to monitor its progression. It has been shown that gait disturbances are an early indicator for cognitive impairments and can predict progression to neurodegenerative diseases. Furthermore, gait performance is a predictor of fall status, morbidity and mortality. Instrumented gait analysis provides quantitative measures to support the investigation of gait pathologies and the definition of targeted rehabilitation programs. In this framework, technologies such as inertial sensors are well accepted, and increasingly employed, as tools to characterize locomotion patterns and their variability in research settings. The general aim of this thesis is the evaluation, comparison and refinement of methods for gait characterization using magneto-inertial measurement units (MIMUs), in order to contribute to the migration of instrumented gait analysis from state of the art to state of the science (i.e.: from research towards its application in standard clinical practice). At first, methods for the estimation of spatio-temporal parameters during straight gait were investigated. Such parameters are in fact generally recognized as key metrics for an objective evaluation of gait and a quantitative assessment of clinical outcomes. Although several methods for their estimate have been proposed, few provided a thorough validation. Therefore an error analysis across different pathologies, multiple clinical centers and large sample size was conducted to further validate a previously presented method (TEADRIP). Results confirmed the applicability and robustness of the TEADRIP method. The combination of good performance, reliability and range of usage indicate that the TEADRIP method can be effectively adopted for gait spatio-temporal parameter estimation in the routine clinical practice. However, while traditionally gait analysis is applied to straight walking, several clinical motor tests include turns between straight gait segments. Furthermore, turning is used to evaluate subjects’ motor ability in more challenging circumstances. The second part of the research therefore headed towards the application of gait analysis on turning, both to segment it (i.e.: distinguish turns and straight walking bouts) and to specifically characterize it. Methods for turn identification based on a single MIMU attached to the trunk were implemented and their performance across pathological populations was evaluated. Focusing on Parkinson’s Disease (PD) subjects, turn characterization was also addressed in terms of onset and duration, using MIMUs positioned both on the trunk and on the ankles. Results showed that in PD population turn characterization with the sensors at the ankles lacks of precision, but that a single MIMU positioned on the low back is functional for turn identification. The development and validation of the methods considered in these works allowed for their application to clinical studies, in particular supporting the spatio-temporal parameters analysis in a PD treatment assessment and the investigation of turning characteristic in PD subjects with Freezing of Gait. In the first application, comparing the pre and post parameters it was possible to objectively determine the effectiveness of a rehabilitation treatment. In the second application, quantitative measures confirmed that in PD subjects with Freezing of Gait turning 360° in place is further compromised (and requires additional cognitive effort) compared to turning 180° while walking