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While working on activity recognition using wearable sensors for healthcare applications, the main issue arises in the classification of activities. When we attempt to classify activities like walking, sitting, or running from accelerometer and gyroscope data, the signals often overlap and noise complicates the classification process. The existing methods do not have solid mathematical foundations to handle this issue. We started with the standard magnitude approach where one can compute $m =  \sqrt{a^2_1 + a^2_2 + a^2_3}$ from the accelerometer readings, but this approach failed because different activities ended up in overlapping regions. We therefore developed a different approach. Instead of collapsing the 6-dimensional sensor data into simple magnitudes, we keep all six dimensions and treat each activity as a rectangular box in this 6D space. We define these boxes using simple interval constraints. For example, walking occurs when the $x$-axis accelerometer reading is between $2$ and $4$, the $y$-axis reading is between $9$ and $10$, and so on. The key breakthrough is what we call a separability index $s = \frac{d_{\min}^{}}{\sigma}$ that determines how accurately the classification will work. Here dmin represents how far apart the activity boxes are, and $\sigma$ represents the amount of noise present. From this simple idea, we derive a mathematical formula $P(\text{error}) \leqslant (n-1)\exp\left(-\frac{s^2}8\right)$  that predicts the error rate even before initiating the experiment. We tested this on the standard UCI-HAR and WISDM datasets and achieved $86.1 %$ accuracy. The theoretical predictions matched the actual results within $3 %$. This approach outperforms the traditional magnitude methods by $30.6 %$ and explains why certain activities overlap with each other.

Modeling of gas-particle suspensions with large pressure and density gradients is of practical interest in the study of volcanic phenomena, explosions at different altitudes, as well as in technogenic problems related to the operation of space technology and the formation of space debris. This work presents numerical and analytical investigations of the expansion of gas suspensions with a high ratio (up to six orders of magnitude) of pressures and densities. For numerical modeling, a high-resolution hybrid large-particle method was employed. Under the conditions considered, the accuracy of the method was confirmed by comparison with asymptotically exact solutions. The study examined the wave and structural characteristics of concentrated gas suspension expansion depending on particle volume fraction, particle size, and initial pressure ratio. It was found that the polytropic index and sound speed in the gas suspension depend not only on temperature but also on pressure and particle concentration. With increasing pressure, both the polytropic index and sound speed rise, while with increasing particle volume fraction they decrease. In the case of an arbitrary discontinuity decay, an unusual effect is observed compared with “pure” gas dynamics: the relative velocity of the mixture in the uniform flow region decreases as the initial pressure increases. This is explained by the nonlinear dependence of the sound speed in a gas-dispersed mixture on pressure. With increasing particle size (Stokes number), the mixture flow splits into gaseous and dispersed components. At the initial moment, the contact discontinuity separating the mixture from the rarefied gas region splits into two contact boundaries: gaseous and dispersed. A practical conclusion is that when the particle size changes by two orders of magnitude, the gas-dynamic parameters of the mixture in the rarefaction wave region and up to the medium interface remain close to each other. During spatial expansion, the initial cylindrical shape of the dispersed medium successively transforms into a cross-section resembling a hexagon. At the next stage of expansion, the particles redistribute to form a bilateral conical structure. Eventually, a dispersed formation close to a spherical shape emerges.