Using Gravity to Estimate Accelerometer Orientation
ISWC '03 Proceedings of the 7th IEEE International Symposium on Wearable Computers
Activity Recognition and Monitoring Using Multiple Sensors on Different Body Positions
BSN '06 Proceedings of the International Workshop on Wearable and Implantable Body Sensor Networks
Shakra: tracking and sharing daily activity levels with unaugmented mobile phones
Mobile Networks and Applications
Activity sensing in the wild: a field trial of ubifit garden
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Gesture Recognition with a 3-D Accelerometer
UIC '09 Proceedings of the 6th International Conference on Ubiquitous Intelligence and Computing
Activity recognition from accelerometer data
IAAI'05 Proceedings of the 17th conference on Innovative applications of artificial intelligence - Volume 3
IMCE '09 Proceedings of the 1st international workshop on Interactive multimedia for consumer electronics
A hybrid discriminative/generative approach for modeling human activities
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
iPhone as a physical activity measurement platform
CHI '10 Extended Abstracts on Human Factors in Computing Systems
A practical approach to recognizing physical activities
PERVASIVE'06 Proceedings of the 4th international conference on Pervasive Computing
Fish'n'Steps: encouraging physical activity with an interactive computer game
UbiComp'06 Proceedings of the 8th international conference on Ubiquitous Computing
A survey on visual surveillance of object motion and behaviors
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Activity classification using realistic data from wearable sensors
IEEE Transactions on Information Technology in Biomedicine
Machine Recognition of Human Activities: A Survey
IEEE Transactions on Circuits and Systems for Video Technology
Physical activity monitoring with mobile phones
ICOST'11 Proceedings of the 9th international conference on Toward useful services for elderly and people with disabilities: smart homes and health telematics
A mobile data collection platform for mental health research
Personal and Ubiquitous Computing
Activity logging using lightweight classification techniques in mobile devices
Personal and Ubiquitous Computing
Estimating heart rate variation during walking with smartphone
Proceedings of the 2013 ACM international joint conference on Pervasive and ubiquitous computing
GaitTrack: Health Monitoring of Body Motion from Spatio-Temporal Parameters of Simple Smart Phones
Proceedings of the International Conference on Bioinformatics, Computational Biology and Biomedical Informatics
Using time use with mobile sensor data: a road to practical mobile activity recognition?
Proceedings of the 12th International Conference on Mobile and Ubiquitous Multimedia
Reliable and secure body fall detection algorithm in a wireless mesh network
BodyNets '13 Proceedings of the 8th International Conference on Body Area Networks
Using multiple sensors for reliable markerless identification through supervised learning
Machine Vision and Applications
Identifying risky environments for COPD patients using smartphones and internet of things objects
International Journal of Computational Intelligence Studies
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This paper uses accelerometer-embedded mobile phones to monitor one's daily physical activities for sake of changing people's sedentary lifestyle. In contrast to the previous work of recognizing user's physical activities by using a single accelerometer-embedded device and placing it in a known position or fixed orientation, this paper intends to recognize the physical activities in the natural setting where the mobile phone's position and orientation are varying, depending on the position, material and size of the hosting pocket. By specifying 6 pocket positions, this paper develops a SVM based classifier to recognize 7 common physical activities. Based on 10-folder cross validation result on a 48.2 hour data set collected from 7 subjects, our solution outperforms Yang's solution and SHPF solution by 5-6%. By introducing an orientation insensitive sensor reading dimension, we boost the overall F-score from 91.5% to 93.1%. With known pocket position, the overall F-score increases to 94.8%.