Nothing Special   »   [go: up one dir, main page]
Skip to main content

Advertisement

Springer Nature Link
Log in

Sensors are Capable to Help in the Measurement of the Results of the Timed-Up and Go Test? A Systematic Review

  • Mobile & Wireless Health
  • Published:
Journal of Medical Systems Aims and scope Submit manuscript

Abstract

The analysis of movements used in physiotherapy areas related to the elderly is becoming increasingly important due to factors such as the increase in the average life expectancy and the rate of elderly people over the whole population. In this systematic review, we try to determine how the inertial sensors embedded in mobile devices are exploited for the measurement of the different parameters involved in the Timed-Up and Go test. The results show the mobile devices equipped with onboard motion sensors can be exploited for these types of studies: the most commonly used sensors are the magnetometer, accelerometer and gyroscope available in consumer off-the-shelf smartphones. Other features typically used to evaluate the Timed-Up and Go test are the time duration, the angular velocity and the number of steps, allowing for the recognition of some diseases as well as the measurement of the subject’s performance during the test execution.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2

Similar content being viewed by others

Data availability

There are no data acquired for this research.

References

  1. World facts and statistics on disabilities and disability issues. www.cdc.gov/ncbddd/disabilityandhealth/dhds.html. (accessed Dec. 17, 2018).

  2. Population ages 65 and above (% of total) | Data. https://data.worldbank.org/indicator/SP.POP.65UP.TO.ZS#fromHistory (accessed Nov. 24, 2018).

  3. WHO | Facts about ageing, WHO, 2015. https://www.who.int/ageing/about/facts/en/ (accessed Dec. 07, 2018).

  4. Dobre, C., Mavromoustakis, C.X, Garcia, N., Goleva, R. I., and Mastorakis, G., Ambient assisted living and enhanced living environments: principles, technologies and control. Butterworth-Heinemann, 2016.

  5. Garcia, N.M., and Rodrigues, J.J.P.C., Ambient assisted living. CRC Press, 2015.

  6. Reis, S., Felizardo, V., Pombo, N., and Garcia, N., 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 - DSAI 2016, pp. 240–245, 2016, https://doi.org/10.1145/3019943.3019978.

  7. Galán-Mercant, A., Barón-López, F.J., Labajos-Manzanares, M.T., and Cuesta-Vargas, A.I., Reliability and criterion-related validity with a smartphone used in timed-up-and-go test. BioMed. Eng. OnLine, 13, 1, 156, 2014, https://doi.org/10.1186/1475-925X-13-156.

    Article  PubMed  PubMed Central  Google Scholar 

  8. Yang, Z., Song, C., Lin, F., Langan, J., and Xu, W., 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), 2017, pp. 340–347, https://doi.org/10.1109/CHASE.2017.117.

  9. Silva, J. and Sousa, I., Instrumented timed up and go: Fall risk assessment based on inertial wearable sensors. In: 2016 IEEE International Symposium on Medical Measurements and Applications (MeMeA), pp. 1–6, https://doi.org/10.1109/MeMeA.2016.7533778. 2016.

  10. BITalino - Biomedical Equipment | Low-Cost Toolkit. http://bitalino.com/en/ (accessed Dec. 07, 2018).

  11. Pires, I.M., Garcia, N.M., Pombo, N., and Flórez-Revuelta, F., “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, https://doi.org/10.3390/s16020184.

  12. Sousa, P.S., Sabugueiro, D., Felizardo, V., Couto, R., Pires, I., and Garcia, N.M., mHealth Sensors and Applications for Personal Aid BT - Mobile Health: A Technology Road Map. In: Adibi, S. (Ed.), Cham: Springer International Publishing, 2015, pp. 265–281.

  13. Pires, I.M., Garcia, N.M., Pombo, N., Flórez-Revuelta, F., and Spinsante, S., Approach for the development of a framework for the identification of Activities of Daily Living using sensors in mobile devices. Sensors (Switzerland). 18, 2, 2018, https://doi.org/10.3390/s18020640.

  14. Pires, I.M., Garcia, N.M., Pombo, N., and Flórez-revuelta, F., Limitations of the Use of Mobile Devices and Smart Environments for the Monitoring of Ageing People, no. Ict4awe, pp. 269–275, 2018.

  15. Zdravevski, E. et al., Automation in systematic, scoping and rapid reviews by an NLP toolkit: a case study in enhanced living environments in Enhanced Living Environments, vol. 11369, I. Ganchev, N. M. Garcia, C. Dobre, C. X. Mavromoustakis, and R. Goleva, Eds. Cham: Springer International Publishing, 2019, pp. 1–18.

