Posture transition analysis with barometers: contribution to accelerometer-based algorithms

  • 119 Accesses


Posture transitions are one of the most mechanically demanding tasks and are useful to evaluate the motor status of patients with motor impairments, frail individuals or the elderly, among others. So far, wearable inertial systems have been one of the most employed tools in the study of these movements due to their suitable size and weight, being non-invasive systems. These devices are mainly composed of accelerometers and, to a lesser extent, gyroscopes, magnetometers or barometers. Although accelerometers provide the most reliable measurement, detecting activities where a change of altitude is observed, such as some posture transitions, may require additional sensors to reliably detect these activities. In this work, we present an algorithm that combines the information of a barometer and an accelerometer to detect posture transitions and falls. In contrast to other works, we test different activities (where altitude is involved) in order to achieve a reliable classifier against false positives. Furthermore, by means of feature selection methods, we obtain optimal subsets of features for the accelerometer and barometer sensors to contextualise these activities. The selected features are tested through several machine learning classifiers, which are assessed with an evaluation data set. Results show that the inclusion of barometer features in addition to those obtained for an accelerometer clearly enhances the detection accuracy up to a 11%, in terms of geometric mean between sensitivity and specificity, compared to algorithms where only the accelerometer is used. Finally, we have also analysed the computer burden; in this sense, the usage of barometers, in addition to increase the accuracy, also reduces the computational resources required to classify a new pattern, as shown by a reduction in the number of support vectors.

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

Access options

Buy single article

Instant unlimited access to the full article PDF.

US$ 39.95

Price includes VAT for USA

Subscribe to journal

Immediate online access to all issues from 2019. Subscription will auto renew annually.

US$ 99

This is the net price. Taxes to be calculated in checkout.

Fig. 1
Fig. 2
Fig. 3
Fig. 4


  1. 1.

    Salarian A, Russmann H, Vingerhoets FJG et al (2007) Ambulatory monitoring of physical activities in patients with Parkinson’s disease. IEEE Trans Biomed Eng 54:2296–2299

  2. 2.

    Fulk GD, Sazonov E (2012) Using sensors to measure activity in people with stroke. Top Stroke Rehabil 18:746–757.

  3. 3.

    Albert MV, Toledo S, Shapiro M, Kording K (2012) Using mobile phones for activity recognition in Parkinson’s patients. Front Neurol 3:158.

  4. 4.

    Samà A, Pérez-López C, Rodríguez-Martín D et al (2017) Estimating bradykinesia severity in Parkinson’s disease by analysing gait through a waist-worn sensor. Comput Biol Med 84:114–123.

  5. 5.

    Rodríguez-Martín D, Samà A, Pérez-López C et al (2015) Posture transition identification on PD patients through a SVM-based technique and a single waist-worn accelerometer. Neurocomputing 164:144–153.

  6. 6.

    Chung P-C, Hsu Y-L, Wang C-Y et al (2012) Gait analysis for patients with Alzheimer’s disease using a triaxial accelerometer. In: IEEE international symposium on circuits and systems. IEEE, pp 1323–1326

  7. 7.

    Wang W-H, Chung P-C, Hsu Y-L et al (2013) Inertial-sensor-based balance analysis system for patients with Alzheimer’s disease. In: Conference on technologies and applications of artificial intelligence. IEEE, pp 128–133

  8. 8.

    Lee I-M, Shiroma EJ (2014) Using accelerometers to measure physical activity in large-scale epidemiological studies: issues and challenges. Br J Sports Med 48:197–201.

  9. 9.

    Troiano RP, McClain JJ, Brychta RJ, Chen KY (2014) Evolution of accelerometer methods for physical activity research. Br J Sports Med 48:1019–1023.

  10. 10.

    Godfrey A, Conway R, Meagher D, OLaighin G (2008) Direct measurement of human movement by accelerometry. Med Eng Phys 30:1364–1386.

  11. 11.

    STMicroelectronics (2016) LPS25H.MEMS pressure sensor: 260–1260 hPa absolute digital output barometer. DocID023722 Rev 5, 45

  12. 12.

    TE Connectivity (2015) MS5637-02BA03. Low voltage barometric pressure sensor, 18

  13. 13.

    Robert Bosch GmbH. Datasheet BMP280 digital pressure sensor

  14. 14.

    Rodriguez-Martin D, Samà A, Perez-Lopez C et al (2013) SVM-based posture identification with a single waist-located triaxial accelerometer. Expert Syst Appl 40:7203–7211.

  15. 15.

    Taraldsen K, Chastin SFM, Riphagen II et al (2012) Physical activity monitoring by use of accelerometer-based body-worn sensors in older adults: a systematic literature review of current knowledge and applications. Maturitas 71:13–19.

  16. 16.

    Margarito J, Helaoui R, Bianchi AM et al (2016) User-independent recognition of sports activities from a single wrist-worn accelerometer: a template-matching-based approach. IEEE Trans Biomed Eng 63:788–796.

  17. 17.

    Peng W, Crouse JC, Lin J-H (2013) Using active video games for physical activity promotion: a systematic review of the current state of research. Heal Educ Behav 40:171–192.

  18. 18.

    Yang CC, Hsu YL (2010) A review of accelerometry-based wearable motion detectors for physical activity monitoring. Sensors 10:7772–7788.

  19. 19.

    Culhane KM, O’Connor M, Lyons D, Lyons GM (2005) Accelerometers in rehabilitation medicine for older adults. Age Ageing 34:556–560.

  20. 20.

    Najafi B, Aminian K, Paraschiv-Ionescu A et al (2003) Ambulatory system for human motion analysis using a kinematic sensor: monitoring of daily physical activity in the elderly. IEEE Trans Biomed Eng 50:711–723

  21. 21.

