Data to accompany paper entitled: Evaluating machine learning-based classification of human locomotor activities for exoskeleton control using inertial measurement unit and pressure insole data

<b>Paper Abstract:</b> Classifying human locomotor activities from wearable sensor data is an important high-level component of control schemes for many wearable robotic exoskeletons. Here we evaluated the use of three machine learning models to classify activity type (walking, running, jumping), speed, and surface incline using input data from body-worn inertial measurement units (IMUs) and e-textile insole pressure sensors. The IMUs were positioned on segments of the lower limb and pelvis during lab-based data collection from 16 healthy participants (11 men, 5 women) who walked and ran on a treadmill at a range of preset speeds and inclines. Logistic Regression (LR), Random Forest (RF) and Light Gradient-Boosting Machine (LGBM) models were trained, tuned and scored on a validation data set (n =14), and then evaluated on a test set (n=2). The LGBM model consistently outperformed the other two, predicting activity and speed well, but not incline. Further interrogation showed that LGBM performed equally well with data from a limited number of IMUs and that speed prediction was challenged by inclusion of abnormally fast walking and slow running trials. Gyroscope data was most important in model performance. LGBM models provide a promising option for implementing locomotor activity prediction from lower limb-mounted IMU data recorded from various anatomical locations.<br>The data uploaded are the .C3D files containing the raw signals used. To access the code used to analyse these data, build and run the machine learning models, please see: https://github.com/UniExeterRSE/LISA