Microprocessor prosthetic knees, able to restore the gait of people with transfemoral amputation, are now often equipped with sensors embedded in the prosthetic shank, which could be used to assess some gait characteristics during real-life activities. In particular, an estimation of the walking speed during the locomotion of those subjects would be a relevant indicator of the performance. However, if methods have already been proposed in the literature to compute this walking speed, none are directly usable in this context and with this population. For these reasons, the current study proposed to estimate the instantaneous walking speed with a shank-embedded Inertial Measurement Units based on a biomechanical model of the prosthetic lower limb. Averaged walking speed estimation has been quantified for nine individuals with transfemoral amputation walking on a treadmill at different speeds and slopes when wearing an instrumented knee ankle prosthesis. Experimental results demonstrated the ability of the model to estimate the walking speed with an accuracy of 9% (normalized root mean squared errors over all the patients), which is consistent with previous reported walking speed estimation errors. In addition, as the walking speed estimation is instantaneous, the proposed method can provide the estimation by the end of the stance phase, which is an originality compared to other methods based on step length estimation. The present method is relevant for the estimation of walking speed during real-life activities of above-knee amputees opening the way to direct activity monitoring from the prosthesis.
SawersABHafnerBJ. Outcomes associated with the use of microprocessor-controlled prosthetic knees among individuals with unilateral transfemoral limb loss: a systematic review. J Rehabil Res Dev2013; 50: 273.
2.
SchmalzTBlumentrittSMarxB. Biomechanical analysis of stair ambulation in lower limb amputees. Gait Posture2007; 25: 267–278.
3.
BurnfieldJMEberlyVJGronelyJK, et al. Impact of stance phase microprocessor-controlled knee prosthesis on ramp negotiation and community walking function in K2 level transfemoral amputees. Prosthet Orthot Int2012; 36: 95–104.
4.
SabatiniAM. Estimating three-dimensional orientation of human body parts by inertial/magnetic sensing. Sensors2011; 11: 1489–1525.
5.
SabatiniAM. Kalman-filter-based orientation determination using inertial/magnetic sensors: observability analysis and performance evaluation. Sensors2011; 11: 9182–9206.
6.
MahonyRHamelTPflimlinJM. Nonlinear complementary filters on the special orthogonal group. IEEE T Automat Contr2008; 53: 1203–1218.
TuraARaggiMRocchiL, et al. Gait symmetry and regularity in transfemoral amputees assessed by trunk accelerations. J Neuroeng Rehabil2010; 7: 4.
9.
MarianiBRochatSBülaCJ, et al. Heel and toe clearance estimation for gait analysis using wireless inertial sensors. IEEE T Biomed Eng2012; 59: 3162–3168.
10.
DadashiFMarianiBRochatS, et al. Gait and foot clearance parameters obtained using shoe-worn inertial sensors in a large-population sample of older adults. Sensors2013; 14: 443–457.
11.
YuanKWangQWangL. Fuzzy-logic-based terrain identification with multisensor fusion for transtibial amputees. IEEE/ASME T Mech2015; 20: 618–630.
12.
RedfieldMTCagleJCHafnerBJ, et al. Classifying prosthetic use via accelerometry in persons with transtibial amputations. J Rehabil Res Dev2013; 50: 1201–1212.
13.
ZhangFFangZLiuM, et al. Preliminary design of a terrain recognition system. Conf Proc IEEE Eng Med Biol Soc2011; 2011: 5452–5455.
14.
SpragerSJuricMB. Inertial sensor-based gait recognition: a review. Sensors2015; 15: 22089–22127.
15.
RamstrandNNilssonK-A. Validation of a patient activity monitor to quantify ambulatory activity in an amputee population. Prosthet Orthot Int2007; 31: 157–166.
16.
BussmannHBReuvekampPJVeltinkPH, et al. Validity and reliability of measurements obtained with an “activity monitor” in people with and without a transtibial amputation. Phys Ther1998; 78: 989–998.
17.
DudekNLKhanODLemaireED, et al. Ambulation monitoring of transtibial amputation subjects with patient activity monitor versus pedometer. J Rehabil Res Dev2008; 45: 577–585.
18.
HalsneEGWaddinghamMGHafnerBJ. Long-term activity in and among persons with transfemoral amputation. J Rehabil Res Dev2013; 50: 515.
19.
JayaramanADeenySEisenbergY, et al. Global position sensing and step activity as outcome measures of community mobility and social interaction for an individual with a transfemoral amputation due to dysvascular disease. Phys Ther2014; 94: 401–410.
20.
