Angular Motion - or Rotation. All parts of an object or the whole body move in a circle about a single axis of rotation. General Motion - A combination of translation and rotation. Most human movement falls into this category. The analysis of motion can be basically performed in three dimensions: 1-D - this analysis applies to motion of a point along a line requiring only one number to describe position, i.e. considering motion of center of mass. 2-D - it applies to motion on a plane requiring two numbers (x and y) to specify position and one number (θ) to specify orientation. 3-D - this analysis applies to motion requiring three numbers (x, y, and z coordinates) to specify position and three numbers (pitch, yaw, and roll) to describe orientation. The reference frames can be absolute (fixed in space) or relative (fixed to a movable object). To find joint and segment angles, relative reference frame is used. To be able to do the analysis of motion, there is a need of an anatomical reference position, which is the starting point for describing later body segment movements and measuring joint angles. This is usually accomplished by having the actor to erect standing, with his feet separated slightly and pointed forward, his harms hanging at the sides, and his palms facing forward.
The inclusion of Labanotation in this approach will allow the analysis of human motion similarity taking time and space into consideration. Table 3.3 - Results comparison between Bribiesca's work and our implementation Contributions: In this Chapter, we provided a compared the results obtained when performing the analysis of human motion between a well-known global alignment algorithm (Needleman-Wunsch), and our 3D ChainCode implementation. Needleman-Wunsh algorithm alters the chain code representation, so an experiment where 3D ChainCode is used with different step sizes was performed. This experiment shown the importance of removing the lack of movement when performing the analysis of human motion: limbs that are almost steady contribute to the value of human motion similarity obtained during the analysis. It is worth to mention that our 3D ChainCode implementation is not an exact implementation of Bribiesca’s work, due to the time spent when performing the analysis of similarity between 3D curves.
Scenario ONE: USER PERFORMING THE EXERCISE Loading exercise from input file based on LABANotation User interacts with GUI HMTR System (1) to select the desired exercise. GUI HMTR System (1) requests to load the exercise from DATABASE (2), which uses its LABAN Files Module (2a). LABAN Files Module (2a) sends the information to LABAN Simulation Block (3) where it is read by Read Input Files Module (3a). Configuring HMTR System for Model Simulation User interacts with GUI HMTR System (1) to enter required settings for model simulation, i.e. type of figure he wants, camera to be used, timing for the simulation, naming the file where performance is going to be saved, etc. GUI HMTR System (1) interacts with LABAN Simulation Block (3) who uses its Animation Settings Module (3b) to accomplish this process. Configuring sensors for use User wear sensors and interacts with GUI HMTR System (1) for the initialization process. GUI HMTR System (1) interacts with User Simulation Block (5) to get this process done. Read Sensors Module (5a) reads from the sensors, Mapping Module (5b) will map that input data into 3D ChainCode system data, which is sent to User Configuration Module (5c) as part of this process. Once that process is done, User Configuration Module (5c) will sends a signal to GUI HMTR System (1) so User knows that he is ready to perform the exercise.
Matching User’s performance vs. Once User is to do the exercise, he interacts with GUI HMTR System (1) to run the simulation. GUI HMTR System (1) triggers LABAN Simulation Module (3c), for Model simulation, and User Simulation Module (5e), for User simulation. LABAN Simulation Module (3c) starts Model simulation, and User Simulation Module (5e) interacts with Read Sensors Module (5a) and Mapping Module (5b) to read data from User. Sampling Module (4a) begins to sample both LABAN Simulation Module (3c) and User Simulation Module (5e) to perform the analysis of similarity. Messages regarding user performances are generated by Messages Generator Module (4b), and then sent to GUI HMTR System (1) so they can be shown to User. Scenario TWO: GETTING A NEW EXERCISE FROM USER’S DOCTOR AND PERFORMING THE EXERCISE Getting the new exercise from Doctor User interacts with GUI HMTR System (1) to get the new exercise from his Doctor. Here we assume User already knows that such notification has been sent. GUI HMTR System (1) interacts with Communications Block (6), and this block establishes a communication with the Doctor to get the new exercise using Input Module (6a). Loading exercise from input file based on LABANotation
simulation. LABAN Simulation Module (3c) starts Model simulation, and User Simulation Module (5e) interacts with Read Sensors Module (5a) and Mapping Module (5b) to read data from User. Sampling Module (4a) begins to sample both LABAN Simulation Module (3c) and User Simulation Module (5e) to perform the analysis of similarity. Messages regarding user performances are generated by Messages Generator Module (4b), and then sent to GUI HMTR System (1) so they can be shown to User. Scenario THREE: EXERCISING AND SENDING PERFORMANCE TO DOCTOR Loading exercise from input file based on LABANotation User interacts with GUI HMTR System (1) to select the desired exercise. GUI HMTR System (1) requests to load the exercise from DATABASE Block (2), which uses its LABAN Files Module (2a). LABAN Files Module (2a) sends the information to LABAN Simulation Block (3) where it is read by Read Input Files Module (3a). Configuring HMTR System for Simulation User interacts with GUI HMTR System (1) to enter required settings for model simulation, i.e. type of figure he wants, camera to be used, timing for the simulation, naming the file where performance is going to be saved, etc. GUI HMTR System (1) interacts with LABAN Simulation Block (3) who uses its Animation Settings Module (3b) to accomplish this process.
Configuring sensors for use User wear sensors and interacts with GUI HMTR System (1) for the initialization process. GUI HMTR System (1) interacts with User Simulation Block (5) to get this process done. Read Sensors Module (5a) reads from the sensors, Mapping Module (5b) will map that input data into 3D ChainCode system data, which is sent to User Configuration Module (5c) as part of this process. Once that process is done, User Configuration Module (5c) will sends a signal to GUI HMTR System (1) so User knows that he is ready to perform the exercise. Matching User’s performance vs. Model Once User is ready to do the exercise, he interacts with GUI HMTR System (1) to run the simulation. GUI HMTR System (1) triggers LABAN Simulation Module (3c), for Model simulation, and User Simulation Module (5e), for User simulation. LABAN Simulation Module (3c) starts Model simulation, and User Simulation Module (5e) interacts with Read Sensors Module (5a) and Mapping Module (5b) to read data from User. Sampling Module (4a) begins to sample both LABAN Simulation Module (3c) and User Simulation Module (5e) to perform the analysis of similarity. Messages regarding user performances are generated by Messages Generator Module (4b), and then sent to GUI HMTR System (1) so they can be shown to User. Additionally, we assume User selects “saving performance into a file” option. By choosing that option, performance User is saved during simulation using Output File Module (5d) of User Simulation Block (5). Sending data to user’s Doctor