Three-Dimensional Measurement of Spinal Kinematics and Whole-Body Activity Recognition

Back pain is one of the leading causes of disability, being the second largest contributor to work days missed, and sixth largest disability when expressed in terms of an overall burden measured in disability-adjusted life years. Back pain is a large economic burden, where indirect costs from work days missed far outweigh the direct costs due to treatment. As such, it is economically better to prevent back pain from occurring, rather than treating it after the onset of pain. Some risk factors of back pain which can be monitored to help in the prevention of pain include poor posture and prolonged sedentary behaviour. Inactivity, being similar to prolonged sedentary behaviour, is also a risk factor for some of the major non-communicable diseases responsible for death including heart diseases, stroke, breast and colon cancer, and diabetes. The aims of the thesis were to: 1) compare a number of commonly used measurement systems, including a low-cost wearable sensor, in their ability to measure motion typically seen in the human spine; 2) develop an activity classification model capable of predicting everyday activities including standing, sitting, lying, and walking; 3) create a new, inexpensive device that can simultaneously track user spine posture/kinematics and activity; and 4) validate the device to have accuracy within ±5° for spine posture, and an average positive activity classification rate of 90% or above. This research demonstrates the accuracy of a low-cost wearable sensor in its ability to track motion similar to that of the human spine under typical conditions and compare this to more expensive systems. Using two accelerometers and machine learning, a new activity recognition model was created with the ability to track 13 distinct activities commonly used in daily living, being: standing, sitting, prone, supine, right-side, and left-side lying, walking, jogging, jumping, stair ascending, stair descending, walking on an incline, and transitions. From this new knowledge, a new concept inertial-sensor-based device was created with the capabilities of measuring spinal kinematics and whole-body activity tracking. The device has been developed to measure spinal motions with mean errors of ±2.5°, and therefore meeting the aim to have an accuracy within ±5°, while also showing that the more superior the position on the spine an inertial sensor is placed, the higher the errors in measurement. The device can also predict standing, sitting, lying, and walking with an average accuracy of 95.6%, and therefore above the desired accuracy of 90%. When including all activities, the classifier has an average accuracy of 90.3%. To reduce the global effect of back pain, the developed device has the capabilities to aid in the prevention, management, and rehabilitation of back pain by focussing on two risk factors: poor posture and inactivity. For use in this research, the definition of a good posture is one that compromises between minimising spinal load and minimise muscle activity, therefore a poor posture is one that doesn’t adhere to this requirement which could significantly increase the risk of the onset of back pain. For widespread use, the device created in this research has been developed to be as inexpensive as possible. To meet these goals, the future work of the device has been outlined, including size and cost reduction, as well as increasing the aesthetic appeal, thus making it a more appealing product to the general population.Thesis (Ph.D.) -- University of Adelaide, School of Mechanical Engineering, 201

Quantification of knee extensor muscle forces: a multimodality approach

Given the growing interest of using musculoskeletal (MSK) models in a large number of clinical applications for quantifying the internal loading of the human MSK system, verification and validation of the model’s predictions, especially at the knee joint, have remained as one of the biggest challenges in the use of the models as clinical tools. This thesis proposes a methodology for more accurate quantification of knee extensor forces by exploring different experimental and modelling techniques that can be used to enhance the process of verification and validation of the knee joint model within the MSK models for transforming the models to a viable clinical tool. In this methodology, an experimental protocol was developed for simultaneous measurement of the knee joint motion, torques, external forces and muscular activation during an isolated knee extension exercise. This experimental protocol was tested on a cohort of 11 male subjects and the measurements were used to quantify knee extensor forces using two different MSK models representing a simplified model of the knee extensor mechanism and a previously-developed three-dimensional MSK model of the lower limb. The quantified knee extensor forces from the MSK models were then compared to evaluate the performance of the models for quantifying knee extensor forces. The MSK models were also used to investigate the sensitivity of the calculated knee extensor forces to key modelling parameters of the knee including the method of quantifying the knee centre of rotation and the effect of joint translation during motion. In addition, the feasibility of an emerging ultrasound-based imaging technique (shear wave elastography) for direct quantification of the physiologically-relevant musculotendon forces was investigated. The results in this thesis showed that a simplified model of the knee can be reliably used during a controlled planar activity as a computationally-fast and effective tool for hierarchical verification of the knee joint model in optimisation-based large-scale MSK models to provide more confidence in the outputs of the models. Furthermore, the calculation of knee extensor muscle forces has been found to be sensitive to knee joint translation (moving centre of rotation of the knee), highlighting the importance of this modelling parameter for quantifying physiologically-realistic knee muscle forces in the MSK models. It was also demonstrated how the movement of the knee axis of rotation during motion can be used as an intuitive tool for understanding the functional anatomy of the knee joint. Moreover, the findings in this thesis indicated that the shear wave elastography technique can be potentially used as a novel method for direct quantification of the physiologically-relevant musculotendon forces for independent validation of the predictions of musculotendon forces from the MSK models.Open Acces

Quantification of performance analysis factors in front crawl swimming using micro electronics: a data rich system for swimming.

