ABSTRACT
The Timed-Up and Go test is a very used test in the physiotherapy area. For the measurement of the results of the test, we propose to use a smartphone with several embedded sensors, including accelerometer, magnetometer, gyroscope, a Bitalino device with the Electromyography (EMG) and Electrocardiography (ECG) sensors, and a second Bitalino device with a pressure sensor connected and positioned in the back of the chair. This architecture allows to capture several types of data from the sensors easily. In this paper, we present a structured method to implement the measurement of the different parameters involved in the Timed-up and Go test, for acquiring, processing and cleaning the collected measurements. This data will help in the classification of the test results initially, and later on to discover more complex patterns and related conditions, such as equilibrium changes, neurological pathologies, degenerative pathologies, lesions of lower limbs and chronic venous diseases.
References
- Data.worldbank.org. 2019. Data.worldbank.org. https://data.worldbank.org/indicator/SP.POP.65UP.TO.ZSGoogle Scholar
- Ciprian Dobre, Constandinos x Mavromoustakis, Nuno Garcia, Rossitza Ivanova Goleva, and George Mastorakis. 2016. Ambient Assisted Living and Enhanced Living Environments: Principles, Technologies and Control. Butterworth-Heinemann, Butterworth-Heinemann. Google Scholar
- Koustabh Dolui, Srijani Mukherjee, and Soumya Kanti Datta. 2013. Smart device sensing architectures and applications. In 2013 International Computer Science and Engineering Conference (ICSEC). IEEE, Bangkok, Thailand, 91--96.Google Scholar
- Nuno M Garcia. 2016. A Roadmap to the Design of a Personal Digital Life Coach. In ICT Innovations 2015, Suzana Loshkovska and SasoEditors Koceski (Eds.). Springer Cham, Ohrid, Macedonia, 21--27.Google Scholar
- Nuno M Garcia and Joel Jose P C Rodrigues. 2015. Ambient assisted living. CRC Press, Boca Ratom, FL.Google Scholar
- Martin Gjoreski, Hristijan Gjoreski, Mitja Luštrek, and Matjaž Gams. 2016. How Accurately Can Your Wrist Device Recognize Daily Activities and Detect Falls? Sensors 16, 6 (2016), 22.Google Scholar
- Shawn R. Jeffery, Gustavo Alonso, Michael J. Franklin, Wei Hong, and Jennifer Widom. 2006. Declarative Support for Sensor Data Cleaning. Springer-Verlag, Dublin, Ireland, 83--100. Google Scholar
- Inês Sousa Joana Silva. 2018. Instrumented Timed Up and Go: Fall Risk Assessment based on Inertial Wearable Sensors. IEEE, Benevento, Italy.Google Scholar
- Seungwoo Kang, Youngki Lee, Chulhong Min, Younghyun Ju, Taiwoo Park, Jinwon Lee, Yunseok Rhee, and Junehwa Song. 2010. Orchestrator: An active resource orchestration framework for mobile context monitoring in sensor-rich mobile environments. In 2010 IEEE International Conference on Pervasive Computing and Communications (PerCom). IEEE, Mannheim, Germany, 135--144.Google Scholar
- Lipyeow Lim, Archan Misra, and Tianli Mo. 2013. Adaptive data acquisition strategies for energy-efficient, smartphone-based, continuous processing of sensor streams. Distributed and Parallel Databases 31, 2 (Jun 2013), 321--351. Google Scholar
- Hong Lu, Jun Yang, Zhigang Liu, Nicholas D. Lane, Tanzeem Choudhury, and Andrew T. Campbell. 2010. The Jigsaw continuous sensing engine for mobile phone applications. In Proceedings of the 8th ACM Conference on Embedded Networked Sensor Systems - SenSys '10. ACM Press, Zurich, Switzerland, 71. Google Scholar
- Mario A. Nascimento, Carlos Guestrin, Samuel R. Madden, Joseph M. Hellerstein, and Wei Hong. 2004. Proceedings of the Thirtieth International Conference on Very Large Data Bases: Toronto, Canada, August 31-September 3, 2004. Morgan Kaufmann Publishers, Toronto, Canada. Google Scholar
