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Hemiplegia is a symptom that is caused by reduced sensory and motor ability on one side of the body due to stroke-related neural defects. Muscular weakness and abnormal sensation that is induced by hemiplegia usually lead to motor impairments, such as difficulty in controlling the trunk, unstable balance, and poor walking ability. Therefore, most hemiplegia patients show defective and asymmetric gait pattern. The purpose of this study is to distinguish hemiplegic gait by extracting simple characteristics of acceleration signals that are caused by asymmetry during walking using a wearable system. The devised wearable system was equipped with a three-axis accelerometer and a three-axis gyroscope. We selected 165 candidate features without step detection. A random forest algorithm was used for the classification, and the forward search algorithm was also used for optimal feature selection. The developed system and algorithms were verified clinically in 15 normal subjects and 20 hemiplegia patients that were undergoing stroke treatment, and 26 subject’s data was used for training, including validation, and nine subject’s data used for test. As a result of test set, the accuracy, sensitivity, specificity and positive predictive value were 100.0%, with the two classification attributes of standard deviation of points perpendicular to the axis of line of identity of Poincaré plot of angular velocity around vertical axis and kurtosis of frequency of angular velocity around longitudinal axis.

Hemiplegic walking is the main symptom of hemiplegia and is the basis for judging the outcome or severity of hemiplegia. In this study, we developed a wearable measurement module for measuring the inertia signal generated during walking and evaluated the possibility of gait measurement as a basic study of the new hemiplegia diagnosis technology using wearable device. The developed measurement module is worn on the waist. It is equipped with 3-axis acceleration sensor and 3-axis angular velocity sensor and has the function to transmit and record data through wireless communication. As a result of measuring the gait signals using the developed measurement module, specific patterns are shown for each axis according to each step, and it is confirmed that gait signals can be distinguished intuitively.

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Background Oesophageal button battery impaction (OBBI) is a medical emergency requiring timely removal due to rapid oesophageal tissue injury. The aim of this study was to characterize circumstances of OBBI and identify potential barriers to timely removal of button batteries (BB) in the paediatric population. Methods This is a retrospective review of OBBI cases between January 2018 to June 2019. Medical records were used to obtain patient demographics, battery size and type, battery source, location of initial presentation and outcomes. Time to obtaining x‐ray, transfer to tertiary centre and to removal of button battery were recorded. Results Eight cases of OBBI were recorded during the 18 months study timeframe. Six patients were male and two were female, aged 0.97 to 2.8 years. Six were from an English‐speaking background and two were from families of non‐English speaking background. Battery removal occurred at Queensland Children's Hospital (QCH) in seven of eight cases. Time from ingestion to initial presentation to hospital ranged from 39 min to 123 h with a mean time of 2 h and 13 min. Overall, the total time from ingestion of BB to removal ranged from 2 h 54 min to 126 h 51 min. Conclusion Despite being recognized as a time critical emergency, diagnostic, geographic and logistic challenges in Queensland make optimal care a challenge. Primary prevention strategies coupled with an expanded network for safe battery removal, and novel management strategies such as honey and acetic acid could improve care and reduce morbidity.