Now showing 1 - 10 of 12
  • Publication
    Leveraging IMU Data for Accurate Exercise Performance Classification and Musculoskeletal Injury Risk Screening
    Inertial measurement units (IMUs) are becoming increasingly prevalent as a method for low cost and portable biomechanical analysis. However, to date they have not tended to be accepted into routine clinical practice. This is often due to the disconnect between translating the data collected by the sensors into meaningful and actionable information for end users. This paper outlines the work completed by our group in attempting to achieve this. We discuss the conceptual framework involved in our work, the methodological approach taken in analysing sensor signals and discuss possible application models. The work completed by our group indicates that IMU based systems have the potential to bridge the gap between laboratory and clinical movement analysis. Future work will focus on collecting a diverse range of movement data and using more sophisticated data analysis techniques to refine systems.
      815Scopus© Citations 26
  • Publication
    The limb movement analysis of rehabilitation exercises using wearable inertial sensors
    Due to no supervision of a therapist in home based exercise programs, inertial sensor based feedback systems which can accurately assess movement repetitions are urgently required. The synchronicity and the degrees of freedom both show that one movement might resemble another movement signal which is mixed in with another not precisely defined movement. Therefore, the data and feature selections are important for movement analysis. This paper explores the data and feature selection for the limb movement analysis of rehabilitation exercises. The results highlight that the classification accuracy is very sensitive to the mount location of the sensors. The results show that the use of 2 or 3 sensor units, the combination of acceleration and gyroscope data, and the feature sets combined by the statistical feature set with another type of feature, can significantly improve the classification accuracy rates. The results illustrate that acceleration data is more effective than gyroscope data for most of the movement analysis.
      292Scopus© Citations 15
  • Publication
    Physical Activity Monitoring in Patients with Neurological Disorders: A Review of Novel Body-Worn Devices
    Aim: The aim was to conduct a systematic review to examine the literature reporting the validityand reliability of wearable physical activity monitoring in individuals with neurologicaldisorders. Method: A systematic search of the literature was performed using a specific searchstrategy in PubMed and CINAHL. A search constraint of articles published in English, includinghuman participants, published between January 2008 and March 2017 was applied. Peerreviewedstudies which enrolled adult participants with any neurological disorder were included.For the studies which sought to explore the validity of activity monitors, the outcomesmeasured using the monitor were compared to a criterion measure of physical activity. Thestudies methodological quality was assessed using an adapted version of the Quality Assessmentof Diagnostic Accuracy Studies (QUADAS) framework. Data extracted from each studyincluded the following: characteristics of the study participants, study setting, devices used,study protocol/methods, outcomes measured, and the validity/reliability of measurementproduced. Results: Twenty-three studies examining the validity and reliability of 16 differentmonitors were included. The identified studies comprised participants with a range of differentdisorders of neurological origin. The available evidence suggests that biaxial or triaxialaccelerometer devices positioned around the ankle produce the most accurate step countmeasurements in patients with neurological disorders. The findings regarding the reliabilityand validity of activity counts and energy expenditure are largely inconclusive in this population.Discussion: Ankle-worn biaxial or triaxial accelerometer-type devices provide the mostaccurate measurement of physical activity. However, further work is required in this field before wearable activity monitoring can be more widely implemented clinically. Standardisedactivity monitoring protocols are required for implementing these devices in clinical trials andclinical practice, and consensus is required as to the reporting and interpretation of derivedvariables
      362Scopus© Citations 24
  • Publication
    Neuromuscular electrical stimulation in the treatment of knee osteoarthritis: a systematic review and meta-analysis
    Objective: To assess the effectiveness of surface neuromuscular electrical stimulation in the treatment of knee osteoarthritis. Design: Systematic review and meta-analysis of randomized controlled and controlled clinical trials Methods: Studies were identified from databases (MEDLINE, EMBASE, CINAHL, Sports Discus, PEDro and the Cochrane Library) searched to January 2011 using a battery of keywords. Two reviewers selected studies meeting inclusion criteria. The methodological quality of the included studies was assessed using the Thomas Test and the strength of the evidence was then graded using the Agency for Health Care Policy and Research guidelines. Data were pooled and meta-analyses were performed. Results: Nine randomized controlled trials and one controlled clinical trial, studying a total of 409 participants (n = 395 for randomized controlled trials, and n = 14 for controlled trial) with a diagnosis of osteoarthritis were included. Inconsistent evidence (level D) was found that neuromuscular electrical stimulation has a significant impact on measures of pain, function and quadriceps femoris muscle strength in knee osteoarthritis. Conclusion: The role of neuromuscular electrical stimulation in the treatment of knee osteoarthritis is ambiguous. Therefore, future work is needed in this field to clearly establish the role of neuromuscular electrical stimulation in this population.
      1177Scopus© Citations 47
  • Publication
    The Limb Movement Analysis of Rehabilitation Exercises using Wearable Inertial Sensors
    Due to no supervision of a therapist in home based exercise programs, inertial sensor based feedback systems which can accurately assess movement repetitions are urgently required. The synchronicity and the degrees of freedom both show that one movement might resemble another movement signal which is mixed in with another not precisely defined movement. Therefore, the data and feature selections are important for movement analysis. This paper explores the data and feature selection for the limb movement analysis of rehabilitation exercises. The results highlight that the classification accuracy is very sensitive to the mount location of the sensors. The results show that the use of 2 or 3 sensor units, the combination of acceleration and gyroscope data, and the feature sets combined by the statistical feature set with another type of feature, can significantly improve the classification accuracy rates. The results illustrate that acceleration data is more effective than gyroscope data for most of the movement analysis.
