Abstract
Activity Recognition (AR) is a subset of pervasive computing that attempts to identify physical actions performed by a user. Previous sensor-based AR systems involve computation and energy overheads incurred by the use of heterogeneous and large number of sensors, however it is possible to arrive at an optimized system where the design involves optimization of energy consumption through number of sensors, computation through minimal set of features and cost through a nominal hardware platform ideally making it a multidimensional optimization. The above mentioned modelling was reflected in the construction of this optimized system as the design employs a single accelerometer and extracts only 7 time-domain features resulting in ease of computation to classify the activities, thus encouraging it to be inherently deployable on an embedded platform. The system was trained and tested on the accelerometer data acquired from three publicly available datasets. The performance of four chosen machine learning based classification models from an initial set of eight was evaluated, analysed and ranked on the grounds of efficiency and computation. The model was implemented on a Raspberry Pi Zero (USD 5) and the average time for feature computation and the maximum time taken to classify an instance of an activity was found to be 0.015 s and 1.094 s respectively, thus validating the viability of the system on an embedded platform and making it affordable to the population in the low-income groups.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Lara, O.D., Labrador, M.A.: A survey on human activity recognition using wearable sensors. IEEE Commun. Surv. Tutor. 15(3), 1192–1209 (2013)
Lester, J., Choudhury, T., Borriello, G.: A practical approach to recognizing physical activities. In: Pervasive, vol. 3968, pp. 1–16 (2006)
Altun, K., Barshan, B., Tunçel, O.: Comparative study on classifying human activities with miniature inertial and magnetic sensors. Pattern Recognition 43(10), 3605–3620 (2010)
Anguita, D., Ghio, A., Oneto, L., Parra, X., Reyes-Ortiz, J.L.: A public domain dataset for human activity recognition using smartphones. In: ESANN (2013)
Casale, P., Pujol, O., Radeva, P.: Personalization and user verification in wearable systems using biometric walking patterns. Pers. Ubiquitous Comput. 16(5), 563–580 (2012)
Casale, P., Pujol, O., Radeva, P.: Human activity recognition from accelerometer data using a wearable device. In: IbPRIA, vol. 6669, pp. 289–296 (2011)
Ronao, C.A., Cho, S.B.: Human activity recognition using smartphone sensors with two-stage continuous hidden Markov models. In: 2014 10th International Conference on Natural Computation (ICNC), Xiamen, pp. 681–686 (2014)
Garcia-Ceja, E., Brena, R.F.: Building personalized activity recognition models with scarce labeled data based on class similarities. In: UCAmI, vol. 9454, pp. 265–276 (2015)
Nguyen, L.T., Tague, P., Zeng, M., Zhang, J.: SuperAD: supervised activity discovery. In: UbiComp/ISWC Adjunct, pp. 1463–1472 (2015)
Ravi, N., Dandekar, N., Mysore, P., Littman, M.L.: Activity recognition from accelerometer data. In: AAAI, pp. 1541–1546 (2005)
Bao, L., Intille, S.S.: Activity recognition from user-annotated acceleration data. In: Pervasive, vol. 3001, pp. 1–17 (2004)
Bedogni, L., Di Felice, M., Bononi, L.: By train or by car? Detecting the user’s motion type through smartphone sensors data. In: 2012 IFIP Wireless Days, Dublin, pp. 1–6 (2012). https://doi.org/10.1109/WD.2012.6402818
Keally, M., Zhou, G., Xing, G., Wu, J., Pyles, A.: PBN: towards practical activity recognition using smartphone-based body sensor networks. In: Proceedings of the 9th ACM Conference on Embedded Networked Sensor Systems, Seattle, Washington, pp. 246–259 (2011)
Chen, T., Guestrin, C.: XGBoost: a scalable tree boosting system, CoRR, vol. abs/1603.02754 (2016)
Geurts, P., Ernst, D., Wehenkel, L.: Extremely randomized trees. Mach. Learn. 63(1), 3–42 (2006)
Acknowledgement
The authors would like to acknowledge Solarillion Foundation for its support and funding of the research work carried out.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Ramesh, A., Ganesan, A.V., Anupkrishnan, S., Rao, A., Vijayaraghavan, V. (2019). Design Optimization of Activity Recognition System on an Embedded Platform. In: Arai, K., Kapoor, S., Bhatia, R. (eds) Advances in Information and Communication Networks. FICC 2018. Advances in Intelligent Systems and Computing, vol 886. Springer, Cham. https://doi.org/10.1007/978-3-030-03402-3_46
Download citation
DOI: https://doi.org/10.1007/978-3-030-03402-3_46
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-03401-6
Online ISBN: 978-3-030-03402-3
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)