Abstract
Human Activity Recognition from accelerometer sensors is key to enable applications such as fitness tracking or health status monitoring at home. However, evaluating the performance of activity recognition systems in real-life deployments is challenging to the multiple differences in sensor number, placement and orientation that may arise in real settings. Considering such differences requires a large amount of labeled data. To overcome the challenges and costs associated to the collection of a wide range of heterogeneous data, we propose a simulator, called MEASURed, which uses motion capture to simulate accelerometer data on different settings. Then, using the simulated data to estimate the performance of activity recognition models under different scenarios. In this chapter, we describe MEASURed and evaluate its performance in estimating the accuracy of activity recognition models. Our results show that MEASURed can estimate the average accuracy of an activity recognition model using real accelerometer magnitude data. By using motion capture to simulate accelerometer data, the sensor research community can profit from visual datasets that have been collected by other communities to evaluate performance of activity recognition in a wide range of activities. MEASURed can be used to evaluate activity recognition classifiers in settings with different number, placement, and sampling rate of accelerometer sensors. The evaluation on a broad spectrum of scenarios gives a more general view of models and their limitations.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Notes
- 1.
We obtain linear acceleration using a high pass filter as described in https://developer.android.com/guide/topics/sensors/sensors_motion#java and apply a median filter to remove noise.
- 2.
- 3.
- 4.
- 5.
- 6.
- 7.
- 8.
- 9.
References
Amft O (2005) On the need for quality standards in activity recognition using ubiquitous sensors. In: UbiComp ’13 Adjunct proceedings of the 2013 ACM conference on pervasive and ubiquitous computing adjunct publication, pp 62–79
Asare P, Dickerson RF et al (2013) BodySim: a multi-domain modeling and simulation framework for body sensor networks research and design. In: Proceedings of SenSys ’13, pp 1–2
Banos O, Calatroni A, et al (2012) Kinect=imu? learning MIMO signal mappings to automatically translate activity recognition systems across sensor modalities. In: Proceedings of the 16th ISWCWashington, DC, USA, 2012. IEEE Computer Society, pp 92–99
Bulling A, Blanke U, Schiele B (2014) A tutorial on human activity recognition using body-worn inertial sensors. ACM Comput Surv 46(3):33:1–33:33
Chavarriaga R, Bayati H, Millán JR (2013) Unsupervised adaptation for acceleration-based activity recognition: robustness to sensor displacement and rotation. Pers Ubiquitous Comput 17(3):479–490
Gjoreski H, Ciliberto M, Morales FJO, Roggen D, Mekki S, Valentin S (2017) A versatile annotated dataset for multimodal locomotion analytics with mobile devices. In: Proceedings of the 15th ACM conference on embedded network sensor systems, SenSys ’17, New York, NY, USA, 2017. ACM, pp 61:1–61:2
Kunze K, Lukowicz P (2014) Sensor placement variations in wearable activity recognition. IEEE Pervasive Comput 13(4):32–41
Lara Oscar D, Labrador Miguel A (2013) A survey on human activity recognition using wearable sensors. IEEE Commun Surv Tutor 15(3):1192–1209
Lockhart JW, Weiss GM, Xue JC, Gallagher ST, Grosner AB, Pulickal TT (2011) Design considerations for the WISDM smart phone-based sensor mining architecture. In: Proceedings of the fifth international workshop on knowledge discovery from sensor data, SensorKDD ’11, New York, NY, USA, 2011. ACM, pp 25–33
Micucci D, Mobilio M, Napoletano P (2017) UniMiB SHAR: a dataset for human activity recognition using acceleration data from smartphones. Appl Sci 7(10)
Ofli F et al (2013) Berkeley MHAD: a comprehensive multimodal human action database. In: 2013 IEEE WACV, pp 53–60
Roggen D, Calatroni A, Rossi M, Holleczek T, Förster K, Tröster G, Lukowicz P, Bannach D, Pirkl G, Ferscha A, Doppler J, Holzmann C, Kurz M, Holl G, Chavarriaga R, Sagha H, Bayati H, Creatura M, Millan JR (2010) Collecting complex activity datasets in highly rich networked sensor environments. In: 2010 seventh international conference on networked sensing systems (INSS), pp 233–240
Ruiz C, Pan S, Bannis A, Chen X, Joe-Wong C, Noh HY, Zhang P (2018) IDrone: robust drone identification through motion actuation feedback. Proc ACM Interact Mob Wearable Ubiquitous Technol 2(2):80:1–80:22
Stisen A, Blunck H, Bhattacharya S, Prentow TS, Kjærgaard MB, Dey A, Sonne T, Jensen MM (2015) Smart devices are different: assessing and mitigating mobile sensing heterogeneities for activity recognition. In: Proceedings of the 13th ACM conference on embedded networked sensor systems, SenSys ’15, New York, NY, USA, 2015. ACM, pp 127–140
Takeda S, Lago P, Okita T, Inoue S (2018) A multi-sensor setting activity recognition simulation tool. In: Proceedings of the 2018 ACM international joint conference and 2018 international symposium on pervasive and ubiquitous computing and wearable computers, UbiComp ’18, New York, NY, USA, 2018. ACM, pp 1444–1448
Ustev YE, Incel OD, Ersoy C (2013) User, device and orientation independent human activity recognition on mobile phones: challenges and a proposal. In: Proceedings of the 2013 ACM conference on pervasive and ubiquitous computing adjunct publication, UbiComp ’13 Adjunct, New York, NY, USA, 2013. ACM, pp 1427–1436
Wüstenberg M, Lukowicz P, Kjaergaard MB, Blunck H, Grønbæk K, Franke T, Bouvin NO (2013) On heterogeneity in mobile sensing applications aiming at representative data collection. In: UbiComp ’13 Adjunct proceedings of the 2013 ACM conference on pervasive and ubiquitous computing adjunct publication. ACM, pp 1087–1098
Yang J (2009) Toward physical activity diary: motion recognition using simple acceleration features with mobile phones. In: Proceedings of the 1st international workshop on interactive multimedia for consumer electronics, IMCE ’09, New York, NY, USA. ACM, pp 1–10
Young AD, Ling MJ, Arvind DK (2011) IMUSim: a simulation environment for inertial sensing algorithm design and evaluation. In: Proceedings of the 10th ACM/IEEE IPSN, pp 199–210
Yurtman A, Barshan B, Fidan B (2018) Activity recognition invariant to wearable sensor unit orientation using differential rotational transformations represented by quaternions. Sensors 18(8)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this chapter
Cite this chapter
Lago, P., Takeda, S., Okita, T., Inoue, S. (2019). MEASURed: Evaluating Sensor-Based Activity Recognition Scenarios by Simulating Accelerometer Measures from Motion Capture. In: Kawaguchi, N., Nishio, N., Roggen, D., Inoue, S., Pirttikangas, S., Van Laerhoven, K. (eds) Human Activity Sensing. Springer Series in Adaptive Environments. Springer, Cham. https://doi.org/10.1007/978-3-030-13001-5_10
Download citation
DOI: https://doi.org/10.1007/978-3-030-13001-5_10
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-13000-8
Online ISBN: 978-3-030-13001-5
eBook Packages: Computer ScienceComputer Science (R0)