Analysis of Multimodal Sensor Systems for Identifying Basic Walking Activities

Falls are a major health issue in societies globally and the second leading cause of unintentional death worldwide. To address this issue, many studies aim to remotely monitor gait to prevent falls. However, these activity data collected in studies must be labelled with the appropriate environmental context through Human Activity Recognition (HAR). Multimodal HAR datasets often achieve high accuracies at the cost of cumbersome sensor systems, creating a need for these datasets to be analysed to identify the sensor types and locations that enable high-accuracy HAR. This paper analyses four datasets, USC-HAD, HuGaDB, Camargo et al.’s dataset, and CSL-SHARE, to find optimal models, methods, and sensors across multiple datasets. Regarding window size, optimal windows are found to be dependent on the sensor modality of a dataset but mostly occur in the 2–5 s range. Support Vector Machines (SVMs) and Artificial Neural Networks (ANNs) are found to be the highest-performing models overall. ANNs are further used to create models trained on the features from individual sensors of each dataset. From this analysis, Inertial Measurement Units (IMUs) and three-axis goniometers are shown to be individually capable of high classification accuracy, with Electromyography (EMG) sensors exhibiting inconsistent and reduced accuracies. Finally, it is shown that the thigh is the optimal location for IMU sensors, with accuracy decreasing as IMUs are placed further down away from the thigh.