Every year 424,000 fatal accidents occur, they are the second cause of unintentional death after road traffic injuries. The difference between fatal and not fatal accidents often is the presence of other people able to promptly provide first aid or call for help. Unfortunately, even during the practice of group activities (e.g. team sports) an accident can happen when a person is alone or out of sight; thus, the availability of devices able to detect if a serious accident is occurred and consequently arise an alarm to other people is an important issue for the safety of people. Starting from these considerations, in this paper we propose a wearable device able to detect accidents occurring during the practice of running. The device uses a one class SVM trained only on the normal activity and classifies as anomalies all the unknown situations. Then, in order to avoid alarms related to non dangerous events, the output of the classifier is analyzed by an additional stage responsible to detect if the person is or not unconscious after an abnormal event. In the former case an alarm is arisen by the system.

A Wearable Embedded System for Detecting Accidents while Running

Vincenzo Carletti;Antonio Greco;Alessia Saggese;Mario Vento;VIGILANTE, VINCENZO
2018

Abstract

Every year 424,000 fatal accidents occur, they are the second cause of unintentional death after road traffic injuries. The difference between fatal and not fatal accidents often is the presence of other people able to promptly provide first aid or call for help. Unfortunately, even during the practice of group activities (e.g. team sports) an accident can happen when a person is alone or out of sight; thus, the availability of devices able to detect if a serious accident is occurred and consequently arise an alarm to other people is an important issue for the safety of people. Starting from these considerations, in this paper we propose a wearable device able to detect accidents occurring during the practice of running. The device uses a one class SVM trained only on the normal activity and classifies as anomalies all the unknown situations. Then, in order to avoid alarms related to non dangerous events, the output of the classifier is analyzed by an additional stage responsible to detect if the person is or not unconscious after an abnormal event. In the former case an alarm is arisen by the system.
978-989-758-290-5
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11386/4710067
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