Passive and active assistive writing devices in suppressing hand tremor

Patients with hand tremor disease frequently experience difficulties in performing their daily tasks, especially in handwriting activities. In order to prevent the ingestion of drugs and intervention of surgeries, a non-invasive solution was presented to improve their writing capabilities. In this study, there were two novel inventions of the hand-held device named as TREMORX and Active Assistive Writing Device (AAWD) with the approaches of passive and active elements respectively. For validation, the patient with tremor was assisted in using a normal pen and TREMORX to perform a handwriting task at the sitting and standing postures. For AAWD, the active suppressing element was the servo motor to control the hand tremor act on the writing tool tip and an accelerometer will measure the necessary parameters values for feedback control signal. The classic Proportional (P) controller and Proportional-Integral- Derivative (PID) were presented. The P controller was tuned with a meta-heuristic method by adjusting the parameters into several values to examine the response and robustness of the controller in suppressing the tremor. The evaluation was based on decreasing the coherence magnitude on the frequency response analysis. To optimise the performances, two types of Evolutionary Algorithms (EA) were employed which were Genetic Algorithm (GA) and Particle Swarm Optimisation (PSO). The optimisation techniques were integrated into the PID controller system to generate the optimum performances in controlling the tremor. For the simulation study, the parametric model representing the actual system of the AAWD was presented. The main objectives of this analysis were to determine the optimum value of PID parameters based on EA optimisation techniques. The determined parameters for both optimisations were then injected into the experimental environment to test and evaluate the performance of the controllers. The findings of the study exhibited that the PID controller for both EA optimisation provided excellent performances in suppressing the tremor signal act on the AAWD in comparison to the classic pure P controller. Based on the fitness evaluation, the GA optimisation significantly enhanced the PID controller performance compared to PSO optimisation. The handwriting performance using both TRREMORX and AAWD was recorded and from a visual justification, it showed that the quality of legibility was improved as compared with using normal handwriting devices. These outcomes provided an important contribution towards achieving novel methods in suppressing hand tremor by means of the invention of the handheld writing devices incorporated with intelligent control techniques

Fall Prevention Using Linear and Nonlinear Analyses and Perturbation Training Intervention

abstract: Injuries and death associated with fall incidences pose a significant burden to society, both in terms of human suffering and economic losses. The main aim of this dissertation is to study approaches that can reduce the risk of falls. One major subset of falls is falls due to neurodegenerative disorders such as Parkinson’s disease (PD). Freezing of gait (FOG) is a major cause of falls in this population. Therefore, a new FOG detection method using wavelet transform technique employing optimal sampling window size, update time, and sensor placements for identification of FOG events is created and validated in this dissertation. Another approach to reduce the risk of falls in PD patients is to correctly diagnose PD motor subtypes. PD can be further divided into two subtypes based on clinical features: tremor dominant (TD), and postural instability and gait difficulty (PIGD). PIGD subtype can place PD patients at a higher risk for falls compared to TD patients and, they have worse postural control in comparison to TD patients. Accordingly, correctly diagnosing subtypes can help caregivers to initiate early amenable interventions to reduce the risk of falls in PIGD patients. As such, a method using the standing center-of-pressure time series data has been developed to identify PD motor subtypes in this dissertation. Finally, an intervention method to improve dynamic stability was tested and validated. Unexpected perturbation-based training (PBT) is an intervention method which has shown promising results in regard to improving balance and reducing falls. Although PBT has shown promising results, the efficacy of such interventions is not well understood and evaluated. In other words, there is paucity of data revealing the effects of PBT on improving dynamic stability of walking and flexible gait adaptability. Therefore, the effects of three types of perturbation methods on improving dynamics stability was assessed. Treadmill delivered translational perturbations training improved dynamic stability, and adaptability of locomotor system in resisting perturbations while walking.Dissertation/ThesisDoctoral Dissertation Biomedical Engineering 201

