Advertisement

Fall detection and human activity classification using wearable sensors and compressed sensing

  • Oussama KerdjidjEmail author
  • Naeem Ramzan
  • Khalida Ghanem
  • Abbes Amira
  • Fatima Chouireb
Original Research
  • 38 Downloads

Abstract

The fall of elderly patients is still a critical medical issue since it can cause irreversible bone injuries due to the elderly bones weakness. To mitigate the likelihood of the occurrence of a fall, continuously tracking the patients with balance and health issues has been envisaged, despite being unpractical. To address this problem, we propose an efficient automatic fall detection system which is also fitted for the detection of different activities of daily living (ADL). The system relies on a wearable Shimmer device, to transmit some inertial signals via a wireless connection to a computer. Aiming at reducing the size of the transmitted data and minimizing the energy consumption, a compressive sensing (CS) method is applied. In this perspective, we started by creating our dataset from 17 subjects performing a set of movements, then three distinct systems were investigated: one which detects the presence or the absence of the fall, a second which detects static or dynamic movements including the fall, and a third which recognizes the fall and six other ADL activities. In the acquisition and classification steps, first only the data collected by the accelerometer are exploited, then a mixture of the accelerometer and gyroscope measurements are taken into consideration. The two configurations are compared and the resulting system incorporating CS capabilities is shown to achieve up to 99.8% of accuracy.

