Algorithm 1 Computing a network.

1:Input:${\mathcal{T}}_{k,l}=\{t_{k,l,1},t_{k,l,2},...,t_{k,l,N_{\mathcal{A}}}\}$: set of $N_{\mathcal{A}}$ time series representing the joint angles of individual $p_k$ when performing the movement $M_l$. 2:Output:$W_{k,l}$: adjacency matrix corresponding to the network $G_{k,l}(S,E_{k,l})$. 3:$\mathrm{Z}:=0$                    ▹$0\in {\mathbb{R}}^{N_{\mathcal{A}}\times N_{\mathcal{A}}}$ is the null matrix 4:for$i:=1$ to $N_{\mathcal{A}}$ do 5:    for $j:=i+1$ to $N_{\mathcal{A}}$ do 6:        ${\mathrm{Z}}_{i,j}:=z_1(i,j)+z_2(i,j)+z_3(i,j)$ 7:        ${\mathrm{Z}}_{i,j}:={\mathrm{Z}}_{j,i}$ 8:    end for 9:end for 10:Solve the optimization problem in the Equation (3), given the pairwise similarity measures $\mathrm{Z}={\left({\mathrm{Z}}_{i,j}\right)}_{i,j=1}^{N_{\mathcal{A}}}$, to obtain the adjacency matrix $W_{k,l}$.