Skip to main content

Advertisement

Springer Nature Link
Log in

Ambient assistance service for fall and heart problem detection

  • Original Research
  • Published:
Journal of Ambient Intelligence and Humanized Computing Aims and scope Submit manuscript

Abstract

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.

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
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19

Similar content being viewed by others

Explore related subjects

Discover the latest articles and news from researchers in related subjects, suggested using machine learning.

References

  • Abbate S, Avvenuti M, Bonatesta F, Cola G, Corsini P, Vecchio A (2012) A smartphone-based fall detection system. Pervasive Mob Comput 8(6):883–899

    Article  Google Scholar 

  • 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)

  • Ahmed F, Ibrahimy MI, Ali MAM, Zahedi E (2002) A portable recorder for long-term fetal heart rate monitoring. Microprocess Microsyst 26(7):325–330

    Article  Google Scholar 

  • 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)

  • 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):290–300

    Article  Google Scholar 

  • 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)

  • 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):1353–1365

    Article  Google Scholar 

  • Bourke AK, O’brien JV, Lyons GM (2007) Evaluation of a threshold-based tri-axial accelerometer fall detection algorithm. Gait posture 26(2):194–199

    Article  Google Scholar 

  • 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)

  • 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):3051–3057

    Article  Google Scholar 

  • 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):2025–2033

    Article  Google Scholar 

  • 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)

  • 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):7316–7330

    Article  Google Scholar 

  • 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)

  • 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)

  • 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 1–12

  • Colon LNV, DeLaHoz Y, Labrador M (2014) Human fall detection with smartphones. Communications (LATINCOM). In: IEEE Latin-America Conference on (p 1–7)

  • 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 24:1287–1293

    Article  Google Scholar 

  • Destatis (2011) Older people in Germany and the EU. Federal Statistical Office of Germany, Wiesbaden

    Google Scholar 

  • 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)

  • 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 128–138

  • 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)

  • 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):823–830

    Article  Google Scholar 

  • 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)

  • 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 35:68–80

    Article  Google Scholar 

  • Hakim A, Huq MS, Shanta S, Ibrahim BSKK (2017) Smartphone based data mining for fall detection: analysis and design. Procedia Comput Sci 105:46–51

    Article  Google Scholar 

  • 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)

  • 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)

  • 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)

  • 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)

  • Jensen K, Kristensen LM (2009) Coloured Petri nets: modelling and validation of concurrent systems. Springer Science & Business Media, Berlin

    Book  MATH  Google Scholar 

  • 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):500–505

    Article  Google Scholar 

  • 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)

  • 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 Heidelberg

  • 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)

  • 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 69–87

    Google Scholar 

  • 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):129–137

    Article  Google Scholar 

  • 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 Heidelberg

  • 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):745–755

    Article  Google Scholar 

  • Liu J, Lockhart TE (2014) Development and evaluation of a prior-to-impact fall event detection algorithm. IEEE Trans Biomed Eng 61(7):2135–2140

    Article  Google Scholar 

  • 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):736–744

    Article  Google Scholar 

  • 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 Publications

  • 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 109:969–974

    Article  Google Scholar 

  • 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)

  • Milner R (1997) The definition of standard ML: revised. MIT Press, Cambridge

    Book  Google Scholar 

  • Mubashir M, Shao L, Seed L (2013) A survey on fall detection: Principles and approaches. Neurocomputing 100:144–152

    Article  Google Scholar 

  • Nageotte MP (2015) Fetal heart rate monitoring. In: Seminars in Fetal and Neonatal Medicine (vol 20, 3, pp 144–148). WB Saunders

  • 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):125–136

    Article  Google Scholar 

  • 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 Saunders

  • 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)

  • 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):611–622

    Article  Google Scholar 

  • 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–e213

    Article  Google Scholar 

  • Shinde BA, Chawan PM (2014) Dementia patient movement detection and fall detection using smart phone technology. Int J Adv Technol Eng Sci 2:155–160

    Google Scholar 

  • 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, Berlin

  • 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)

  • 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)

  • Valenti G, Westerterp KR (2013) Optical heart rate monitoring module validation study. In: Consumer Electronics (ICCE), IEEE International Conference on (pp. 195–196)

  • 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):400–406

    Article  Google Scholar 

  • 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)

  • 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 1–29

    Google Scholar 

  • 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)

  • 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)

  • 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 Inf

  • 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):e33

    Article  Google Scholar 

  • 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)

  • 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)

  • 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)

  • 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):1274–1286

    Article  Google Scholar 

  • 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):1002–1014

    Article  Google Scholar 

  • 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)

Download references

Author information

Authors and Affiliations

  1. LRPE Laboratory, University of Sciences and Technology Houari Boumediene, BP 32 EL ALIA, 16111, Bab Ezzouar Algiers, Algeria

    Amina Makhlouf, Isma Boudouane & Nadia Saadia

  2. LISV Laboratory, University of Versailles Saint-Quentin-en-yvelines, 10-12 avenue de l’Europe, 78140, Velizy, France

    Amar Ramdane Cherif

Authors
  1. Amina Makhlouf
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Isma Boudouane
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Nadia Saadia
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Amar Ramdane Cherif
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Amina Makhlouf.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Makhlouf, A., Boudouane, I., Saadia, N. et al. Ambient assistance service for fall and heart problem detection. J Ambient Intell Human Comput 10, 1527–1546 (2019). https://doi.org/10.1007/s12652-018-0724-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12652-018-0724-4

Keyword