Keywords

Fall detection Human activity Wearable sensors Compressed sensing Classification 

Notes

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

References

  1. Alhimale L, Zedan H, Al-Bayatti A (2014) The implementation of an intelligent and video-based fall detection system using a neural network. Appl Soft Comput 18:59–69.  https://doi.org/10.1016/j.asoc.2014.01.024 CrossRefGoogle Scholar
  2. Ando B, Baglio S, Lombardo CO, Marletta V (2016) A multisensor data-fusion approach for adl and fall classification. IEEE Trans Instrum Meas 65(9):1960–1967.  https://doi.org/10.1109/TIM.2016.2552678 CrossRefGoogle Scholar
  3. Aslan M, Sengur A, Xiao Y, Wang H, Ince MC, Ma X (2015) Shape feature encoding via fisher vector for efficient fall detection in depth-videos. Appl Soft Comput 37(C):1023–1028.  https://doi.org/10.1016/j.asoc.2014.12.035 CrossRefGoogle Scholar
  4. Burns A, Doheny E, Greene B, Foran T, Leahy D, O’Donovan K, McGrath M (2010a) An extensible platform for physiological signal capture. In: Engineering in medicine and biology society (EMBC), 2010 annual international conference of the IEEE, pp 3759–3762.  https://doi.org/10.1109/IEMBS.2010.5627535
  5. Burns A, Greene BR, McGrath MJ, O’Shea TJ, Kuris B, Ayer SM, Stroiescu F, Cionca V (2010) A wireless sensor platform for noninvasive biomedical research. IEEE Sens J 10(9):1527–1534.  https://doi.org/10.1109/JSEN.2010.2045498 CrossRefGoogle Scholar
  6. Candes E, Wakin M (2008) An introduction to compressive sampling. Signal Process Mag IEEE 25(2):21–30.  https://doi.org/10.1109/MSP.2007.914731 CrossRefGoogle Scholar
  7. Candes E, Romberg J, Tao T (2006) Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information. Inf Theory IEEE Trans 52(2):489–509.  https://doi.org/10.1109/TIT.2005.862083 MathSciNetCrossRefzbMATHGoogle Scholar
  8. Casilari E, Santoyo-Ramn JA, Cano-Garca JM (2017) Analysis of public datasets for wearable fall detection systems. Sensors 17(7):1513.  https://doi.org/10.3390/s17071513 CrossRefGoogle Scholar
  9. Cheffena M (2016) Fall detection using smartphone audio features. IEEE J Biomed Health Inf 20(4):1073–1080.  https://doi.org/10.1109/JBHI.2015.2425932 CrossRefGoogle Scholar
  10. Cheng L, Guan Y, Zhu K, Li Y (2017a) Recognition of human activities using machine learning methods with wearable sensors. In: 2017 IEEE 7th annual computing and communication workshop and conference (CCWC), pp 1–7.  https://doi.org/10.1109/CCWC.2017.7868369
  11. Cheng L, Li Y, Guan Y (2017b) Human activity recognition based on compressed sensing. In: 2017 IEEE 7th annual computing and communication workshop and conference (CCWC), pp 1–7.  https://doi.org/10.1109/CCWC.2017.7868489
  12. Cheng L, Li Y, Guan Y (2017c) Human activity recognition based on compressed sensing. In: 2017 IEEE 7th annual computing and communication workshop and conference (CCWC), pp 1–7.  https://doi.org/10.1109/CCWC.2017.7868489
  13. Daher M, Diab A, Najjar MEBE, Khalil MA, Charpillet F (2017) Elder tracking and fall detection system using smart tiles. IEEE Sens J 17(2):469–479.  https://doi.org/10.1109/JSEN.2016.2625099 CrossRefGoogle Scholar
  14. Donoho DL (2006) Compressed sensing. IEEE Trans Inf Theor 52(4):1289–1306.  https://doi.org/10.1109/TIT.2006.871582 MathSciNetCrossRefzbMATHGoogle Scholar
  15. Duda R, Hart P, Stork D (2012) Pattern classification. Wiley, OxfordzbMATHGoogle Scholar
  16. Feng G, Mai J, Ban Z, Guo X, Wang G (2016) Floor pressure imaging for fall detection with fiber-optic sensors. IEEE Pervas Comput 15(2):40–47.  https://doi.org/10.1109/MPRV.2016.27 CrossRefGoogle Scholar
  17. Gao L, Bourke A, Nelson J (2014) Evaluation of accelerometer based multi-sensor versus single-sensor activity recognition systems. Med Eng Phys 36(6):779–785CrossRefGoogle Scholar
  18. Ghanem K (2013) Effect of channel correlation and path loss on average channel capacity of body-to-body systems. IEEE Trans Antenn Propag 61(12):6260–6265.  https://doi.org/10.1109/TAP.2013.2283035 CrossRefGoogle Scholar
