Applying deep neural networks and inertial measurement unit in recognizing irregular walking differences in the real world.

Falling injuries pose serious health risks to people of all ages, and knowing the extent of exposure to irregular surfaces will increase the ability to measure fall risk. Current gait analysis methods require overly complicated instrumentation and have not been tested for external factors such as walking surfaces that are encountered in the real-world, thus the results are difficult to extrapolate to real-world situations. Artificial intelligence approaches (in particular deep learning networks of varied architectures) to analyze data collected from wearable sensors were used to identify irregular surface exposure in a real-world setting. Thirty young adults wore six Inertial Measurement Unit (IMU) sensors placed on their body (right wrist, trunks at the L5/S1 level, left and right thigh, left and right shank) while walking over eight different surfaces commonly encountered in the living community as well as occupational settings. Three variations of deep learning models were trained to solve this walking surface recognition problem: 1) convolution neural network (CNN); 2) long short term memory (LSTM) network and 3) LSTM structure with an extra global pooling layer (Global-LSTM) which learns the coordination between different data streams (e.g. different channels of the same sensor as well as different sensors). Results indicated that all three deep learning models can recognize walking surfaces with above 0.90 accuracy, with the Global-LSTM yielding the best performance at 0.92 accuracy. In terms of individual sensors, the right thigh based Global-LSTM model reported the highest accuracy (0.90 accuracy). Results from this study provide further evidence that deep learning and wearable sensors can be utilized to recognize irregular walking surfaces induced motion alteration and applied to prevent falling injuries.