Original article| Volume 56, 103270, November 2021

Comparing fall detection methods in people with multiple sclerosis: A prospective observational cohort study

Published:September 18, 2021DOI:


      • People with MS fall frequently.
      • The gold standard for fall reporting is prospective self-report fall calendars.
      • There is increasing interest in automated fall detection.
      • We compare here three methods of fall counting, used concurrently.


      Background Falls occur across the population but are more common, and have more negative sequelae, in people with multiple sclerosis (MS). Given the prevalence and impact of falls, accurate measures of fall frequency are needed. This study compares the sensitivity and false discovery rates of three methods of fall detection: the current gold standard, prospective paper fall calendars, real-time self-reporting and automated detection, the latter two from a novel body-worn device.
      Methods Falls in twenty-five people with MS were recorded for eight weeks with prospective fall calendars, real-time body-worn self-report, and an automated body-worn detector concurrently. Eligible individuals were adults with MS enrolled in a randomized controlled trial of a fall prevention intervention. Entry criteria were at least two falls or near-falls in the previous two months, Expanded Disability Status Scale ≤ 6.0, community dwelling, and no MS relapse in the previous month. The sensitivity (proportion of true falls detected) and false discovery rates (proportion of false reports generated) of the fall detection methods were compared. A true fall was a fall reported by at least two methods. A false report was a fall reported by only one method. The trial is registered on (NCT02583386) and is closed.
      Results In the 1,276 person-days of fall counting with all three methods in use simultaneously there were 1344 unique fall events. Of these, 8.5% (114) were true falls and 91.5% (1230) were false reports. Fall calendars had the lowest sensitivity (0.614) and the lowest false discovery rate (0.067). The automated detector had the highest sensitivity (0.921) and the highest false discovery rate (0.919). All methods generated under one false report per day. There were no fall detection-related adverse events.
      Conclusion Fall calendars likely underestimate fall frequency by around 40%. The automated detector evaluated here misses very few falls but likely overestimates the number of falls by around one fall per day. Additional research is needed to produce an ideal fall detection and counting method for use in clinical and research applications.
      Funding United States Department of Veterans Affairs, Rehabilitations Research and Development Service



      ADL (activities of daily living), EDSS (expanded disability status scale), FFF (free from falls), GPS (global positioning system), IMU (inertial monitoring unit), IQR (interquartile range), MS (multiple sclerosis), OHSU (oregon health & science university), PwMS (people with MS), sd (standard deviation), ToF (time-of-flight), VAPORHCS (veterans affairs portland health care system)
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      1. Aguiar B., Rocha T., Silva J., Sousa I., 2014. Accelerometer-based fall detection for smartphones. 2014 IEEE International Symposium on Medical Measurements and Applications (MeMeA), 1-6.

