  Fall detection is a serious healthcare issue that needs to be solved. Falling
without quick medical intervention would lower the chances of survival for the
elderly, especially if living alone. Hence, the need is there for developing
fall detection algorithms with high accuracy. This paper presents a novel
IoT-based system for fall detection that includes a sensing device transmitting
data to a mobile application through a cloud-connected gateway device. Then,
the focus is shifted to the algorithmic aspect where multiple features are
extracted from 3-axis accelerometer data taken from existing datasets. The
results emphasize on the significance of Continuous Wavelet Transform (CWT) as
an influential feature for determining falls. CWT, Signal Energy (SE), Signal
Magnitude Area (SMA), and Signal Vector Magnitude (SVM) features have shown
promising classification results using K-Nearest Neighbors (KNN) and E-Nearest
Neighbors (ENN). For all performance metrics (accuracy, recall, precision,
specificity, and F1 Score), the achieved results are higher than 95% for a
dataset of small size, while more than 98.47% score is achieved in the
aforementioned criteria over the UniMiB-SHAR dataset by the same algorithms,
where the classification time for a single test record is extremely efficient
and is real-time
