Oonagh M. Giggins

Biofeedback in rehabilitation.

This paper reviews the literature relating to the biofeedback used in physical rehabilitation. The biofeedback methods used in rehabilitation are based on biomechanical measurements and measurements of the physiological systems of the body. The physiological systems of the body which can be measured to provide biofeedback are the neuromuscular system, the respiratory system and the cardiovascular system. Neuromuscular biofeedback methods include electromyography (EMG) biofeedback and real-time ultrasound imaging (RTUS) biofeedback. EMG biofeedback is the most widely investigated method of biofeedback and appears to be effective in the treatment of many musculoskeletal conditions and in post cardiovascular accident (CVA) rehabilitation. RTUS biofeedback has been demonstrated effective in the treatment of low back pain (LBP) and pelvic floor muscle dysfunction. Cardiovascular biofeedback methods have been shown to be effective in the treatment of a number of health conditions such as hypertension, heart failure, asthma, fibromyalgia and even psychological disorders however a systematic review in this field has yet to be conducted. Similarly, the number of large scale studies examining the use of respiratory biofeedback in rehabilitation is limited. Measurements of movement, postural control and force output can be made using a number of different devices and used to deliver biomechanical biofeedback. Inertial based sensing biofeedback is the most widely researched biomechanical biofeedback method, with a number of studies showing it to be effective in improving measures of balance in a number of populations. Other types of biomechanical biofeedback include force plate systems, electrogoniometry, pressure biofeedback and camera based systems however the evidence for these is limited. Biofeedback is generally delivered using visual displays, acoustic or haptic signals, however more recently virtual reality (VR) or exergaming technology have been used as biofeedback signals. VR and exergaming technology have been primarily investigated in post-CVA rehabilitation, however, more recent work has shown this type of biofeedback to be effective in improving exercise technique in musculoskeletal populations. While a number of studies in this area have been conducted, further large scale studies and reviews investigating different biofeedback applications in different clinical populations are required.

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.

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.

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 validity and reliability of wearable physical activity monitoring in individuals with neurological disorders. Method: A systematic search of the literature was performed using a specific search strategy in PubMed and CINAHL. A search constraint of articles published in English, including human participants, published between January 2008 and March 2017 was applied. Peer-reviewed studies which enrolled adult participants with any neurological disorder were included. For the studies which sought to explore the validity of activity monitors, the outcomes measured using the monitor were compared to a criterion measure of physical activity. The studies’ methodological quality was assessed using an adapted version of the Quality Assessment of Diagnostic Accuracy Studies (QUADAS) framework. Data extracted from each study included the following: characteristics of the study participants, study setting, devices used, study protocol/methods, outcomes measured, and the validity/reliability of measurement produced. Results: Twenty-three studies examining the validity and reliability of 16 different monitors were included. The identified studies comprised participants with a range of different disorders of neurological origin. The available evidence suggests that biaxial or triaxial accelerometer devices positioned around the ankle produce the most accurate step count measurements in patients with neurological disorders. The findings regarding the reliability and validity of activity counts and energy expenditure are largely inconclusive in this population. Discussion: Ankle-worn biaxial or triaxial accelerometer-type devices provide the most accurate measurement of physical activity. However, further work is required in this field before wearable activity monitoring can be more widely implemented clinically. Standardised activity monitoring protocols are required for implementing these devices in clinical trials and clinical practice, and consensus is required as to the reporting and interpretation of derived variables.

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 been accepted into routine clinical practice. This is often due to a 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. Our work indicates that IMU based systems have the potential to bridge the gap between laboratory and clinical movement analysis. Future studies will focus on collecting a diverse range of movement data and using more sophisticated data analysis techniques to refine systems.

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.

53 Using inertial sensors to quantify exercise performance in ankle rehabilitation: a case report

Background Neuromuscular training programmes have demonstrated success in the rehabilitation of ankle joint injuries, as well having proven success in reducing the risk of injury recurrence. However athlete motivation to do these exercises can be poor, with many athletes performing their exercises incorrectly when they are not supervised by their trainer/therapist. Objective The objective of this study was to investigate whether inertial sensors on the leg can be used to track exercise performance, and therefore be used to provide feedback on exercise performance. Design A single case study. Setting University research laboratory. Participants A healthy (no injuries/conditions that would affect postural stability/proprioception) adult male (age = 25 years, body mass = 75 kg, height = 189 cm) participated in this study. Assessment The participant performed ten repetitions of a single leg squat exercise (SLS). Skeleton and video data were recorded using a Microsoft Kinect for post-labelling of exercise performance. An inertial sensor (Shimmer, Dublin, Ireland) was secured to the participant’s left shank. The sensor contained a tri-axial accelerometer, gyroscope and magnetometer sampling at 51.2 Hz. Main outcome measurements The following signals were obtained from the sensor during the SLS; acceleration magnitude, pitch, roll and yaw. The skeleton and video data were labelled by a physiotherapist. The sensor signals were then inspected to determine if the various labels of SLS performance could be discriminated. Results The following sensors signals from the left shank can discriminate between the various labels of SLS performance; Acceleration magnitude, roll, pitch and accelerometer Z. Conclusions This preliminary analysis reveals that variations in SLS performance, which may indicate poor neuromuscular control of the ankle joint can be identified with an inertial sensors on the shin. While the results of this case study are encouraging further quantitative analyses of the data are required.