Cortico-muscular coherence in sensorimotor synchronisation

This thesis sets out to investigate the neuro-muscular control mechanisms underlying the ubiquitous phenomenon of sensorimotor synchronisation (SMS). SMS is the coordination of movement to external rhythms, and is commonly observed in everyday life. A large body of research addresses the processes underlying SMS at the levels of behaviour and brain. Comparatively, little is known about the coupling between neural and behavioural processes, i.e. neuro-muscular processes. Here, the neuro-muscular processes underlying SMS were investigated in the form of cortico-muscular coherence measured based on Electroencephalography (EEG) and Electromyography (EMG) recorded in human healthy participants. These neuro-muscular processes were investigated at three levels of engagement: passive listening and observation of rhythms in the environment, imagined SMS, and executed SMS, which resulted in the testing of three hypotheses: (i) Rhythms in the environment, such as music, spontaneously modulate cortico-muscular coupling, (ii) Movement intention modulates cortico-muscular coupling, and (iii) Cortico-muscular coupling is dynamically modulated during SMS time-locked to the stimulus rhythm. These three hypotheses were tested through two studies that used Electroencephalography (EEG) and Electromyography (EMG) recordings to measure Cortico-muscular coherence (CMC). First, CMC was tested during passive music listening, to test whether temporal and spectral properties of music stimuli known to induce groove, i.e., the subjective experience of wanting to move, can spontaneously modulate the overall strength of the communication between the brain and the muscles. Second, imagined and executed movement synchronisation was used to investigate the role of movement intention and dynamics on CMC. The two studies indicate that both top-down, and somatosensory and/or proprioceptive processes modulate CMC during SMS tasks. Although CMC dynamics might be linked to movement dynamics, no direct correlation between movement performance and CMC was found. Furthermore, purely passive auditory or visual rhythmic stimulation did not affect CMC. Together, these findings thus indicate that movement intention and active engagement with rhythms in the environment might be critical in modulating CMC. Further investigations of the mechanisms and function of CMC are necessary, as they could have important implications for clinical and elderly populations, as well as athletes, where optimisation of motor control is necessary to compensate for impaired movement or to achieve elite performance

Effects of balance training on balance, gait and non-motor symptoms in individuals with Parkinson's disease

Postural instability (PI) is one of the most disabling symptoms of Parkinson’s disease (PD). PI is a well-known risk factor for falls in individuals with PD that worsens with disease progression. About 50-70% of people with PD fall once or more in a year, which is much higher than the 30% fall rate reported for community dwelling older individuals. Impaired balance associated with PI and fear of falling are factors related to decreased mobility and poor quality of life in individuals with PD. Several studies have examined the effects of exercise particularly strengthening and aerobic training on various motor and non-motor symptoms of PD. However to date, few studies have examined effects of balance specific interventions on balance, spatiotemporal gait, and non-motor symptoms such as fatigue, pain and depression. Moreover, none have used a commercially available device, Biodex Balance System (BSS) to implement a challenging balance training protocol. BSS consists of a moving platform that can be used to progressively challenge one’s balance while providing visual feedback. Finally, most of the previous studies did not report information pertaining to clinically meaningful changes in balance and its implications to physical function and quality of life in individuals with PD. The overall objective of this study was to evaluate whether short term progressively challenging balance specific training using the BSS improves balance, spatiotemporal gait and non-motor symptoms including fatigue, pain and depression in individuals with PD compared to usual non-progressive balance exercises. The central hypothesis is that challenging balance exercises, where individuals with PD are challenged out of their comfort zone for static and dynamic balance can significantly improve balance and spatiotemporal gait. Chapter 2 describes aims 1 and 2, utilizing 4 weeks of BBS balance training to determine changes in sway measures and spatio-temporal gait variables in individuals with PD. Ten individuals in a balance exercise group using the BSS and 10 individuals in general balance exercise group without Biodex (Non-BSS) completed the study. This study showed that 4 weeks of balance exercises using BSS resulted in significant within group improvement in sway area, center of pressure (CoP), path length in antero-posterior (AP) direction in the BSS group. We also found significant within group improvements in the balance measured by Berg Balance Scale, gait velocity, and step length in both groups. Additionally, we found significant within group improvements in functional scores measured by the Timed Up and Go and 6 Minute Walk Test in both groups. However, we did not find significant between group differences for any of the outcome variables. Due to technical failure in the system, we were not able to report force plate data from the non-BSS group. Chapter 3 describes aim 3, where 4 weeks of BSS training was utilized to determine changes in fatigue, pain, depression, fear of falling and quality of life in individuals with PD. Although motor symptoms of PD are described widely in the literature, and several studies report improvement in motor symptoms following various exercise trainings, little has been done to determine the efficacy of exercise interventions on the non - motor symptoms of PD. Aerobic exercise, strengthening, gait, tai-chi, qigong, and yoga therapy have been shown to improve motor deficits in PD. However, no study has examined the effects of balance training with BSS on non-motor features such as depression, fatigue, pain and fear of falling in individuals with PD. In our study, we determined the effects of balance training on non - motor symptoms of PD. The results demonstrated that 4 weeks of balance training resulted in a non-significant trend toward improvement in depression, pain, and fear of falling, and only the BSS training group demonstrated statistically significant improvement in fatigue. In summary, this dissertation work provides evidence that the use of the BSS is feasible, safe, and effective in improving balance, gait, and function in individuals with PD. However further study with a larger sample size, randomized control design, and biomechanical (force plate) data in both groups is required to better understand the role of challenging balance training in this population. The findings of this dissertation work have implications about designing future studies with specific intensity of balance exercises needed to make meaningful changes in balance, gait and non-motor symptoms of not only the individuals with PD but also in individuals with other neurological disorders resulting in PI

