PhysioLab - a multivariate physiological computing toolbox for ECG, EMG and EDA signals: a case of study of cardiorespiratory fitness assessment in the elderly population


The exponential increase of wearable health-tracking technologies offers new possibilities but also poses new challenges in signal processing to enable fitness monitoring through multimodal physiological recordings. Although there are several software tools used for post-processing in physiological computing applications, limitations in the analysis, incorporating signals from multiple sources, integrating contextual information and providing information visualization tools prevent a widespread use of this technology. To address these issues, we introduce PhysioLab, a multimodal processing Matlab tool for the data analysis of Electromyography (EMG), Electrocardiography (ECG) and Electrodermal Activity (EDA). The software is intended to facilitate the processing and comprehension of multimodal physiological data with the aim of assessing fitness in several domains. A unique feature of PhysioLab is that is informed by normative data grouped by age and sex, allowing contextualization of data based on users’ demographics. Besides signal processing, PhysioLab includes a novel approach to multivariable data visualization with the aim of simplifying interpretation by non-experts users. The system computes a set of ECG features based on heart rate variability analysis, EMG parameters to quantify force and fatigue levels, and galvanic skin level/responses from EDA signals. Furthermore, PhysioLab provides compatibility with data from multiple low-cost wearable sensors. We conducted an experiment with 17 community-dwelling older adults (64.5 ± 6.4) to assess the feasibility of the tool in characterizing cardiorespiratory profiles during physical activity. Correlation analyses and regression models showed significant interactions between physiology and fitness evaluations. Our results suggest novel ways that physiological parameters could be effectively used to complement traditional fitness assessment.

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  1. 1.

  2. 2.

  3. 3.

  4. 4.


  1. 1.

    Al Hazzouri AZ, Haan MN, Deng Y, Neuhaus J, Yaffe K (2014) Reduced heart rate variability is associated with worse cognitive performance in elderly Mexican Americans. Hypertension 63(1):181–187

    Article  Google Scholar 

  2. 2.

    Albinet CT, Boucard G, Bouquet CA, Audiffren M (2010) Increased heart rate variability and executive performance after aerobic training in the elderly. Eur J Appl Physiol 109(4):617–624

    Article  Google Scholar 

  3. 3.

    Bartlett R (2007) Introduction to sports biomechanics: Analysing human movement patterns. Routledge

  4. 4.

    Blanch A, Balada F, Aluja A (2013) Presentation and AcqKnowledge: an application of software to study human emotions and individual differences. Comput Methods Prog Biomed 110(1):89–98

    Article  Google Scholar 

  5. 5.

    Boucsein W (2012) Electrodermal activity. Springer Science & Business Media

  6. 6.

    Cacioppo JT, Tassinary LG, Berntson G (2007) Handbook of psychophysiology. Cambridge University Press

  7. 7.

    Cardiology TFotESo, Cardiology TFotESo (1996) The north American Society of Pacing and Electrophysiology. Heart rate variability: standards of measurement, physiological interpretation and clinical use. Circulation 93(5):1043–1065

    Article  Google Scholar 

  8. 8.

    Cifrek M, Medved V, Tonković S, Ostojić S (2009) Surface EMG based muscle fatigue evaluation in biomechanics. Clin Biomech 24(4):327–340

    Article  Google Scholar 

  9. 9.

    Compostella L, Nicola R, Tiziana S, Caterina C, Fabio B (2014) Autonomic dysfunction predicts poor physical improvement after cardiac rehabilitation in patients with heart failure. Research in Cardiovascular Medicine 3(4):e25237.

  10. 10.

    da Silva HP, Guerreiro J, Lourenço A, Fred AL, Martins R (2014). BITalino: A Novel Hardware Framework for Physiological Computing. In: PhyCS, Citeseer, pp 246–253

  11. 11.

    Dawson ME, Schell AM, Filion DL (2007) 7 The Electrodermal System. Handbook of psychophysiology 159

  12. 12.

    De Luca CJ (1997) The use of surface electromyography in biomechanics. J Appl Biomech 13:135–163

    Article  Google Scholar 

  13. 13.

    Edlin G, Golanty E (2012) Health & wellness. Jones & Bartlett Publishers

  14. 14.

    Ernst G (2014) Heart rate variability. Springer

  15. 15.

    Fairclough SH (2009) Fundamentals of physiological computing. Interact Comput 21(1):133–145

    Article  Google Scholar 

  16. 16.

    Gacek A, Pedrycz W (2011) ECG signal processing, classification and interpretation: a comprehensive framework of computational intelligence. Springer Science & Business Media

  17. 17.

    Goldberger AL (2012) Clinical electrocardiography: a simplified approach. Elsevier Health Sciences

  18. 18.

