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Chapter 3
Correlations of Gait Phase
Kinematics and Cortical EEG:
Modelling Human Gait with Data
from Sensors
Chaitanya Nutakki, Sandeep Bodda and Shyam Diwakar
Abstract
Neural coding of gait intent and continuous gait kinematics have advanced brain
computer interface (BCI) technology for detection and predicting human upright
walking movement. However, the dynamics of cortical involvement in upright
walking and upright standing has not been clearly understood especially with the
focus of off-laboratory assessments. In this study, wearable low-cost mobile phone
accelerometers were used to extract position and velocity at 12 joints during walking and the cortical changes involved during gait phases of walking were explored
using non-invasive electroencephalogram (EEG). Extracted gait data included,
accelerometer values proximal to brachium of arm, antecubitis, carpus, coxal,
femur and tarsus by considering physical parameters including height, weight and
stride length. Including EEG data as features, the spectral and temporal features
were used to classify and predict the swing and stance instances for healthy subjects. While focusing on stance and swing classification in healthy subjects, this
chapter relates to gait features that help discriminate walking movement and its
neurophysiological counterparts. With promising initial results, further exploration
of gait may help change detection of movement neurological conditions in regions
where specialists and clinical facilities may not be at par.
Keywords: human gait, cortical activation, electroencephalography, stance, swing,
accelerometer sensors
1. Introduction
Upright gait has been used as a peculiar biometric characteristic and can offer
clues to help develop detection mechanisms for walking-related neurological
disorders, if detected can help reduce cost and help propose diagnostic approaches
[1]. Gait and locomotion are complex sequential processes involving timed coordination between central nervous system, muscles and bones [2]. The action of
numerous muscles and the variability in joint kinematics, leads to changes between
different phases in gait, mainly swing and stance [3]. Human gait analysis involves
the measurement and assessment of kinematic and inverse dynamic parameters
that characterize the different phases of gait and quantifies the musculoskeletal
functions [4, 5]. Within the context of gait measurement, sensors used in assessing
Chapter 3
Correlations of Gait Phase
Kinematics and Cortical EEG:
Modelling Human Gait with Data
from Sensors
Chaitanya Nutakki, Sandeep Bodda and Shyam Diwakar
Abstract
Neural coding of gait intent and continuous gait kinematics have advanced brain
computer interface (BCI) technology for detection and predicting human upright
walking movement. However, the dynamics of cortical involvement in upright
walking and upright standing has not been clearly understood especially with the
focus of off-laboratory assessments. In this study, wearable low-cost mobile phone
accelerometers were used to extract position and velocity at 12 joints during walking and the cortical changes involved during gait phases of walking were explored
using non-invasive electroencephalogram (EEG). Extracted gait data included,
accelerometer values proximal to brachium of arm, antecubitis, carpus, coxal,
femur and tarsus by considering physical parameters including height, weight and
stride length. Including EEG data as features, the spectral and temporal features
were used to classify and predict the swing and stance instances for healthy subjects. While focusing on stance and swing classification in healthy subjects, this
chapter relates to gait features that help discriminate walking movement and its
neurophysiological counterparts. With promising initial results, further exploration
of gait may help change detection of movement neurological conditions in regions
where specialists and clinical facilities may not be at par.
Keywords: human gait, cortical activation, electroencephalography, stance, swing,
accelerometer sensors
1. Introduction
Upright gait has been used as a peculiar biometric characteristic and can offer
clues to help develop detection mechanisms for walking-related neurological
disorders, if detected can help reduce cost and help propose diagnostic approaches
[1]. Gait and locomotion are complex sequential processes involving timed coordination between central nervous system, muscles and bones [2]. The action of
numerous muscles and the variability in joint kinematics, leads to changes between
different phases in gait, mainly swing and stance [3]. Human gait analysis involves
the measurement and assessment of kinematic and inverse dynamic parameters
that characterize the different phases of gait and quantifies the musculoskeletal
functions [4, 5]. Within the context of gait measurement, sensors used in assessing