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
  • [41] Neural Networks for Pathological Gait Classification Using Wearable Motion Sensors
    Yin, Shubao
    Chen, Chen
    Zhu, Hangyu
    Wang, Xinping
    Chen, Wei
    2019 IEEE BIOMEDICAL CIRCUITS AND SYSTEMS CONFERENCE (BIOCAS 2019), 2019,
  • [42] Human Gait Analysis using Wearable Sensors of Acceleration and Angular Velocity
    Takeda, R.
    Tadano, S.
    Todoh, M.
    Yoshinari, S.
    13TH INTERNATIONAL CONFERENCE ON BIOMEDICAL ENGINEERING, VOLS 1-3, 2009, 23 (1-3): : 1069 - +
  • [43] Real-Time Gait Phase Detection Using Wearable Sensors
    Mazhar, Osama
    Bari, Abu Zeeshan
    Faudzi, Ahmad 'Athif Mohd
    2015 10TH ASIAN CONTROL CONFERENCE (ASCC), 2015,
  • [44] Gait Kinematic Analysis in Water Using Wearable Inertial Magnetic Sensors
    Fantozzi, Silvia
    Giovanardi, Andrea
    Borra, Davide
    Gatta, Giorgio
    PLOS ONE, 2015, 10 (09):
  • [45] Wearable sensors for gait pattern examination in glaucoma patients
    Ma, Yuchao
    Amini, Navid
    Ghasemzadeh, Hassan
    MICROPROCESSORS AND MICROSYSTEMS, 2016, 46 : 67 - 74
  • [46] A Gait Retraining Feedback System Based on Wearable Sensors
    He, Zexia
    Shen, Yang
    Liu, Tao
    Yi, Jingang
    Ferreira, Jodo Paulo
    2017 IEEE INTERNATIONAL CONFERENCE ON ADVANCED INTELLIGENT MECHATRONICS (AIM), 2017, : 1029 - 1034
  • [47] Wearable Gait Sensors to Measure Degenerative Cerebellar Ataxia
    Sakakibara, R.
    Terayama, K.
    Akihiro, O.
    Haruta, H.
    Akiba, T.
    Tateno, F.
    Kishi, M.
    Tsuyusaki, Y.
    Aiba, Y.
    Ogata, T.
    MOVEMENT DISORDERS, 2017, 32
  • [48] Wearable gait sensors to measure degenerative cerebellar ataxia
    Sakakibara, R.
    Terayama, K.
    Ogawa, A.
    Haruta, H.
    Akiba, T.
    Tateno, F.
    Kishi, M.
    Tsuyusaki, Y.
    Aiba, Y.
    Ogata, T.
    JOURNAL OF THE NEUROLOGICAL SCIENCES, 2017, 381 : 56 - 57
  • [49] Remote Gait Analysis Using Wearable Sensors Detects Asymmetric Gait Patterns in Patients Recovering from ACL Reconstruction
    Gurchiek, Reed D.
    Choquette, Rebecca H.
    Beynnon, Bruce D.
    Slauterbeck, James R.
    Tourville, Timothy W.
    Toth, Michael J.
    McGinnis, Ryan S.
    2019 IEEE 16TH INTERNATIONAL CONFERENCE ON WEARABLE AND IMPLANTABLE BODY SENSOR NETWORKS (BSN), 2019,
  • [50] Improving the reliability of underwater gait analysis using wearable pressure and inertial sensors
    Monoli, Cecilia
    Galli, Manuela
    Tuhtan, Jeffrey A.
    PLOS ONE, 2024, 19 (03):