    Gupta R, Mitra M, Bera J (2014) ECG Acquisition in a Computer. In: ECG Acquisition and Automated Remote Processing. Springer, pp 51–72

  19. 19.

    Hallman DM, Mathiassen SE, Lyskov E (2015) Long-term monitoring of physical behavior reveals different cardiac responses to physical activity among subjects with and without chronic neck pain. BioMed Res Int 2015:11.

  20. 20.

    Hansen AL, Johnsen BH, Thayer JF (2003) Vagal influence on working memory and attention. Int J Psychophysiol 48(3):263–274

    Article  Google Scholar 

  21. 21.

    Hautala AJ, Kiviniemi AM, Mäkikallio TH, Kinnunen H, Nissilä S, Huikuri HV, Tulppo MP (2006) Individual differences in the responses to endurance and resistance training. Eur J Appl Physiol 96(5):535–542

    Article  Google Scholar 

  22. 22.

    Heyward VH, Gibson A (2014) Advanced Fitness Assessment and Exercise Prescription 7th Edition. Human Kinetics

  23. 23.

    Jenkins SP (2005) Sports science handbook: the essential guide to kinesiology, sport and exercise science volume 2 (IZ). Multi-Science, Brentwood

    Google Scholar 

  24. 24.

    Jung J, Heisel A, Butz B, Fries R, Schieffer H, Tscholl D, Schäfers HJ (1997) Factors influencing heart rate variability in patients with severe aortic valve disease. Clin Cardiol 20(4):341–344

    Article  Google Scholar 

  25. 25.

    Kaikkonen KM, Korpelainen RI, Tulppo MP, Kaikkonen HS, Vanhala ML, Kallio MA, Keinänen-Kiukaanniemi SM, Korpelainen JT (2014) Physical activity and aerobic fitness are positively associated with heart rate variability in obese adults. J Phys Act Health 11(8):1614–1621

    Article  Google Scholar 

  26. 26.

    Kamen G, Gabriel D (2010) Essentials of electromyography. Human Kinetics

  27. 27.

    Keytel L, Goedecke J, Noakes T, Hiiloskorpi H, Laukkanen R, Van Der Merwe L, Lambert E (2005) Prediction of energy expenditure from heart rate monitoring during submaximal exercise. J Sports Sci 23(3):289–297

    Article  Google Scholar 

  28. 28.

    Kiviniemi AM, Hautala AJ, Kinnunen H, Tulppo MP (2007) Endurance training guided individually by daily heart rate variability measurements. Eur J Appl Physiol 101(6):743–751

    Article  Google Scholar 

  29. 29.

    Kleiger RE, Stein PK, Bigger JT (2005) Heart rate variability: measurement and clinical utility. Annals of Noninvasive Electrocardiology 10(1):88–101

    Article  Google Scholar 

  30. 30.

    Loue S, Sajatovic M (2008) Encyclopedia of aging and public health. Springer Science & Business Media

  31. 31.

    Macfarlane PW, van Oosterom A, Janse M (2010) Comprehensive electrocardiology, vol 4. Springer Science & Business Media

  32. 32.

    Mahinrad S, Van Heemst D, Macfarlane P, Stott D, Jukema J, De Craen A, Sabayan B (2015) 4C. 03: short-term heart rate variability and cognitive function in older subjects at risk of cardiovascular disease. J Hypertens 33:e57

    Article  Google Scholar 

  33. 33.

    Muñoz JE, Pereira F, Karapanos E (2016) Workload management through glanceable feedback: The role of heart rate variability. In: e-Health Networking, Applications and Services (Healthcom), 2016 I.E. 18th International Conference on, IEEE, pp 1–6

  34. 34.

    Niskanen J-P, Tarvainen MP, Ranta-Aho PO, Karjalainen PA (2004) Software for advanced HRV analysis. Comput Methods Prog Biomed 76(1):73–81

    Article  Google Scholar 

  35. 35.

    Nuwer R (2013) Armband adds a twitch to gesture control. New Scientist 217(2906):21

    Article  Google Scholar 

  36. 36.

    Pfurtscheller G, Brunner C, Schlögl A, Da Silva FL (2006) Mu rhythm (de) synchronization and EEG single-trial classification of different motor imagery tasks. NeuroImage 31(1):153–159

    Article  Google Scholar 

  37. 37.

    Placido da Silva H, Fred A, Martins RP (2014) Biosignals for everyone. Pervasive Computing, IEEE 13(4):64–71

    Article  Google Scholar 

  38. 38.

    Poon LW, Chodzko-Zajko WJ, Tomporowski PD (2006) Active living, cognitive functioning, and aging, vol 1. Human Kinetics

  39. 39.

    Prokasy W (2012) Electrodermal activity in psychological research. Elsevier

  40. 40.

    Rikli RE, Jones CJ (2012) Senior fitness test manual. Human Kinetics

  41. 41.