  19. Gibson RM, Amira A, Ramzan N, de la Higuera PC, Pervez Z (2016) Multiple comparator classifier framework for accelerometer-based fall detection and diagnostic. Appl Soft Comput 39:94–103.  https://doi.org/10.1016/j.asoc.2015.10.062 CrossRefGoogle Scholar
  20. Gibson RM, Amira A, Ramzan N, de la Higuera PC, Pervez Z (2017) Matching pursuit-based compressive sensing in a wearable biomedical accelerometer fall diagnosis device. Biomed Signal Process Control 33:96–108.  https://doi.org/10.1016/j.bspc.2016.10.016 CrossRefGoogle Scholar
  21. Hall KIGKPS (2016) Advances in Body-Centric Wireless Communication: applications and state-of-the-art, Institution of Engineering and Technology, chap Diversity and MIMO for efficient front-end design of body-centric wireless communications devicesGoogle Scholar
  22. Han J, Qian C, Wang X, Ma D, Zhao J, Xi W, Jiang Z, Wang Z (2016) Twins: Device-free object tracking using passive tags. IEEE/ACM Trans Netw 24(3):1605–1617.  https://doi.org/10.1109/TNET.2015.2429657 CrossRefGoogle Scholar
  23. Harrou F, Zerrouki N, Sun Y, Houacine A (2017) Vision-based fall detection system for improving safety of elderly people. IEEE Instrum Meas Mag 20(6):49–55.  https://doi.org/10.1109/MIM.2017.8121952 CrossRefGoogle Scholar
  24. Hui S, Zhongmin W (2017) Compressed sensing method for human activity recognition using tri-axis accelerometer on mobile phone. J China Univ Posts Telecommun 24(2):31–71.  https://doi.org/10.1016/S1005-8885(17)60196-1 CrossRefGoogle Scholar
  25. Igual R, Medrano C, Plaza I (2013) Challenges, issues and trends in fall detection systems. BioMed Eng OnLine 12(1):66.  https://doi.org/10.1186/1475-925X-12-66 CrossRefGoogle Scholar
  26. Jokanovic B, Amin M, Ahmad F (2016) Radar fall motion detection using deep learning. In: 2016 IEEE radar conference (RadarConf), pp 1–6.  https://doi.org/10.1109/RADAR.2016.7485147
  27. Kerdjidj O, Ghanem K, Amira A, Harizi F, Chouireb F (2014) Concatenation of dictionaries for recovery of ecg signals using compressed sensing techniques. In: 2014 26th international conference on microelectronics (ICM), pp 112–115.  https://doi.org/10.1109/ICM.2014.7071819
  28. Kwolek B, Kepski M (2016) Fuzzy inference-based fall detection using kinect and body-worn accelerometer. Appl Soft Comput 40:305–318.  https://doi.org/10.1016/j.asoc.2015.11.031 CrossRefGoogle Scholar
  29. Lee RYW, Carlisle AJ (2011) Detection of falls using accelerometers and mobile phone technology. Age Age 40(6):690–696.  https://doi.org/10.1093/ageing/afr050 CrossRefGoogle Scholar
  30. Li Q, Stankovic JA, Hanson MA, Barth AT, Lach J, Zhou G (2009) Accurate, fast fall detection using gyroscopes and accelerometer-derived posture information. In: 2009 sixth international workshop on wearable and implantable body sensor networks, pp 138–143.  https://doi.org/10.1109/BSN.2009.46
  31. Litvak D, Zigel Y, Gannot I (2008) Fall detection of elderly through floor vibrations and sound. In: 2008 30th annual international conference of the IEEE engineering in medicine and biology society, pp 4632–4635.  https://doi.org/10.1109/IEMBS.2008.4650245
  32. Lusardi MM (2017) Determining risk of falls in community dwelling older adults: a systematic review and meta-analysis using posttest probability. J Geriatr Phys Ther 40:1–36CrossRefGoogle Scholar
  33. Makhlouf A, Boudouane I, Saadia N, Ramdane Cherif A (2018) Ambient assistance service for fall and heart problem detection. J Amb Intell Hum Comput 2018:1–20.  https://doi.org/10.1007/s12652-018-0724-4 Google Scholar
  34. Mallat S, Zhang Z (1993) Matching pursuits with time-frequency dictionaries. Signal Process IEEE Trans 41(12):3397–3415.  https://doi.org/10.1109/78.258082 CrossRefzbMATHGoogle Scholar
  35. Micucci D, Mobilio M, Napoletano P, Tisato F (2017) Falls as anomalies? an experimental evaluation using smartphone accelerometer data. J Amb Intell Hum Comput 8(1):87–99.  https://doi.org/10.1007/s12652-015-0337-0 CrossRefGoogle Scholar
