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Human gait analysis has been widely employed in many areas of application such as in sport, medical rehabilitation, and even in product design (Whittle, 2007; Winter, 2009; Perry, 1992; Medved, 2001; Huston, 2009). In the field of human motion study, optical-based motion analyzer systems have been widely used, most notably in gait analysis for medical rehabilitation purposes (Medved, 2001) where it can be used to monitor a patient’s response during medical rehabilitation. Gait parameters, such as step length, cadence, cycle time, stance time, speed), of a patient could be observed and analyzed to indicate whether improvement or deterioration has taken place. These parameters could also be compared against sex- and age-matched normal population distributions to determine whether the patient is approaching normal performance (Bohannon, 1988; Himann, et al., 1988; Ostrosky, et al., 1994). It is also reported that a knowledge of the expected changes in joint angle allow therapists to better understand a patient’s gait pathology (Stanic, et al. 1977; Rancho Los Amigos Research Centre, 1989; Brandstater, et al., 1983; Olney, et al., 1991; Steadman, et al., 1997).
In general, human gait could be measured by direct measurement techniques and also imaging (optical) measurement techniques. Examples of direct measurement techniques include goniometry, accelerometers, foot-switches and others. However, direct measurement techniques are sometimes inaccurate and do not provide information with adequate details. The main problem in direct measurement techniques is subject has to carry many cables or other components that could affects walking motion (Winter, 2009). Most of the problems encountered by direct measurement techniques could be overcome by imaging (optical) measurement techniques. A variety of automatic visual measurement technique have been developed using special purpose hardware. This involves placing active and passive markers on different joints of the subject under consideration (Orr, 1997). The only system that can capture all the motion data is an imaging system due to complexity of most walking movements (Winter, 2009).
At present, many commercial camera-based motions capture for both 2D and 3D gait analyzer systems are available. In such systems, the cameras send out infrared light signals and detect the reflection from the markers attached on the body. The measurement is based on the angle and time delay between the original and reflected signal, which make triangulation of the marker in space is possible. The sampling rates of such systems can be as high as 1000 Hz, although typically most systems operate in the range of 60–250 Hz. These systems are highly accurate, and often locating a marker to within 1 mm, having less than 1 mm of error (Richards, 1999). However such benefits come at a price and may not within the budget of most hospitals, especially in Indonesia.
As alternative to the above system, inexpensive quantitative methods based on video technology and personal computers are available such as ones reported in (Wall & Crosbie, 1997; Stillman & McMeeken, 1996). To overcome the lack of system availability in Indonesia, a series of research on the development of affordable 2D and 3D optical motion analyzer system has been conducted in Faculty of Mechanical and Aerospace Engineering, Institut Teknologi Bandung (FMAE-ITB), Indonesia.