Automatic individual calibration in fall detection - an integrative ambulatory measurement framework

Computer Methods in Biomechanics and Biomedical Engineering

Volumn 16, Issue 5, 2013, Pages 504-510

  Liu, Jian ^a   Lockhart, Thurmon E ^b  

^a UNIVERSITY OF HOUSTON   (United States)

^b Virginia Polytechnic Institute and State University   (United States)

Author keywords

ambulatory measurement; fall detection; fall intervention; slips and falls

Indexed keywords

ACTIVITIES OF DAILY LIVING; AMBULATORY MEASUREMENT; FALL DETECTION; FALL INTERVENTION; INDIVIDUAL CALIBRATIONS; INERTIAL MEASUREMENT UNIT; LABORATORY STUDIES; SLIPS AND FALLS;

UNITS OF MEASUREMENT;

CALIBRATION;

ACCELERATION; ACCELEROMETER; AGED; ALGORITHM; AMBULATORY MONITORING; ARTICLE; CALIBRATION; CONCEPTUAL FRAMEWORK; DAILY LIFE ACTIVITY; FALL RISK ASSESSMENT; HUMAN; HUMAN EXPERIMENT; INTEGRATIVE AMBULATORY MEASUREMENT; KINEMATICS; NORMAL HUMAN; RESPONSE TIME; SENSITIVITY AND SPECIFICITY; TASK PERFORMANCE; THIGH; TRUNK;

ACCELERATION; ACCIDENTAL FALLS; ACTIVITIES OF DAILY LIVING; AGED; ALGORITHMS; BIOMECHANICAL PHENOMENA; CALIBRATION; HUMANS; MONITORING, AMBULATORY; REFERENCE VALUES;

EID: 84878122295     PISSN: 10255842     EISSN: 14768259     Source Type: Journal    
DOI: 10.1080/10255842.2011.627329     Document Type: Article

References (27)

1. AminianK, IrevisanC, NajafiB, DejnabadiH, FrigoC, PavanE et al. 2004. Evaluation of an ambulatory system for gait analysis in hip osteoarthritis and after total hip replacement. Gait Posture. 20(1):102-107.
2. AminianK, NajafiB. 2004. Capturing human motion using body-fixed sensors: outdoor measurement and clinical applications. Comput Animat Virt Worlds. 15:79-94.
3. BaoL. 2003. Physical activity recognition from accelerometer data under semi-naturalistic conditions. MS thesis, MIT.
4. ChenKY, BassettDR. 2005. The technology of accelerometry-based activity monitors: current and future. Med Sci Sports Exer.37(11):S490-S500.
5. FukayaK, UchidaM. 2008. Protection against impact with the ground using wearable airbags. Indus Health. 46:59-65.
6. GiansantiD. 2006. Investigation of fall-risk using a wearable device with accelerometers and rate gyroscopes. Physiol Measure.27:1081-1090.
7. KarantonisDM, NarayananMR, MathieM, LovellNH, CellerBG. 2006. Implementation of a real-time human movement classifier using a triaxial accelerometer for ambulatory monitoring. IEEE Trans Inf Technol Biomed.10(1):156-167.
8. LiuJ. Aging effect on successful recovery from unexpected slips: a 3D lower extremity joint moment analysis. Saarbrucken, Germany: VDM Publishing House.
9. LiuJ, LockhartTE. 2009a. Age-related joint moment characteristics during normal gait and successful reactive-recovery from unexpected slip perturbations. Gait Posture. 30:276-281.
10. Liu J, Lockhart TE. 2009b. Trunk angular kinematics during slip-induced falls and activities of daily living - towards developing a fall detector. In: Human Factors and Ergonomics Society 53rd Annual Meeting, San Antonio, TX. p. 892-896.
11. LiuJ, LockhartTE. Development and evaluation of a prior-to-impact fall event detection algorithm. J Biomech, under review.
12. LockhartTE, LiuJ.2008. Differentiating fall-prone and healthy adults using local dynamic stability. Ergonomics. 51:1860-1872.
13. LuingeHJ, VeltinkPH, BatenCTM. 2007. Ambulatory measurement of arm orientation. J Biomech.40(1):78-85.
14. LyonsGM, CulhaneKM, HiltonD, GracePA, LyonsD. 2005. A description of an accelerometer-based mobility monitoring technique. Med Eng Phys.27(6):497-504.
15. MansfieldA, LyonsGM. 2003. The use of accelerometry to detect heel contact events for use as a sensor in FES assisted walking. Med Eng Phys.25:879-885.
16. MathieMJ, CellerBG, LovellNH, CosterACF. 2004. Classification of basic daily movements using a triaxial accelerometer. Med Biol Eng Comput.42(5):679-687.
17. Najafi B, Aminian K, Bula C, Ruggieri G, Robert P. 2001. Falling risk evaluation in elderly using miniature gyroscope: relation between gait and risk of falling. Control of Posture and Gait. Symposium of the International Society for Postural and Gait Research (ISPG 2001), Maastricht. p. 135-139.
18. NajafiB, AminianK, Paraschiv-IonescuA, LoewF, BulaC, RobertP. 2003. Ambulatory system for human motion analysis using a kinematic sensor: monitoring of daily physical activity in the elderly. IEEE Trans Biomed Eng.50(6):711-723.
19. NouryN, BarralonP, VironeG, BoissyP, HamelM, RumeauP. 2003. A smart sensor based on rules and its evaluation in daily routines. Paper Presented at the 25th Annual International Conference of theIEEE EMBS.
20. NyanMN, TayFEH, TanAWY, SeahKHW. 2006a. Distinguishing fall activities from normal activities by angular rate characteristics and high-speed camera characterization. Med Eng Phys.28(8):842-849.
21. NyanMN, FrancisEHT, ManimaranM, SeahKHW. 2006b. Garment-based detection of falls and activities of daily living using 3-axis MEMS accelerometer. J Phys Conf Ser. 34:1059-1067.
22. SabatiniAM, MartelloniC, ScapellatoS, CavalloF. 2005. Assessment of walking features from foot inertial sensing. IEEE Trans Biomed Eng.52(3):486-494.
23. SekineM, TamuraT, TogawaT, FukuiY. 2000. Classification of waist-acceleration signals in a continuous walking record. Med Eng Phys.22(4):285-291.
24. SimcoxS, ParkerS, DavisGM, SmithRW, MiddletonJW. 2005. Performance of orientation sensors for use with a functional electrical stimulation mobility system. J Biomech.38(5):1185-1190.
25. ZhangK, WernerP, SunM, Pi-SunyerFX, BoozerCN. 2003. Measurement of human daily physical activity. Obes Res.11(1):33-40.
26. ZijlstraW, HofAL. 2003. Assessment of spatio-temporal gait parameters from trunk accelerations during human walking. Gait Posture. 18:1-10.
27. ZweigMH, CampbellG. 1993. Receiver operating characteristic (ROC) plots - a fundamental evaluation tool in clinical medicine. Clin Chem.39:561-577.