Detection of Pill Intake Associated Gestures using Smart Wearables and Machine Learning

Abstract

Gesture identification represents one way of monitoring adherence to medical treatment for cognitive-impaired individuals, dementia-related conditions being severely dependent on precise medication. This paper proposes a gesture identification algorithm used to detect the pill ingestion, that runs on an inexpensive smart wearable device. We use techniques pertaining to supervised machine learning and the data set is processed with the Keras framework. Data collected is represented by acceleration values supplied by the wearable and is proceed on the wearable device itself, showing high accuracy results in identifying the pill intake gesture. The trained model is deployed in a resource constrained embedded device and the inferences is carried locally onto the device.