Now showing 1 - 4 of 4
  • Publication
    Quantitative assessment of therapeutic interventions for neurological disorders using electromyography and accelerometery
    (University College Dublin. School of Electrical and Electronic Engineering, 2022) ;
    Neurological disorders are the leading cause of disability and the second leading cause of death worldwide. Increasing global populations and life expectancies are resulting in greater numbers of individuals impacted by various neurological disorders. A wide variety of treatment options are currently available, however, due to restrictions in clinical time, subjective rating of motor function, inter-patient variability and daily fluctuations of motor symptoms, the optimal treatment is often difficult to achieve. Prompted by these limitations, there is growing interest in the use of wearable sensors for the quantification of movement and to improve the understanding of the pathological physiological processes that impair motor function. The development and application of signal processing based algorithms for the quantification of movement and motor control strategies in neurological disorders could further advance the clinical understanding of the changes that occur throughout disease progression as well as to provide quantitative measures of treatment efficacy. The focus of this thesis is therefore to explore the application of signal processing methods to surface electromyography (sEMG) and accelerometery (ACC) to quantify the effects of therapeutic interventions for neurological disorders, namely Parkinson’s Disease (PD) and stroke. In conclusion, the findings in this thesis demonstrate the ability for wearable sensing technologies to provide clinically relevant information for the assessment of therapeutic interventions as well as illustrating their ability to be used as biomarkers in the design and advancement of therapeutic devices for the rehabilitation and treatment of neurological disorders. Since the sudden onset of the COVID-19 pandemic, there is a substantially increased need for remote healthcare solutions, particularly those providing quantitative clinical measures. This thesis demonstrates the ability for wearable sensors to quantify the effects of therapeutic interventions and may help to reduce the current barriers that have so far limited the widespread uptake of quantitative sensors in the clinic.
  • Publication
    Quantitative clinical assessment of motor function during and following LSVT-BIG® therapy
    Background LSVT-BIG® is an intensively delivered, amplitude-oriented exercise therapy reported to improve mobility in individuals with Parkinson’s disease (PD). However, questions remain surrounding the efficacy of LSVT-BIG® when compared with similar exercise therapies. Instrumented clinical tests using body-worn sensors can provide a means to objectively monitor patient progression with therapy by quantifying features of motor function, yet research exploring the feasibility of this approach has been limited to date. The aim of this study was to use accelerometer-instrumented clinical tests to quantify features of gait, balance and fine motor control in individuals with PD, in order to examine motor function during and following LSVT-BIG® therapy. Methods Twelve individuals with PD undergoing LSVT-BIG® therapy, eight non-exercising PD controls and 14 healthy controls were recruited to participate in the study. Functional mobility was examined using features derived from accelerometry recorded during five instrumented clinical tests: 10 m walk, Timed-Up-and-Go, Sit-to-Stand, quiet stance, and finger tapping. PD subjects undergoing therapy were assessed before, each week during, and up to 13 weeks following LSVT-BIG®. Results Accelerometry data captured significant improvements in 10 m walk and Timed-Up-and-Go times with LSVT-BIG® (p <  0.001), accompanied by increased stride length. Temporal features of the gait cycle were significantly lower following therapy, though no change was observed with measures of asymmetry or stride variance. The total number of Sit-to-Stand transitions significantly increased with LSVT-BIG® (p <  0.001), corresponding to a significant reduction of time spent in each phase of the Sit-to-Stand cycle. No change in measures related to postural or fine motor control was observed with LSVT-BIG®. PD subjects undergoing LSVT-BIG® showed significant improvements in 10 m walk (p <  0.001) and Timed-Up-and-Go times (p = 0.004) over a four-week period when compared to non-exercising PD controls, who showed no week-to-week improvement in any task examined. Conclusions This study demonstrates the potential for wearable sensors to objectively quantify changes in motor function in response to therapeutic exercise interventions in PD. The observed improvements in accelerometer-derived features provide support for instrumenting gait and sit-to-stand tasks, and demonstrate a rescaling of the speed-amplitude relationship during gait in PD following LSVT-BIG®.
      15Scopus© Citations 12
  • Publication
    Gait Event Detection from Accelerometry using the Teager-Kaiser Energy Operator
    Objective: A novel method based on the application of the Teager-Kaiser Energy Operator is presented to estimate instances of initial contact (IC) and final contact (FC) from accelerometry during gait. The performance of the proposed method was evaluated against four existing gait event detection (GED) methods under three walking conditions designed to capture the variance of gait in real-world environments. Methods: A symmetric discrete approximation of the Teager-Kaiser energy operator was used to capture simultaneous amplitude and frequency modulations of the shank acceleration signal at IC and FC during flat treadmill walking, inclined treadmill walking, and flat indoor walking. Accuracy of estimated gait events were determined relative to gait events detected using force-sensitive resistors. The performance of the proposed algorithm was assessed against four established methods by comparing mean-absolute error, sensitivity, precision and F1-score values. Results: The proposed method demonstrated high accuracy for GED in all walking conditions, yielding higher F1-scores (IC: >0.98, FC: >0.9) and lower mean-absolute errors (IC: <0.018s, FC: <0.039s) than other methods examined. Estimated ICs from shank-based methods tended to exhibit unimodal distributions preceding the force-sensitive resistor estimated ICs, whereas estimated gait events for waist-based methods had quasi-uniform random distributions and lower accuracy. Conclusion: Compared to established gait event detection methods, the proposed method yielded comparably high accuracy for IC detection, and was more accurate than all other methods examined for FC detection. Significance: The results support the use of the Teager-Kaiser Energy Operator for accurate automated GED across a range of walking conditions.
      742Scopus© Citations 17