## 2 Materials and Methods
### Signal Segmentation
#### 2.6.2 Cycle Detection  
To segment the recorded signals, the self-similarity matrix (SSM) [42] was applied, a method that consists of transforming the signal into a feature vector using a sliding window of size \(w\) with an overlap of \(o\). The resulting matrix has a shape of \(n\) features by \(m\) subsequences of the signal (\(m=(N-w)/o\)), \(N\) is the length of the time-series [43]. This method was chosen as some conditions could not be controlled by investigators; for example, line stops and bathroom breaks. Furthermore, it was only possible to videotape the first cycles of the tasks, and time stamps for the start and end of the cycle were not possible to acquire.  
To accomplish this, multiple steps were followed:  
1. Signal selection and downsampling: the appropriate accelerometer signal was chosen by identifying which axis (x, y, or z) of the three ACC sensors (back, biceps, or trapezius) represented the cyclic nature of the work task in the best way. Downsampling of the chosen signal to the frame rate of the camera was also accomplished.
2. Feature extraction: feature extraction from the signal, namely, peak to peak distance, absolute energy, mean, standard deviation (SD), autocorrelation, traveled distance, kurtosis, and skewness of the signal, was computed with TSFEL [44]. The window  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**HRV (ms)** & **CVL (\%)** & **Frequency (bpm)** & **Synchronization (\%)** \\ \hline SDRRi & RHR & ABD RR & Correlation \\ RMSSD & CVL & RC RR & Phase Synchrony \\ SD1 & CVS & & RC \\ SD2 & & & \\ \hline \hline \end{tabular}  
* HRV—heart rate variability; SDRRi—standard deviation of consecutive RR peak intervals; RMSSD—root mean square of the consecutive RR peak intervals; RHR—relative heart ate; CVS—cardiovascular strain; CVL—cardiovascular load; SD1—Poincaré plot standard deviation perpendicular to the line of identity; SD2—Poincaré plot standard deviation along the line of identity; ABD—abdominal; RC—rb cage; RR—respiratory rate.  
\end{table}
Table 1: Extracted metrics from ECG and RIP signals.  
(\(w\)) and overlap (\(o\)) sizes for each workstation and subject had to be adapted manually. H1: \(w\in\) [50,120] samples, \(o\) = 5%; H2: \(w\in\) [100,150] samples, \(o\in\) [1,5]%; H3: \(w\in\) [25,40], \(o\in\) [5,10]%.
3. SSM computation: a z-normalization of the feature series (row-wise normalization) and a further z-normalization of the windows (column-wise) was performed. SSM computation was carried out by applying the dot product between the feature matrix's transpose by itself (cosine distance between each segment of the feature matrix) [42].
4. Self-similarity function: a column-wise sum of features that enhance parts of the signal with similar structure [42] was made.
5. Anomaly and cycle detection: thresholding the self-similarity function and detecting its fiducial points were carried out [45].
6. Confirmation: synchronization of the signals and video by the last detected jump was performed. Comparison of the positions from the last step with annotations on the video were carried out.  
These are presented in Figure 4, where the illustrated recording presents an extended pause of activity, which is excluded from the analysis.