Freezing of gait (FoG) is one of the most alarming motor symptoms of Parkinson's disease, most commonly experienced by subjects who have suffered from the disease for a long period of time. Furthermore, freezing of gait can lead to falls and may result to nursing home admissions which can negatively affect the quality of life for patients, in addition to raising a broader set of socioeconomic consequences. In this research, machine learning algorithms are utilised as means of identifying the freezing of gait event prior to its onset. An accelerometer time series dataset containing 237 individual Freezing of Gait events over 8 patients was considered, from which features were extracted and presented to 7 machine learning classifiers. Our simulation results indicated that machine learning algorithms are powerful tools for the early prediction of freezing of gait, capable of obtaining high sensitivity and specificity for the identification of the onset of Freezing of Gait. Support Vector Machines with Polynomial kernel achieved the highest performance in comparison with the benchmarked techniques. The classification algorithm was applied to 5 second windows using 18 features, obtaining balanced accuracies (the mean value of sensitivity and specificity) of 91 %, 90%, and 82% over the Walk, FoG and Transition classes, respectively.

Fencing in livestock management is essential for location and movement control yet with conventional methods to require close labour supervision, leading to increased costs and reduced flexibility. Consequently, virtual fencing systems (VF) have recently gained noticeable attention as an effective method for the maintenance and control of restricted areas for animals. Existing systems to control animal movement use audio followed by controversial electric shocks which are prohibited in various countries. Accordingly, the present work has investigated the sole application of audio signals in training and managing animal behaviour. Audio cues in the range of 125–17 kHz were used to prohibit the entrance of seven Hebridean ewes from a restricted area with a feed bowl. Two trials were performed over the period of a year which were video recorded. Sound signals were activated when the animal approached a feed bowl and a restricted area with no feed bowl present. Results from both trials demonstrated that white noise and sounds in the frequency ranges of 125–440 Hz to 10–17 kHz successfully discouraged animals from entering a specific area with an overall success rate of 89.88% (white noise: 92.28%, 10–14 kHz: 89.13%, 15–17 kHz: 88.48%, 125–440 Hz: 88.44%). The study demonstrated that unaided audio stimuli were effective at managing virtual fencing for sheep.