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Floor Sensors (FS) are used to capture information from the force induced on the contact surface by feet during gait. On the other hand, the Ambulatory Inertial Sensors (AIS) are used to capture the velocity, acceleration and orientation of the body during different activities. In this paper, fusion of the stated modalities is performed to overcome the challenge of gait classification from wearable sensors on the lower portion of human body not in contact with ground as in FS. Deep learning models are utilized for the automatic feature extraction of the ground reaction force obtained from a set of 116 FS and body movements from AIS attached at 3 different locations of lower body, which is novel. Spatio-temporal information of disproportionate inputs obtained from the two modalities is balanced and fused within deep learning network layers whilst reserving the categorical content for each gait activity. Our approach of fusion compensates the degradation in spatio-temporal accuracies in individual modalities and makes the overall classification outcomes more accurate. Further assessment of multi-modality based results show significant improvements in f-scores using different deep learning models i.e., LSTM (99.90%), 2D-CNN (88.73%), 1D-CNN (94.97%) and ANN (89.33%) respectively. [ABSTRACT FROM AUTHOR]

Gait activity classifications from single-modality data, e.g. acquired by separate vision, pressure, sound and inertial measurements, can be improved by complementary multi-modality fusion to capture a larger set of distinctive gait activity features. We demonstrate a feature-level based sensor fusion of spatio-temporal data obtained from a set of 116 collaborative floor sensors for spatio-temporal sampling of the ground reaction force and ambulatory inertial sensors at 3 positions on the human body. Principle Component Analysis and Canonical Correlation Analysis are used for automatic feature extraction. Fusion at feature level elucidates the balance between otherwise disproportional number of inputs from the two modalities, while reducing the overall number of inputs for classification without degrading substantially the information content. Improvement in the classification is achieved using K-Nearest Neighbor and Kernel Support Vector Machine, manifesting f-scores of 0.95 and 0.94 respectively. [ABSTRACT FROM AUTHOR]

We demonstrate accurate spatio-temporal gait data classification from raw tomography sensor data without the need to reconstruct images. This is based on a simple yet efficient machine learning methodology based on a convolutional neural network architecture for learning spatio-temporal features, automatically end-to-end from raw sensor data. In a case study on a floor pressure tomography sensor, experimental results show an effective gait pattern classification F-score performance of 97.88 $\pm$ 1.70%. It is shown that the automatic extraction of classification features from raw data leads to a substantially better performance, compared to features derived by shallow machine learning models that use the reconstructed images as input, implying that for the purpose of automatic decision-making it is possible to eliminate the image reconstruction step. This approach is portable across a range of industrial tasks that involve tomography sensors. The proposed learning architecture is computationally efficient, has a low number of parameters and is able to achieve reliable classification F-score performance from a limited set of experimental samples. We also introduce a floor sensor dataset of 892 samples, encompassing experiments of 10 manners of walking and 3 cognitive-oriented tasks to yield a total of 13 types of gait patterns. [ABSTRACT FROM PUBLISHER]