    Gouwanda D, Senanayake SM (2011) Periodical gait asymmetry assessment using real-time wireless gyroscopes gait monitoring system. J Med Eng Technol 35:432–440

  22. 22.

    Li Q, Stankovic JA, Hanson MA et al (2009) Accurate, fast fall detection using gyroscopes and accelerometer-derived posture information. In: 2009 6th international workshop on wearable and implantable body sensor networks, pp 138–143

  23. 23.

    Schepers HM, Roetenberg D, Veltink PH (2010) Ambulatory human motion tracking by fusion of inertial and magnetic sensing with adaptive actuation. Med Biol Eng Comput 48:27–37.

  24. 24.

    Preece SJ, Goulermas JY, Kenney LPJ et al (2009) Activity identification using body-mounted sensors—a review of classification techniques. Physiol Meas 30:R1–R33.

  25. 25.

    Reyes-Ortiz J-L, Oneto L, Samà A et al (2016) Transition-aware human activity recognition using smartphones. Neurocomputing 171:754–767.

  26. 26.

    Lara OD, Labrador MA (2013) A survey on human activity recognition using wearable sensors. IEEE Commun Surv Tutor 15:1192–1209.

  27. 27.

    Massé F, Bourke AK, Chardonnens J et al (2014) Suitability of commercial barometric pressure sensors to distinguish sitting and standing activities for wearable monitoring. Med Eng Phys 36:739–744.

  28. 28.

    Masse F, Gonzenbach R, Paraschiv-Ionescu A et al (2016) Wearable barometric pressure sensor to improve postural transition recognition of mobility-impaired stroke patients. IEEE Trans Neural Syst Rehabil Eng 24:1210–1217.

  29. 29.

    Massé F, Gonzenbach RR, Arami A et al (2015) Improving activity recognition using a wearable barometric pressure sensor in mobility-impaired stroke patients. J Neuroeng Rehabil 12:72.

  30. 30.

    Moncada-Torres A, Leuenberger K, Gonzenbach R et al (2014) Activity classification based on inertial and barometric pressure sensors at different anatomical locations. Physiol Meas 35:1245–1263.

  31. 31.

    Gjoreski M, Gjoreski H, Luštrek M, Gams M (2016) How accurately can your wrist device recognize daily activities and detect falls? Sensors 16:800.

  32. 32.

    Tolkiehn M, Atallah L, Lo B, Yang G-Z (2011) Direction sensitive fall detection using a triaxial accelerometer and a barometric pressure sensor. In: 33rd annual international conference of the IEEE engineering in medicine and biology society, pp 369–372

  33. 33.

    Bianchi F, Redmond SJ, Narayanan MR et al (2010) Barometric pressure and triaxial accelerometry-based falls event detection. IEEE Trans Neural Syst Rehabil Eng 18:619–627.

  34. 34.

    Samà A, Perez-Lopez C, Rodriguez-Martin D et al (2013) A heterogeneous database for movement knowledge extraction in Parkinson’s disease. In: European symposium on artificial neural networks, computational intelligence and machine learning

  35. 35.

    Rodríguez-Martín D, Pérez-López C, Samà A et al (2017) A waist-worn inertial measurement unit for long-term monitoring of Parkinson’s disease patients. Sensors 17:827.

  36. 36.

    Zhou S, Shan Q, Fei F et al (2009) Gesture recognition for interactive controllers using MEMS motion sensors. In: 2009 4th IEEE international conference on nano/micro engineered and molecular systems, pp 935–940

  37. 37.

    Antonsson EK, Mann RW (1985) The frequency content of gait. J Biomech 18:39–47

  38. 38.

    Kerr KM, White JA, Barr DA, Mollan RAB (1997) Analysis of the sit-stand-sit cycle in normal subjects movement. Clin Biomech 12:236–245

  39. 39.

    Anguita D, Ghio A, Oneto L et al (2013) Energy efficient smartphone-based activity recognition using fixed-point arithmetic. Special session in ambient assisted living: home care. J Univers Comput Sci 19:1295–1314

  40. 40.

    Samà A, Rodríguez-Martín D, Pérez-López C et al (2017) Determining the optimal features in freezing of gait detection through a single waist accelerometer in home environments. Pattern Recognit Lett.

  41. 41.

    Robnik-Sikonja M, Kononenko I (2003) Theoretical and empirical analysis of ReliefF and RReliefF. Machine 53:23–69

  42. 42.

    Hall M, Frank E, Holmes G et al (2009) The WEKA data mining software. ACM SIGKDD Explor Newsl 11:10.

  43. 43.

    Rodríguez-Martín D, Samà A, Pérez-López C, Català A (2017) Posture transitions identification based on a triaxial accelerometer and a barometer sensor. Adv Comput Intell.

  44. 44.

    Faust O, Hagiwara Y, Jen Hong T et al (2018) Deep learning for healthcare applications based on physiological signals: a review. Comput Methods Programs Biomed 161:1–13.

Download references


This project has been performed within the framework of MASPARK Project which is funded by La Fundació La Marató de TV3 436/C/2014. Authors would like to thank to all participants who took part of these tests.

Author information

Correspondence to Daniel Rodríguez-Martín.

Ethics declarations

Conflict of interest

All authors declare that they have no conflict of interest.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Rodríguez-Martín, D., Samà, A., Pérez-López, C. et al. Posture transition analysis with barometers: contribution to accelerometer-based algorithms. Neural Comput & Applic 32, 335–349 (2020).

Download citation


  • Accelerometer
  • Barometer
  • Human activity recognition