Classifying Unstable and Stable Walking Patterns Using Electroencephalography Signals and Machine Learning Algorithms

Analyzing unstable gait patterns from Electroencephalography (EEG) signals is vital to develop real-time brain-computer interface (BCI) systems to prevent falls and associated injuries. This study investigates the feasibility of classification algorithms to detect walking instability utilizing EEG signals. A 64-channel Brain Vision EEG system was used to acquire EEG signals from 13 healthy adults. Participants performed walking trials for four different stable and unstable conditions: (i) normal walking, (ii) normal walking with medial-lateral perturbation (MLP), (iii) normal walking with dual-tasking (Stroop), (iv) normal walking with center of mass visual feedback. Digital biomarkers were extracted using wavelet energy and entropies from the EEG signals. Algorithms like the ChronoNet, SVM, Random Forest, gradient boosting and recurrent neural networks (LSTM) could classify with 67 to 82% accuracy. The classification results show that it is possible to accurately classify different gait patterns (from stable to unstable) using EEG-based digital biomarkers. This study develops various machine-learning-based classification models using EEG datasets with potential applications in detecting unsteady gait neural signals and intervening by preventing falls and injuries

An Exploration of Tactile Warning Design Based on Perceived Urgency

When there is information overload on the visual modality, another system of warnings must be adopted to prevent potential risks—tactile warning systems present a viable alternative. Building on work on design approaches for auditory warning systems that match appropriate warnings to the severity of risk, this thesis presents an approach to design tactile warnings based on perceived urgency. To do this, I use a subjective rating technique. I performed three experiments to demonstrate this approach. Our research approach uses subjective rating technique to evaluate perceived urgency. Three experiments were conducted to design tactile warnings with a tactile interface developed by attaching a grid of tactors on a vest. In Experiment 1 and 2, I evaluated perceived urgency of several warning designs with three important parameters of tactile warnings with subjective rating. In Experiment 3 I examined one warning design in the context of flight simulation. The results of Experiment 1 and 2 showed that participants can discriminate between all levels of perceived urgency from most warning parameters. In Experiment 3, the results showed that selected warning design was correctly mapped with the severity of most events. The findings suggest that tactile warnings based on perceived urgency can be a possible approach, but further studies will be required to evaluate different parameters of tactile warnings

The appraisal of Facebook online community: An exposition of mobile commerce in social media reviews

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