    Google Scholar 

  16. Yang, Z., Song, C., Lin, F., Langan, J., and Xu, W., A Smart Environment-Adapting Timed-Up-and-Go System Powered by Sensor-Embedded Insoles. IEEE Internet of Things J. 1, 2018, https://doi.org/10.1109/JIOT.2018.2844837.

  17. Meigal, A., Reginya, S., Gerasimova-Meigal, L., Prokhorov, K., and Moschevikin, A., Analysis of Human Gait Based on Smartphone Inertial Measurement Unit: A Feasibility Study. In: Proceedings of the 22st Conference of Open Innovations Association FRUCT, p. 21:151--21:158, 2018.

  18. Bao, T. et al., Effects of long-term balance training with vibrotactile sensory augmentation among community-dwelling healthy older adults: a randomized preliminary study. J. Neuroeng. Rehabil. 15, 1: 5, 2018, https://doi.org/10.1186/s12984-017-0339-6.

    Article  PubMed  PubMed Central  Google Scholar 

  19. Hellmers, S. et al., Towards an automated unsupervised mobility assessment for older people based on inertial TUG measurements. Sensors (Basel, Switzerland) 18, 10, 2018, https://doi.org/10.3390/s18103310.

  20. Chigateri, N. G., Kerse, N., Wheeler, L., MacDonald, B., and Klenk, J., Validation of an accelerometer for measurement of activity in frail older people. Gait Posture 66: 114–117, 2018, https://doi.org/10.1016/j.gaitpost.2018.08.024.

    Article  PubMed  Google Scholar 

  21. Mellone, S., Tacconi, S., and Chiari, L., Validity of a Smartphone-based instrumented Timed Up and Go. Gait Posture. 36, 1: 163–165, 2012, https://doi.org/10.1016/j.gaitpost.2012.02.006.

    Article  PubMed  Google Scholar 

  22. Madhushri, P., Jovanov, E., Milenkovic, A., and Shtessel, Y., A model based analysis of optimality of sit-to-stand transition. In: 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2017, pp. 2398–2401, https://doi.org/10.1109/EMBC.2017.8037339.

  23. Beyea, J., McGibbon, C.A., Sexton, A., Noble, J., and O’Connell, C., Convergent validity of a wearable sensor system for measuring sub-task performance during the timed Up-and-Go Test. Sensors (Basel, Switzerland). 17, 4: 934, 2017, https://doi.org/10.3390/s17040934.

    Article  Google Scholar 

  24. Coni, A., Mellone, S., Colpo, M., Bandinelli, S., and Chiari, L., Influence of age and gender on sensor-based functional measures: A factor analysis approach. In: 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2015, pp. 5054–5057, https://doi.org/10.1109/EMBC.2015.7319527.

  25. Salarian, A., Horak, F. B., Zampieri, C., Carlson-Kuhta, P., Nutt, J.G., and Aminian, K., iTUG, a sensitive and reliable measure of mobility. IEEE Trans. Neural Syst. Rehabil. Eng. 18, 3: 303–310, 2010, https://doi.org/10.1109/TNSRE.2010.2047606.

    Article  PubMed  PubMed Central  Google Scholar 

  26. Suppa, A. et al., l-DOPA and freezing of gait in Parkinson’s disease: objective assessment through a wearable wireless system. Front. Neurol. 8: 406, 2017, https://doi.org/10.3389/fneur.2017.00406.

    Article  PubMed  PubMed Central  Google Scholar 

  27. Harmelink, K.E.M., Zeegers, A.V.C.M., Tonis, T.M., Hullegie, W., Nijhuis-van der Sanden, M.W.G., and Staal, J.B., The effectiveness of the use of a digital activity coaching system in addition to a two-week home-based exercise program in patients after total knee arthroplasty: study protocol for a randomized controlled trial. BMC Musculoskelet. Disord. 18, 1: 290, 2017, https://doi.org/10.1186/s12891-017-1647-5.

    Article  PubMed  PubMed Central  Google Scholar 

  28. Madhushri, P., Dzhagaryan, A.A., Jovanov, E., and Milenkovic, A., A Smartphone Application Suite for Assessing Mobility. In: 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2016, pp. 3117–3120, https://doi.org/10.1109/EMBC.2016.7591389.

