TY - GEN
T1 - Activity recognition in WSN
T2 - 2012 7th International Conference on Computing and Convergence Technology (ICCIT, ICEI and ICACT), ICCCT 2012
AU - Awan, Muhammad Arshad
AU - Guangbin, Zheng
AU - Kim, Shin Dung
PY - 2012
Y1 - 2012
N2 - Activity recognition is a key component in identifying the context of a user for providing services based on the application. In this study, we propose a model that is based on the recognition of users' activities through wireless sensors network technologies. The model is composed of four components: set of sensors, set of activities, backend server with machine learning algorithms and a GUI application for the interaction with the user. New sensors can be added to the system based on the novel activities. In order to train the model, a sequence of steps involved in an activity need to be performed and then the model is applied for the identification of the same activity in future and visualize through GUI application. A prototype is developed to show the usability of the proposed model. As a pilot testing only accelerometer data of android phone is used to identify the activities of daily living (ADL); sitting, standing, walking and jogging. The model is trained by getting the sensors data while performing activities and tested on real data. A good accuracy of results i.e. about 96% on average is achieved in all activities.
AB - Activity recognition is a key component in identifying the context of a user for providing services based on the application. In this study, we propose a model that is based on the recognition of users' activities through wireless sensors network technologies. The model is composed of four components: set of sensors, set of activities, backend server with machine learning algorithms and a GUI application for the interaction with the user. New sensors can be added to the system based on the novel activities. In order to train the model, a sequence of steps involved in an activity need to be performed and then the model is applied for the identification of the same activity in future and visualize through GUI application. A prototype is developed to show the usability of the proposed model. As a pilot testing only accelerometer data of android phone is used to identify the activities of daily living (ADL); sitting, standing, walking and jogging. The model is trained by getting the sensors data while performing activities and tested on real data. A good accuracy of results i.e. about 96% on average is achieved in all activities.
UR - http://www.scopus.com/inward/record.url?scp=84881147895&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84881147895&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:84881147895
SN - 9788994364216
T3 - Proceedings - 2012 7th International Conference on Computing and Convergence Technology (ICCIT, ICEI and ICACT), ICCCT 2012
SP - 15
EP - 20
BT - Proceedings - 2012 7th International Conference on Computing and Convergence Technology (ICCIT, ICEI and ICACT), ICCCT 2012
Y2 - 3 December 2012 through 5 December 2012
ER -