For centuries, man has been intrigued by the complexities of human locomotion and movement. Over 300 years ago Borelli published an amazingly detailed description of various activities involving animal motion, but was limited by the technology available [1]. In the late 19^th century, Muybridge advanced the field of movement analysis when he used emerging photographic technology to document animal movement patterns [2]. Around the same time, Marey [3] developed then-advanced techniques for creating time-series photographic records of movement patterns called chronophotography. Marey’s analyses also made it obvious to him that his kinematic data only told part of the story, and he developed the traction dynamograph (now known as a load cell) and the dynamographic platform (now known as a force plate).

Advances were made in the 20^th century as the modern force plate was developed [4]. At the same time, cinematography technology was progressing along with an understanding of how to best use it to study human movement [5]. In addition, the use of multiple cameras for 3-dimensional analyses became possible with the development of synchronizing methods [6]. Eventually video techniques were developed which greatly facilitated data collection and analyses [7]. This was soon followed by the creation of a computer-interfaced charged-coupled device that could detect markers placed on the body [8], and eventually light-emitting diodes that signaled position data [9].

As both kinetic and kinematic analysis capabilities improved, it became readily apparent that the combination of body position data and the resulting ground reaction forces (GRF) were critical for a complete understanding of the biomechanics of human activity [10]. Although complex and tedious, the merging of these variables is possible when data collection is properly performed [11]. Perhaps one of the most critical developments was the creation of direct linear transformation methods to convert video records to computerized data [12].

With the increasing rate of technology development, the limitations of needing to use a force plate for motion analysis are apparent. Many activities cannot be performed on a small force plate, while others need to be measured in the field. There is concern over the inability to collect valid GRF data without a force plate, and the lack of user-friendly software to facilitate analyses [13]. Moreover, although inverse dynamics can be used to determine kinetic data for specific joints and body segments, there is a need for appropriate technology and software that can accurately and easily derive these large data sets [14]. Recently, such a technology has been developed [15], and it is the purpose of this study to examine the validity of this system to accurately determine GRFs during human exercise without the use of a force plate. Combined with an accurate motion capture system (MCS), this technology would ultimately permit the substantial and complex calculations of individual joint torques and segmental forces to be determined without being constrained to the space of a force plate.

The major finding of the present study is that the MCS described by Moodie [15] resulted in consistently accurate GRFs for the concentric phase of a body weight squat. Not only was the squatting task very repeatable, but both the force plate and the MCS-derived data were similar. The precision of the derived GRFs is reflected in the SEM of only 11.1 N. In order to statistically compare the resulting force-time curves for both measuring systems, regression analysis was used, resulting in an R ²  = 0.989. This type of analysis permitted comparison of the actual mean force-time curves for each testing modality.

Although force plates are often the testing device of choice for determining GRF, numerous other devices has been developed to access kinetic data for human performance. Small force sensors worn like a shoe have produced acceptable GRF data, and although somewhat bulky, appear to be appropriate for assessing gait [16]. On the other hand, when similar devices have been used to assess tasks such as lifting, greater error resulted, suggesting these types of devices may not be appropriate for other forms of activity [17]. Other research has examined force sensors inserted in the shoe. These devices have produced valid data when testing GRF during various types of gait [18, 19]. Another approach to assessing GRF during gait successfully used force sensors embedded under the running surface of a treadmill [20]. Regardless of the type of activity performed, the resonant frequency of the force plates used must be appropriate for the type of activity being analyzed [21]. Recently, a tri-axial force sensor has been developed that favorably compares with force plate and optical motion capture system data [22]. Alternatively, multiple force plates have been synchronized to create increased testing areas, thus permitting more types of activities to be monitored [23]. It is clear that numerous attempts have been made to develop force-sensing devices that improve human kinetics testing.

For many analyses, segmental and joint positions are required [10]. For these purposes, systems have been developed that combine GRF data with body orientation information [24], although results have been somewhat modest. When only kinematic data is used, double integration techniques have permitted determination of the system center of mass (COM) by calculating the segmental COM centroid [25]. While results have been favorable, this method requires use of segmental masses from reference data [26], thus limiting the precision for any one individual. Other attempts at using kinetic data to derive GRFs have had mixed results, possibly due to movement of markers on the surface of the skin [27].

When GRFs have been directly measured from a force plate, COM accelerations and displacements can be derived [28], although it has been noted that corresponding video data is required to obtain a more thorough understanding of the mechanics of the activity [10]. Regardless, the proper collection and use of GRF data for motion analyses has been nicely described by Miller [29]. The MCS used in the present study, along with the accompanying software for calculating the large amount of data generated, was user friendly, and more importantly, accurate and permitted determinations of complex calculations.

Though the MCS employed in the present investigation provides several advantages over traditional force plating testing, it is not without limitations. Accurate GRF measurement is dependent on a properly calibrated space, free of interfering obstacles. Additionally, accurate force measurement requires anatomically correct marker positioning and segmental mass determination via DXA. In comparison with force plate analysis, the MCS requires more complex and lengthy preparation. The MCS software provides rapid force calculations; therefore, the reduction in time associated with data analysis exceeds any additional time spent in preparation. The derivation of the estimated GRF requires a massive number of calculations from a large data set, and is greatly facilitated using this MCS software. Furthermore, the ability to measure force outside of the confines of a force plate seemingly outweighs the disadvantages associated with marker placement and DXA measures.

The use of the system employed in the present study negates the need for a force plate. Additionally, precise measures of each relevant body segment mass are available for each subject, thus contributing to the accuracy of the GRFs reported. The system used also permits relatively free movement in the measurement field as determined by how the sensors are arranged. As evident by the very small SEM of 11.1 N, the result is an accurate and repeatable method to determine GRFs without the use of a force plate.