A Discreet Wearable Long-Range Emergency System Based on Embedded Machine Learning

Low-Power Wide Area Networks have contributed in several parts of the Internet of Things ecosystem during the last years by enabling long range, robust and low power communication. Machine Learning for embedded systems has also assisted the advancement of the Internet of Things by identifying patterns and increasing the accuracy of predicting events and behaviours. At the same time, wearable and mobile systems are less obtrusive, consuming less energy and have more computing resources. In this paper we combine these three components and propose a low cost wearable system based on a regular shoe and off-the-shelf electronics which is able to recognize foot gestures and transmit messages over long range, in cases of emergency. The evaluation considers an application scenario where the user performs specific foot gestures to trigger the transmission of an emergency message, during other activities (e.g., walking). The proposed wearable system would benefit a user who is in danger and attempts to notify her/his emergency contacts in a discreet manner. Results show that the proposed system is able to identify the intended foot gestures with 98% accuracy.