Ambient assistance service for fall and heart problem detection

  • Amina MakhloufEmail author
  • Isma Boudouane
  • Nadia Saadia
  • Amar Ramdane Cherif
Original Research


Continuous monitoring of vital signs and activity measures has the potential to provide remote health monitoring and rapid detection of critical events such as heart attacks and falls. This paper proposes a multimodal system for monitoring the elderly at their homes. The system proposed contains three ambient assistance services (Fall detection, Heart disorder detection and Location) and an emergency service. A three-axis accelerometer, pulse oximeter and eight photoelectric sensors are applied for fall detection, cardiac problems detection and location respectively. The emergency service provides data fusion of this sensors and sends detailed information about the statue of the followed person to the doctor. This multimodal system is modeled by Colored Timed and Stochastic Petri nets (CTSPN) simulated in CPNTools. Experimental tests for each service have been performed on 10 subjects. The results show that falls can be detected from walking or standing with 87% of accuracy, 82% of sensitivity and 92% of specificity, from a total data set of 50 emulates falls and 50 normal activities daily living. The results obtained during the tests validate the detection of tachycardia with 100% of success. The location was done with 94% of sensitivity. The proposed system minimizes the false positive and false negative.


Ambient assistance service Multimodal systems Fall detection Heart disorder detection Location 


  1. Abbate S, Avvenuti M, Bonatesta F, Cola G, Corsini P, Vecchio A (2012) A smartphone-based fall detection system. Pervasive Mob Comput 8(6):883–899CrossRefGoogle Scholar
  2. Aguiar B, Rocha T, Silva J, Sousa I (2014) Accelerometer-based fall detection for smartphones. In: Medical Measurements and Applications (MeMeA), IEEE International Symposium on (pp 1–6)Google Scholar
  3. Ahmed F, Ibrahimy MI, Ali MAM, Zahedi E (2002) A portable recorder for long-term fetal heart rate monitoring. Microprocess Microsyst 26(7):325–330CrossRefGoogle Scholar
  4. Alwan M, Rajendran PJ, Kell S, Mack D, Dalal S, Wolfe M, Felder R (2006) A smart and passive floor-vibration based fall detector for elderly. Information and Communication Technologies. ICTTA’06. 2nd (vol 1, pp 1003–1007)Google Scholar
  5. Auvinet E, Multon F, Saint-Arnaud A, Rousseau J, Meunier J (2011) Fall detection with multiple cameras: An occlusion-resistant method based on 3-d silhouette vertical distribution. IEEE Trans Inf Technol Biomed 15(2):290–300CrossRefGoogle Scholar
  6. Baek WS, Kim DM, Bashir F, Pyun JY (2013) Real life applicable fall detection system based on wireless body area network. In: Consumer Communications and Networking Conference (CCNC), IEEE (pp 62–67)Google Scholar
  7. Bauer A, Malik M, Schmidt G, Barthel P, Bonnemeier H et al (2008) Heart rate turbulence: standards of measurement, physiological interpretation, and clinical use: International Society for Holter and Noninvasive Electrophysiology Consensus. J Am Coll Cardiol 52(17):1353–1365CrossRefGoogle Scholar
  8. Bourke AK, O’brien JV, Lyons GM (2007) Evaluation of a threshold-based tri-axial accelerometer fall detection algorithm. Gait posture 26(2):194–199CrossRefGoogle Scholar
  9. Bourke AK, Van de Ven PW, Chaya A, ÓLaighin G, Nelson J (2008) Design and test of a long-term fall detection system incorporated into a custom vest for the elderly. In: Signals and Systems Conference, 208.(ISSC 2008). IET Irish (pp 307–312)Google Scholar
  10. Bourke AK, Van de Ven P, Gamble M, O’Connor R et al (2010) Evaluation of waist-mounted tri-axial accelerometer based fall-detection algorithms during scripted and continuous unscripted activities. J Biomech 43(15):3051–3057CrossRefGoogle Scholar
  11. Bradley TD, Logan AG, Kimoff RJ, Sériès F et al (2005) Continuous positive airway pressure for central sleep apnea and heart failure. N Engl J Med 353(19):2025–2033CrossRefGoogle Scholar
  12. Chan AM, Selvaraj N, Ferdosi N, Narasimhan R (2013) Wireless patch sensor for remote monitoring of heart rate, respiration, activity, and falls. In Engineering in Medicine and Biology Society (EMBC). In: 35th Annual International Conference of the IEEE (pp. 6115–6118)Google Scholar
