---
title: "EMG and MoCap Integration Workflow"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{EMG and MoCap Integration Workflow}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r, include = FALSE}
knitr::opts_chunk$set(collapse = TRUE, comment = "#>")
```

This vignette shows how to process EMG and align it with motion-capture data.

## 1. Simulate MoCap and EMG data

```{r}
library(PhysioMoCap)

set.seed(42)

mocap_sr <- 120
emg_sr <- 1000

n_mocap <- 600
n_emg <- 5000

mocap <- cbind(
  knee_angle = sin(seq(0, 8 * pi, length.out = n_mocap)) * 40,
  hip_angle = sin(seq(0, 8 * pi, length.out = n_mocap) + 0.5) * 30
)

emg <- cbind(
  tibialis_anterior = rnorm(n_emg, 0, 0.2),
  gastrocnemius = rnorm(n_emg, 0, 0.2),
  rectus_femoris = rnorm(n_emg, 0, 0.2)
)
```

## 2. Process EMG (rectify + RMS envelope + smoothing)

```{r}
emg_proc <- processEMG(
  x = emg,
  sampling_rate = emg_sr,
  bandpass = c(20, 450),
  envelope_cutoff = 6,
  rms_window_ms = 50,
  filter_method = "moving_average"
)

str(emg_proc, max.level = 1)
```

## 3. Align EMG to MoCap timeline

```{r}
emg_aligned <- alignEMGtoMoCap(
  emg = emg_proc$envelope,
  emg_sampling_rate = emg_sr,
  mocap_length = n_mocap,
  mocap_sampling_rate = mocap_sr
)

dim(emg_aligned)
```

## 4. Build an integrated analysis table

```{r}
integrated <- integrateEMGMoCap(
  mocap = mocap,
  emg = emg,
  mocap_sampling_rate = mocap_sr,
  emg_sampling_rate = emg_sr,
  process = TRUE,
  rms_window_ms = 50,
  envelope_cutoff = 6,
  filter_method = "moving_average"
)

head(integrated$combined)
```

## Notes

- If MVC trials are available, pass `mvc = ...` to `processEMG()` for `%MVC`
  scaling.
- For event-locked studies, align on synchronization pulses or known trigger
  markers before integrating.