  29. Cippitelli, E., Gasparrini, S., Gambi, E., and Spinsante, S., An Integrated Approach to Fall Detection and Fall Risk Estimation Based on RGB-Depth and Inertial Sensors. In: Proceedings of the 7th International Conference on Software Development and Technologies for Enhancing Accessibility and Fighting Info-exclusion, 2016, pp. 246–253, https://doi.org/10.1145/3019943.3019979.

  30. Pedrana, A., Comotti, D., Locatelli, P., and Traversi, G., Development of a telemedicine-oriented gait analysis system based on inertial sensors. In: 2018 7th International Conference on Modern Circuits and Systems Technologies (MOCAST), 2018, pp. 1–4, https://doi.org/10.1109/MOCAST.2018.8376592.

  31. Harris, A., True, H., Hu, Z., Cho, J, Fell, N., and Sartipi, M., Fall recognition using wearable technologies and machine learning algorithms. In: 2016 IEEE International Conference on Big Data (Big Data), 2016, pp. 3974–3976, https://doi.org/10.1109/BigData.2016.7841080.

  32. Williams, B., Allen, B., True, H., Fell, N., Levine, D., and Sartipi, M., A real-time, mobile timed up and go system. In: 2015 IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks (BSN), 2015, pp. 1–6, https://doi.org/10.1109/BSN.2015.7299382.

  33. Galan-Mercant, A. and Cuesta-Vargas, A.I., Clinical frailty syndrome assessment using inertial sensors embedded in smartphones. Physiol. Meas. 36, 9: 1929, 2015.

    Article  CAS  Google Scholar 

  34. Milosevic, M., Jovanov, E., and Milenković, A., Quantifying Timed-Up-and-Go test: A smartphone implementation. In: 2013 IEEE International Conference on Body Sensor Networks, 2013, pp. 1–6, https://doi.org/10.1109/BSN.2013.6575478.

  35. Dzhagaryan, A., Milenkovic, A., Jovanov, E., and Milosevic, M., Smart Button: A wearable system for assessing mobility in elderly. In: 2015 17th International Conference on E-health Networking, Application & Services (HealthCom), 2015, pp. 416–421, https://doi.org/10.1109/HealthCom.2015.7454536.

  36. Schliessmann, D. et al., 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. J. Neuroeng. Rehabil. 15, 1: 44, 2018, https://doi.org/10.1186/s12984-018-0389-4.

    Article  PubMed  PubMed Central  Google Scholar 

  37. Greene, B.R., Doheny, E.P, Kenny, R.A., and Caulfield, B., Classification of frailty and falls history using a combination of sensor-based mobility assessments. Physiol. Meas. 35, 10, 2053, 2014.

  38. Galán-Mercant, A. and Cuesta-Vargas, A.I., Differences in trunk accelerometry between frail and non-frail elderly persons in functional tasks. BMC Res. Notes 7, 1: 100, 2014, https://doi.org/10.1186/1756-0500-7-100.

    Article  PubMed  PubMed Central  Google Scholar 

  39. Galán-Mercant, A. and Cuesta-Vargas, A. I., Mobile Romberg test assessment (mRomberg). BMC Res. Notes 7, 1: 640, 2014, https://doi.org/10.1186/1756-0500-7-640.

    Article  PubMed  PubMed Central  Google Scholar 

  40. Greene, B.R., Healy, M., Rutledge, S., Caulfield, B., and Tubridy, N., Quantitative assessment of multiple sclerosis using inertial sensors and the TUG test. In: 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2014, pp. 2977–2980, https://doi.org/10.1109/EMBC.2014.6944248.

  41. Tacconi, C., Mellone, S., and Chiari, L., Smartphone-based applications for investigating falls and mobility. In: 2011 5th International Conference on Pervasive Computing Technologies for Healthcare (PervasiveHealth) and Workshops, 2011, pp. 258–261, https://doi.org/10.4108/icst.pervasivehealth.2011.246060.

  42. Mellone, S., Tacconi, C., Schwickert, L., Klenk, J., Becker, C., and Chiari, L., Smartphone-based solutions for fall detection and prevention: the FARSEEING approach. Zeitschrift fur Gerontologie und Geriatrie. 45, 8: 722–727, 2012, https://doi.org/10.1007/s00391-012-0404-5.

    Article  CAS  PubMed  Google Scholar 

  43. Bernhard, F. P. et al., Wearables for gait and balance assessment in the neurological ward - study design and first results of a prospective cross-sectional feasibility study with 384 inpatients. BMC Neurol. 18, 1: 114, 2018, https://doi.org/10.1186/s12883-018-1111-7.