Development and Evaluation of AI-based Parkinson's Disease Related Motor Symptom Detection Algorithms

Parkinson's Disease (PD) is a chronic, progressive, neurodegenerative disorder that is typically characterized by a loss of (motor) function, increased slowness and rigidity. Due to a lack of feasible biomarkers, progression cannot easily be quantified with objective measures. For the same reason, neurologists have to revert to monitoring of (motor) symptoms (i.e. by means of subjective and often inaccurate patient diaries) in order to evaluate a medication's effectiveness. Replacing or supplementing these diaries with an automatic and objective assessment of symptoms and side effects could drastically reduce manual efforts and potentially help in personalizing and improving medication regime. In turn, appearance of symptoms could be reduced and the patient's quality of life increased. The objective of this thesis is two-fold: (1) development and improvement of algorithms for detecting PD related motor symptoms and (2) to develop a software framework for time series analysis

A Search For Principles of Basal Ganglia Function

The basal ganglia are a group of subcortical nuclei that contain about 100 million neurons in humans. Different modes of basal ganglia dysfunction lead to Parkinson's disease and Huntington's disease, which have debilitating motor and cognitive symptoms. However, despite intensive study, both the internal computational mechanisms of the basal ganglia, and their contribution to normal brain function, have been elusive. The goal of this thesis is to identify basic principles that underlie basal ganglia function, with a focus on signal representation, computation, dynamics, and plasticity. This process begins with a review of two current hypotheses of normal basal ganglia function, one being that they automatically select actions on the basis of past reinforcement, and the other that they compress cortical signals that tend to occur in conjunction with reinforcement. It is argued that a wide range of experimental data are consistent with these mechanisms operating in series, and that in this configuration, compression makes selection practical in natural environments. Although experimental work is outside the present scope, an experimental means of testing this proposal in the future is suggested. The remainder of the thesis builds on Eliasmith & Anderson's Neural Engineering Framework (NEF), which provides an integrated theoretical account of computation, representation, and dynamics in large neural circuits. The NEF provides considerable insight into basal ganglia function, but its explanatory power is potentially limited by two assumptions that the basal ganglia violate. First, like most large-network models, the NEF assumes that neurons integrate multiple synaptic inputs in a linear manner. However, synaptic integration in the basal ganglia is nonlinear in several respects. Three modes of nonlinearity are examined, including nonlinear interactions between dendritic branches, nonlinear integration within terminal branches, and nonlinear conductance-current relationships. The first mode is shown to affect neuron tuning. The other two modes are shown to enable alternative computational mechanisms that facilitate learning, and make computation more flexible, respectively. Secondly, while the NEF assumes that the feedforward dynamics of individual neurons are dominated by the dynamics of post-synaptic current, many basal ganglia neurons also exhibit prominent spike-generation dynamics, including adaptation, bursting, and hysterses. Of these, it is shown that the NEF theory of network dynamics applies fairly directly to certain cases of firing-rate adaptation. However, more complex dynamics, including nonlinear dynamics that are diverse across a population, can be described using the NEF equations for representation. In particular, a neuron's response can be characterized in terms of a more complex function that extends over both present and past inputs. It is therefore straightforward to apply NEF methods to interpret the effects of complex cell