The aim of this study is to increase the depth of data available to swimming coaches in order to allow them to make more informed coaching decisions for their athletes in front crawl swimming. A coach’s job is to assist with various factors of an individual athlete to allow them to perform at an optimum level. The demands of the swimming coach require objective data on the swim performance in order to offer efficient solutions (Burkett and Mellifont, 2008). The main tools available to a coach are their observation and perceptions, however it is known that these used alone can often result in poor judgment. Technological progress has allowed video cameras to become an established technology for swim coaching and more recently when combined with software, for quantitative measurement of changes in technique. This has allowed assessment of swimming technique to be included in the more general discipline of sports performance analysis. Within swimming, coaches tend to observe from the pool edge, limiting vision of technique, but some employ underwater cameras to combat this limitation. Video cameras are a reliable and established technology for the measurement of kinematic parameters in sport, however, accelerometers are increasingly being employed due to their ease of use, performance, and comparatively low cost. Previous accelerometer based studies in swimming have tended to focus on easily observable factors such as stroke count, stroke rate and lap times. To create a coaching focused system, a solution to the problem of synchronising multiple accelerometers was developed using a maxima detection method. Results demonstrated the effectiveness of the method with 52 of 54 recorded data sets showing no time lag error and two tests showing an error of 0.04s. Inter-instrument and instrument-video correlations are all greater than r = .90 (p < .01), with inter-instrument precision (Root Mean Square Error; RMSE) ≈ .1ms−2, demonstrating the efficacy of the technique. To ensure the design was in line with coaches' expectations and with the ASA coaching guidelines, interviews were conducted with four ASA swim coaches. Results from this process identified the factors deemed important: lap time, velocity, stroke count, stroke rate, distance per stroke, body roll angle and the temporal aspects of the phases of the stroke. These factors generally agreed with the swimming literature but extended upon the general accelerometer system literature. Methods to measure these factors were then designed and recorded from swimmers. The data recorded from the multi-channel system was processed using software to extract and calculate temporal maxima and minima from the signal to calculate the factors deemed important to the coach. These factors were compared to video derived data to determine the validity and reliability of the system, all results were valid and reliable. From these validated factors additional factors were calculated, including, distance per stroke and index of coordination and the symmetry of these factors. The system was used to generate individual profiles for 12 front crawl swimmers. The system produced eight full profiles with no issues. Four profiles required individualisation in the processing algorithm for the phases of the stroke. This was found to be due to the way in which these particular swimmers varied in the way they fatigued. The outputs from previous systems have tended to be either too complicated for a coach to understand and interpret e.g. raw data (Ohgi et al. 2000), or quite basic in terms of output e.g. stroke rate and counts (Le Sage et al. 2011). This study has added to the current literature by developing a system capable of calculating and displaying a breadth of factors to a coach. The creation of this system has also created a biomechanical research tool for swimming, but the process and principles can be applied to other sports. The use of accelerometers was also shown to be particularly useful at recording temporal activities within sports activities. Using PC based processing allows for quick turnaround times in the processing of detailed results of performance. There has been substantial development of scientific knowledge in swimming, however, the exchange of knowledge between sport science and coaches still requires development (Reade et al. 2008; Williams and Kendall 2007). This system has started to help bridge the gap between science and coaching, however there is still substantial work needed. This includes a better understanding of the types of data needed, how these can be displayed and level of detail required by the coach to allow them to enact meaningful coaching programmes for their athletes

The efficacy of the load-velocity profile to predict one repetition maximum

Autoregulation is the process of acutely manipulating training variables in response to an individual’s fluctuations in strength and fatigue and is vital for optimising programming. Load-velocity profiles (LVPs) have been proposed as effective flexible programming strategies to optimise resistance training load (kg), often through the daily estimation of one repetition maximum (1RM). This PhD, therefore, adopted a pragmatic, mixed methods research design and followed an applied research model (ARMSS) to devise a series of studies to ascertain a novel, efficient, and valid approach to LVP-based 1RM prediction. Prior to choosing an autoregulatory method, strength and conditioning (S&C) practitioners must first determine an appropriate non-flexible programming strategy. A systematic review of literature revealed percentages of 1RM (% 1RM) as the superior method for increasing maximal strength (study one). After thematic analyses (study two) revealed barriers such as inaccurate 1RM predictions, time-costly protocols, and “iPad coaching” to the implementation of LVPs within practice; common velocity-based technology used by coaches; and the combination of ballistic and non-ballistic exercise when profiling, a new LVP method addressing these factors was devised in a key training, but under-researched exercise, the free-weight back squat. The new approach to LVP-based 1RM prediction developed from this thesis utilised the Gymaware linear-position transducer given its superior reliability and validity (study three); individualised profiling due to stronger load-velocity relationships and large between-participant variability observed (study four); ballistic (jump squat) exercise after larger mechanical output was revealed in 0-60% 1RM when compared to non-ballistic (back squat) (study five); a submaximal point of extrapolation (80% 1RM mean velocity) due to poor within-participant reliability of loads > 85% 1RM (study four); quadratic modelling (study four); and as few as four incremental loads. Results revealed this combination to be an effective method for estimating 1RM and autoregulating daily load for S&C coaches