- Ivan Miguel Pires, Nuno M. Garcia, Nuno Pombo, and Francisco Flórez-Revuelta. 2016. From data acquisition to data fusion: A comprehensive review and a roadmap for the identification of activities of daily living using mobile devices. Sensors (Switzerland) 16, 2 (2016), 27.Google Scholar
- Ivan Miguel Pires, Nuno M Garcia, Nuno Pombo, and Francisco Flórez-revuelta. 2018. Limitations of the Use of Mobile Devices and Smart Environments for the Monitoring of Ageing People. HSP 1, Ict4awe (2018), 269--275.Google Scholar
- IDC: The premier global market intelligence company. 2019. IDC: The premier global market intelligence company. https://www.idc.com/promo/smartphone-market-share/osGoogle Scholar
- Bodhi Priyantha, Dimitrios Lymberopoulos, and Jie Liu. 2010. Enabling energy efficient continuous sensing on mobile phones with LittleRock. In Proceedings of the 9th ACM/IEEE International Conference on Information Processing in Sensor Networks - IPSN '10. ACM Press, Stockholm, Sweden, 420. Google Scholar
- Facts Publications. 2018. Disability Statistics: Information, Charts, Graphs and Tables. https://www.disabled-world.com/disability/statistics/Google Scholar
- Sandra Reis, Virginie Felizardo, Nuno Pombo, and Nuno Garcia. 2016. Elderly mobility analysis during Timed Up and Go test using biosignals. Proceedings of the 7th International Conference on Software Development and Technologies for Enhancing Accessibility and Fighting Info-exclusion 1 (2016), 240--245. Google Scholar
- Daniel Schliessmann, Maria Nisser, Christian Schuld, Till Gladow, Steffen Derlien, Laura Heutehaus, Norbert Weidner, Ulrich Smolenski, and Rudiger Rupp. 2018. Trainer in a pocket - proof-of-concept of mobile, real-time, foot kinematics feedback for gait pattern normalization in individuals after stroke, incomplete spinal cord injury and elderly patients. Journal of neuroengineering and rehabilitation 15, 1 (May 2018), 44.Google Scholar
- J Silva and I Sousa. 2016. Instrumented timed up and go: Fall risk assessment based on inertial wearable sensors. In 2016 IEEE International Symposium on Medical Measurements and Applications (MeMeA). IEEE, Benevento, Italy, 1--6.Google Scholar
- P S Sousa, D Sabugueiro, V Felizardo, R Couto, I Pires, and N M Garcia. 2015. mHealth Sensors and Applications for Personal Aid BT - Mobile Health: A Technology Road Map. Springer International Publishing, Switzerland, 265--281.Google Scholar
- C Tacconi, S Mellone, and L Chiari. 2011. Smartphone-based applications for investigating falls and mobility. In 2011 5th International Conference on Pervasive Computing Technologies for Healthcare (PervasiveHealth) and Workshops. IEEE, Dublin, Ireland, 258--261.Google Scholar
- Narseo Vallina-Rodriguez and Narseo Vallina-Rodriguez. 2011. ErdOS: Achieving Energy Savings in Mobile OS. Procedings of MobiArch'11 1 (2011), 37--42. Google Scholar
- Z Yang, C Song, F Lin, J Langan, and W Xu. 2017. Empowering a Gait Feature-Rich Timed-Up-and-Go System for Complex Ecological Environments. In 2017 IEEE/ACM International Conference on Connected Health: Applications, Systems and Engineering Technologies (CHASE). IEEE, Philadelphia, USA, 340--347. Google Scholar
- Z Yang, C Song, F Lin, J Langan, and W Xu. 2018. A Smart Environment-Adapting Timed-Up-and-Go System Powered by Sensor-Embedded Insoles. IEEE Internet of Things Journal 1 (2018), 1.Google Scholar
- E. Zdravevski, P. Lameski, V. Trajkovik, A. Kulakov, I. Chorbev, R. Goleva, N. Pombo, and N. Garcia. 2017. Improving Activity Recognition Accuracy in Ambient-Assisted Living Systems by Automated Feature Engineering. IEEE Access 5 (2017), 5262--5280.Google Scholar
Index Terms
Smartphone-based automatic measurement of the results of the Timed-Up and Go test

Susanna Spinsante

Comments