    Schäfer A, Vagedes J (2013) How accurate is pulse rate variability as an estimate of heart rate variability?: a review on studies comparing photoplethysmographic technology with an electrocardiogram. Int J Cardiol 166(1):15–29

    Article  Google Scholar 

  42. 42.

    Shaffer F, McCraty R, Zerr CL (2014) A healthy heart is not a metronome: an integrative review of the heart's anatomy and heart rate variability. Front Psychol 5:1040.

  43. 43.

    Shah AJ, Su S, Veledar E, Bremner JD, Goldstein FC, Lampert R, Goldberg J, Vaccarino V (2011) Is heart rate variability related to memory performance in middle aged men? Psychosom Med 73(6):475

    Article  Google Scholar 

  44. 44.

    Singh B, Bharti N (2015) Software tools for heart rate variability analysis. International Journal of Recent Scientific Research 6(4):3501–3506

    Google Scholar 

  45. 45.

    Smith DR (2009) Use of the 6-Min Walk Test: A Pro and Con Review. PCCSU Article 6 (09). Accessed 17 Aug 2017

  46. 46.

    Soleymani M, Villaro-Dixon F, Pun T, Chanel G (2017) Toolbox for Emotional fEAture extraction from Physiological signals (TEAP). Front ICT 4:1.

  47. 47.

    Stein PK, Ehsani AA, Domitrovich PP, Kleiger RE, Rottman JN (1999) Effect of exercise training on heart rate variability in healthy older adults. Am Heart J 138(3):567–576

    Article  Google Scholar 

  48. 48.

    Tanaka H, Monahan KD, Seals DR (2001) Age-predicted maximal heart rate revisited. J Am Coll Cardiol 37(1):153–156

    Article  Google Scholar 

  49. 49.

    Tarvainen MP, Niskanen J-P, Lipponen J, Ranta-Aho P, Karjalainen P (2009). Kubios HRV—a software for advanced heart rate variability analysis. In: 4th European Conference of the International Federation for Medical and Biological Engineering, Springer, pp 1022–1025

  50. 50.

    Thongpanja S, Phinyomark A, Phukpattaranont P, Limsakul C (2013) Mean and median frequency of EMG signal to determine muscle force based on time-dependent power spectrum. Elektronika ir Elektrotechnika 19(3):51–56

    Article  Google Scholar 

  51. 51.

    Uth N, Sørensen H, Overgaard K, Pedersen PK (2004) Estimation of VO2max from the ratio between HRmax and HRrest–the heart rate ratio method. Eur J Appl Physiol 91(1):111–115

    Article  Google Scholar 

  52. 52.

    Vidaurre C, Sander TH, Schlögl A (2011) BioSig: the free and open source software library for biomedical signal processing. Comput Intell Neurosci 2011:12.

  53. 53.

    Voss A, Schroeder R, Heitmann A, Peters A, Perz S (2015) Short-term heart rate variability—influence of gender and age in healthy subjects. PLoS One 10(3):e0118308.

  54. 54.

    Wagner J (2006) Augsburg biosignal toolbox (AuBT) user guide

  55. 55.

    Wang H-M, Huang S-C (2012) SDNN/RMSSD as a surrogate for LF/HF: a revised investigation. Modelling and Simulation in Engineering 2012:16

    Article  Google Scholar 

  56. 56.

    Ware JE, Kosinski M, Dewey JE, Gandek B (2000) SF-36 health survey: manual and interpretation guide. Quality Metric Inc.

  57. 57.

    Wilhelm F, Peyk P (2005) ANSLAB: Autonomic Nervous System Laboratory (Version 4.0). Available at the SPR Software Repository: Accessed 17 Aug 2017

  58. 58.

    Wilkinson N (2014) Personal Training: Theory and Practice. Routledge

  59. 59.

    Zhang F, Chen S, Zhang H, Zhang X, Li G (2014) Bioelectric signal detrending using smoothness prior approach. Med Eng Phys 36(8):1007–1013

    Article  Google Scholar 

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This work is supported by the Portuguese Foundation for Science and Technology through the Augmented Human Assistance project (CMUPERI/HCI/0046/2013), Projeto Estratégico LA 9 - UID/EEA/50009/2013 and ARDITI (Agência Regional para o Desenvolvimento da Investigação, Tecnologia e Inovação) institution.

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Correspondence to John Edison Muñoz.

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Muñoz, J.E., Gouveia, E.R., Cameirão, M.S. et al. PhysioLab - a multivariate physiological computing toolbox for ECG, EMG and EDA signals: a case of study of cardiorespiratory fitness assessment in the elderly population. Multimed Tools Appl 77, 11521–11546 (2018).

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  • Physiological computing
  • Electrocardiography
  • Electromyography
  • Electrodermal activity
  • Cardiorespiratory fitness
  • Elderly