      470Scopus© Citations 15
  • Publication
    An investigation into the effects of neuromuscular electrical stimulation exercise in type 2 diabetes : a case study
    Exercise is a vital component in the management and prevention of type 2 diabetes (T2D). Both the American College of Sports Medicine (ACSM) and the American Diabetes Association (ADA) advocate exercise as a treatment method for T2D. However, given the benefits of engaging in physical activity, many T2D patients are often unable to partake in physical activity secondary to complications of their diabetes or other musculoskeletal problems. Neuromuscular electrical stimulation (NMES) exercise is a likely alternative for diabetic individuals who face barriers to physical activity. NMES has received much attention in recent years as a new form of inducing exercise. The ability of NMES to stimulate innervated muscle has resulted in it’s use as a training tool for individuals without neuromuscular pathology. Banerjee and colleagues showed that prolonged NMES exercise in sedentary adults resulted in significant improvements in maximal aerobic capacity, muscle strength and capacity for physical activity. The aim of this case study was to investigate the use of NMES exercise in T2D.
      1352
  • Publication
    The Use of Inertial Sensors for the Classification of Rehabilitation Exercises
    The benefits of exercise in rehabilitation after orthopaedic surgery or following a musculoskeletal injury has been widely established. Within a hospital or clinical environment, adherence levels to rehabilitation exercise programs are high due to the supervision of the patient during the rehabilitation process. However, adherence levels drop significantly when patients are asked to perform the program at home. This paper describes the use of simple inertial sensors for the purpose of developing a biofeedback system to monitor adherence to rehabilitation programs. The results show that a single sensor can accurately distinguish between seven commonly prescribed rehabilitation exercises with accuracies between 93% and 95%. Results also show that the use of multiple sensor units does not significantly improve results therefore suggesting that a single sensor unit can be used as an input to an exercise biofeedback system.
      564Scopus© Citations 18
  • Publication
    Towards fully instrumented and automated assessment of motor function tests
    Quantitative assessment of mobility and motor function is critical to our understanding and treatment of musculoskeletal and neurological diseases. Instrumented tests augment traditional approaches by moving from a single, often subjective, performance metric to multiple objective measures. In this study, we investigated ways of automatically capturing motor performance by leveraging data from a network of six wearable sensors worn at five different locations by 17 healthy volunteers while performing a battery of motor function tests. We developed a framework to segment motor tasks, e.g. walking and standing up, from 3D acceleration and angular velocity data, and extracted features. Results were compared to clinical test scores and manual annotations of the data. For the best performing sensors, we achieved a rate of correct classification of 82 to 100% and mean temporal accuracy of 0.1 to 0.6 s. We provided guidelines on sensor placement to maximize accuracy of the motor assessment, and a better interpretation of the data using our unsupervised subject-specific approach.
      413Scopus© Citations 4
  • Publication
    Proposed Design Approach for Interactive Feedback Technology Supports in Rehabilitation
    Despite the anticipated benefits associated with the use interactive feedback technology, the adoption of these technologies into clinical practice has not yet been widespread. Initial usability studies and anecdotal evidence suggests that interactive feedback technology supports are effective in rehabilitation, yet they dontseem to satisfy the needs or the expectations of clinicians and patients. This paper outlines a proposed approach to the design,development and selection of interactive feedback supports for use in rehabilitation. The proposed process comprises seven stages; 1) Understand the problem, 2) Identify the benefits that a technology support may offer, 3) Devise a modified care pathway,4) Outline the design specification for an interactive technology support, 5) Ascertain whether other solutions exist, 6) Develop interactive feedback technology platform, 7) And finally evaluate.In this paper we outline this approach using orthopaedic rehabilitation as a specific example, however the same basic principles can be applied to many different rehabilitation groups.
      185Scopus© Citations 1
  • Publication
    Rehabilitation exercise assessment using inertial sensors: a cross-sectional analytical study
    Background: Accurate assessments of adherence and exercise performance are required in order to ensure that patients adhere to and perform their rehabilitation exercises correctly within the home environment. Inertial sensors have previously been advocated as a means of achieving these requirements, by using them as an input to an exercise biofeedback system. This research sought to investigate whether inertial sensors, and in particular a single sensor, can accurately classify exercise performance in patients performing lower limb exercises for rehabilitation purposes. Methods:Fifty-eight participants (19 male, 39 female, age: 53.9 +/- 8.5 years, height: 1.69 +/- 0.08 m, weight: 74.3 +/- 13.0 kg) performed ten repetitions of seven lower limb exercises (hip abduction, hip flexion, hip extension, knee extension, heel slide, straight leg raise, and inner range quadriceps). Three inertial sensor units, secured to the thigh, shin and foot of the leg being exercised, were used to acquire data during each exercise. Machine learning classification methods were applied to quantify the acquired data. Results:The classification methods achieved relatively high accuracy at distinguishing between correct and incorrect performance of an exercise using three, two, or one sensor while moderate efficacy scores were also achieved by the classifier when attempting to classify the particular error in exercise performance. Results also illustrated that a reduction in the number of inertial sensor units employed has little effect on the overall efficacy results. Conclusion:The results revealed that it is possible to classify lower limb exercise performance using inertial sensors with satisfactory levels of accuracy and reducing the number of sensors employed does not reduce the accuracy of the method
      316Scopus© Citations 91