## 2 Materials and Methods
### Dataset
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Marker \#** & **Label** & **Type** & **Name** \\ \hline
1 & R.ASIS & T/A & Right anterior superior illac spine \\
2 & L.ASIS & T/A & Left anterior superior illac spine \\
3 & R.PSIS & T/A & Right posterior illac spine \\
4 & LPSIS & T/A & Left posterior illac spine \\
5 & R.Lilac.Crest & T & Right illac crest \\
6 & L.Lilac.Crest & T & Left illac crest \\ \hline
7 & R.Thigh.Top.Lateral & T & Right thigh top lateral marker \\
8 & R.Thigh.Bottom.Lateral & T & Right thigh bottom lateral marker \\
9 & R.Thigh.Top.Medial & T & Right thigh top medial marker \\
10 & R.Thigh.Bottom.Medial & T & Right thigh bottom medial marker \\
11 & R.Shank.Top.Lateral & T & Right blank top lateral marker \\
12 & R.Shank.Bottom.Lateral & T & Right hand bottom lateral marker \\
13 & R.Shank.Top.Medial & T & Right shank top medial marker \\
14 & R.Shank.Bottom.Medial & T & Right shank bottom medial marker \\
15 & R.Heel.Top & T/A & Right heel top \\
16 & R.Heel.Bottom & T/A & Right heel bottom \\
17 & R.Heel.Lateral & T & Right heel lateral \\ \hline
18 & L.Thigh.Top.Lateral & T & Left thigh top lateral marker \\
19 & L.Thigh.Bottom.Lateral & T & Left thigh bottom lateral marker \\
20 & L.Thigh.Top.Medial & T & Left thigh top medial marker \\
21 & L.Thigh.Bottom.Medial & T & Left thigh bottom medial marker \\
22 & L.Shank.Top.Lateral & T & Left shank top lateral marker \\
23 & L.Shank.Bottom.Lateral & T & Left shank bottom lateral marker \\
24 & L.Shank.Top.Medial & T & Left shank top medial marker \\
25 & L.Shank.Bottom.Medial & T & Left shank bottom medial marker \\
26 & L.Heel.Top & T/A & Left heel top \\
27 & L.Heel.Bottom & T/A & Left heel bottom \\
28 & L.Heel.Lateral & T & Left heel lateral \\ \hline
29 & R.GTR & A & Right greater trochanter \\
30 & R.Knee & A & Right knee \\
31 & R.Knee.Medial & A & Right knee medial \\
32 & R.HF & A & Right head of fibula \\
33 & R.TT & A & Right tibial tuberosity \\
34 & R.Ankle & A & Right ankle \\
35 & R.Arkle.Medial & A & Right ankle medial \\
36 & R.MT1 & A & Right 1 st metatarsal \\
37 & R.MT5 & A & Right 5 th metatarsal \\
38 & R.MT2 & A & Right 2 nd metatarsal \\ \hline
39 & L.GTR & A & Left greater trochanter \\
40 & L.Knee & A & Left knee \\
41 & L.Knee.Medial & A & Left knee medial \\
42 & L.HF & A & Left head of fibula \\
43 & L.TT & A & Left tibial tuberosity \\
44 & L.Ankle & A & Left ankle \\
45 & L.Ankle.Medial & A & Left ankle medial \\
46 & L.MT1 & A & Left 1 st metatarsal \\
47 & L.MT5 & A & Left 5 th metatarsal \\
48 & L.MT2 & A & Left 2 nd metatarsal \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Description of each marker’s ID, label, and type to determine whether it is either anatomical (A) or technical (T), based on the anatomical position, obtained from [8]. these forces while the subjects performed the requested trials. Trials consisted of three phases: the subject walking at 1.2 m/s for 1 min; then the treadmill increased its speed incrementally to 2.5 m/s, and after 3 min, data were recorded for 30 s, which was repeated at speeds of 3.5 m/s and 4.5 m/s; after the trials, the speed was set back to 1.2 m/s for a 1-min cool-down period before stopping completely. The sampling frequency of target features was doubled to 300 Hz, compared to the sampling frequency of source features of 150 Hz, generating more data for the target features concerning source features. Thus, for each sample in the markers (source) dataset, two examples are in the forces (target) dataset, making it impossible to merge both datasets and create a prediction using a model. Two methods solved the data granularity issue, matched both datasets, and combined them into a single dataset to fit an ML model:  
* Down-sampling: Given that the forces dataset has double the samples of the markers dataset, index numbers could eliminate odd index numbers and thus cut the dataset by half. This sequential elimination of samples removed data from the doubled dataset but allowed us to join both datasets without bias in predicting a sample using the current data.