Motor disorders in Parkinson’s Disease (PD) show high inter-individual variability which challenges the current observational-based strategies in the clinical setting to determine the actual disease evolution and monitoring the therapy response. In spite the recent development of the motion capture technology, it is still hardly transferable to the routine exam and the non-linear disease patterns are poorly explored. Because gait pattern could be approached as deterministic chaotic system, this work aimed to non-linearly represent lower limb kinematic standing out the differences among PD stages. For doing so, a widely used deep learning framework was implemented for obtaining the body landmarks and their temporal series and thereafter, construing the phase space based on the first order derivatives. Largest Lyapunov exponent, correlation dimension and approximate entropy were then computed resulting in statistically significant differences (Wilcoxon rank test, p < 0.05), particularly between healthy controls and stages 3, the most advanced stage, and comparing stage 1 face to stage 3. These finding providing insights how the complex patterns may be related with the disease progression in PD and easily implemented using affordable video devices.
Observation of Gait patterns is the available evaluation in clinical routine of the motor manifestations in Parkinson’s Disease (PD). Lately, different investigations have attempted to quantitatively analyze gait patterns by linear methods facing several limitations since the non-stationary nature of the gait patterns. This study presents a non-linear characterization of the Parkinson's disease gait by a deterministic chaotic analysis which represents the temporal gait dynamics with a minimum set of parameters. Specifically, delay and embedding dimension parameters were obtained for reconstructing the phase space and its characteristic coefficients, namely Lyapunov, correlation dimension, and approximate entropy. Statistical differences (p < 0.05, Mann-Whitney test) were found for the Lyapunov exponent and the approximated entropy when describing two gait patterns, i.e., control and PD groups.
Visual disorders are one of the common side problems in Cerebral Palsy (CP) being reported with an incidence between 50% to 90% of the cases. These visual disorders may interfere with the developmental process and motor learning of these children. This work presents a robust method to spatio-temporally characterize the ocular motion in CP. Smooth pursuit and saccadic eye tasks were assessed by using simple visual stimuli and recording the eye trajectories during the task. A dense optical flow estimation of the ocular movement is modulated by a visual attention model which extracts relevant eye motion information. Analyses in time and frequency domain were performed suggesting statistical differences (p-value < 0.01) in Signal to Noise Ratio (SNR) and Wavelet coefficients for both saccadic and smooth pursuit tasks.
External auditory cues stimulate motor related areas of the brain, activating motor ways parallel to the basal ganglia circuits and providing a temporary pattern for gait. In effect, patients may re-learn motor skills mediated by compensatory neuroplasticity mechanisms. However, long term functional gains are dependent on the nature of the pathology, follow-up is usually limited and reinforcement by healthcare professionals is crucial. Aiming to cope with these challenges, several researches and device implementations provide auditory or visual stimulation to improve Parkinsonian gait pattern, inside and outside clinical scenarios. The current work presents a semiautomated strategy for spatio-temporal feature extraction to study the relations between auditory temporal stimulation and spatiotemporal gait response. A protocol for auditory stimulation was built to evaluate the integrability of the strategy in the clinic practice. The method was evaluated in transversal measurement with an exploratory group of people with Parkinson’s (n = 12 in stage 1, 2 and 3) and control subjects (n =6). The result showed a strong linear relation between auditory stimulation and cadence response in control subjects (R=0.98 ±0.008) and PD subject in stage 2 (R=0.95 ±0.03) and stage 3 (R=0.89 ±0.05). Normalized step length showed a variable response between low and high gait velocity (0.2> R >0.97). The correlation between normalized mean velocity and stimulus was strong in all PD stage 2 (R>0.96) PD stage 3 (R>0.84) and controls (R>0.91) for all experimental conditions. Among participants, the largest variation from baseline was found in PD subject in stage 3 (53.61 ±39.2 step/min, 0.12 ± 0.06 in step length and 0.33 ± 0.16 in mean velocity). In this group these values were higher than the own baseline. These variations are related with direct effect of metronome frequency on cadence and velocity. The variation of step length involves different regulation strategies and could need others specific external cues. In conclusion the current protocol (and their selected parameters, kind of sound time for training, step of variation, range of variation) provide a suitable gait facilitation method specially for patients with the highest gait disturbance (stage 2 and 3). The method should be adjusted for initial stages and evaluated in a rehabilitation program.
Several approaches using auditory feedback have been proposed to improve gait rehabilitation in Parkinson Disease. Despite auditory cues have shown to be useful, there are still unanswered questions about their optimal usage regarding parameters like frequency, number of beats and their integration with rehabilitation protocols, among others. Most approaches have attempted to resolve these questions by measuring their direct effect on spatiotemporal gait variables. However, few studies have assessed how synchronized the auditory feedback and the gait pattern are. The main goal was to quantify synchronization between the gait temporal patterns and the auditory stimuli. The group of participants consisted of seven (7) healthy subjects, aged between 50-70 years (average 57.28, ± 5.87 years), with average height of 1.64±0.09m and independent community ambulation. Each candidate was asked to sign an informed consent, given their good cognitive conditions for understanding the nature and purpose of the study. Participants were instructed to follow the sounds provided by a metronome. Feet tracking yielded the temporal gait pattern. The temporal coherence metric was developed to evaluate synchronization between audio signal and subject motion, in terms of phase shift (π radian). Results show a good fit to auditory stimulus in metronome rates between 140-150 and 60-80 beats/min (bpm) for the selected participants. A lower temporal coherence was observed at the beginning and the end of the test. The proposed metric allows quantification of the temporal coherence between gait and auditory cues in healthy elder subjects. Other exploratory trials should be directed to evaluate the temporal coherence between auditory stimuli and generated movements in population with Parkinson Disease.
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