Postural control and functional balance play a key role in athletic performance as well as prevention of sport related injuries (Hale et al., 2007; Hume et al., 2005; Tropp et al., 1985). Many times, athletic trainers attempt to improve the balance or handle a weight shift during performance (Sell et al., 2007) of their athletes in order to enhance performance, prevent injury, or recover after injury (e.g., ankle sprain) (Wikstrom et al., 2010). It is known that low handicap golfers have better balance abilities (Sell et al., 2007). Therefore, an accurate quantification of an athlete's dynamic balance control in the field may play an important role in many sport applications.
Due to measurement ease, postural control is often assessed by measuring center of pressure (CoP) sway or its trajectory using a force platform (Najafi et al., 2012). However, standing on an instrumented platform makes it difficult to examine postural control on different surface types which better replicate an athlete's natural competitive environment. Recently, several wearable technologies have been developed to evaluate body motion (e.g. body segment sway) based on microelectro-mechanical sensors (MEMS) (Adlerton et al., 2003; Allum et al., 2001a; 2001b; Allum et al., 2002, Mayagoitia et al., 2002, Najafi et al., 2002,). Key advantages of body-wearable sensors are their low cost and portable usage in many environments (Aminian et al., 2002; Najafi et al., 2003; 2009; 2010a; 2010b; Russmann et al. 2004). Body-wearable sensors often consist of one or a combination of accelerometers, angular velocity sensors, and magnetometers providing valuable data in research (Russmann et al., 2004). By attaching these sensors to a body segment, they allow measurement of segment motion or body sway while balance tasks are performed (Najafi et al., 2010a; 2012; Mancini et al. 2012). Several studies have demonstrated that sensor derived parameters from wearable technologies are useful as an objective metric for assessing postural control deterioration due to a disorder (e.g. Parkinson's, diabetes) or aging (Mancini et al., 2011; Mancini et al., 2012; Najafi et al., 2012; Toosizadeh et al., 2014b). Wearable technologies are also sensitive enough to track changes in post-intervention postural control (Grewal et al., 2013, Toosizadeh et al., 2014a) or predicting prospective falls (Schwenk et al., 2014).
Key challenges of using wearable sensors include their inability to extract useful clinical data when there is too much noise, restriction on the number of sensors that can be attached, and their ease of management. Therefore, a simplified biomechanical human body model with the minimum number of sensor attachments required should be implemented with such technology to make them suitable for clinical applications. On the other hand, model simplification may alter system accuracy, thus, an optimum tradeoff between accuracy and minimum number of sensor attachments should be provided.
Previous studies addressing wearable technology for assessing postural control often focus on sacral region motion (e.g., one link) and deem to be sufficient in estimating the center of mass (CoM) sway assuming hip joint movement is limited (Allum et al., 2001a, Aramaki et al., 2001; Adlerton et al., 2003). This approach may produce accurate results during quiet standing or walking a straight line but may be inappropriate for measuring CoM trajectory during athletic movements with significant body segment motions.
This study aims to explore the accuracy of wearable sensors together with simplified biomechanical models for estimating CoM trajectory during significant body segment movements. To achieve this goal, three studies were designed to explore the accuracy of main inputs of the proposed model. First, sensor accuracy for estimating 3D body segment angle was assessed. For this purpose, accuracy of the system to estimate trunk 3D angle was estimated. We assumed similar accuracy would be obtained if sensor was attached to other body segments. Second, accuracy of three simplified models to estimate CoM trajectory were evaluated and compared to the reference system. These models estimate CoM trajectory using either 1) a single sensor model attached to subject's lower back (Model 1); 2) two sensor modules, respectively attached to subject's lower back and shank (Model 2); or 3) three sensor modules, respectively attached to subject's lower back, thigh, and shank (Model 3). Through this study, the optimum model, which includes minimum number of sensor module attachments and an acceptable accuracy, was selected. Finally, the accuracy of the optimum model for estimation of CoM trajectory in medial-lateral (M-L) and anterior-posterior (A-P) planes with respect to foot position were examined during a series of movements including golf swings.
Three studies were performed to examine different inputs of the final model. Study 1 explored the accuracy of the proposed sensors for estimating 3D body segment angle. In this study, a camera-based motion analyzer (Vicon[R], Oxford, UK) was determined as the reference standard.