        • Amin M.G.
        • Zhang Y.D.
        • Ahmad F.
        • Ho K.D.
        Radar signal processing for elderly fall detection: the future for in-home monitoring.
        IEEE Signal Process. Mag. 2016; 33: 71-80
        • Aziz O.
        • Musngi M.
        • Park E.J.
        • Mori G.
        • Robinovitch S.N.
        A comparison of accuracy of fall detection algorithms (threshold-based vs. machine learning) using waist-mounted tri-axial accelerometer signals from a comprehensive set of falls and non-fall trials.
        Med. Biol. Eng. Comput. 2017; 55: 45-55
        • Bagala F.
        • Becker C.
        • Cappello A.
        • et al.
        Evaluation of accelerometer-based fall detection algorithms on real-world falls.
        PLoS ONE. 2012; 7: e37062
        • Bagala F.
        • Becker C.
        • Cappello A.
        • et al.
        Evaluation of accelerometer-based fall detection algorithms on real-world falls.
        PLoS ONE. 2012; 7: e37062
        • Bazelier M.T.
        • van Staa T.
        • Uitdehaag B.M.
        • et al.
        The risk of fracture in patients with multiple sclerosis: the UK general practice research database.
        J. Bone Miner. Res. 2011; 26: 2271-2279
        • Bian Z.P.
        • Hou J.
        • Chau L.P.
        • Magnenat-Thalmann N.
        Fall detection based on body part tracking using a depth camera.
        IEEE J. Biomed. Health Inform. 2015; 19: 430-439
        • Bronnum-Hansen H.
        • Hansen T.
        • Koch-Henriksen N.
        • Stenager E.
        Fatal accidents among danes with multiple sclerosis.
        Mult. Scler. 2006; 12: 329-332
        • Cameron M.
        • McMillan G.
        • Hugos C.
        • Hildebrand A.
        • Judd G.
        • Jacobs P.
        Free from falls education and exercise program for reducing falls in people with multiple sclerosis: a randomized controlled trial.
        Mult. Scler. J. 2021; (in press)
        • Cameron M.H.
        • Asano M.
        • Bourdette D.
        • Finlayson M.L.
        People with multiple sclerosis use many fall prevention strategies but still fall frequently.
        Arch. Phys. Med. Rehabil. 2013; 94: 1562-1566
        • Cameron M.H.
        • Nilsagard Y.
        Balance, gait, and falls in multiple sclerosis.
        Handb. Clin. Neurol. 2018; 159: 237-250
        • Castillo J.C.
        • Carneiro D.
        • Serrano-Cuerda J.
        • Novais P.
        • Fernandez-Caballero A.
        • Neves J.
        A muti-modal approach for activity classification and fall detection.
        Int. J. Syst. Sci. 2014; 45: 810-824
        • Chao P.K.
        • Chan H.L.
        • Tang F.T.
        • Chen Y.C.
        • Wong M.K.
        A comparison of automatic fall detection by the cross-product and magnitude of tri-axial acceleration.
        Physiol. Meas. 2009; 30: 1027-1037
        • Chowdhury N.
        • Hildebrand A.
        • Folsom J.
        • Jacobs P.
        • Cameron M.
        Are "gold standard" prospective daily self-report fall calendars accurate? A comparison with a real-time daily self-report device in multiple sclerosis.
        Neurology. 2018; 90 (suppl)(P4): 404
        • Chua J.L.
        • Chang Y.C.
        • Lim W.K.
        A simple vision-based fall detection technique for indoor video surveillance.
        Signal Image Video Process. 2015; 9: 623-633
        • Coote S.
        • Comber L.
        • Quinn G.
        • Santoyo-Medina C.
        • Kalron A.
        • Gunn H.
        Falls in people with multiple sclerosis: risk identification, intervention, and future directions.
        Int. J. MS Care. 2020; 22: 247-255
        • Coote S.
        • Sosnoff J.J.
        • Gunn H.
        Fall incidence as the primary outcome in multiple sclerosis falls-prevention trials: recommendation from the international MS falls prevention research network.
        Int. J. MS Care. 2014; 16: 178-184
        • Dolci E.
        • Scharer B.
        • Grossmann N.
        • et al.
        Automated fall detection algorithm with global trigger tool, incident reports, manual chart review, and patient-reported falls: algorithm development and validation with a retrospective diagnostic accuracy study.
        J. Med. Internet Res. 2020; 22: e19516
        • Fleming J.
        • Matthews F.E.
        • Brayne C.
        Cambridge city over-75s cohort (CC75C) study collaboration. Falls in advanced old age: recalled falls and prospective follow-up of over-90-year-olds in the cambridge city over-75s cohort study.
        BMC Geriatr. 2008; 8 (6-2318-8-6)
        • Gasparrini S.
        • Cippitelli E.
        • Spinsante S.
        • Gambi E.
        A depth-based fall detection system using a kinect(R) sensor.
        Sensors. 2014; 14 (Basel): 2756-2775
        • Gianni C.
        • Prosperini L.
        • Jonsdottir J.
        • Cattaneo D.
        A systematic review of factors associated with accidental falls in people with multiple sclerosis: a meta-analytic approach.