dynamics at the network level. The role of spike timing in basal ganglia function is also examined. Although the basal ganglia have been interpreted in the past to perform computations on the basis of mean firing rates (over windows of tens or hundreds of milliseconds) it has recently become clear that patterns of spikes on finer timescales are also functionally relevant. Past work has shown that precise spike times in sensory systems contain stimulus-related information, but there has been little study of how post-synaptic neurons might use this information. It is shown that essentially any neuron can use this information to perform flexible computations, and that these computations do not require spike timing that is very precise. As a consequence, irregular and highly-variable firing patterns can drive behaviour with which they have no detectable correlation. Most of the projection neurons in the basal ganglia are inhibitory, and the effect of one nucleus on another is classically interpreted as subtractive or divisive. Theoretically, very flexible computations can be performed within a projection if each presynaptic neuron can both excite and inhibit its targets, but this is hardly ever the case physiologically. However, it is shown here that equivalent computational flexibility is supported by inhibitory projections in the basal ganglia, as a simple consequence of inhibitory collaterals in the target nuclei. Finally, the relationship between population coding and synaptic plasticity is discussed. It is shown that Hebbian plasticity, in conjunction with lateral connections, determines both the dimension of the population code and the tuning of neuron responses within the coded space. These results permit a straightforward interpretation of the effects of synaptic plasticity on information processing at the network level. Together with the NEF, these new results provide a rich set of theoretical principles through which the dominant physiological factors that affect basal ganglia function can be more clearly understood

The computational neurology of active vision

In this thesis, we appeal to recent developments in theoretical neurobiology – namely, active inference – to understand the active visual system and its disorders. Chapter 1 reviews the neurobiology of active vision. This introduces some of the key conceptual themes around attention and inference that recur through subsequent chapters. Chapter 2 provides a technical overview of active inference, and its interpretation in terms of message passing between populations of neurons. Chapter 3 applies the material in Chapter 2 to provide a computational characterisation of the oculomotor system. This deals with two key challenges in active vision: deciding where to look, and working out how to look there. The homology between this message passing and the brain networks solving these inference problems provide a basis for in silico lesion experiments, and an account of the aberrant neural computations that give rise to clinical oculomotor signs (including internuclear ophthalmoplegia). Chapter 4 picks up on the role of uncertainty resolution in deciding where to look, and examines the role of beliefs about the quality (or precision) of data in perceptual inference. We illustrate how abnormal prior beliefs influence inferences about uncertainty and give rise to neuromodulatory changes and visual hallucinatory phenomena (of the sort associated with synucleinopathies). We then demonstrate how synthetic pharmacological perturbations that alter these neuromodulatory systems give rise to the oculomotor changes associated with drugs acting upon these systems. Chapter 5 develops a model of visual neglect, using an oculomotor version of a line cancellation task. We then test a prediction of this model using magnetoencephalography and dynamic causal modelling. Chapter 6 concludes by situating the work in this thesis in the context of computational neurology. This illustrates how the variational principles used here to characterise the active visual system may be generalised to other sensorimotor systems and their disorders