Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 30836
Investigating Activity Recognition Using 9-Axis Sensors and Filters in Wearable Devices

Authors: Jong Tae Kim, Jun Gil Ahn, Jong Kang Park


In this paper, we analyze major components of activity recognition (AR) in wearable device with 9-axis sensors and sensor fusion filters. 9-axis sensors commonly include 3-axis accelerometer, 3-axis gyroscope and 3-axis magnetometer. We chose sensor fusion filters as Kalman filter and Direction Cosine Matrix (DCM) filter. We also construct sensor fusion data from each activity sensor data and perform classification by accuracy of AR using Naïve Bayes and SVM. According to the classification results, we observed that the DCM filter and the specific combination of the sensing axes are more effective for AR in wearable devices while classifying walking, running, ascending and descending.

Keywords: Activity Recognition, Kalman Filter, gyroscope, accelerometer, magnetometer, directional cosine matrix filter

Digital Object Identifier (DOI):

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1222


[1] Adil Mehmood Khan, Y-K Lee, SY Lee, T-S Kim, “Human activity recognition via an accelerometer-enabled-smartphone using kernel discriminant analysis,” Proc. of 5th IEEE International Conference on Future Information Technology, pp.1-6, 2010.
[2] Mortazavi, Bobak Jack, et al. "Determining the single best axis for exercise repetition recognition and counting on smartwatches." 2014 11th International Conference on Wearable and Implantable Body Sensor Networks. IEEE, 2014.
[3] Maurer, Uwe, et al. "Activity recognition and monitoring using multiple sensors on different body positions." International Workshop on Wearable and Implantable Body Sensor Networks (BSN'06). IEEE, 2006.
[4] Stehman, Stephen V. "Selecting and interpreting measures of thematic classification accuracy." Remote sensing of Environment 62.1 (1997): 77-89.
[5] Seyed Amir Hoseini-Tabatabaei, Alexander Gluhak, Rahim Tafazolli, "A survey on smartphone-based systems for opportunistic user context recognition," ACM Computing Surveys, vol.45, no.3, pp.27, 2013
[6] Ling Pei, Jingbin Liu, Robert Guinness, Yuwei Chen, Heidi Kuusniemi, Ruizhi Chen, "Using LS-SVM based motion recognition for smartphone indoor wireless positioning,"
[7] Muhammad Shoaib, Stephan Bosch, Ozlem Durmaz Incel, Hans cholten, Paul JM Havinga, “Fusion of smartphone motion sensors for physical activity recognition,” Sensors, vol.14, no.6, pp.10146-10176, 2014
[8] Chernbumroong S., Shuang Cang, Hongnian Yu, "GA-based Classifiers fusion for multi-sensor activity recognition of elderly people," IEEE Journal of Biomedical and Health Informatics, vol.19, no.1, pp. 282?289, 2015.
[9] Kalman, Rudolph Emil. "A new approach to linear filtering and prediction problems." Journal of basic Engineering 82.1 (1960): 35-45.
[10] Premerlani, William, and Paul Bizard. "Direction cosine matrix imu: Theory." DIY DRONE: USA (2009): 13-15.
[11] Pomares, Ignacio Rojas, et al. "A benchmark dataset to evaluate sensor displacement in activity recognition." (2012).
[12] Erik K Antonsson, Robert W Mann, "The frequency content of gait," Journal of biomechanics, vol.18, no.1, pp.39-47, 1985
[13] Muhammad Shoaib, Stephan Bosch, Ozlem Durmaz Incel, Hans cholten, Paul JM Havinga, "A Survey of Online Activity Recognition Using Mobile Phones," Sensors, vol.15, no.1, pp.2059-2085, 2015.
[14] George Dimitoglou, James A. Adams, and Carol M. Jim,” Comparison of the C4.5 and a Naïve Bayes Classifier for the Prediction of Lung Cancer Survivability”
[15] Smola, Alex, and Vladimir Vapnik. "Support vector regression machines." Advances in neural information processing systems 9 (1997): 155-161.
[16] Powers, David Martin. "Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation." (2011).
[17] Hall, Mark, et al. "The WEKA data mining software: an update." ACM SIGKDD explorations newsletter 11.1 (2009): 10-18.
[18] Kohavi, Ron. "A study of cross-validation and bootstrap for accuracy estimation and model selection." Ijcai. Vol. 14. No. 2. 1995.