  13. Charlon Y, Fourty N, Bourennane W et al (2013) Design and evaluation of a device worn for fall detection and localization: Application for the continuous monitoring of risks incurred by dependents in an Alzheimer’s care unit. Expert Syst Appl 40(18):7316–7330CrossRefGoogle Scholar
  14. Chen J, Kwong K, Chang D, Luk J, Bajcsy R (2006) Wearable sensors for reliable fall detection. In: Engineering in Medicine and Biology Society. IEEE-EMBS. 27th Annual International Conference of the (pp 3551–3554)Google Scholar
  15. Chen D, Feng W, Zhang Y, Li X, Wang T (2011) A wearable wireless fall detection system with accelerators. In: Robotics and Biomimetics (ROBIO), IEEE International Conference on (pp 2259–2263)Google Scholar
  16. Choi S, Youm S (2017) A study on a fall detection monitoring system for falling elderly using open source hardware. Multimedia Tools and Applications, pp 1–12Google Scholar
  17. Colon LNV, DeLaHoz Y, Labrador M (2014) Human fall detection with smartphones. Communications (LATINCOM). In: IEEE Latin-America Conference on (p 1–7)Google Scholar
  18. Coppetti T, Brauchlin A, Müggler S, Attinger-Toller A, Templin C et al (2017) Accuracy of smartphone apps for heart rate measurement. Eur J Prevent Cardiol 24:1287–1293CrossRefGoogle Scholar
  19. Destatis (2011) Older people in Germany and the EU. Federal Statistical Office of Germany, WiesbadenGoogle Scholar
  20. Diab MO, Marak RA, Dichari M, Moslem B (2013) The smartphone accessory heart rate monitor. In: Computer Medical Applications (ICCMA), International Conference on (pp 1–5)Google Scholar
  21. Foko TE, Dlodlo N, Montsi L (2013) An integrated smart system for ambient-assisted living. In: Internet of things, smart spaces, and next generation networking. Springer, Berlin Heidelberg, pp 128–138Google Scholar
  22. Fortino G, Gravina R (2015) Fall-MobileGuard: a smart real-time fall detection system. In: Proceedings of the 10th EAI International Conference on Body Area Networks (pp 44–50). ICST (Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering)Google Scholar
  23. Fox K, Borer JS, Camm AJ, Danchin N, Ferrari R et al (2007) Resting heart rate in cardiovascular disease. J Am Coll Cardiol 50(9):823–830CrossRefGoogle Scholar
  24. Furman GD, Baharav A, Cahan C, Akselrod S (2008) Early detection of falling asleep at the wheel: a heart rate variability approach. In: Computers in Cardiology, IEEE (pp 1109–1112)Google Scholar
  25. Gravina R, Alinia P, Ghasemzadeh H, Fortino G (2017) Multi-sensor fusion in body sensor networks: state-of-the-art and research challenges. Inf Fusion 35:68–80CrossRefGoogle Scholar
  26. Hakim A, Huq MS, Shanta S, Ibrahim BSKK (2017) Smartphone based data mining for fall detection: analysis and design. Procedia Comput Sci 105:46–51CrossRefGoogle Scholar
  27. Hermans B, Verheyden B, Beckers F, Aubert A, Puers R (2005) A portable multi-sensor datalogger for heart rate variability (HRV) study during skydiver’s free fall. In Solid-State Sensors, Actuators and Microsystems. Digest of Technical Papers. In: IEEE, The 13th International Conference on (Vol 1, pp 465–469)Google Scholar
  28. Huang JH, Wang TT, Su TY, Lan KC (2013) Design and deployment of a heart rate monitoring system in a senior center. Sensor, Mesh and Ad Hoc Communications and Networks (SECON). In: 10th Annual IEEE Communications Society Conference on (pp 71–75)Google Scholar
  29. Hui G (2010) Real-time human heart rate monitoring using a wireless sensor network based on stochastic resonance. In: E-Health Networking, Digital Ecosystems and Technologies (EDT), IEEE, International Conference on (Vol 1, pp 15–18)Google Scholar
  30. Humenberger M, Schraml S, Sulzbachner C, Belbachir AN, Srp A, Vajda F (2012) Embedded fall detection with a neural network and bio-inspired stereo vision. In: Computer Vision and Pattern Recognition Workshops (CVPRW), IEEE, Computer Society Conference on (pp 60–67)Google Scholar
  31. Jensen K, Kristensen LM (2009) Coloured Petri nets: modelling and validation of concurrent systems. Springer Science & Business Media, BerlinCrossRefzbMATHGoogle Scholar
  32. Kangas M, Vikman I, Nyberg L, Korpelainen R, Lindblom J, Jämsä T (2012) Comparison of real-life accidental falls in older people with experimental falls in middle-aged test subjects. Gait Posture 35(3):500–505CrossRefGoogle Scholar
  33. Khawandi S, Ballit A, Daya B (2013) Applying machine learning algorithm in fall detection monitoring system. In: Computational Intelligence and Communication Networks (CICN), IEEE, 5th International Conference on (pp 247–250)Google Scholar
