Top

Published in:

01-05-2019 | Mobile & Wireless Health

Development of a Strategy to Predict and Detect Falls Using Wearable Sensors

Authors: Nuno Ferrete Ribeiro, João André, Lino Costa, Cristina P. Santos

Published in: Journal of Medical Systems | Issue 5/2019

Abstract

Falls are a prevalent problem in actual society. Some falls result in injuries and the cost associated with their treatment is high. This is a complex problem that requires several steps in order to be tackled. Firstly, it is crucial to develop strategies that recognize the locomotion mode, indicating the state of the subject in various situations. This article aims to develop a strategy capable of identifying normal gait, the pre-fall condition, and the fall situation, based on a wearable system (IMUs-based). This system was used to collect data from healthy subjects that mimicked falls. The strategy consists, essentially, in the construction and use of classifiers as tools for recognizing the locomotion modes. Two approaches were explored. Associative Skill Memories (ASMs) based classifier and a Convolutional Neural Network (CNN) classifier based on deep learning. Finally, these classifiers were compared, providing for a tool with a good accuracy in recognizing the locomotion modes. Results have shown that the accuracy of the classifiers was quite acceptable. The CNN presented the best results with 92.71% of accuracy considering the pre-fall step different from normal steps, and 100% when not considering.

Available only for authorised users

Rueterbories, J., Spaich, E. G., Larsen, B., and Andersen, O. K., Methods for gait event detection and analysis in ambulatory systems. Med. Eng. Phys. 32(6):545–552, 2010. https://doi.org/10.1016/j.medengphy.2010.03.007.CrossRefPubMed

Fish, D. J., Pathology forum: Characteristic gait patterns in neuromuscular pathologies. Journal of Prosthetics and Orthotics 9(2):163–167, 1997.CrossRef

Amboni, M., Iuppariello, L., Iavarone, A., Fasano, A., Palladino, R., Rucco, R., Picillo, M., Lista, I., Varriale, P., Vitale, C., Cesarelli, M., Sorrentino, G., and Barone, P., Step length predicts executive dysfunction in Parkinson’s disease: a 3-year prospective study. J. Neurol. 265(10):2211–2220, 2018. https://doi.org/10.1007/s00415-018-8973-x.CrossRefPubMed

Liparoti, M., Della Corte, M., Rucco, R., Sorrentino, P., Sparaco, M., Capuano, R., Minino, R., Lavorgna, L., Agosti, V., Sorrentino, G., and Bonavita, S., Gait abnormalities in minimally disabled people with multiple sclerosis: a 3D-motion analysis study. Mult. Scler. Relat. Disord. 29:100–107, 2019. https://doi.org/10.1016/J.MSARD.2019.01.028.CrossRefPubMed

Rucco, R., Agosti, V., Jacini, F., Sorrentino, P., Varriale, P., De Stefano, M., Milan, G., Montella, P., and Sorrentino, G., Spatio-temporal and kinematic gait analysis in patients with Frontotemporal dementia and Alzheimer’s disease through 3D motion capture. Gait Posture 52:312–317, 2017. https://doi.org/10.1016/J.GAITPOST.2016.12.021.CrossRefPubMed

Sorrentino, P., Barbato, A., Del Gaudio, L., Rucco, R., Varriale, P., Sibilio, M., Strazzullo, P., Sorrentino, G., and Agosti, V., Impaired gait kinematics in type 1 Gaucher’s disease. J. Park. Dis. 6(1): 191–195, 2016. https://doi.org/10.3233/JPD-150660.CrossRef

Barone, P., Aarsland, D., Burn, D., Emre, M., Kulisevsky, J., and Weintraub, D.: Cognitive impairment in nondemented Parkinson’s disease. https://doi.org/10.1002/mds.23919, 2011CrossRef

Chen, P. H., Wang, R. L., Liou, D. J., and Shaw, J. S.: Gait disorders in Parkinson’s disease: Assessment and management. https://doi.org/10.1016/j.ijge.2013.03.005, 2013CrossRef

Thompson, A.J., Toosy, A.T., and Ciccarelli, O., Pharmacological management of symptoms in multiple sclerosis: current approaches and future directions. Lancet Neurol. 9(12):1182–1199, 2010. https://doi.org/10.1016/S1474-4422(10)70249-0.CrossRefPubMed

10.

