Where is my Device? - Detecting the Smart Device's Wearing Location in the Context of Active Safety for Vulnerable Road Users
This article describes an approach to detect the wearing location of smart devices worn by pedestrians and cyclists. The detection, which is based solely on the sensors of the smart devices, is important context-information which can be used to parametrize subsequent algorithms, e.g. for dead reckoning or intention detection to improve the safety of vulnerable road users. The wearing location recognition can in terms of Organic Computing (OC) be seen as a step towards self-awareness and self-adaptation. For the wearing location detection a two-stage process is presented. It is subdivided into moving detection followed by the wearing location classification. Finally, the approach is evaluated on a real world dataset consisting of pedestrians and cyclists.
Code (0)
등록된 구현이 없습니다.
Tasks
General ClassificationSimilar Papers 제목 키워드 기반
Starting Movement Detection of Cyclists Using Smart Devices
In near future, vulnerable road users (VRUs) such as cyclists and pedestrians will be equipped with smart devices and wearables which are capable to communicate with intelligent vehicles and other traffic participants. R…
Activity Recognitionfeature selectionHuman Activity RecognitionTask Offloading for Smart Glasses in Healthcare: Enhancing Detection of Elevated Body Temperature
Wearable devices like smart glasses have gained popularity across various applications. However, their limited computational capabilities pose challenges for tasks that require extensive processing, such as image and vid…
Egocentric Activity Recognition on a Budget
Recent advances in embedded technology have enabled more pervasive machine learning. One of the common applications in this field is Egocentric Activity Recognition (EAR), where users wearing a device such as a smartphon…
Activity RecognitionEgocentric Activity RecognitionReinforcement LearningSmartMask- Developing an automated self-care system
COVID-19 has changed our world and has filled people with fear and anxiety. Everyone has a fear of coming in contact with people having the Coronavirus. In Spite of releasing full lockdowns, there is still a pressing nee…
ARGUS: Context-Based Detection of Stealthy IoT Infiltration Attacks
IoT application domains, device diversity and connectivity are rapidly growing. IoT devices control various functions in smart homes and buildings, smart cities, and smart factories, making these devices an attractive ta…
Intrusion DetectionSelf-Learning