Human falls are one of the leading causes of fatal unintentional injuries
worldwide. Falls result in a direct financial cost to health systems, and indirectly,
to society’s productivity. Unsurprisingly, human fall detection and prevention is
a major focus of health research. In this chapter, we present and evaluate several
bidirectional long short-term memory (Bi-LSTM) models using a data set provided
by the Challenge UP competition. The main goal of this study is to detect 12 human
daily activities (six daily human activities, five falls, and one post-fall activity)
derived from multi-modal data sources - wearable sensors, ambient sensors, and
vision devices. Our proposed Bi-LSTM model leverages data from accelerometer
and gyroscope sensors located at the ankle, right pocket, belt, and neck of the subject.
We utilize a grid search technique to evaluate variations of the Bi-LSTM model and
identify a configuration that presents the best results. The best Bi-LSTM model
achieved good results for precision and f1-score, 43.30% and 38.50%, respectively
Item Type:
Book Section
Refereed:
Yes
Uncontrolled Keywords:
Bi-LSTM; Human falls; Multimodal sensors; Human activities