Estimation of spatio-temporal parameters of gait from magneto-inertial measurement units: multicenter validation among Parkinson, mildly cognitively impaired and healthy older adults

被引:61
作者
Bertoli, Matilde [1 ,2 ]
Cereatti, Andrea [1 ,2 ,3 ]
Trojaniello, Diana [4 ]
Avanzino, Laura [5 ,6 ]
Pelosin, Elisa [7 ]
Del Din, Silvia [8 ]
Rochester, Lynn [8 ,9 ]
Ginis, Pieter [10 ]
Bekkers, Esther M. J. [10 ,11 ]
Mirelman, Anat [12 ,13 ,14 ]
Hausdorff, Jeffrey M. [12 ,13 ,14 ,15 ,16 ]
Della Croce, Ugo [1 ,2 ]
机构
[1] Univ Sassari, Dept Biomed Sci, Sassari, Italy
[2] Interuniv Ctr Bioengn Human Neuromusculoskeletal, Sassari, Italy
[3] Politecn Torino, Dept Elect & Telecommun, Turin, Italy
[4] Osped San Raffaele, E Serv Life & Hlth, Milan, Italy
[5] Univ Genoa, Sect Human Physiol, Dept Expt Med, Genoa, Italy
[6] Univ Genoa, Ctr Polifunz Sci Motorie, Genoa, Italy
[7] Univ Genoa, Dept Neurosci Rehabil Ophthalmol Genet & Maternal, Genoa, Italy
[8] Newcastle Univ, Inst Neurosci, Inst Ageing, Clin Ageing Res Unit, Campus Ageing & Vital, Newcastle, England
[9] Newcastle Upon Tyne Hosp NHS Fdn Trust, Newcastle, England
[10] Katholieke Univ Leuven, Dept Rehabil Sci, Neuromotor Rehabil Res Grp, Louvain, Belgium
[11] Radboud Univ Nijmegen, Med Ctr, Donders Inst Brain Cognit & Behav, Dept Neurol,Parkinson Ctr Nijmegen, Nijmegen, Netherlands
[12] Tel Aviv Sourasky Med Ctr, Ctr Study Movement Cognit & Mobil, Neurol Inst, Tel Aviv, Israel
[13] Tel Aviv Univ, Sagol Sch Neurosci, Tel Aviv, Israel
[14] Tel Aviv Univ, Sackler Sch Med, Tel Aviv, Israel
[15] Rush Univ, Med Ctr, Rush Alzheimers Dis Ctr, Tel Aviv, Israel
[16] Rush Univ, Med Ctr, Dept Orthopaed Surg, Tel Aviv, Israel
来源
BIOMEDICAL ENGINEERING ONLINE | 2018年 / 17卷
关键词
Clinical gait analysis; Spatial and temporal gait parameters; Magneto-inertial sensors; Wearable sensors; Parkinson; Elderly; Multicentric study; CONVOLUTIONAL NEURAL-NETWORKS; STRIDE LENGTH; FALL RISK; SENSORS; SYSTEM; FOOT; TIME;
D O I
10.1186/s12938-018-0488-2
中图分类号
R318 [生物医学工程];
学科分类号
0831 ;
摘要
Background: The use of miniaturized magneto-inertial measurement units (MIMUs) allows for an objective evaluation of gait and a quantitative assessment of clinical outcomes. Spatial and temporal parameters are generally recognized as key metrics for characterizing gait. Although several methods for their estimate have been proposed, a thorough error analysis across different pathologies, multiple clinical centers and on large sample size is still missing. The aim of this study was to apply a previously presented method for the estimate of spatio-temporal parameters, named Trusted Events and Acceleration Direct and Reverse Integration along the direction of Progression (TEADRIP), on a large cohort (236 patients) including Parkinson, mildly cognitively impaired and healthy older adults collected in four clinical centers. Data were collected during straight-line gait, at normal and fast walking speed, by attaching two MIMUs just above the ankles. The parameters stride, step, stance and swing durations, as well as stride length and gait velocity, were estimated for each gait cycle. The TEADRIP performance was validated against data from an instrumented mat. Results: Limits of agreements computed between the TEADRIP estimates and the reference values from the instrumented mat were -27 to 27 ms for Stride Time, -68 to 44 ms for Stance Time, -31 to 31 ms for Step Time and -67 to 52 mm for Stride Length. For each clinical center, the mean absolute errors averaged across subjects for the estimation of temporal parameters ranged between 1 and 4%, being on average less than 3% (< 30 ms). Stride length mean absolute errors were on average 2% (approximate to 25 mm). Error comparisons across centers did not show any significant difference. Significant error differences were found exclusively for stride and step durations between healthy elderly and Parkinsonian subjects, and for the stride length between walking speeds. Conclusions: The TEADRIP method was effectively validated on a large number of healthy and pathological subjects recorded in four different clinical centers. Results showed that the spatio-temporal parameters estimation errors were consistent with those previously found on smaller population samples in a single center. The combination of robustness and range of applicability suggests the use of the TEADRIP as a suitable MIMU-based method for gait spatio-temporal parameter estimate in the routine clinical use. The present paper was awarded the "SIAMOC Best Methodological Paper 2017".
