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- PublicationAn interactive exercise biofeedback Android application utilizing a single inertial measurement unit to support joint replacement rehabilitationBoomerang Ortho is an Android application developed with the aim to better support patients in their exercise rehabilitation program following total knee replacement. The use of a single inertial measurement unit (IMU) attached to the lower leg allows for classification of exercise technique, real-time biofeedback, and both self and remote monitoring of patient data. The prototype application for demonstration is currently undergoing pilot testing prior to an assessment of impact on clinical outcome.
- PublicationEvaluating the use of machine learning in the assessment of joint angle using a single inertial sensorIntroduction: Joint angle measurement is an important objective marker in rehabilitation. Inertial measurement units may provide an accurate and reliable method of joint angle assessment. The objective of this study was to assess whether a single sensor with the application of machine learning algorithms could accurately measure hip and knee joint angle, and investigate the effect of inertial measurement unit orientation algorithms and person-specific variables on accuracy. Methods: Fourteen healthy participants completed eight rehabilitation exercises with kinematic data captured by a 3D motion capture system, used as the reference standard, and a wearable inertial measurement unit. Joint angle was calculated from the single inertial measurement unit using four machine learning models, and was compared to the reference standard to evaluate accuracy. Results: Average root-mean-squared error for the best performing algorithms across all exercises was 4.81 (SD ¼ 1.89). The use of an inertial measurement unit orientation algorithm as a pre-processing step improved accuracy; however, the addition of person-specific variables increased error with average RMSE 4.99 (SD ¼ 1.83). Conclusions: Hip and knee joint angle can be measured with a good degree of accuracy from a single inertial measurement unit using machine learning. This offers the ability to monitor and record dynamic joint angle with a single sensor outside of the clinic.
- PublicationThe development and evaluation of a connected health exercise biofeedback platform to support orthopaedic rehabilitation(University College Dublin. School of Public Health, Physiotherapy and Sports Science, 2020)Home exercise programmes play a key role in patient rehabilitation following knee replacement surgery. The demand for these operations is increasing significantly due to the aging population, and in an attempt to reduce costs to healthcare providers, growing numbers of patients are being discharged directly home from hospital. These patients are typically provided with a home exercise programme which they are expected to complete on a routine basis, placing greater responsibility on the self-management skills of the patient. However, adherence to home exercise programmes is poor, with many reasons for lack of engagement, leading to sub-optimal rehabilitation outcomes and negative implications for the healthcare provider. Connected health technologies utilising ubiquitous mobile devices, alongside sensing platforms such as inertial measurement units, can be used to provide exercise biofeedback to patients in an automated manner. By harnessing machine learning to interpret sensor data captured during the exercise programme, the patient and clinician can receive personalised, accurate and timely information to support the rehabilitation process. Despite a number of systems being developed, acceptance and uptake remains poor. This is partly due to the lack of technical and user evaluation being undertaken, limiting the understanding of key components such as usability, feasibility and functionality. Hence, there is a need to thoroughly evaluate any newly developed systems from an early stage with key stakeholders. The focus of this programme of research was to develop and evaluate a prototype connected health exercise biofeedback system comprising of an Android tablet application and single inertial sensor for use in home exercise rehabilitation following knee replacement surgery. The research presented throughout this thesis suggests that whilst the developed prototype was easy to use and may aid engagement, there are numerous technical challenges in providing technique-based biofeedback using supervised machine learning. Furthermore, this thesis provides preliminary evidence that a more suitable and feasible method of providing feedback may be based on measuring joint angle, as opposed to the current classification approach.
- PublicationRehabilitation Exercise Segmentation for Autonomous Biofeedback Systems with ConvFSMSegmenting physical movements is a key step for any accelerometry-based autonomous biofeedback system oriented to rehabilitation and physiotherapy activities. Fundamentally, this can be reduced to the detection of recurrent patterns, also called motion primitives, in longer inertial signals. Most of the solutions developed in the literature require extensive domain knowledge, or are incapable of scaling to complex motion patterns and new exercises. In this paper, we explore the capabilities of inertial measurement units for the segmentation of upper limb rehabilitation exercises. To do so, we introduce a novel segmentation technique based on Convolutional Neural Networks and Finite State Machines, called ConvFSM. ConvFSM is able to isolate motion primitives from raw streaming data, using very little domain knowledge. We also investigate different combinations of sensors, in order to identify the most effective and flexible setup that could fit a home-based rehabilitation feedback system. Experimental results are presented, based on a dataset obtained from a combination of common upper limb and lower limb exercises.