  34. Klack L, Möllering C, Ziefle M, Schmitz-Rode T (2010) Future care floor: a sensitive floor for movement monitoring and fall detection in home environments. In: International Conference on Wireless Mobile Communication and Healthcare (pp 211–218). Springer Berlin HeidelbergGoogle Scholar
  35. Lai C, Lei Z, Hao M, Lu G (2014) Experimental research of picosecond pulsed laser irradiating in GaAs photoelectric detectors. In Reliability, Maintainability and Safety (ICRMS), IEEE, International Conference on (pp. 157–159)Google Scholar
  36. Lauterbach C, Steinhage A, Techmer A (2013) A large-area sensor system underneath the floor for ambient assisted living applications. Pervasive and mobile sensing and computing for healthcare. Springer, Berlin, Heidelberg, pp 69–87Google Scholar
  37. Lee ES, Lee JS, Joo MC, Kim JH, et Noh SE (2017) Accuracy of heart rate measurement using smartphones during treadmill exercise in male patients with ischemic heart disease. Ann Rehabil Med 41(1):129–137CrossRefGoogle Scholar
  38. LeMay R, Choi S, Youn JH, Newstorm J (2013) Postural transition detection using a wireless sensor activity monitoring system. In: International Conference on Grid and Pervasive Computing (pp. 393–402). Springer, Berlin HeidelbergGoogle Scholar
  39. Li Y, Ho KC, Popescu M (2014) Efficient source separation algorithms for acoustic fall detection using a Microsoft Kinect. IEEE Trans Biomed Eng 61(3):745–755CrossRefGoogle Scholar
  40. Liu J, Lockhart TE (2014) Development and evaluation of a prior-to-impact fall event detection algorithm. IEEE Trans Biomed Eng 61(7):2135–2140CrossRefGoogle Scholar
  41. Maddox TM, Ross C, Ho PM, Masoudi FA, Magid D et al (2008) The prognostic importance of abnormal heart rate recovery and chronotropic response among exercise treadmill test patients. Am Heart J 156(4):736–744CrossRefGoogle Scholar
  42. Makhlouf A, Saadia N, Ramdane-Cherif A (2015) Services of ambient assistance for elderly and/or disabled person in health intelligent habitat. In: Proceedings of the International Conference on Agents and Artificial Intelligence-Volume 2 (pp. 225–231). SCITEPRESS-Science and Technology PublicationsGoogle Scholar
  43. Makhlouf A, Nedjai I, Saadia N, et Ramdane-Cherif A (2017) Multimodal system for fall detection and location of person in an intelligent habitat. Procedia Comput Sci 109:969–974CrossRefGoogle Scholar
  44. Miah MAR, Basak S, Huda MR, Roy A (2013) Low cost computer based heart rate monitoring system using fingertip and microphone port. In: Informatics, Electronics & Vision (ICIEV), IEEE, International Conference on (pp. 1–4)Google Scholar
  45. Milner R (1997) The definition of standard ML: revised. MIT Press, CambridgeCrossRefGoogle Scholar
  46. Mubashir M, Shao L, Seed L (2013) A survey on fall detection: Principles and approaches. Neurocomputing 100:144–152CrossRefGoogle Scholar
  47. Nageotte MP (2015) Fetal heart rate monitoring. In: Seminars in Fetal and Neonatal Medicine (vol 20, 3, pp 144–148). WB SaundersGoogle Scholar
  48. Ozcan K, Mahabalagiri AK, Casares M, Velipasalar S (2013) Automatic fall detection and activity classification by a wearable embedded smart camera. IEEE J Emerg Select Top Circuit Syst 3(2):125–136CrossRefGoogle Scholar
  49. Pike K, Pillow JJ, Lucas JS (2012) Long term respiratory consequences of intrauterine growth restriction. In: Seminars in Fetal and Neonatal Medicine (vol 17, No. 2, pp 92–98). WB SaundersGoogle Scholar
  50. Rotariu C, Pasarica A, Costin H, Adochiei F, Ciobotariu R (2011) Telemedicine system for remote blood pressure and heart rate monitoring. In: E-Health, Conference Bioengineering (eds) (EHB), IEEE, (pp 1–4)Google Scholar
  51. Rougier C, Meunier J, St-Arnaud A, Rousseau J (2011) Robust video surveillance for fall detection based on human shape deformation. IEEE Trans Circuits Syst Video Technol 21(5):611–622CrossRefGoogle Scholar
  52. Segerståhl K. Oinas-Kukkonen H (2011) Designing personal exercise monitoring employing multiple modes of delivery: implications from a qualitative study on heart rate monitoring. Int J Med Inf 80(12):e203–e213CrossRefGoogle Scholar
  53. Shinde BA, Chawan PM (2014) Dementia patient movement detection and fall detection using smart phone technology. Int J Adv Technol Eng Sci 2:155–160Google Scholar