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页数:14
相关论文
共 35 条
  • [1] A Wearable Magneto-Inertial System for Gait Analysis (H-Gait): Validation on Normal Weight and Overweight/Obese Young Healthy Adults
    Agostini, Valentina
    Gastaldi, Laura
    Rosso, Valeria
    Knaflitz, Marco
    Tadano, Shigeru
    [J]. SENSORS, 2017, 17 (10)
  • [2] Gait recording with inertial sensors - How to determine initial and terminal contact
    Boetzel, Kai
    Martinez Marti, Fernando
    Carvajal Rodriguez, Miguel Angel
    Plate, Annika
    Olivares Vicente, Alberto
    [J]. JOURNAL OF BIOMECHANICS, 2016, 49 (03) : 332 - 337
  • [3] Cereatti Andrea, 2015, 2 IEEE INT S IN SENS
  • [4] A Wearable Inertial Measurement System With Complementary Filter for Gait Analysis of Patients With Stroke or Parkinson's Disease
    Chang, Hsing-Cheng
    Hsu, Yu-Liang
    Yang, Shih-Chin
    Lin, Jung-Chin
    Wu, Zhi-Hao
    [J]. IEEE ACCESS, 2016, 4 : 8442 - 8453
  • [5] Toward Pervasive Gait Analysis With Wearable Sensors: A Systematic Review
    Chen, Shanshan
    Lach, John
    Lo, Benny
    Yang, Guang-Zhong
    [J]. IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS, 2016, 20 (06) : 1521 - 1537
  • [6] Validation of an Accelerometer to Quantify a Comprehensive Battery of Gait Characteristics in Healthy Older Adults and Parkinson's Disease: Toward Clinical and at Home Use
    Del Din, Silvia
    Godfrey, Alan
    Rochester, Lynn
    [J]. IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS, 2016, 20 (03) : 838 - 847
  • [7] Della Croce U., 2017, HDB HUMAN MOTION, P1
  • [8] A Mobile Kalman-Filter Based Solution for the Real-Time Estimation of Spatio-Temporal Gait Parameters
    Ferrari, Alberto
    Ginis, Pieter
    Hardegger, Michael
    Casamassima, Filippo
    Rocchi, Laura
    Chiari, Lorenzo
    [J]. IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING, 2016, 24 (07) : 764 - 773
  • [9] Mobile Stride Length Estimation With Deep Convolutional Neural Networks
    Hannink, Julius
    Kautz, Thomas
    Pasluosta, Cristian F.
    Barth, Jens
    Schuelein, Samuel
    Gassmann, Karl-Guenter
    Klucken, Jochen
    Eskofier, Bjoern M.
    [J]. IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS, 2018, 22 (02) : 354 - 362
  • [10] Benchmarking Foot Trajectory Estimation Methods for Mobile Gait Analysis
    Hannink, Julius
    Ollenschlaeger, Malte
    Kluge, Felix
    Roth, Nils
    Klucken, Jochen
    Eskofier, Bjoern M.
    [J]. SENSORS, 2017, 17 (09)