An event-triggered machine learning approach for accelerometer-based fall detection

The fixed-size non-overlapping sliding window (FNSW)
and fixed-size overlapping sliding window (FOSW)
approaches are the most commonly used
data-segmentation techniques in machine
learning-based fall detection using accelerometer
sensors. However, these techniques do not segment by
fall stages (pre-impact, impact, and post-impact)
and thus useful information is lost, which may
reduce the detection rate of the
classifier. Aligning the segment with the fall stage
is difficult, as the segment size varies. We propose
an event-triggered machine learning (EvenT-ML)
approach that aligns each fall stage so that the
characteristic features of the fall stages are more
easily recognized. To evaluate our approach, two
publicly accessible datasets were
used. Classification and regression tree (CART),
k-nearest neighbor (k-NN), logistic regression (LR),
and the support vector machine (SVM) were used to
train the classifiers. EvenT-ML gives classifier
F-scores of 98% for a chest-worn sensor and
92% for a waist-worn sensor, and significantly
reduces the computational cost compared with the
FNSW- and FOSW-based approaches, with reductions of
up to 8-fold and 78-fold, respectively. EvenT-ML
achieves a significantly better F-score than
existing fall detection approaches. These results
indicate that aligning feature segments with fall
stages significantly increases the detection rate
and reduces the computational cost.