A Kinematic Information Acquisition Model That Uses Digital Signals from an Inertial and Magnetic Motion Capture System
Abstract
:1. Introduction
2. Materials and Methods
2.1. Description of the Imocap-GIS Inertial and Magnetic System
2.2. Signal Transformation Model
2.2.1. Stage 1. Data Collection and Transmission Using the Imocap-GIS Motion Capture System
- Data collection and filtering
- Data transmission from Imocap-GIS to an external device
2.2.2. Stage 2. Message Routing on External Device
- Access point (AP) mode: this mode is enabled when Imocap-GIS does not detect a network. In this case, the system behaves as an access point, creating a new WiFi network on the least busy channel found within the 2.4 GHz frequency.
- Station (STA) mode: in this mode, Imocap-GIS works as a station, i.e., the system is connected to an existing access point (a router, for example), where the transmission frequency is given by the already established WiFi network.
2.2.3. Stage 3. Kinematic Parameter Determination
- Step 1: Initial system setup
- Step 2: Determination of initial orientation and reference parameters
- Step 3: Orientation estimation during movement
2.2.4. Stage 4. Sensor Assignment and Rotation Settings According to the Analytical Units of Movement
2.2.5. Stage 5. Model Implementation in a Virtual Environment and Visualization of Kinematic Information
- Step 1: Assignment of each sensor to a virtual body segment and physical sensor location on the test subject.
- Step 2: Data reception and motion simulation
2.3. Validation of the Signal Transformation Model
- Motion selection: Table 4 shows the protocol for the execution of elbow flexion-extension and forearm pronation-supination movements applied in the experiment, considering the suggestions of professionals in motor recovery therapy.
- Configuration and calibration of the optical motion capture system (OptiTrack/Motive): calibration was carried out in such a way that the cameras detected the reflective markers properly.
- Initialization of the virtual environment and configuration of the inertial and magnetic motion capture system (Imocap-GIS): the virtual environment was initialized to assign the sensors to the body segments (icon A in Figure 6) and based on this assignment, the possible rotations and form of information representation were configured (icon B in Figure 6). It was also possible to define the sampling rate for the session: 30, 60, or 120 samples per second (default 60 Hz, see the upper-left corner of Figure 6).
- Location of the markers and sensors on the body segments: for the optical system, the markers were fixed on the two body segments (rigid body): 3 markers on the arm and 3 markers on the forearm. In the case of the inertial and magnetic system, using elastic bands fixed with hook-and-loop tape, the arm and forearm sensors were placed (Figure 4c), considering the calibration of body posture.
- Initiate recording of session data: for both the optical, inertial and magnetic systems, the process of displaying on-screen information and recording the data associated with the movement of the upper limb was initiated.
- Motion execution and kinematic information visualization: the participant started the execution of movements and, simultaneously, the Motive software screen showed the movement of the body segments formed by the markers. For the inertial and magnetic system software, the movements were simulated by the avatar and the corresponding kinematic information was displayed in a quantitative way (area (3) of Figure 6).
- Session end: at the end of the movement protocol, the recording process was stopped in the two systems, which generated the respective data files.
- Finally, it was necessary to apply the RMSE (root mean square error) equation for the selected motions, and to analyze the results.
3. Results
3.1. Digital Signal to Kinematic Information Transformation Model
3.2. Results of the Validation Tests of the Proposed Model
- Optical system: a plaintext file was generated, and organized in columns corresponding to the current capture time (first column). Then, for each rigid body, a column associated with the name of the body segment was created, followed by the three columns of the corresponding axes, as follows: Y_Yaw_#rigidbody, X_Pitch_#rigidbody, Z_Roll_#rigidbody (subsequent columns).
- Inertial and magnetic system: a plaintext file containing a column of data for each analytical unit of movement of the upper limb (flexion-extension, pronation-supination). In other words, by applying the transformation model proposed in this work, the value of the joint amplitude or range of motion (ROM) calculated from the value of the positions of the X, Y, and Z axes of each of the sensors were generated.
