Evaluation of Fall and Seizure Detection with Smartphone and Smartwatch Devices

Epilepsy and falls incur a great social and economic cost globally. Automatically detecting their occurrence would help mitigate the myriad of issues that arise from not receiving assistance after such an event. Despite existing research showing the potential advantages in using the ever-improving sensor technology incorporated within commercially available smartphone and smartwatch devices for human activity recognition, most available solutions for fall and seizure detection are still offered with dedicated hardware, which is often more expensive and less practical. This paper presents a comparison and evaluation of algorithms for detecting convulsions and falls, separately and combined, using smartphone and smartwatch devices. With a dataset of ordinary activities and simulated falls and convulsions, recorded by 15 test subjects, we found the devices a viable option for the successful detection of the activities, achieving accuracy rates between 89.7% and 98.5% with C4.5 decision tree algorithms.

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