Braak, H., Thal, D. R., Ghebremedhin, E., and Del Tredici, K., Stages of the pathologic process in alzheimer disease: Age categories from 1 to 100 years. J. Neuropathol. Exp. Neurol. 70(11):960–969, 2011. https://doi.org/10.1097/NEN.0b013e318232a379.CrossRefPubMed

11.

Brady, R. O., Kanfer, J. N., and Shapiro, D., Metabolism of glucocerebrosides II. Evidence of an enzymatic deficiency in Gaucher’s disease. Biochem. Biophys. Res. Commun. 18(2):221–225, 1965. https://doi.org/10.1016/0006-291X(65)90743-6.CrossRefPubMed

12.

Muro-de-la Herran, A., García-Zapirain, B., and Méndez-Zorrilla, A., Gait analysis methods: an overview of wearable and non-wearable systems, highlighting clinical applications. Sensors (Switzerland) 14(2):3362–3394, 2014. https://doi.org/10.3390/s140203362.CrossRef

13.

Berry, S. D., and Miller, R., Falls: Epidemiology, pathophysiology, and relationship to fracture. Curr. Osteoporos. Rep. 6(4):149–154, 2008.CrossRef

14.

Medicine, I., and Prevention, D.: The second fifty years: Promoting health and preventing disability national academies press, 1992

15.

Khusainov, R., Azzi, D., Achumba, I. E., and Bersch, S. D., Real-time human ambulation, activity, and physiological monitoring: taxonomy of issues, techniques, applications, challenges and limitations. Sensors (Basel, Switzerland) 13(10):12852–12902, 2013. https://doi.org/10.3390/s131012852.CrossRef

16.

Hsieh, C. Y., Liu, K. C., Huang, C. N., Chu, W. C., and Chan, C. T.: Novel hierarchical fall detection algorithm using a multiphase fall model. Sensors (Switzerland) 17(2). https://doi.org/10.3390/s17020307, 2017CrossRef

17.

Burns, E. R., Stevens, J. A., and Lee, R., The direct costs of fatal and non-fatal falls among older adults — United States. J. Safety Res. 58:99–103, 2016. https://doi.org/10.1016/j.jsr.2016.05.001.CrossRefPubMed

18.

Cates, B., Sim, T., Heo, H. M., Kim, B., Kim, H., and Mun, J. H.: A novel detection model and its optimal features to classify falls from low- and high-acceleration activities of daily life using an insole sensor system. Sensors (Switzerland) 18(4). https://doi.org/10.3390/s18041227, 2018CrossRef

19.

SENSO 2012: SENSO supports, 2012

20.

Tunstall Healthcare: iVi intelligent pendant, 2017

21.

Tamura, T., Yoshimura, T., Sekine, M., Uchida, M., and Tanaka, O., A wearable airbag to prevent fall injuries. IEEE Trans. Inf. Technol. Biomed. 13(6):910–914, 2009. https://doi.org/10.1109/TITB.2009.2033673.CrossRefPubMed

22.

Ribeiro, N. F., and Santos, C. P.: An intuitive visual interface for a real-time monitoring system for human gait using imus. In: 2017 IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC). https://doi.org/10.1109/ICARSC.2017.7964068, pp. 153–158, 2017.

23.

Macedo, P., Afonso, J. A., Rocha, L. A., and Simoes, R.: A telerehabilitation system based on wireless motion capture sensors. In: PhyCS - Proceedings of the International Conference on Physiological Computing Systems. https://doi.org/10.5220/0004873800550062, pp. 55–62, 2014.

24.

Ribeiro, N. F., and Santos, C. P.: Inertial measurement units: a brief state of the art on gait analysis. In: 2017 IEEE 5th Portuguese Meeting on Bioengineering (ENBENG). https://doi.org/10.1109/ENBENG.2017.7889458, pp. 1–4, 2017.

25.

Igual, R., Medrano, C., and Plaza, I., Challenges, issues and trends in fall detection systems. BioMedical Engineering OnLine 12(1):66, 2013. https://doi.org/10.1186/1475-925X-12-66.CrossRefPubMedPubMedCentral

26.

Rucco, R., Sorriso, A., Liparoti, M., Ferraioli, G., Sorrentino, P., Ambrosanio, M., and Baselice, F.: Type and location of wearable sensors for monitoring falls during static and dynamic tasks in healthy elderly: a review. https://doi.org/10.3390/s18051613, 2018CrossRef

27.

Kangas, M., Konttila, A., Winblad, I., and Jamsa, T.: Determination of simple thresholds for accelerometry-based parameters for fall detection. In: 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. https://doi.org/10.1109/IEMBS.2007.4352552, pp. 1367–1370, 2007.

