Fig. 1From: Wearable airbag technology and machine learned models to mitigate falls after strokeAirbag Device and Model Pipeline. Sequence of steps in processing and developing the fall prediction models. Kinematic data were collected from individuals with a history of stroke (n = 20) and individuals who had not experienced a stroke (i.e. control, n = 15) while wearing an IMU airbag device. Raw IMU accelerometer and gyroscope signals were filtered and segmented for the pre-impact fall data window, ending at the detected impact time minus the selected lead time duration. Statistical features were extracted from the pre-impact fall data window and labeled according to cohort membership (control or stroke) and activity type (fall or non-fall, fall type). All features were used as input to two models, each model trained on either control or stroke data. The control-trained model was trained on all control features, and each subject of the stroke cohort was tested iteratively. In the stroke-trained model, a single subject was iteratively held out for testing while the remaining subjects were used for model training (leave-one-subject-out cross-validation scheme). The dashed grey rectangle signifies an iterative selection process of each held out test subject. Performance metrics to pre-impact detect falls in stroke were compared between the two exclusive cohort trained modelsBack to article page