  36. Mubashir M, Shao L, Seed L (2013) A survey on fall detection: Principles and approaches. Neurocomputer 100:144–152.  https://doi.org/10.1016/j.neucom.2011.09.037 CrossRefGoogle Scholar
  37. Ntanasis P, Pippa E, Özdemir AT, Barshan B, Megalooikonomou V (2017) Investigation of sensor placement for accurate fall detection. Springer, Cham, pp 225–232.  https://doi.org/10.1007/978-3-319-58877-3-30 Google Scholar
  38. Ozcan K, Velipasalar S, Varshney PK (2017) Autonomous fall detection with wearable cameras by using relative entropy distance measure. IEEE Trans Hum Mach Syst 47(1):31–39.  https://doi.org/10.1109/THMS.2016.2620904 Google Scholar
  39. Ozdemir AT (2016) An analysis on sensor locations of the human body for wearable fall detection devices: Principles and practice. Sensors 16(8):1161.  https://doi.org/10.3390/s16081161 CrossRefGoogle Scholar
  40. Ruan W, Sheng QZ, Yao L, Gu T, Ruta M, Shangguan L (2016) Device-free indoor localization and tracking through human-object interactions. In: 2016 IEEE 17th international symposium on a world of wireless, mobile and multimedia networks (WoWMoM), pp 1–9.  https://doi.org/10.1109/WoWMoM.2016.7523524
  41. Sheltami TR, Bala A, Shakshuki EM (2016) Wireless sensor networks for leak detection in pipelines: a survey. J Amb Intell Hum Comput 7(3):347–356.  https://doi.org/10.1007/s12652-016-0362-7 CrossRefGoogle Scholar
  42. Sherrington C, Tiedemann A (2017) Physiotherapy in the prevention of falls in older people. J Physiother 61:54–60.  https://doi.org/10.1016/j.jphys.2015.02.011 CrossRefGoogle Scholar
  43. Sigg S, Scholz M, Shi S, Ji Y, Beigl M (2013) Rf-sensing of activities from non-cooperative subjects in device-free recognition systems using ambient and local signals. IEEE Trans Mob Comput 13:907–920.  https://doi.org/10.1109/TMC.2013.28 CrossRefGoogle Scholar
  44. Tropp JA, Gilbert AC (2005) Signal recovery from partial information via orthogonal matching pursuit. IEEE Trans Inf Theory 53:4655–4666CrossRefzbMATHGoogle Scholar
  45. Tropp JA, Gilbert AC (2007) Signal recovery from random measurements via orthogonal matching pursuit. IEEE Trans Inf Theory 53:4655–4666MathSciNetCrossRefzbMATHGoogle Scholar
  46. Vallabh P, Malekian R (2018) Fall detection monitoring systems: a comprehensive review. Journal of Ambient Intelligence and Humanized Computing 9(6):1809–1833.  https://doi.org/10.1007/s12652-017-0592-3 CrossRefGoogle Scholar
  47. Yao L, Sheng QZ, Li X, Wang S, Gu T, Ruan W, Zou W (2015) Freedom: Online activity recognition via dictionary-based sparse representation of rfid sensing data. In: 2015 IEEE international conference on data mining, pp 1087–1092.  https://doi.org/10.1109/ICDM.2015.102
  48. Yao L, Sheng QZ, Li X, Gu T, Tan M, Wang X, Wang S, Ruan W (2018) Compressive representation for device-free activity recognition with passive rfid signal strength. IEEE Trans Mob Comput 17(2):293–306.  https://doi.org/10.1109/TMC.2017.2706282 CrossRefGoogle Scholar
  49. Zerrouki N, Harrou F, Sun Y, Houacine A (2016) Accelerometer and camera-based strategy for improved human fall detection. J Med Syst 40(12):284.  https://doi.org/10.1007/s10916-016-0639-6 CrossRefGoogle Scholar
  50. Zhang S, Feng R, Wu Y, Yu N (2017) Adaptive compressed sensing for acceleration data transmission in human motion capture. In: 2017 10th international congress on image and signal processing, biomedical engineering and informatics (CISP-BMEI), pp 1–6.  https://doi.org/10.1109/CISP-BMEI.2017.8302268

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Laboratory of Telecommunications, Signals and Systems, Department of ElectronicsUniversity Amar TelidjiLaghouatAlgeria
  2. 2.Division TélécomCentre de développement des technologies avancées (CDTA)AlgiersAlgeria
  3. 3.School of Engineering and ComputingUniversity of the West of ScotlandPaisleyScotland, UK

Personalised recommendations