Motion capture

Motion capture (Mocap) records the movement of a person by tracking the position and orientation of key points on the body and translating them into digital data, producing a real-time digital representation of the user, an avatar. It is used across animation, gaming, sports, biomechanics, and healthcare. Unlike camera-based systems, TESLASUIT mocap is inertial: it derives motion entirely from sensors worn on the body, so it needs no external cameras, line of sight, or fixed capture volume.

IMU basics#

TESLASUIT mocap uses set of 14 inertial measurement units (IMUs), distributed across the body (upper arms, wrists, hands, the back, thighs, shins, and feet). Each IMU combines three sensors:

• an accelerometer (linear acceleration),
• a gyroscope (angular velocity)
• a magnetometer (orientation relative to the Earth's magnetic field).

Together they describe how each body segment moves in space. Fusing all 14 IMUs produces the full-body avatar. See Hardware - Components for sensor placement.

What Mocap produces#

Fusing the sensors yields three layers of data, from lowest to highest level:

• Raw data is the unprocessed per-sensor output: gyroscope (angular velocity), accelerometer (linear acceleration), magnetometer (magnetic field), fused orientation quaternions, linear acceleration with gravity removed, and a timestamp.
• Skeleton data is a digital skeletal model of interconnected bones that mirrors the wearer's posture in real time. Each bone carries a position (its x, y, z coordinates) and a rotation (its orientation, stored as a quaternion).
• Biomechanical angles are the relative angles between connected bones at each joint.

In the Python API these three layers are streamed and read through the TsMocap subsystem. For the exact field names and units, see Mocap API and Core Data Structures.

Biomechanical angles#

A biomechanical angle is the angle between two bones meeting at a joint, measured in degrees. Angles are grouped by the type of movement and the anatomical plane it occurs in:

• Flexion and extension bend or straighten a joint, in the sagittal plane. A knee increases its flexion angle as the leg bends.
• Abduction and adduction move a limb away from or toward the body's midline, in the frontal plane. Raising an arm sideways abducts the shoulder.
• Internal and external rotation turn a bone around its long axis, in the transverse plane.

These angles support analysis of joint flexibility, posture, and movement efficiency, and are widely used in sports performance, rehabilitation, and ergonomics.

6-axis vs 9-axis modes#

The IMUs can operate in two modes, each balancing robustness and absolute-heading accuracy:

Mode	Sensors used	Strength	Weakness
9-axis	accelerometer + gyroscope + magnetometer	Better absolute heading reference	Magnetometer is sensitive to nearby ferromagnetic objects and stray fields
6-axis	accelerometer + gyroscope	Immune to magnetic interference	More prone to heading drift; calibration is not persisted between sessions

Use 9-axis mode only in a magnetically clean environment for test scenarios, and use 6-axis mode in all standard cases, especially where nearby metal or electronics could corrupt the magnetometer. See Hardware - Wearing and Concept - Calibration.

Accuracy and drift#

Reported orientation accuracy is on the order of 0.5° in roll and pitch and 1° in heading, at up to 100 frames per second (Hardware - Specifications).

Where this is used#

• Studio - Data Capturing: streaming, recording, and playback of mocap
• Control Center - Calibration: initial sensor set-up
• Mocap API: the data fields, units, and TsMocap methods in code
• Stream motion data: a runnable script for streaming IMU and biomechanical data
• Body & channel map: interactive Mocap and Skeleton views of joints and bones
• Concept - Known Limitations