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Fig. 1 | Journal of NeuroEngineering and Rehabilitation

Fig. 1

From: Wearable airbag technology and machine learned models to mitigate falls after stroke

Fig. 1

Airbag 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 models

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