Inertial measurement units based on microelectromechanical systems are perspectives for motion capture applications due to their numerous advantages. A motion trajectory is restored using a well-known navigation algorithm, which assumes integration of the signals from accelerometers and gyroscopes. Readings of both sensors contain errors, which quickly accumulate due to integration. The applicability of an inertial measurement unit for motion capture depends on the trajectory being tracked and can be predicted due to the simulation of signals from inertial sensors. The first simulation step is prescribing a motion trajectory and corresponding velocities. The existing simulation software provides no user-friendly graphical tools for the completion of this step. This work introduces an algorithm for the simulation of accelerometer signals upon a two-dimensional trajectory drawn with a computer mouse and then vectorized. We propose a modification of the Potrace algorithm for tracing motion trajectories. Thus, a trajectory and velocities can be set simultaneously. The obtained results form a basis for simulating three-dimensional motion trajectories since the latter can be represented by three mutually orthogonal two-dimensional projections.

Inertial sensors are used for human motion capture in a wide range of applications. Some kinds of human motion can be tracked by inertial sensors incorporated in smartphones or smartwatches. However, the latter can scarcely be used if misclassification of user activities is highly undesirable. In this case electronics and embedded software engineers should design, implement and verify their own human motion capture embedded systems, and oftentimes they have to do so from scratch. One of the issues the engineers should face is selection of suitable components, primarily accelerometers, gyroscopes and magnetometers, after thorough examination of commercially available items. Among technical characteristics of inertial sensors their sample frequency determines whether the sensor will be able to capture a specific motion kind or not. We propose a novel algorithm that allows the researcher or embedded software engineer to calculate the minimal sample frequency sufficient for tracking a prescribed motion scenario without significant signal losses. The algorithm utilizes the Poisson equation for motion of a triaxial rigid body, the Shoemake’s algorithm for interpolating quaternions on the unit hypersphere, and the frequency analysis of a discrete-time signal. One can use the proposed algorithm as an argument for acceptance or rejection of a gyroscope when selecting hardware components for a human motion tracking system.

The paper deals with the problem of estimating the execution time of firmware. Any firmware is bound to wait for a response from peripheral devices such as external memory chips, displays, analog-to-digital converters, etc. The firmware’s execution is frozen until the expected response is obtained. Thus, any firmware’s execution time depends not only on the computational resources of the embedded system being inspected but also on peripheral devices each of which is able to perform a set of operations during some random time period residing, however, within a known interval. The paper introduces a model of a computer application for evaluation of microcontroller-based embedded systems’ firmware’s execution time that takes into consideration the type of the microcontroller, the total duration of all the assembler-like instructions for a specific microcontroller, all the occasions of waiting for a response from hardware components, and the possible time periods for all the responses being waited for. Besides, we proposed the architecture of the computer application that assumes a reusable database retaining data on microcontrollers’ instructions.