Study 2 measured accuracy of three simplified models for estimating CoM compared to a full-body CoM estimation model using Vicon during a series of voluntary movements. Study 3 compared the output of the selected model for estimation of CoM trajectory during golf swings.
A total of 25 participants including groups of 3, 4, and 18 were recruited from the Rosalind Franklin University campus for each respective study. All studies were approved by the local Institutional Review Board at Rosalind Franklin University, North Chicago, IL, USA and participants signed an approved informed consent form prior to participation. Three healthy male participants participated in Study 1 (age: 23.3 [+ or -] 0.6 years; height: 1.80 [+ or -] 0.07 m; body mass: 70.3 [+ or -] 8.1 kg), four healthy males in Study 2 (age: 38.4 [+ or -] 17.5 years; height: 1.84 [+ or -] 0.03 cm; body mass: 84.1 [+ or -] 12.3 kg), and 18 golf players (12 males and 6 females) with an established handicap level (9-19; average handicap: 14.9 [+ or -] 3.1) were recruited (age: 38.4[+ or -]12.3 years; height: 1.78 [+ or -] 0.09 cm; body mass: 80.4 [+ or -] 14.2 kg).
Equipment and reference system
Depending on the study, up to three inertial sensors, each including a triaxial accelerometer, triaxial gyroscope, and a triaxial magnometer (LEGSys[TM], BioSensics LLC, Boston, MA), were attached respectively, to subject's shin, thigh, and lower back using comfortable Velcro bands as shown in Figure 3. The sensors provided real-time kinematic data (sample frequency 60Hz) including acceleration and speed of rotation as well as quaternion ([q.sub.w], [q.sub.x], [q.sub.y], [q.sub.z]) components (Dumas et al., 2004; Hart et al., 1994; Kingston and Beard, 2004) that were subsequently converted to Euler angles (Najafi et al., 2010a). These angles were used to describe a sequence of three rotations determining the orientation of a rigid body in three dimensions, in their order of application are: i) Yaw ([phi], M-L), ii) Pitch ([theta], A-P) and iii) Roll ([phi], I-E). The resulting three-dimensional angles were used to estimate the trajectory of a participant's segments such as shank, thigh or upper back depending on the simplified human body model. We assumed each body segment to be rigid, thus considering that the wearable sensors directly provide segment angle, our models were not sensitive to the exact location of sensor attachment.
A five-camera based motion analyzer system (Vicon[R]) was used as the reference standard. The sample frequency of Vicon system was set to 60Hz to facilitate synchronization between inertial sensors and output of Vicon system. To synchronize between two systems, at the beginning of each measurement, subjects were asked to bend their trunk forward and back. The time of maximum trunk tilt measured by two systems via the markers and sensors attached to the lower back was used to synchronize between two systems as illustrated in Figure 5. For all experiments, the anterior-posterior (A-P) direction was defined as the rotation in the sagittal plane, rotation in the frontal plane characterized as medial-lateral (M-L) direction, and the internal-external (I-E) was assumed as rotation in the transversal plane, all in respect to the subject's upright position.
To explore the degree of agreement between range of CoM sway estimated by each model and the range of center of pressure (CoP) sway, measured by a standard pressure platform, subjects in Study 2, were asked to perform golf swing trials while standing on a pressure platform (Emed[R] System, Novel, Germany, Figure 2). This may limit the base of support favored by golfers. Thus, after selection of the optimum model, further evaluation was performed without a pressure platform (Study 3, Figure 3), and participants were allowed to self-select their base of support prior to each swing.
Protocol of measurement
Study 1: Estimation of a typical 3D body segment angle (lower back angle)
To reduce the impact of potential artifact due to movement of skin for the reference system, all Vicon markers and inertial sensor were placed on a rigid plate attached to the participant's lower back between the 10th thoracic vertebra (t10) and 5th lumbar vertebra (Figure 1). The reference trunk tilt was calculated from the 3-D components of the markers in each direction as described in our previous publication (Najafi et al., 2002).
Before each test, the A-P direction of the participant was visually aligned to the Vicon's y-axis origin. Using the coordinates extracted from the markers, the projection of lower back angles along each axis were estimated.
Participants executed a series of voluntary movements while standing, including bending forward and backward (A-P), bending side-to-side (M-L) and rotating right to left (I-E)....