        Clin. Rehabil. 2014; 28: 704-716
        • Gunn H.
        • Creanor S.
        • Haas B.
        • Marsden J.
        • Freeman J.
        Frequency, characteristics, and consequences of falls in multiple sclerosis: findings from a cohort study.
        Arch. Phys. Med. Rehabil. 2014; 95: 538-545
        • Gunn H.J.
        • Newell P.
        • Haas B.
        • Marsden J.F.
        • Freeman J.A.
        Identification of risk factors for falls in multiple sclerosis: a systematic review and meta-analysis.
        Phys. Ther. 2013; 93: 504-513
        • Hannan M.T.
        • Gagnon M.M.
        • Aneja J.
        • et al.
        Optimizing the tracking of falls in studies of older participants: comparison of quarterly telephone recall with monthly falls calendars in the MOBILIZE boston study.
        Am. J. Epidemiol. 2010; 171: 1031-1036
        • Hugos C.L.
        • Frankel D.
        • Tompkins S.A.
        • Cameron M.
        Community delivery of a comprehensive fall-prevention program in people with multiple sclerosis: a retrospective observational study.
        Int. J. MS Care. 2016; 18: 42-48
        • Kangas M.
        • Konttila A.
        • Lindgren P.
        • Winblad I.
        • Jamsa T.
        Comparison of low-complexity fall detection algorithms for body attached accelerometers.
        Gait Posture. 2008; 28: 285-291
        • Kangas M.
        • Vikman I.
        • Nyberg L.
        • Korpelainen R.
        • Lindblom J.
        • Jamsa T.
        Comparison of real-life accidental falls in older people with experimental falls in middle-aged test subjects.
        Gait Posture. 2012; 35: 500-505
        • Kau L.J.
        • Chen C.S.
        A smart phone-based pocket fall accident detection, positioning, and rescue system.
        IEEE J. Biomed. Health Inform. 2015; 19: 44-56
        • Klenk J.
        • Becker C.
        • Lieken F.
        • et al.
        Comparison of acceleration signals of simulated and real-world backward falls.
        Med. Eng. Phys. 2011; 33: 368-373
        • Klenk J.
        • Schwickert L.
        • Palmerini L.
        • et al.
        The FARSEEING real-world fall repository: a large-scale collaborative database to collect and share sensor signals from real-world falls.
        Eur. Rev. Aging Phys. Act. 2016; 13 (8-016-0168-9. eCollection 2016)
        • Kwolek B.K.M
        Improving fall detection by the use of depth sensor and accelerometer.
        Neurocomputing. 2015; 168: 637-645
        • Kwolek B.
        • Kepski M.
        Human fall detection on embedded platform using depth maps and wireless accelerometer.
        Comput. Methods Progr. Biomed. 2014; 117: 489-501
        • Lamb S.E.
        • Jorstad-Stein E.C.
        • Hauer K.
        • Becker C.
        • Prevention of Falls Network Europe and Outcomes Consensus Group
        Development of a common outcome data set for fall injury prevention trials: the prevention of falls network europe consensus.
        J. Am. Geriatr. Soc. 2005; 53: 1618-1622
        • Lee J.K.
        • Robinovitch S.N.
        • Park E.J.
        Inertial sensing-based pre-impact detection of falls involving near-fall scenarios.
        IEEE Trans. Neural Syst. Rehabil. Eng. 2015; 23: 258-266
        • Li Y.
        • Ho K.C.
        • Popescu M.
        Efficient source separation algorithms for acoustic fall detection using a microsoft kinect.
        IEEE Trans. Biomed. Eng. 2014; 61: 745-755
        • Lipsitz L.A.
        • Tchalla A.E.
        • Iloputaife I.
        • et al.
        Evaluation of an automated falls detection device in nursing home residents.
        J. Am. Geriatr. Soc. 2016; 64: 365-368
        • Luque R.
        • Casilari E.
        • Moron M.J.
        • Redondo G.
        Comparison and characterization of android-based fall detection systems.
        Sensors. 2014; 14 (Basel): 18543-18574
        • Ma X.
        • Wang H.
        • Xue B.
        • Zhou M.
        • Ji B.
        • Li Y
        Depth-based human fall detection via shape features and improved extreme learning machine.
        IEEE J. Biomed. Health Inform. 2014; 18: 1915-1922
        • Matsuda P.N.
        • Shumway-Cook A.
        • Ciol M.A.
        • Bombardier C.H.
        • Kartin D.A.
        Understanding falls in multiple sclerosis: association of mobility status, concerns about falling, and accumulated impairments.
        Phys. Ther. 2012; 92: 407-415
        • Mauldin T.R.
        • Canby M.E.
        • Metsis V.
        • Ngu A.H.H.
        • Rivera C.C.
        SmartFall: a smartwatch-based fall detection system using deep learning.
        Sensors. 2018; 18 (Basel)
        • Mazumder R.
        • Murchison C.
        • Bourdette D.
        • Cameron M.
        Falls in people with multiple sclerosis compared with falls in healthy controls.
        PLoS ONE. 2014; 9e107620
        • Mosquera-Lopez C.
        • Wan E.
        • Shastry M.
        • et al.
