Hershey, Pennsylvania
New York, New YorkBeijing, China
Inertial Measurement Units in Gait and Sport Motion Analysis

Inertial Measurement Units in Gait and Sport Motion Analysis

Braveena K. Santhiranayagagam (Victoria University, Australia), XiaoChen Wei (Victoria University, Australia), Daniel T. H. Lai (College of Engineering and Science, Victoria University, Australia), and Rezaul K. Begg (Victoria University, Australia)
Copyright: © 2015 |Pages: 13
DOI: 10.4018/978-1-4666-5888-2.ch679
OnDemand:
(Individual Chapters)
Available
$37.50
Current Special Offers
No Current Special Offers
TOTAL SAVINGS: $37.50

Chapter Preview

Top

Background

On-field motion tracking is still a huge gap to identify the practical issues with human motion. It requires light weight, portable and wearable sensors to fill this gap. The advent of micro-electromechanical systems (MEMs) inertial sensors, as a technological evolution, has opened new avenues to measuring human movement analysis.

Applications of IMUs in Human Gait Activities

Gait is the study of walking of an individual and is often related to the movement analysis of the lower limbs. IMUs are widely being deployed in gait applications after the invention of MEMs. The applications of IMUs in gait could be classified into four main areas as shown in Figure 1.

Figure 1.

IMU application areas in Gait related studies

978-1-4666-5888-2.ch679.f01

Studies have reported application of IMUs in assessing and diagnosing patients’ gait function for Parkinson’s disease (PD) (Djuric et al., 2010; Tien, Glaser, & Aminoff, 2010), diabetes (Petrofsky, Lee, & Bweir, 2005) and drop foot syndrome (Lau & Tong, 2008). Inertial sensors could also be used to monitor the recovery of patients after severe falls or surgeries (Cooper et al., 2008). Researchers also have demonstrated the applicability of IMU sensors for early detection of certain pathologies such as PD and risk of falls (Najafi, Aminian, Loew, Blanc, & Robert, 2002).

In STUDY 1, we present an intelligent automatic gait classification system for identifying different walking conditions (walking normally with preferred walking speed (PWS), walking while carrying a glass of water, and walking blind folded) using foot kinematics data.

Key Terms in this Chapter

Gait Cycle: The time interval between the exact same repetitive events of walking.

Scatter Plot: A kind of plot which uses Cartesian coordinates to display values as a collection of points for two variables. The average peaks against CV values are plotted for all subjects.

Skilled Low Handicap Golfers: A numerical representation of a golfer’s playing ability. The lower handicap represents a good skilled golfer.

Golf Swing: A movement requiring an efficient sequence of movements, including address, backswing, downswing, impact, and follow-through.

Inertial Measurement Units (IMUs): Comprise of gyroscopes and accelerometers to measure rotational velocities and accelerations

Support Vector Machines (SVMs): Widely used advanced classification tools in machine learning which have directed learning models with associated learning algorithms to recognize patterns and analyze data.

Complete Chapter List

Search this Book:
Reset