  54. Steg H, Strese H, Loroff C, Hull J, Schmidt S (2006) Europe is facing a demographic challenge. Ambient Assisted Living Offers Solutions. VDI/VDE/IT, BerlinGoogle Scholar
  55. Tetzlaff T, Boor M, Witkowski U, Zandian R (2014) Low power network node for ambient monitoring and heart rate measurement. In: Education and Research Conference (EDERC), IEEE, 6th European Embedded Design in (pp 75–79)Google Scholar
  56. Torres-Pereira L, Ruivo P, Torres-Pereira C, Couto C (1997) A noninvasive telemetric heart rate monitoring system based on phonocardiography. In Industrial Electronics, ISIE’97. In: Proceedings of the IEEE International Symposium on (pp. 856–859)Google Scholar
  57. Valenti G, Westerterp KR (2013) Optical heart rate monitoring module validation study. In: Consumer Electronics (ICCE), IEEE International Conference on (pp. 195–196)Google Scholar
  58. Valle R, Aspromonte N, Carbonieri E, D’Eri A, Feola M et al (2008) Fall in readmission rate for heart failure after implementation of B-type natriuretic peptide testing for discharge decision: a retrospective study. Int J Cardiol 126(3):400–406CrossRefGoogle Scholar
  59. Vallejo M, Isaza CV, Lopez JD (2013) Artificial neural networks as an alternative to traditional fall detection methods. In: Engineering in Medicine and Biology Society (EMBC), 35th Annual International Conference of the IEEE (pp. 1648–1651)Google Scholar
  60. Van de Ven P, Bourke A, Nelson J, O’Brien H (2010) Design and integration of fall and mobility monitors in health monitoring platforms. Wearable and autonomous biomedical devices and systems for smart environment. Springer, Berlin Heidelberg, pp 1–29Google Scholar
  61. Wagner M, Kuch B, Cabrera C, Enoksson P, Sieber A (2012) Android based body area network for the evaluation of medical parameters. In: Intelligent Solutions in Embedded Systems (WISES), IEEE, Proceedings of the Tenth Workshop on (pp. 33–38)Google Scholar
  62. Wang C, Narayanan MR, Lord SR, Redmond SJ, et Lovell NH (2014) A low-power fall detection algorithm based on triaxial acceleration and barometric pressure. In: Engineering in Medicine and Biology Society (EMBC), 2014 36th Annual International Conference of the IEEE (pp. 570–573)Google Scholar
  63. Wang C, Lu W, Redmond SJ, Stevens MC, Lord SR, et Lovell NH (2017) A low-power fall detector balancing sensitivity and false alarm rate. IEEE J Biomed Health InfGoogle Scholar
  64. Yan BP, Chan CK, Li CK, To OT, Lai WH et al (2017) Resting and postexercise heart rate detection from fingertip and facial photoplethysmography using a smartphone camera: a validation study. JMIR mHealth and uHealth 5(3):e33CrossRefGoogle Scholar
  65. Yang W, Yang K, Jiang H, Wang Z, Lin Q, Jia W (2014) Fetal heart rate monitoring system with mobile internet. In: Circuits and Systems (ISCAS), IEEE International Symposium on (pp 443–446)Google Scholar
  66. Ye W, Xiang-Yu B (2013) Research of fall detection and alarm applications for the elderly. In: Mechatronic Sciences, Electric Engineering and Computer (MEC), IEEE, Proceedings International Conference on (pp 615–619)Google Scholar
  67. Yu M, Naqvi SM, et Chambers J (2010) A robust fall detection system for the elderly in a Smart Room. In: Acoustics Speech and Signal Processing (ICASSP), 2010 IEEE International Conference on (pp 1666–1669)Google Scholar
  68. Yu M, Rhuma A, Naqvi SM, Wang L, Chambers J (2012) A posture recognition-based fall detection system for monitoring an elderly person in a smart home environment. IEEE Trans Inf Technol Biomed 16(6):1274–1286CrossRefGoogle Scholar
  69. Yu M, Yu Y, Rhuma A, Naqvi SMR, Wang L, Chambers JA (2013) An online one class support vector machine-based person-specific fall detection system for monitoring an elderly individual in a room environment. IEEE J Biomed Health Inf 17(6):1002–1014CrossRefGoogle Scholar
  70. Zhou CC, Tu CL, Gao Y, Wang FX, Gong HW et al (2014) A low-power, wireless, wrist-worn device for long time heart rate monitoring and fall detection. In: Orange Technologies (ICOT), IEEE International Conference on (pp 33–36)Google Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.LRPE LaboratoryUniversity of Sciences and Technology Houari BoumedieneBab Ezzouar AlgiersAlgeria
  2. 2.LISV LaboratoryUniversity of Versailles Saint-Quentin-en-yvelinesVelizyFrance

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