28.

Boissy, P., Choquette, S., Hamel, M., and Noury, N., User-based motion sensing and fuzzy logic for automated fall detection in older adults. Telemedicine Journal and E-Health: the Official Journal of the American Telemedicine Association 13(6):683–693, 2007. https://doi.org/10.1089/tmj.2007.0007.CrossRef

29.

Lai, C. F., Chang, S. Y., Chao, H. C., and Huang, Y. M., Detection of cognitive injured body region using multiple triaxial accelerometers for elderly falling. IEEE Sensors J. 11(3):763–770, 2011. https://doi.org/10.1109/JSEN.2010.2062501.CrossRef

30.

Bianchi, F., Redmond, S.J., Narayanan, M.R., Cerutti, S., and Lovell, N.H., Barometric pressure and triaxial accelerometry-based falls event detection. IEEE Trans. Neural Syst. Rehabil. Eng. 18(6):619–627, 2010. https://doi.org/10.1109/TNSRE.2010.2070807.CrossRefPubMed

31.

Klucken, J., Barth, J., Kugler, P., Schlachetzki, J., Henze, T., Marxreiter, F., Kohl, Z., Steidl, R., Hornegger, J., Eskofier, B., and Winkler, J., Unbiased and mobile gait analysis detects motor impairment in Parkinson’s disease. PLOS ONE 8(2):1–9, 2013. https://doi.org/10.1371/journal.pone.0056956.CrossRef

32.

Kim, E.A.N., Mordiffi, S.Z., Bee, W.H., Devi, K., and Evans, D., Evaluation of three fall-risk assessment tools in an acute care setting. J. Adv. Nurs. 60(4):427–435, 2007. https://doi.org/10.1111/j.1365-2648.2007.04419.x.CrossRefPubMed

33.

Leone, A., Rescio, G., Caroppo, A., and Siciliano, P., A wearable EMG-based system pre-fall detector. Procedia Engineering 120:455–458, 2015. https://doi.org/10.1016/j.proeng.2015.08.667.CrossRef

34.

Andrė, J., Santos, C., and Costa, L., Skill memory in biped locomotion: Using perceptual information to predict task outcome. Journal of Intelligent and Robotic Systems: Theory and Applications 82(3-4):379–397, 2016. https://doi.org/10.1007/s10846-015-0197-z.CrossRef

35.

Jimenez, A. R., Seco, F., Prieto, C., and Guevara, J.: A comparison of pedestrian dead-reckoning algorithms using a low-cost mems imu. In: 2009 IEEE International Symposium on Intelligent Signal Processing. https://doi.org/10.1109/WISP.2009.5286542, pp. 37–42, 2009.

36.

InvenSense: MPU-6000 and MPU-6050 Product specification. Tech. Rep. 408 InvenSense Inc, 2013

37.

Honeywell: 3-Axis Digital Compass IC HMC5883L. Tech. rep., Honeywell International Inc, 2011

38.

Ribeiro, N. F., Ferreira, C., Reis, L. P., Silva, H., Macedo, P., Rocha, L., and Santos, C. P.: Validation of a knee angle measurement system based on IMUs. In: CLAWAR 2017 - The 20th International Conference on Climbing and Walking Robots and the Support Technologies for Mobile Machines. https://doi.org/10.1142/9789813231047_0078, pp. 645–652. World Scientific, Porto, 2017.

39.

Kavanagh, J. J., and Menz, H.B., Accelerometry: a technique for quantifying movement patterns during walking. Gait Posture 28(1):1–15, 2008. https://doi.org/10.1016/j.gaitpost.2007.10.010.CrossRefPubMed

40.

Lockhart, T. E., Soangra, R., Zhang, J., and Wu, X., Wavelet based automated postural event detection and activity classification with single IMU. Biomed. Sci. Instrum. 49(Cdc 2010):224–233, 2013. https://doi.org/10.1016/j.micinf.2011.07.011.Innate.CrossRefPubMedPubMedCentral

41.

Jolliffe, I. T., Principal Component Analysis. 2 ed. Berlin: Springer, 2002. https://doi.org/10.1007/b98835.

42.

Wei-min, L., and Chein-I, C.: Variants of principal components analysis. In: 2007 IEEE International Geoscience and Remote Sensing Symposium, pp. 1083–1086, 2007. https://doi.org/10.1109/IGARSS.2007.4422989.

