Gait Variability to Phenotype Common Orthopedic Gait Impairments Using Wearable Sensors

被引:2
|
作者
Kushioka, Junichi [1 ]
Sun, Ruopeng [1 ,2 ]
Zhang, Wei [3 ]
Muaremi, Amir [4 ]
Leutheuser, Heike [5 ]
Odonkor, Charles A. [6 ]
Smuck, Matthew [1 ,2 ]
机构
[1] Stanford Univ, Dept Orthopaed Surg, Stanford, CA 94305 USA
[2] Stanford Univ, Div Phys Med & Rehabil, Stanford, CA 94305 USA
[3] Ecole Polytech Fed Lausanne EPFL, Lab Movement Anal & Measurement, CH-1015 Lausanne, Switzerland
[4] Novartis Inst Biomed Res, CH-4056 Basel, Switzerland
[5] Friedrich Alexander Univ Erlangen Nurnberg FAU, Dept Artificial Intelligence Biomed Engn AIBE, Machine Learning & Data Analyt Lab MaD Lab, D-91052 Erlangen, Germany
[6] Yale Sch Med, Dept Orthoped & Rehabil, Div Physiatry, New Haven, CT 06510 USA
关键词
lumbar spinal stenosis; knee osteoarthritis; wearable IMU sensor; gait variability; gait impairment; LUMBAR SPINAL STENOSIS; SPATIOTEMPORAL PARAMETERS; OLDER-ADULTS; FALL RISK; OSTEOARTHRITIS; WALKING; PEOPLE; PERFORMANCE; DISABILITY; DISORDERS;
D O I
10.3390/s22239301
中图分类号
O65 [分析化学];
学科分类号
070302 ; 081704 ;
摘要
Mobility impairments are a common symptom of age-related degenerative diseases. Gait features can discriminate those with mobility disorders from healthy individuals, yet phenotyping specific pathologies remains challenging. This study aims to identify if gait parameters derived from two foot-mounted inertial measurement units (IMU) during the 6 min walk test (6MWT) can phenotype mobility impairment from different pathologies (Lumbar spinal stenosis (LSS)-neurogenic diseases, and knee osteoarthritis (KOA)-structural joint disease). Bilateral foot-mounted IMU data during the 6MWT were collected from patients with LSS and KOA and matched healthy controls (N = 30, 10 for each group). Eleven gait parameters representing four domains (pace, rhythm, asymmetry, variability) were derived for each minute of the 6MWT. In the entire 6MWT, gait parameters in all four domains distinguished between controls and both disease groups; however, the disease groups demonstrated no statistical differences, with a trend toward higher stride length variability in the LSS group (p = 0.057). Additional minute-by-minute comparisons identified stride length variability as a statistically significant marker between disease groups during the middle portion of 6WMT (3rd min: p <= 0.05; 4th min: p = 0.06). These findings demonstrate that gait variability measures are a potential biomarker to phenotype mobility impairment from different pathologies. Increased gait variability indicates loss of gait rhythmicity, a common feature in neurologic impairment of locomotor control, thus reflecting the underlying mechanism for the gait impairment in LSS. Findings from this work also identify the middle portion of the 6MWT as a potential window to detect subtle gait differences between individuals with different origins of gait impairment.
引用
收藏
页数:10
相关论文
共 50 条
  • [21] Objective Measures of Ataxic Gait Using Wearable Inertial Sensors
    El-Gohary, M.
    Horak, L.
    Gomez, C.
    MOVEMENT DISORDERS, 2018, 33 : S292 - S292
  • [22] Gait and Dynamic Balance Sensing Using Wearable Foot Sensors
    Refai, Mohamed Irfan Mohamed
    van Beijnum, Bert-Jan F.
    Buurke, Jaap H.
    Veltink, Peter H.
    IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING, 2019, 27 (02) : 218 - 227
  • [23] Gait analysis using gravitational acceleration measured by wearable sensors
    Takeda, Ryo
    Tadano, Shigeru
    Todoh, Masahiro
    Morikawa, Manabu
    Nakayasu, Minoru
    Yoshinari, Satoshi
    JOURNAL OF BIOMECHANICS, 2009, 42 (03) : 223 - 233
  • [24] Comparing different methods of gait speed estimation using wearable sensors in individuals with varying levels of mobility impairments
    Nunez, Erick H.
    Parhar, Sanjit
    Iwata, Isao
    Setoguchi, Soko
    Chen, Haoqian
    Daneault, Jean-Francois
    42ND ANNUAL INTERNATIONAL CONFERENCES OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY: ENABLING INNOVATIVE TECHNOLOGIES FOR GLOBAL HEALTHCARE EMBC'20, 2020, : 3792 - 3798
  • [25] Wearable sensors used for human gait analysis
    Tarnita, Daniela
    ROMANIAN JOURNAL OF MORPHOLOGY AND EMBRYOLOGY, 2016, 57 (02): : 373 - 382
  • [26] A dataset for wearable sensors validation in gait analysis
    Pierleoni, Paola
    Pinti, Federica
    Belli, Alberto
    Palma, Lorenzo
    DATA IN BRIEF, 2020, 31
  • [27] A Survey on Gait Recognition via Wearable Sensors
    De Marsico, Maria
    Mecca, Alessio
    ACM COMPUTING SURVEYS, 2019, 52 (04)
  • [28] Insights into freezing of gait from wearable sensors
    Horak, F. B.
    Nutt, J. G.
    Mancini, M.
    MOVEMENT DISORDERS, 2015, 30 : S37 - S38
  • [29] Reliability of videotaped observational gait analysis in patients with orthopedic impairments
    Jaap J Brunnekreef
    Caro JT van Uden
    Steven van Moorsel
    Jan GM Kooloos
    BMC Musculoskeletal Disorders, 6
  • [30] Reliability of videotaped observational gait analysis in patients with orthopedic impairments
    Brunnekreef, JJ
    van Uden, CJT
    van Moorsel, S
    Kooloos, JGM
    BMC MUSCULOSKELETAL DISORDERS, 2005, 6 (1)