AlbertMVDeenySMcCarthyC, et al. Monitoring daily function in persons with transfemoral amputations using a commercial activity monitor: a feasibility study. PM&R2014; 6: 1120–1127.
21.
AlbertMVMcCarthyCValentinJ, et al. Monitoring functional capability of individuals with lower limb amputations using mobile phones. PLoS ONE2013; 8: e65340.
22.
SunJWaltersMSvenssonN, et al. The influence of surface slope on human gait characteristics: a study of urban pedestrians walking on an inclined surface. Ergonomics1996; 39: 677–692.
23.
AminianKNajafiBBülaC, et al. Spatio-temporal parameters of gait measured by an ambulatory system using miniature gyroscopes. J Biomech2002; 35: 689–699.
24.
SalarianABurkhardPRVingerhoetsFJG, et al. A novel approach to reducing number of sensing units for wearable gait analysis systems. IEEE T Biomed Eng2013; 60: 72–77.
25.
MiyazakiS. Long-term unrestrained measurement of stride length and walking velocity utilizing a piezoelectric gyroscope. IEEE T Biomed Eng1997; 44: 753–759.
AminianKRobertPJéquierE, et al. Incline, speed, and distance assessment during unconstrained walking. Med Sci Sport Exer1995; 27: 226–234.
28.
DudaORHartEPStorkGD, et al. Pattern classification. 2nd Revised. Berlin: Springer, 2000.
29.
SabatiniAM. Inertial sensing in biomechanics. In: PalaniswamiMBeggR (eds) Computational intelligence for movement sciences neural networks and other emerging techniques. London: Idea Group Publishing, pp.70–100.
30.
ManniniASabatiniAM. Walking speed estimation using foot-mounted inertial sensors: comparing machine learning and strap-down integration methods. Med Eng Phys2014; 36: 1312–1321.
31.
SabatiniAMMartelloniCScapellatoS, et al. Assessment of walking features from foot inertial sensing. IEEE T Biomed Eng2005; 52: 486–494.
32.
LiQYoungMNaingV, et al. Walking speed estimation using a shank-mounted inertial measurement unit. J Biomech2010; 43: 1640–1643.
33.
YangSLaudanskiALiQ. Inertial sensors in estimating walking speed and inclination: an evaluation of sensor error models. Med Biol Eng Comput2012; 50: 383–393.
34.
SegalADOrendurffMSKluteGK, et al. Kinematic and kinetic comparisons of transfemoral amputee gait using C-Leg and Mauch SNS prosthetic knees. J Rehabil Res Dev2006; 43: 857–870.
35.
MartinEShiaVBajcsyR. Determination of a patient’s speed and stride length minimizing hardware requirements. In: Proceedings of the international conference on body sensor networks, Dallas, TX, 23–25 May 2011, pp.144–149. New York: IEEE.
36.
BonnetXDjianFDrevelleX, et al. Design and preliminary evaluation of a microprocessor controlled ankle-knee prosthetic system for above knee amputees. In: Proceedings of the 13th international symposium on 3D analysis of human movement, Lausanne, 14–17 July 2014.
37.
BellmannMSchmalzTLudwigsE, et al. Immediate effects of a new microprocessor-controlled prosthetic knee joint: a comparative biomechanical evaluation. Arch Phys Med Rehabil2012; 93: 541–549.
38.
HansenAHChildressDSMiffSC, et al. The human ankle during walking: implications for design of biomimetic ankle prostheses. J Biomech2004; 37: 1467–1474.
39.
McGeerT. Passive dynamic walking. Int J Rob Res1990; 9: 62–82.
40.
HansenAHChildressDS. Effects of shoe heel height on biologic rollover characteristics during walking. J Rehabil Res Dev2004; 41: 547–554.
41.
ClauserCEMcConvilleJTYoungJW, et al. Weight, volume and center of mass of segments of the human body. AMRL technical report, Wright Patterson Air Force Base, May1969.
42.
VirmavirtaMIsolehtoJ. Determining the location of the body’s center of mass for different groups of physically active people. J Biomech2014; 47: 1909–1913.
43.
GaileyRAllenKCastlesJ, et al. Review of secondary physical conditions associated with lower-limb amputation and long-term prosthesis use. J Rehabil Res Dev2008; 45: 15–29.
44.
SchaarschmidtMLipfertSWMeier-GratzC, et al. Functional gait asymmetry of unilateral transfemoral amputees. Hum Mov Sci2012; 31: 907–917.
45.
RileyPOPaoliniGDella CroceU, et al. A kinematic and kinetic comparison of overground and treadmill walking in healthy subjects. Gait Posture2007; 26: 17–24.
