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Background: Postural instability is a common symptom of progressive supranuclear palsy (PSP). Retropulsion is one form of postural instability. Spontaneous retropulsion involves loss of balance without external provocation. Others have reported on retropulsion in the clinical setting while testing for postural instability but rates of spontaneous retropulsion in the community have not been described. This study examines the prevalence of spontaneous retropulsion in PSP. Methods: A retrospective chart review examined 60 patients from the Saskatchewan Movement Disorders Program with clinical and pathology-confirmed diagnosis of PSP. We identified patients who endorsed spontaneous retropulsion. The data was analysed with univariate logistic regression. Results: The study included 43 males and 17 females. Spontaneous retropulsion was reported in 18 (30%) patients. Among the variables, only sex showed a statistical significance (p = 0.0184) with females more likely to report spontaneous retropulsion (OR = 4.25). Other variables (PSP onset age, onset age of balance impairment, gait impairment, and disease duration) were not statistically significant. Conclusions: Our data suggest that spontaneous retropulsion is common in PSP, with females being at a significantly higher risk than males. This is useful information when counselling patients on risk-avoidance behaviour to prevent falls.

This work presents a method for detecting internal leakage faults in hydraulic actuator cylinders using signal analysis and a supervised artificial neural network classifier. An artificial leakage is introduced into the hydraulic cylinder actuator, and a signal-based fault detection method is employed to process and transform the signals for internal leakage detection. The analysis focuses on extracting features from the pressure signal, particularly the peaks, which include information such as location, height, and width. After the neural network has undergone the training process, it is deployed for the purpose of categorizing fault levels into three distinct categories: “a healthy system,”“a system with a low fault,” and “a system with a high fault.” The proposed technique utilizes pressure difference signals from the cylinder chambers and extracts features from the peak signals to reduce dimensionality. The extracted features are then used for supervised training of an artificial neural network using a feed-forward algorithm. The trained network is capable of predicting the leakage class of unknown datasets. This method offers advantages such as reduced computational cost through feature extraction and dimensionality reduction, and it is capable of detecting multiple leakage classes. This study introduces a notably efficient approach, predicated on artificial neural networks, for the identification of internal leakage faults in hydraulic cylinders, with specific emphasis on faults arising from component wear and seal damage. The practical value of this research lies in its potential to significantly improve the reliability and efficiency of hydraulic systems used in heavy machinery. By utilizing neural networks for internal leakage detection, this research addresses a critical issue that can lead to costly downtime and maintenance in industrial operations.