43.

Ijspeert, A. J., Nakanishi, J., Hoffmann, H., Pastor, P., and Schaal, S., Dynamical movement primitives: Learning attractor models for motor behaviors. Neural Comput. 25(2):328–373, 2013. https://doi.org/10.1162/NECO_a_00393. PMID: 23148415.CrossRefPubMed

44.

Pastor, P., Kalakrishnan, M., Meier, F., Stulp, F., Buchli, J., Theodorou, E., and Schaal, S., From dynamic movement primitives to associative skill memories. Robot. Auton. Syst. 61(4):351–361, 2013. https://doi.org/10.1016/j.robot.2012.09.017.CrossRef

45.

Pastor, P., Kalakrishnan, M., Righetti, L., and Schaal, S.: Towards associative skill memories. In: IEEE-RAS International Conference on Humanoid Robots, pp. 309–315, 2012. https://doi.org/10.1109/HUMANOIDS.2012.6651537.

46.

Hubel, H., and Wiesel, T., Receptive fields of single neurones in the cat’s striate cortex. J. Physiol. 148: 574–591, 1959. https://doi.org/10.1113/jphysiol.2009.174151.CrossRefPubMedPubMedCentral

47.

MathWorks: Deep learning: 3 things you need to know, 2017

48.

Murphy, K.P., Machine learning: a Probabilistic Perspective. Cambridge: MIT Press, 2012. https://doi.org/10.1111/j.1467-9310.1986.tb01158.x.

49.

Birenbaum, A., and Greenspan, H., Multi-view longitudinal CNN for multiple sclerosis lesion segmentation. Eng. Appl. Artif. Intel. 65:111–118, 2017. https://doi.org/10.1016/j.engappai.2017.06.006.CrossRef

50.

Ahmad, J., Muhammad, K., Lee, M.Y., and Baik, S.W., Endoscopic image classification and retrieval using clustered convolutional features. J. Med. Syst. 41(12):196, 2017. https://doi.org/10.1007/s10916-017-0836-y.CrossRefPubMed

51.

Ramos-Pollán, R., Guevara-López, M. Á, and Oliveira, E., A software framework for building biomedical machine learning classifiers through grid computing resources. J. Med. Syst. 36(4):2245–2257, 2012. https://doi.org/10.1007/s10916-011-9692-3.CrossRefPubMed

52.

Garty, H.: hagaygarty/mdCNN, 2017

53.

Zhang, J., Lockhart, T.E., and Soangra, R., Classifying lower extremity muscle fatigue during walking using machine learning and inertial sensors. Ann. Biomed. Eng. 42(3):600–612, 2014. https://doi.org/10.1007/s10439-013-0917-0.CrossRefPubMed

54.

Muniz, A.M.S., Liu, H., Lyons, K.E., Pahwa, R., Liu, W., Nobre, F.F., and Nadal, J., Comparison among probabilistic neural network, support vector machine and logistic regression for evaluating the effect of subthalamic stimulation in Parkinson disease on ground reaction force during gait. J. Biomech. 43(4):720–726, 2010. https://doi.org/10.1016/j.jbiomech.2009.10.018.CrossRefPubMed

Title: Development of a Strategy to Predict and Detect Falls Using Wearable Sensors
Authors: Nuno Ferrete Ribeiro
João André
Lino Costa
Cristina P. Santos
Publication date: 01-05-2019
Publisher: Springer US
Published in: Journal of Medical Systems / Issue 5/2019
Print ISSN: 0148-5598
Electronic ISSN: 1573-689X
DOI: https://doi.org/10.1007/s10916-019-1252-2

At a glance: The STEP trials

Springer Medicine

Development of a Strategy to Predict and Detect Falls Using Wearable Sensors

Abstract

At a glance: The STEP trials

Springer Medicine

Abstract

Please log in to get access to this content

Other articles of this Issue 5/2019

Adaptive Fruitfly Based Modified Region Growing Algorithm for Cardiac Fat Segmentation Using Optimal Neural Network

A Novel Distributed Multitask Fuzzy Clustering Algorithm for Automatic MR Brain Image Segmentation

Achieving Efficient and Privacy-Preserving k-NN Query for Outsourced eHealthcare Data

LSTM Model for Prediction of Heart Failure in Big Data

Geometric Model for the Postural Characterization in the Sagital Plane of Lumbar Raquis

Evaluation of the Applicability of 3d Models as Perceived by the Students of Health Sciences