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Item | Device | Description | |
---|---|---|---|
Hardware | Embedded hardware | Mass Storage | Micro SD card (SDHC card of arbitrary capacity). Can store sensor configurations, calibration data, collected information from sensors, among others. |
Main Processor (SoC) | Interprets firmware instructions by performing basic arithmetic, logic and input/output operations on the system. | ||
Printed Circuit Board | Performs electrical interconnection of the functional elements of the system through the conductive tracks, and mechanically supports the set of electronic components. | ||
Power Source and Management | Composed of a LiPo (lithium polymer) battery and an electronic charge regulator. | ||
Modem and Radio Modules | Provides real-time wireless communication between the motion capture device and a personal computer. It can be implemented with XBEE (ZigBee), Bluetooth, or WiFi modules. | ||
MPUs | The motion processing units used are Invensense™ MPU-9250 based on MEMS (microelectromechanical systems). This MPU is an SiP technology device with 9 DOF (degrees of freedom) and motion tracking technology (specialized in motion capture) designed for low power consumption, low cost, and high-performance characteristics. Among its main typologies are the combination of a 3-axis gyroscope, 3-axis accelerometer, and 3-axis digital compass (magnetometer) in a single encapsulated chip, together with a DMP (digital motion processor) capable of processing complex onboard data fusion algorithms. | ||
Firmware | Source code embedded in the hardware that executes the instructions for performing the functions of the motion capture system. |
Item | Sensor 1: Right Arm | Sensor 2: Right Forearm | ||
---|---|---|---|---|
Rotation to place the sensors in the initial position. | 1. 270° rotation with a Delta1 angle () around X axis. | | 1. Rotation with a Delta3 angle () around X axis. | |
2. Orthogonal rotation with a Delta2 angle () around actual Y axis (y′). | | 2. Orthogonal rotation with a Delta4 angle () around actual Y axis (y′). | | |
Rotation equation |
Motion | Definition | Rotation Equation | Rotation Matrix |
---|---|---|---|
Flexion- extension. | Rotation in the sagittal plane around the anatomical Z axis (transverse axis). | where represents the flexion/extension angle. | |
Pronation- supination. | From the initial position (upright): rotation in the transverse plane around the anatomical X axis (vertical axis). | where represents the pronation- supination angle. |
Item | Motion | Initial Position | Density/Intensity |
---|---|---|---|
1 | Flexo-extension | Upper extremities at the sides of the body and palms of the hands facing the body. |
|
2 | Prono- supination | Flex the elbow to ≈90° and position the thumb upward. | Rotate the forearm towards the inside of the midline, i.e., pronate (palm downwards) as far as possible, and then rotate the forearm in the opposite direction so that the palm is facing upwards, i.e., supinated, until the maximum possible value is obtained: 5 repetitions. |
Motion | RMSE (Degrees) |
---|---|
Elbow flexion-extension | 3.82 |
Forearm pronation-supination | 3.46 |
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Alarcón-Aldana, A.C.; Callejas-Cuervo, M.; Bastos-Filho, T.; Bó, A.P.L. A Kinematic Information Acquisition Model That Uses Digital Signals from an Inertial and Magnetic Motion Capture System. Sensors 2022, 22, 4898. https://doi.org/10.3390/s22134898
Alarcón-Aldana AC, Callejas-Cuervo M, Bastos-Filho T, Bó APL. A Kinematic Information Acquisition Model That Uses Digital Signals from an Inertial and Magnetic Motion Capture System. Sensors. 2022; 22(13):4898. https://doi.org/10.3390/s22134898
Chicago/Turabian StyleAlarcón-Aldana, Andrea Catherine, Mauro Callejas-Cuervo, Teodiano Bastos-Filho, and Antônio Padilha Lanari Bó. 2022. "A Kinematic Information Acquisition Model That Uses Digital Signals from an Inertial and Magnetic Motion Capture System" Sensors 22, no. 13: 4898. https://doi.org/10.3390/s22134898