    Article  PubMed  PubMed Central  Google Scholar 

  44. Palmerini, L., Mellone, S., Rocchi, L., and Chiari, L., Dimensionality reduction for the quantitative evaluation of a smartphone-based Timed Up and Go test. In: 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2011, pp. 7179–7182, https://doi.org/10.1109/IEMBS.2011.6091814.

  45. King, R.C., Atallah, L., Wong, C., Miskelly, F., and Yang, G., Elderly Risk Assessment of Falls with BSN. In: 2010 International Conference on Body Sensor Networks, 2010, pp. 30–35, https://doi.org/10.1109/BSN.2010.42.

  46. Pires, I., Felizardo, V., Pombo, N., and Garcia, N.M., Limitations of energy expenditure calculation based on a mobile phone accelerometer. In: 2017 International Conference on High Performance Computing & Simulation (HPCS), 2017, pp. 124–127.

  47. Pires, I.M., Andrade, M., Garcia, N.M., Crisóstomo, R., and Florez-Revuelta, F., Measurement of Heel-Rise Test Results using a Mobile Device, Feb. 2015, vol. 2, pp. 9–18, Accessed: May 06, 2020. [Online]. Available: http://www.scitepress.org/documents/2015/53663.

  48. Marques, D.L. et al., Validation of a Method to Determine the Reaction Time in the 30-s Chair Stand Test in Elderly People, 2018.

  49. Garcia, N.M., A Roadmap to the Design of a Personal Digital Life Coach BT - ICT Innovations 2015, 2016, pp. 21–27.

Download references

Acknowledgments

This work is funded by FCT/MEC through national funds and co-funded by FEDER – PT2020 partnership agreement under the project UIDB/EEA/50008/2020 (Este trabalho é financiado pela FCT/MEC através de fundos nacionais e cofinanciado pelo FEDER, no âmbito do Acordo de Parceria PT2020 no âmbito do projeto UIDB/EEA/50008/2020). This work is also funded by National Funds through the FCT - Foundation for Science and Technology, I.P., within the scope of the project UIDB/00742/2020. This article is based upon work from COST Action IC1303–AAPELE–Architectures, Algorithms and Protocols for Enhanced Living Environments and COST Action CA16226–SHELD-ON–Indoor living space improvement: Smart Habitat for the Elderly, supported by COST (European Cooperation in Science and Technology). More information in www.cost.eu. Furthermore we would like to thank the Politécnico de Viseu for their support.

Funding

This work is funded by FCT/MEC through national funds and, when applicable, co-funded by the FEDER-PT2020 partnership agreement under the project UIDB/50008/2020. (Este trabalho é financiado pela FCT/MEC através de fundos nacionais e cofinanciado pelo FEDER, no âmbito do Acordo de Parceria PT2020 no âmbito do projeto UIDB/50008/2020).

Author information

Authors and Affiliations

  1. R&D Unit in Digital Services, Applications and Content, Polytechnic Institute of Castelo Branco, Castelo Branco, Portugal

    Vasco Ponciano & Fernando Reinaldo Ribeiro

  2. Altranportugal, Lisbon, Portugal

    Vasco Ponciano

  3. Instituto de Telecomunicações, Universidade da Beira Interior, Covilhã, Portugal

    Ivan Miguel Pires

  4. Computer Science Department, Polytechnic Institute of Viseu, Viseu, Portugal

    Ivan Miguel Pires

  5. UICISA:E Research Centre, School of Health, Polytechnic Institute of Viseu, Viseu, Portugal

    Ivan Miguel Pires

  6. Department of Information Engineering, Marche Polytechnic University, Ancona, Italy

    Susanna Spinsante

Authors
  1. Vasco Ponciano
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ivan Miguel Pires
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Fernando Reinaldo Ribeiro
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Susanna Spinsante
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

All of the authors have contributed to the structure, content and writing of the paper.

Corresponding author

Correspondence to Vasco Ponciano.

Ethics declarations

Competing interests

There are no competitions interests.

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

This chapter does not contain any studies with human participants or animals performed by any of the authors, because it is a literature review.

Informed consent

There are no needs of an informed consent, because there are no participants in the study, because it is a literature review.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

This article is part of the Topical Collection on Mobile & Wireless Health

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ponciano, V., Pires, I.M., Ribeiro, F.R. et al. Sensors are Capable to Help in the Measurement of the Results of the Timed-Up and Go Test? A Systematic Review. J Med Syst 44, 199 (2020). https://doi.org/10.1007/s10916-020-01666-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10916-020-01666-8

Keywords

Search

Navigation