        Automated detection of real-world falls: modeled from people with multiple sclerosis.
        IEEE J. Biomed. Health Inform. 2021; 25 (PP:10.1109/JBHI.2020.3041035): 1975-1984
        • Nilsagard Y.
        • Carling A.
        • Forsberg A.
        Activities-specific balance confidence in people with multiple sclerosis.
        Mult. Scler. Int. 2012; 2012613925
        • Nilsagard Y.
        • Gunn H.
        • Freeman J.
        • et al.
        Falls in people with MS–an individual data meta-analysis from studies from Australia, Sweden, United Kingdom and the United States.
        Mult. Scler. 2015; 21: 92-100
        • O'Loughlin J.L.
        • Robitaille Y.
        • Boivin J.F.
        • Suissa S.
        Incidence of and risk factors for falls and injurious falls among the community-dwelling elderly.
        Am. J. Epidemiol. 1993; 137: 342-354
        • Paul A.
        • Wan E.A.
        • Jacobs P.G.
        Sigma-point kalman smoothing for indoor tracking and auto-calibration using time-of-flight ranging.
        in: Proceedings of the Institute of Navigation GNSS. 2011: 3461-3469
        • Perry L.
        • Kendrick D.
        • Morris R.
        • et al.
        Completion and return of fall diaries varies with participants' level of education, first language, and baseline fall risk.
        J. Gerontol. A Biol. Sci. Med. Sci. 2012; 67: 210-214
        • Peterson E.W.
        • Cho C.C.
        • Finlayson M.L.
        Fear of falling and associated activity curtailment among middle aged and older adults with multiple sclerosis.
        Mult. Scler. 2007; 13: 1168-1175
        • Peterson E.W.
        • Cho C.C.
        • von Koch L.
        • Finlayson M.L.
        Injurious falls among middle aged and older adults with multiple sclerosis.
        Arch. Phys. Med. Rehabil. 2008; 89: 1031-1037
        • Pierleoni P.
        • Belli A.
        • Palma L.
        • Pellegrini M.
        • Pernini L.
        • Valenti S.
        A high reliability wearable device for elderly fall detection.
        IEEE Sens. J. 2015; 15: 4544-4553
        • Podsiadlo D.
        • Richardson S.
        The timed "up & go": a test of basic functional mobility for frail elderly persons.
        J. Am. Geriatr. Soc. 1991; 39: 142-148
        • Powell L.E.
        • Myers A.M.
        The activities-specific balance confidence (ABC) scale.
        J. Gerontol. A Biol. Sci. Med. Sci. 1995; 50A: M28-M34
        • Rapp K.
        • Freiberger E.
        • Todd C.
        • et al.
        Fall incidence in germany: results of two population-based studies, and comparison of retrospective and prospective falls data collection methods.
        BMC Geriatr. 2014; 14 (-2318-14-105): 105
        • Shastry S.C.
        • Asgari M.
        • Wan E.A.
        • Leitschuh J.
        • Preiser N.
        • Folsom J.
        • Condon J.
        • Cameron M.
        • Jacobs P.G.
        Context-aware fall detection using inertial sensors and time-of-flight transceivers.
        in: Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society. 2016
        • Stack E.
        Falls are unintentional: studying simulations is a waste of faking time.
        J. Rehabil. Assist. Technol. Eng. 2017; 4 (2055668317732945-Dec)
        • Stone E.E.
        • Skubic M.
        Fall detection in homes of older adults using the microsoft kinect.
        IEEE J. Biomed. Health Inform. 2015; 19: 290-301
        • Su B.Y.
        • Ho K.C.
        • Rantz M.J.
        • Skubic M.
        Doppler radar fall activity detection using the wavelet transform.
        IEEE Trans. Biomed. Eng. 2015; 62: 865-875
        • Sucerquia A.
        • Lopez J.D.
        • Vargas-Bonilla J.F.
        SisFall: a fall and movement dataset.
        Sensors. 2017; 17 (Basel)
        • Sucerquia A.
        • Lopez J.D.
        • Vargas-Bonilla J.F.
        Real-life/real-time elderly fall detection with a triaxial accelerometer.
        Sensors. 2018; 18 (Basel)
      2. The National Multiple Sclerosis Society, 2021. Free From Falls: A comprehensive fall prevention program. . Updated 2020.

        • Tsinganos P.
        • Skodras A.
        On the comparison of wearable sensor data fusion to a single sensor machine learning technique in fall detection.
        Sensors. 2018; 18 (Basel)
        • Wang J.
        • Zhang Z.
        • Li B.
        • Lee S.
        • Sherratt R.S.
        An enhanced fall detection system for elderly person monitoring using consumer home networks.
        IEEE Trans. Consum. Electron. 2014; 60: 489-501
        • Wang Y.
        • Wu K.
        • Ni L.M.
        Wifall: device-free fall detection by wireless networks.
        IEEE Trans. Mobile Comput. 2017; 16: 581-594
        • Zieschang T.
        • Schwenk M.
        • Becker C.
        • Oster P.
        • Hauer K.
        Feasibility and accuracy of fall reports in persons with dementia: a prospective observational study.
        Int. Psychogeriatr. 2012; 24: 587-598