359Scopus© Citations 5
- PublicationReliability, Validity and Utility of Inertial Sensor Systems for Postural Control Assessment in Sport Science and Medicine Applications: A Systematic ReviewBackground Recent advances in mobile sensing and computing technology have provided a means to objectively and unobtrusively quantify postural control. This has resulted in the rapid development and evaluation of a series of wearable inertial sensor-based assessments. However, the validity, reliability and clinical utility of such systems is not fully understood. Objectives This systematic review aims to synthesise and evaluate studies that have investigated the ability of wearable inertial sensor systems to validly and reliably quantify postural control performance in sports science and medicine applications. Methods A systematic search strategy utilising the PRISMA guidelines was employed to identify eligible articles through ScienceDirect, Embase and PubMed databases. In total, 47 articles met the inclusion criteria and were evaluated and qualitatively synthesised under two main headings: measurement validity and measurement reliability. Furthermore, studies that investigated the utility of these systems in clinical populations were summarised and discussed. Results After duplicate removal, 4374 articles were identified with the search strategy, with 47 papers included in the final review. In total, 28 studies investigated validity in healthy populations, and 15 studies investigated validity in clinical populations; 13 investigated the measurement reliability of these sensor-based systems. Conclusions The application of wearable inertial sensors for sports science and medicine postural control applications is an evolving field. To date, research has primarily focused on evaluating the validity and reliability of a heterogeneous set of assessment protocols, in a laboratory environment. While researchers have begun to investigate their utility in clinical use cases such as concussion and musculoskeletal injury, most studies have leveraged small sample sizes, are of low quality and use a variety of descriptive variables, assessment protocols and sensor-mounting locations. Future research should evaluate the clinical utility of these systems in large high-quality prospective cohort studies to establish the role they may play in injury risk identification, diagnosis and management. This systematic review was registered with the International Prospective Register of Systematic Reviews on 10 August 2018 (PROSPERO registration: CRD42018106363): https://www.crd.york.ac.uk/PROSPERO/display_record.php?RecordID=106363.
905Scopus© Citations 40
- PublicationClinician perceptions of a prototype wearable exercise biofeedback system for orthopaedic rehabilitation: a qualitative explorationObjectives: This study explores the opinions of orthopaedic healthcare professionals regarding the opportunities and challenges of using wearable technology in rehabilitation. It continues to assess the perceived impact of an exemplar exercise biofeedback system that incorporates wearable sensing, involving the clinician in the user-centred design process, a valuable step in ensuring ease of implementation, sustained engagement and clinical relevance. Design: This is a qualitative study consisting of one-to-one semi-structured interviews, including a demonstration of a prototype wearable exercise biofeedback system. Interviews were audio-recorded and transcribed, with thematic analysis conducted of all transcripts. Setting: The study was conducted in the orthopaedic department of an acute private hospital. Participants Ten clinicians from a multidisciplinary team of healthcare professionals involved in the orthopaedic rehabilitation pathway participated in the study. Results: Participants reported that there is currently a challenge in gathering timely and objective data for the monitoring of patients in orthopaedic rehabilitation. While there are challenges in ensuring reliability and engagement of biofeedback systems, clinicians perceive significant value in the use of wearable biofeedback systems such as the exemplar demonstrated for use following total knee replacement. Conclusions: Clinicians see an opportunity for wearable technology to continuously track data in real-time, and feel that feedback provided to users regarding exercise technique and adherence can further support the patient at home, although there are clear design and implementation challenges relating to ensuring technical accuracy and tailoring rehabilitation to the individual. There was perceived value in the prototype system demonstrated to participants which supports the ongoing development of such exercise biofeedback platforms.
297Scopus© Citations 14
- PublicationAutomatic Classification of Knee Rehabilitation Exercises Using a Single Inertial Sensor: a Case StudyInertial measurement units have the ability to accurately record the acceleration and angular velocity of human limb segments during discrete joint movements. These movements are commonly used in exercise rehabilitation programmes following orthopaedic surgery such as total knee replacement. This provides the potential for a biofeedback system with data mining technique for patients undertaking exercises at home without physician supervision. We propose to use machine learning techniques to automatically analyse inertial measurement unit data collected during these exercises, and then assess whether each repetition of the exercise was executed correctly or not. Our approach consists of two main phases: signal segmentation, and segment classification. Accurate pre-processing and feature extraction are paramount topics in order for the technique to work. In this paper, we present a classification method for unsupervised rehabilitation exercises, based on a segmentation process that extracts repetitions from a longer signal activity. The results obtained from experimental datasets of both clinical and healthy subjects, for a set of 4 knee exercises commonly used in rehabilitation, are very promising.
468Scopus© Citations 18
- PublicationPatient Involvement With Home-Based Exercise Programs: Can Connected Health Interventions Influence Adherence?Adherence to home exercise in rehabilitation is a significant problem, with estimates of nonadherence as high as 50%, potentially having a detrimental effect on clinical outcomes. In this viewpoint, we discuss the many reasons why patients may not adhere to a prescribed exercise program and explore how connected health technologies have the ability to offer numerous interventions to enhance adherence; however, it is hard to judge the efficacy of these interventions without a robust measurement tool. We highlight how well-designed connected health technologies, such as the use of mobile devices, including mobile phones and tablets, as well as inertial measurement units, provide us with the opportunity to better support the patient and clinician, with a data-driven approach that incorporates features designed to increase adherence to exercise such as coaching, self-monitoring and education, as well as remotely monitor adherence rates more objectively.
460Scopus© Citations 107